electricsheepafrica/nigerian_transport_and_logistics_smart_city_mobility

# Nigeria Transport & Logistics – Smart City Mobility

Multimodal inflow/outflow, transfers, occupancy across Nigeria cities.

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

## Schema

| column | dtype |

|---|---| | timestamp | object | | city | object | | lat | float64 | | lon | float64 | | mode | object | | inflow | int64 | | outflow | int64 | | transfers | int64 | | avg_occupancy | float64 |

## Usage

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

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