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timestamp
stringdate
2024-10-11 22:29:13
2025-10-11 22:26:38
segment_id
stringlengths
11
11
lat
float64
4.17
13.9
lon
float64
2.68
14.7
hour
int64
0
23
avg_speed_kmh
float64
5
120
density_veh_per_km
float64
2
136
incidents
int64
0
4
congestion_index
float64
0
94.4
2024-12-16 14:39:24
SEG-0162239
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2024-11-16 11:28:45
SEG-0024260
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2025-06-11 11:54:33
SEG-0093490
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2024-12-28 07:33:06
SEG-0162248
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2025-07-23 07:55:43
SEG-0017321
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2025-07-20 06:39:03
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2025-05-17 20:11:41
SEG-0111810
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2025-04-17 16:36:34
SEG-0043383
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2025-08-24 07:24:24
SEG-0154859
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2025-04-20 17:12:08
SEG-0168185
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2025-09-15 22:54:25
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2024-11-13 07:57:58
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2024-11-19 19:40:30
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End of preview. Expand in Data Studio
# Nigeria Transport & Logistics – Traffic Flow

Segment-level average speeds, density, incidents and congestion index across Nigeria.

- **[category]** Route & Trip Data
- **[rows]** ~180,000
- **[formats]** CSV + Parquet (snappy)
- **[geography]** Nigeria (major cities, corridors, ports, airports)

## Schema

| column | dtype |

|---|---| | timestamp | object | | segment_id | object | | lat | float64 | | lon | float64 | | hour | int64 | | avg_speed_kmh | float64 | | density_veh_per_km | float64 | | incidents | int64 | | congestion_index | float64 |

## Usage

```python
import pandas as pd
df = pd.read_parquet('data/nigerian_transport_and_logistics_traffic_flow/nigerian_transport_and_logistics_traffic_flow.parquet')
df.head()
```

```python
from datasets import load_dataset
ds = load_dataset('electricsheepafrica/nigerian_transport_and_logistics_traffic_flow')
ds
```

## Notes

- Nigeria-specific parameters (fleets, roads, traffic, fuel prices)
- Time-of-day traffic effects and seasonal impacts where applicable
- Physical plausibility checks embedded during generation
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Collection including electricsheepafrica/nigerian_transport_and_logistics_traffic_flow