Split (1)

train · 119k rows

conversations listlengths 3 3
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 3 in Yolo, California, USA, along the US50-E freeway, lane 4, direction of eastbound. - Today's weather: Sunny. Temperature is 6.0\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 27, 21, 15, 19, 44, 98, 150, 156, 178, 208, 246 and 248, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [257, 263, 269, 262, 229, 221, 214, 152, 127, 100, 58, 38]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 3 in Sacramento, California, USA, along the US50-E freeway, lane 4, direction of eastbound. - Today's weather: Sunny. Temperature is 5.9\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 32, 23, 24, 42, 124, 250, 332, 296, 287, 307, 334 and 359, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [376, 381, 384, 366, 317, 282, 242, 194, 164, 111, 76, 46]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 3 in Sacramento, California, USA, along the US50-E freeway, lane 3, direction of eastbound. - Today's weather: Sunny. Temperature is 5.9\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 16, 11, 12, 15, 39, 91, 134, 138, 142, 166, 198 and 212, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [226, 242, 260, 264, 232, 191, 141, 112, 80, 58, 35, 21]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 3 in Yolo, California, USA, along the US50-W freeway, lane 3, direction of westbound. - Today's weather: Sunny. Temperature is 6.0\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 30, 33, 47, 85, 140, 195, 220, 222, 262, 304, 304 and 326, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [315, 326, 286, 284, 248, 228, 253, 231, 161, 116, 57, 35]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 3 in El Dorado, California, USA, along the US50-W freeway, lane 3, direction of westbound. - Today's weather: Sunny. Temperature is 5.9\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas and commercial areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 20, 19, 20, 30, 60, 105, 128, 138, 156, 158, 167 and 167, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [165, 162, 177, 163, 158, 126, 103, 85, 62, 50, 27, 16]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 3 in Sacramento, California, USA, along the SR51-N freeway, lane 3, direction of northbound. - Today's weather: Sunny. Temperature is 6.0\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 59, 42, 42, 62, 136, 254, 314, 330, 387, 577, 581 and 581, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [616, 646, 576, 488, 420, 385, 332, 256, 220, 155, 107, 69]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 3 in Sacramento, California, USA, along the SR51-N freeway, lane 3, direction of northbound. - Today's weather: Sunny. Temperature is 5.9\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 70, 34, 29, 36, 83, 164, 207, 205, 231, 279, 300 and 320, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [332, 342, 328, 314, 307, 269, 241, 188, 169, 112, 74, 47]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 3 in Sacramento, California, USA, along the SR51-S freeway, lane 3, direction of southbound. - Today's weather: Sunny. Temperature is 5.9\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 60, 56, 54, 71, 135, 250, 256, 267, 320, 350, 378 and 404, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [371, 378, 374, 359, 365, 344, 314, 246, 198, 136, 95, 68]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 3 in Placer, California, USA, along the I80-W freeway, lane 3, direction of westbound. - Today's weather: Sunny. Temperature is 5.9\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 25, 17, 19, 34, 65, 125, 157, 192, 238, 288, 356 and 343, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [344, 352, 340, 292, 291, 282, 271, 237, 202, 67, 35, 26]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 3 in Sacramento, California, USA, along the SR99-N freeway, lane 4, direction of northbound. - Today's weather: Sunny. Temperature is 6.0\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 42, 35, 48, 78, 153, 272, 270, 278, 318, 370, 384 and 384, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [400, 408, 371, 335, 325, 296, 238, 190, 164, 120, 82, 54]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 3 in Sacramento, California, USA, along the SR99-S freeway, lane 2, direction of southbound. - Today's weather: Sunny. Temperature is 6.0\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, residential areas and educational areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 48, 46, 46, 49, 64, 90, 111, 122, 137, 141, 156 and 164, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [163, 160, 160, 156, 156, 151, 137, 116, 99, 78, 66, 52]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 3 in Sacramento, California, USA, along the SR99-S freeway, lane 2, direction of southbound. - Today's weather: Sunny. Temperature is 5.6\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 8, 9, 19, 40, 63, 103, 107, 94, 106, 112, 114 and 119, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [117, 112, 112, 116, 106, 95, 65, 49, 38, 22, 14, 11]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 4 in Contra Costa, California, USA, along the SR24-W freeway, lane 4, direction of westbound. - Today's weather: Sunny. Temperature is 7.9\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas and commercial areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 0, 2, 2, 105, 218, 273, 228, 293, 337, 391, 361 and 400, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [425, 416, 378, 394, 377, 334, 264, 221, 193, 133, 17, 2]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 4 in Solano, California, USA, along the I80-W freeway, lane 3, direction of westbound. - Today's weather: Sunny. Temperature is 6.0\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, educational areas and residential areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 25, 20, 18, 28, 79, 156, 244, 256, 228, 229, 250 and 276, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [293, 318, 294, 274, 288, 281, 229, 169, 144, 106, 64, 37]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 4 in Santa Clara, California, USA, along the US101-N freeway, lane 4, direction of northbound. - Today's weather: Rain. Temperature is 8.4\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, residential areas and educational areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 30, 27, 38, 78, 146, 171, 166, 169, 202, 235, 259 and 268, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [258, 274, 272, 283, 293, 284, 288, 200, 173, 120, 76, 52]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 4 in San Mateo, California, USA, along the US101-N freeway, lane 5, direction of northbound. - Today's weather: Sunny. Temperature is 8.3\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 57, 44, 66, 126, 237, 322, 384, 410, 456, 495, 496 and 539, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [532, 530, 518, 518, 491, 448, 406, 402, 371, 252, 148, 99]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 4 in Marin, California, USA, along the US101-N freeway, lane 4, direction of northbound. - Today's weather: Sunny. Temperature is 7.9\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 89, 81, 68, 70, 87, 120, 187, 245, 278, 319, 349 and 349, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [350, 355, 364, 352, 330, 285, 244, 216, 190, 164, 120, 98]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 4 in Santa Clara, California, USA, along the US101-S freeway, lane 4, direction of southbound. - Today's weather: Rain. Temperature is 8.4\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 18, 15, 14, 25, 66, 118, 176, 180, 176, 175, 185 and 196, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [197, 218, 212, 209, 207, 204, 172, 137, 115, 86, 60, 35]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 4 in Marin, California, USA, along the US101-S freeway, lane 4, direction of southbound. - Today's weather: Sunny. Temperature is 7.9\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 74, 70, 73, 89, 126, 211, 239, 250, 288, 307, 325 and 311, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [324, 316, 331, 356, 328, 279, 216, 177, 158, 120, 91, 75]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 4 in San Mateo, California, USA, along the I280-N freeway, lane 4, direction of northbound. - Today's weather: Sunny. Temperature is 8.3\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 58, 34, 26, 33, 76, 158, 228, 270, 305, 377, 402 and 395, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [413, 434, 470, 520, 452, 375, 318, 285, 255, 194, 130, 86]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 4 in Alameda, California, USA, along the I580-W freeway, lane 4, direction of westbound. - Today's weather: Sunny. Temperature is 7.9\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas and commercial areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 34, 28, 24, 38, 106, 208, 324, 342, 303, 306, 333 and 367, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [390, 424, 393, 365, 384, 375, 306, 226, 191, 142, 85, 49]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 4 in Alameda, California, USA, along the I680-N freeway, lane 3, direction of northbound. - Today's weather: Rain. Temperature is 8.4\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 60, 58, 62, 74, 110, 157, 182, 195, 213, 229, 237 and 240, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [244, 246, 247, 234, 228, 208, 190, 165, 134, 105, 81, 66]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 4 in Contra Costa, California, USA, along the I680-N freeway, lane 5, direction of northbound. - Today's weather: Sunny. Temperature is 7.9\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 179, 140, 137, 162, 196, 278, 382, 449, 498, 622, 682 and 732, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [750, 810, 803, 809, 699, 607, 532, 478, 420, 341, 304, 229]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 4 in Santa Clara, California, USA, along the I680-S freeway, lane 4, direction of southbound. - Today's weather: Rain. Temperature is 8.4\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 39, 26, 25, 49, 109, 160, 185, 226, 273, 335, 352 and 368, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [373, 414, 399, 403, 383, 370, 292, 247, 205, 161, 96, 48]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 4 in Contra Costa, California, USA, along the I680-S freeway, lane 6, direction of southbound. - Today's weather: Sunny. Temperature is 7.9\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 84, 80, 79, 119, 193, 278, 337, 350, 404, 455, 456 and 466, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [470, 478, 492, 484, 433, 386, 325, 288, 232, 188, 128, 94]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 4 in Santa Clara, California, USA, along the I880-N freeway, lane 6, direction of northbound. - Today's weather: Rain. Temperature is 8.4\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 83, 73, 77, 95, 176, 246, 298, 313, 380, 448, 514 and 561, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [572, 601, 597, 584, 573, 514, 486, 394, 321, 229, 170, 104]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 4 in Alameda, California, USA, along the I880-S freeway, lane 4, direction of southbound. - Today's weather: Sunny. Temperature is 8.1\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas and commercial areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 52, 44, 48, 96, 186, 280, 282, 308, 384, 418, 497 and 470, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [475, 468, 453, 452, 430, 370, 310, 262, 216, 168, 112, 69]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 5 in Santa Cruz, California, USA, along the SR17-N freeway, lane 3, direction of northbound. - Today's weather: Rain. Temperature is 8.4\\u00b0C, and visibility reaches 10.0 miles. - Region information: including commercial areas, transportation areas and residential areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 11, 10, 10, 31, 76, 117, 126, 0, 102, 244, 258 and 263, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [276, 300, 308, 300, 280, 52, 0, 0, 0, 10, 26, 15]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 6 in Kern, California, USA, along the SR99-S freeway, lane 3, direction of southbound. - Today's weather: Sunny. Temperature is 9.9\\u00b0C, and visibility reaches 9.9 miles. - Region information: including transportation areas, residential areas and educational areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 33, 30, 38, 61, 165, 210, 146, 145, 147, 190, 212 and 216, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [258, 260, 278, 284, 226, 190, 142, 114, 83, 76, 46, 49]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the SR2-W freeway, lane 4, direction of westbound. - Today's weather: Sunny. Temperature is 11.2\\u00b0C, and visibility reaches 10.0 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 91, 90, 94, 114, 186, 263, 285, 306, 315, 302, 314 and 332, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [305, 328, 343, 364, 339, 284, 217, 176, 171, 139, 112, 98]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the SR2-W freeway, lane 4, direction of westbound. - Today's weather: Sunny. Temperature is 11.2\\u00b0C, and visibility reaches 10.0 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 12, 8, 11, 30, 86, 173, 208, 204, 196, 182, 170 and 167, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [178, 185, 226, 226, 197, 156, 107, 75, 70, 48, 27, 16]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I5-S freeway, lane 5, direction of southbound. - Today's weather: Sunny. Temperature is 11.2\\u00b0C, and visibility reaches 10.0 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 64, 55, 62, 132, 378, 552, 572, 542, 513, 506, 508 and 521, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [530, 560, 575, 572, 568, 522, 440, 338, 306, 238, 153, 99]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I10-E freeway, lane 4, direction of eastbound. - Today's weather: Sunny. Temperature is 13.9\\u00b0C, and visibility reaches 9.7 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 46, 40, 44, 91, 262, 414, 482, 456, 399, 390, 397 and 412, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [423, 457, 476, 481, 482, 437, 358, 267, 238, 185, 117, 77]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I10-W freeway, lane 4, direction of westbound. - Today's weather: Rain. Temperature is 12.3\\u00b0C, and visibility reaches 9.1 miles. - Region information: including residential areas, transportation areas and educational areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 238, 227, 254, 352, 390, 390, 418, 414, 428, 449, 454 and 404, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [453, 464, 448, 465, 458, 436, 386, 304, 270, 228, 195, 174]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the SR47-S freeway, lane 2, direction of southbound. - Today's weather: Sunny. Temperature is 13.9\\u00b0C, and visibility reaches 9.7 miles. - Region information: including residential areas, commercial areas and transportation areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 10, 20, 11, 6, 15, 38, 54, 60, 56, 60, 84 and 61, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [69, 82, 87, 114, 86, 70, 52, 43, 51, 32, 23, 19]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the SR60-E freeway, lane 4, direction of eastbound. - Today's weather: Sunny. Temperature is 13.9\\u00b0C, and visibility reaches 9.7 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 82, 72, 78, 126, 222, 297, 364, 346, 353, 408, 438 and 492, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [501, 489, 478, 478, 510, 446, 405, 339, 279, 214, 143, 101]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the SR60-E freeway, lane 5, direction of eastbound. - Today's weather: Sunny. Temperature is 13.9\\u00b0C, and visibility reaches 9.7 miles. - Region information: including residential areas, transportation areas and educational areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 162, 150, 177, 231, 299, 374, 450, 472, 450, 487, 505 and 541, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [560, 573, 564, 556, 588, 531, 448, 416, 358, 305, 227, 187]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the SR60-E freeway, lane 4, direction of eastbound. - Today's weather: Sunny. Temperature is 13.9\\u00b0C, and visibility reaches 9.7 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 117, 99, 115, 184, 278, 357, 434, 452, 453, 476, 469 and 504, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [537, 544, 460, 488, 480, 465, 424, 388, 348, 286, 198, 164]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the SR60-E freeway, lane 4, direction of eastbound. - Today's weather: Rain. Temperature is 13.5\\u00b0C, and visibility reaches 9.8 miles. - Region information: including residential areas, transportation areas and educational areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 74, 54, 68, 111, 212, 318, 379, 364, 351, 366, 392 and 411, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [459, 480, 488, 508, 495, 440, 381, 350, 300, 228, 144, 93]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the SR91-W freeway, lane 4, direction of westbound. - Today's weather: Sunny. Temperature is 13.9\\u00b0C, and visibility reaches 9.7 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 46, 40, 44, 91, 262, 414, 482, 456, 399, 390, 397 and 412, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [423, 457, 476, 481, 482, 437, 358, 267, 238, 185, 117, 77]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the US101-N freeway, lane 4, direction of northbound. - Today's weather: Sunny. Temperature is 10.6\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 51, 33, 37, 80, 174, 298, 391, 365, 385, 460, 453 and 471, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [462, 439, 475, 472, 452, 391, 327, 289, 253, 183, 110, 67]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the US101-S freeway, lane 4, direction of southbound. - Today's weather: Sunny. Temperature is 11.2\\u00b0C, and visibility reaches 10.0 miles. - Region information: including residential areas, commercial areas and transportation areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 166, 140, 138, 138, 254, 323, 295, 322, 372, 398, 400 and 392, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [378, 372, 371, 376, 392, 363, 346, 344, 342, 282, 200, 137]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I110-N freeway, lane 4, direction of northbound. - Today's weather: Sunny. Temperature is 12.9\\u00b0C, and visibility reaches 10.0 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 146, 124, 134, 218, 408, 454, 449, 466, 498, 504, 504 and 462, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [441, 482, 452, 487, 493, 476, 466, 414, 387, 295, 236, 173]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the SR134-E freeway, lane 4, direction of eastbound. - Today's weather: Sunny. Temperature is 11.2\\u00b0C, and visibility reaches 10.0 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 62, 38, 37, 55, 124, 256, 324, 338, 392, 462, 469 and 480, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [486, 466, 453, 421, 419, 406, 381, 303, 261, 194, 123, 74]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the SR134-E freeway, lane 4, direction of eastbound. - Today's weather: Sunny. Temperature is 11.2\\u00b0C, and visibility reaches 10.0 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 98, 88, 83, 92, 132, 200, 290, 304, 310, 324, 358 and 356, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [354, 358, 358, 374, 364, 333, 300, 248, 208, 174, 133, 112]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I210-E freeway, lane 5, direction of eastbound. - Today's weather: Sunny. Temperature is 11.2\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 90, 77, 75, 99, 174, 306, 424, 440, 500, 551, 597 and 610, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [563, 632, 628, 602, 590, 526, 473, 372, 311, 225, 164, 114]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I405-N freeway, lane 5, direction of northbound. - Today's weather: Sunny. Temperature is 13.9\\u00b0C, and visibility reaches 9.7 miles. - Region information: including residential areas, transportation areas and educational areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 98, 85, 102, 240, 500, 515, 552, 551, 574, 680, 674 and 688, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [655, 673, 712, 649, 673, 636, 551, 485, 424, 311, 200, 126]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I405-N freeway, lane 5, direction of northbound. - Today's weather: Sunny. Temperature is 13.9\\u00b0C, and visibility reaches 9.7 miles. - Region information: including residential areas, transportation areas and educational areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 137, 123, 144, 261, 513, 543, 567, 557, 566, 658, 659 and 684, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [651, 671, 675, 662, 680, 628, 537, 488, 426, 330, 233, 160]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I405-N freeway, lane 4, direction of northbound. - Today's weather: Sunny. Temperature is 12.9\\u00b0C, and visibility reaches 10.0 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 64, 43, 44, 80, 187, 288, 275, 312, 333, 367, 362 and 364, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [374, 387, 309, 315, 312, 337, 322, 309, 266, 230, 161, 101]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I405-N freeway, lane 5, direction of northbound. - Today's weather: Sunny. Temperature is 10.6\\u00b0C, and visibility reaches 10.0 miles. - Region information: including residential areas, transportation areas and educational areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 196, 153, 141, 161, 254, 363, 446, 497, 562, 604, 614 and 620, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [648, 637, 633, 639, 597, 565, 550, 506, 446, 393, 306, 232]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I405-N freeway, lane 6, direction of northbound. - Today's weather: Sunny. Temperature is 10.6\\u00b0C, and visibility reaches 10.0 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 141, 96, 81, 104, 219, 365, 476, 536, 615, 682, 685 and 714, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [763, 752, 770, 739, 692, 669, 664, 567, 509, 411, 292, 197]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I405-N freeway, lane 4, direction of northbound. - Today's weather: Sunny. Temperature is 10.6\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 84, 62, 51, 72, 160, 250, 346, 362, 370, 406, 448 and 468, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [502, 487, 418, 448, 456, 427, 404, 348, 307, 244, 178, 110]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I405-S freeway, lane 4, direction of southbound. - Today's weather: Sunny. Temperature is 12.9\\u00b0C, and visibility reaches 10.0 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 74, 54, 57, 120, 335, 478, 576, 560, 516, 506, 521 and 550, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [561, 550, 497, 458, 465, 457, 542, 464, 368, 282, 185, 127]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I405-S freeway, lane 5, direction of southbound. - Today's weather: Sunny. Temperature is 12.9\\u00b0C, and visibility reaches 10.0 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 140, 94, 89, 149, 336, 575, 594, 624, 667, 693, 680 and 702, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [699, 672, 494, 528, 611, 637, 624, 574, 545, 455, 309, 198]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I605-N freeway, lane 4, direction of northbound. - Today's weather: Sunny. Temperature is 13.9\\u00b0C, and visibility reaches 9.7 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 38, 28, 36, 70, 166, 241, 308, 342, 378, 388, 424 and 432, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [403, 508, 523, 484, 523, 439, 326, 265, 234, 154, 82, 49]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I605-N freeway, lane 4, direction of northbound. - Today's weather: Sunny. Temperature is 13.9\\u00b0C, and visibility reaches 9.7 miles. - Region information: including residential areas, transportation areas and educational areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 95, 77, 72, 126, 242, 382, 444, 457, 476, 495, 520 and 534, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [552, 547, 513, 542, 543, 488, 420, 379, 335, 259, 178, 116]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I605-N freeway, lane 4, direction of northbound. - Today's weather: Sunny. Temperature is 11.2\\u00b0C, and visibility reaches 10.0 miles. - Region information: including residential areas, transportation areas and educational areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 56, 44, 48, 80, 184, 282, 330, 332, 348, 378, 418 and 448, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [434, 370, 343, 354, 316, 285, 289, 254, 209, 157, 102, 68]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I710-S freeway, lane 4, direction of southbound. - Today's weather: Sunny. Temperature is 13.9\\u00b0C, and visibility reaches 9.7 miles. - Region information: including residential areas, transportation areas and educational areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 92, 91, 93, 144, 272, 332, 324, 290, 304, 327, 338 and 352, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [334, 364, 380, 366, 383, 322, 287, 234, 225, 184, 140, 114]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I710-S freeway, lane 6, direction of southbound. - Today's weather: Sunny. Temperature is 13.9\\u00b0C, and visibility reaches 9.7 miles. - Region information: including residential areas, transportation areas and educational areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 47, 40, 44, 91, 262, 414, 481, 456, 399, 390, 397 and 412, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [423, 458, 476, 481, 482, 437, 358, 267, 238, 186, 117, 78]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I710-S freeway, lane 4, direction of southbound. - Today's weather: Sunny. Temperature is 13.9\\u00b0C, and visibility reaches 9.7 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 46, 40, 44, 91, 262, 414, 482, 456, 399, 390, 397 and 412, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [423, 457, 476, 481, 482, 437, 358, 267, 238, 185, 117, 77]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 7 in Los Angeles, California, USA, along the I710-S freeway, lane 5, direction of southbound. - Today's weather: Sunny. Temperature is 12.9\\u00b0C, and visibility reaches 10.0 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 28, 24, 28, 57, 168, 277, 325, 302, 254, 244, 246 and 253, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [258, 286, 322, 337, 339, 287, 214, 158, 142, 109, 70, 48]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 8 in San Bernardino, California, USA, along the I10-W freeway, lane 4, direction of westbound. - Today's weather: Rain. Temperature is 12.3\\u00b0C, and visibility reaches 9.1 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 79, 73, 109, 262, 302, 326, 370, 377, 416, 451, 468 and 258, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [466, 496, 474, 516, 492, 438, 358, 317, 275, 211, 154, 103]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 8 in San Bernardino, California, USA, along the I10-W freeway, lane 4, direction of westbound. - Today's weather: Rain. Temperature is 12.3\\u00b0C, and visibility reaches 9.1 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 58, 73, 147, 339, 416, 433, 420, 378, 416, 464, 481 and 482, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [459, 488, 436, 418, 386, 365, 320, 279, 226, 145, 103, 67]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 8 in San Bernardino, California, USA, along the I10-W freeway, lane 4, direction of westbound. - Today's weather: Rain. Temperature is 12.3\\u00b0C, and visibility reaches 9.1 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 67, 68, 103, 216, 298, 382, 349, 334, 367, 422, 436 and 441, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [448, 485, 466, 448, 409, 363, 302, 268, 221, 148, 106, 70]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 8 in San Bernardino, California, USA, along the I10-W freeway, lane 4, direction of westbound. - Today's weather: Rain. Temperature is 12.3\\u00b0C, and visibility reaches 9.1 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 69, 60, 79, 165, 344, 433, 476, 500, 468, 469, 478 and 491, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [506, 551, 591, 570, 589, 500, 396, 335, 294, 233, 170, 112]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 8 in Riverside, California, USA, along the I10-W freeway, lane 4, direction of westbound. - Today's weather: Rain. Temperature is 9.8\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 97, 95, 95, 104, 112, 146, 185, 214, 247, 274, 282 and 294, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [290, 282, 300, 284, 263, 212, 189, 174, 152, 134, 111, 98]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 8 in Riverside, California, USA, along the I15-N freeway, lane 4, direction of northbound. - Today's weather: Rain. Temperature is 9.8\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 128, 117, 122, 138, 172, 216, 248, 287, 326, 372, 383 and 355, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [348, 353, 298, 306, 340, 368, 378, 269, 230, 195, 156, 128]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 8 in San Bernardino, California, USA, along the I15-N freeway, lane 4, direction of northbound. - Today's weather: Rain. Temperature is 12.3\\u00b0C, and visibility reaches 9.1 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 122, 109, 100, 120, 152, 194, 212, 229, 249, 264, 275 and 284, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [313, 344, 315, 327, 370, 335, 306, 260, 240, 208, 169, 150]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 8 in San Bernardino, California, USA, along the I15-N freeway, lane 4, direction of northbound. - Today's weather: Rain. Temperature is 12.3\\u00b0C, and visibility reaches 9.1 miles. - Region information: including transportation areas and commercial areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 51, 41, 42, 64, 102, 143, 193, 209, 250, 290, 323 and 327, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [366, 384, 394, 418, 450, 352, 277, 238, 194, 155, 106, 80]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 8 in Riverside, California, USA, along the SR60-E freeway, lane 2, direction of eastbound. - Today's weather: Rain. Temperature is 9.8\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 46, 36, 33, 55, 90, 136, 150, 176, 208, 244, 250 and 244, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [242, 254, 273, 214, 240, 241, 214, 186, 161, 125, 94, 63]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 8 in San Bernardino, California, USA, along the SR71-S freeway, lane 3, direction of southbound. - Today's weather: Rain. Temperature is 13.5\\u00b0C, and visibility reaches 9.8 miles. - Region information: including transportation areas, residential areas and educational areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 29, 28, 32, 60, 116, 158, 206, 213, 209, 230, 246 and 251, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [290, 278, 284, 284, 319, 273, 215, 174, 147, 106, 60, 39]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 8 in Riverside, California, USA, along the SR91-W freeway, lane 3, direction of westbound. - Today's weather: Rain. Temperature is 9.8\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, residential areas and educational areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 54, 54, 88, 182, 183, 270, 332, 316, 328, 350, 360 and 364, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [374, 386, 361, 365, 358, 336, 289, 256, 201, 156, 102, 66]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 8 in San Bernardino, California, USA, along the I210-W freeway, lane 4, direction of westbound. - Today's weather: Rain. Temperature is 12.3\\u00b0C, and visibility reaches 9.1 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 69, 70, 100, 234, 241, 272, 291, 291, 314, 329, 345 and 364, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [378, 374, 349, 348, 308, 327, 274, 238, 202, 161, 116, 68]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 8 in San Bernardino, California, USA, along the I215-N freeway, lane 3, direction of northbound. - Today's weather: Rain. Temperature is 12.3\\u00b0C, and visibility reaches 9.1 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 40, 29, 39, 73, 146, 220, 237, 254, 259, 286, 291 and 303, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [318, 336, 345, 338, 339, 284, 227, 190, 152, 119, 73, 49]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 8 in San Bernardino, California, USA, along the I215-N freeway, lane 5, direction of northbound. - Today's weather: Rain. Temperature is 12.3\\u00b0C, and visibility reaches 9.1 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 68, 62, 58, 92, 156, 207, 266, 289, 316, 343, 370 and 410, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [440, 480, 548, 548, 541, 429, 355, 274, 236, 180, 134, 93]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 8 in Riverside, California, USA, along the I215-S freeway, lane 3, direction of southbound. - Today's weather: Rain. Temperature is 12.3\\u00b0C, and visibility reaches 9.1 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 115, 119, 131, 186, 179, 251, 273, 260, 266, 278, 294 and 279, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [276, 285, 284, 275, 300, 299, 258, 233, 210, 180, 153, 122]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 8 in San Bernardino, California, USA, along the I215-S freeway, lane 2, direction of southbound. - Today's weather: Rain. Temperature is 12.3\\u00b0C, and visibility reaches 9.1 miles. - Region information: including transportation areas, educational areas and commercial areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 34, 30, 40, 82, 172, 216, 238, 250, 234, 234, 239 and 246, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [254, 276, 296, 286, 295, 250, 198, 167, 147, 116, 86, 56]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 10 in Merced, California, USA, along the I5-N freeway, lane 2, direction of northbound. - Today's weather: Sunny. Temperature is 6.7\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 49, 41, 36, 34, 32, 39, 56, 70, 103, 111, 129 and 152, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [172, 198, 187, 202, 219, 223, 230, 225, 154, 138, 113, 76]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 10 in San Joaquin, California, USA, along the I5-N freeway, lane 3, direction of northbound. - Today's weather: Sunny. Temperature is 7.7\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 35, 35, 36, 46, 58, 58, 65, 74, 81, 97, 99 and 112, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [113, 134, 160, 130, 130, 115, 88, 87, 78, 64, 53, 45]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 10 in Merced, California, USA, along the SR99-N freeway, lane 2, direction of northbound. - Today's weather: Sunny. Temperature is 6.7\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, residential areas and educational areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 31, 24, 32, 49, 86, 106, 101, 116, 125, 150, 172 and 179, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [191, 184, 190, 196, 180, 151, 134, 112, 98, 75, 60, 46]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 10 in Stanislaus, California, USA, along the SR99-N freeway, lane 3, direction of northbound. - Today's weather: Sunny. Temperature is 7.6\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 45, 56, 89, 138, 216, 285, 246, 250, 269, 305, 326 and 363, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [381, 364, 353, 362, 361, 295, 258, 211, 176, 118, 78, 52]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 11 in San Diego, California, USA, along the I5-S freeway, lane 4, direction of southbound. - Today's weather: Sunny. Temperature is 14.9\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, residential areas and educational areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 72, 62, 57, 63, 91, 147, 203, 236, 313, 364, 378 and 379, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [371, 378, 385, 361, 362, 338, 312, 295, 276, 167, 108, 76]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 11 in San Diego, California, USA, along the I8-E freeway, lane 2, direction of eastbound. - Today's weather: Sunny. Temperature is 14.9\\u00b0C, and visibility reaches 10.0 miles. - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 21, 22, 21, 33, 28, 60, 96, 120, 118, 133, 135 and 146, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [149, 151, 145, 130, 126, 125, 81, 67, 51, 36, 23, 23]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 11 in San Diego, California, USA, along the I8-E freeway, lane 4, direction of eastbound. - Today's weather: Sunny. Temperature is 14.9\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 42, 29, 21, 38, 53, 120, 209, 259, 287, 319, 360 and 392, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [427, 476, 491, 446, 428, 360, 262, 222, 198, 145, 100, 50]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 11 in San Diego, California, USA, along the I8-E freeway, lane 2, direction of eastbound. - Today's weather: Sunny. Temperature is 14.9\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas and commercial areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 28, 25, 28, 44, 101, 146, 178, 206, 228, 246, 256 and 271, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [262, 269, 267, 256, 228, 179, 140, 104, 72, 62, 46, 27]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 11 in San Diego, California, USA, along the I15-N freeway, lane 5, direction of northbound. - Today's weather: Sunny. Temperature is 14.9\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, educational areas and residential areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 60, 51, 64, 118, 327, 466, 454, 412, 425, 406, 441 and 463, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [469, 492, 498, 456, 430, 368, 288, 224, 189, 134, 96, 62]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 11 in San Diego, California, USA, along the I15-S freeway, lane 6, direction of southbound. - Today's weather: Sunny. Temperature is 14.9\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 60, 68, 68, 73, 137, 250, 375, 455, 486, 516, 572 and 571, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [598, 658, 669, 652, 615, 502, 387, 315, 256, 189, 131, 276]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 11 in San Diego, California, USA, along the SR52-E freeway, lane 2, direction of eastbound. - Today's weather: Sunny. Temperature is 14.9\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas and commercial areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 16, 11, 11, 21, 48, 96, 127, 138, 152, 170, 194 and 206, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [225, 262, 297, 269, 240, 178, 133, 103, 86, 58, 43, 24]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 11 in San Diego, California, USA, along the I805-N freeway, lane 5, direction of northbound. - Today's weather: Sunny. Temperature is 14.9\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, educational areas and residential areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 69, 65, 94, 186, 414, 538, 494, 448, 466, 457, 446 and 418, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [415, 404, 393, 358, 358, 319, 257, 211, 180, 133, 104, 67]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 11 in San Diego, California, USA, along the I805-S freeway, lane 5, direction of southbound. - Today's weather: Sunny. Temperature is 14.9\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, educational areas and residential areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 58, 41, 30, 34, 80, 175, 282, 301, 329, 392, 443 and 500, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [556, 694, 722, 648, 603, 497, 415, 347, 312, 243, 177, 96]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 11 in San Diego, California, USA, along the I805-S freeway, lane 4, direction of southbound. - Today's weather: Sunny. Temperature is 14.9\\u00b0C, and visibility reaches 10.0 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 47, 30, 30, 38, 86, 202, 294, 304, 337, 390, 422 and 457, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [474, 550, 539, 485, 472, 375, 313, 254, 220, 170, 125, 72]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 12 in Orange, California, USA, along the I5-S freeway, lane 5, direction of southbound. - Today's weather: Rain. Temperature is 13.5\\u00b0C, and visibility reaches 9.8 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 118, 93, 122, 296, 552, 678, 650, 618, 648, 660, 664 and 654, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [680, 685, 696, 702, 692, 644, 570, 512, 449, 339, 226, 134]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 12 in Orange, California, USA, along the I5-S freeway, lane 4, direction of southbound. - Today's weather: Sunny. Temperature is 13.9\\u00b0C, and visibility reaches 9.7 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 136, 120, 137, 220, 404, 481, 506, 514, 514, 534, 524 and 517, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [528, 526, 567, 573, 556, 505, 438, 394, 352, 264, 199, 134]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 12 in Orange, California, USA, along the SR55-N freeway, lane 4, direction of northbound. - Today's weather: Rain. Temperature is 13.5\\u00b0C, and visibility reaches 9.8 miles. - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 50, 36, 27, 62, 174, 310, 437, 446, 437, 484, 500 and 509, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [497, 406, 360, 376, 377, 410, 386, 341, 301, 237, 139, 63]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 12 in Orange, California, USA, along the SR57-S freeway, lane 6, direction of southbound. - Today's weather: Rain. Temperature is 13.5\\u00b0C, and visibility reaches 9.8 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 71, 56, 86, 230, 478, 512, 546, 578, 588, 591, 606 and 622, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [651, 636, 649, 612, 600, 554, 445, 385, 326, 242, 151, 84]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 12 in Orange, California, USA, along the SR73-N freeway, lane 3, direction of northbound. - Today's weather: Rain. Temperature is 13.5\\u00b0C, and visibility reaches 9.8 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 4, 8, 6, 11, 29, 51, 105, 96, 101, 102, 103 and 104, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [109, 122, 143, 125, 112, 76, 52, 41, 22, 14, 10, 16]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 12 in Orange, California, USA, along the SR73-S freeway, lane 4, direction of southbound. - Today's weather: Rain. Temperature is 13.5\\u00b0C, and visibility reaches 9.8 miles. - Region information: including educational areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 4, 2, 2, 5, 14, 35, 66, 88, 92, 94, 114 and 111, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [128, 138, 171, 224, 230, 144, 77, 55, 41, 25, 11, 5]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 12 in Orange, California, USA, along the SR73-S freeway, lane 3, direction of southbound. - Today's weather: Rain. Temperature is 13.5\\u00b0C, and visibility reaches 9.8 miles. - Region information: including transportation areas, residential areas and educational areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 5, 2, 2, 5, 16, 39, 66, 81, 81, 88, 106 and 103, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [110, 122, 150, 195, 196, 120, 72, 49, 39, 24, 12, 7]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 12 in Orange, California, USA, along the SR91-W freeway, lane 5, direction of westbound. - Today's weather: Rain. Temperature is 13.5\\u00b0C, and visibility reaches 9.8 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 85, 83, 155, 460, 514, 431, 461, 478, 525, 495, 472 and 474, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [502, 523, 484, 483, 463, 452, 381, 330, 312, 222, 142, 89]}" } ]
[ { "from": "system", "value": "Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data. Context knowledge you could consider: - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000. - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern. - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time. For examples, Airports, and train stations - increased volume on weekends and holidays. Residential areas - more activities during morning and evening rush hours. Commercial areas - busy during lunch hours and after-work periods. Educational locations - high volume during peak hours near schools. Think carefully about the following questions about how spatial-temporal factors affect traffic flow. - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)? - What are the traffic patterns of this area, and what is the change in different time slots? - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?" }, { "from": "human", "value": "Some important information is listed as follows: - Location: District 12 in Orange, California, USA, along the I405-S freeway, lane 5, direction of southbound. - Today's weather: Rain. Temperature is 13.5\\u00b0C, and visibility reaches 9.8 miles. - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km. - Current Time: 12 PM, 2018-2-19, Monday, Washington's Birthday. - Traffic volume data in the past 12 hours were 101, 87, 101, 172, 296, 483, 517, 466, 437, 430, 470 and 465, respectively. According to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 1 PM to 0 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]" }, { "from": "gpt", "value": "{Traffic volume data in the next 12 hours: [488, 530, 512, 522, 520, 458, 412, 362, 321, 227, 159, 116]}" } ]

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