electricsheepafrica/nigerian_transport_and_logistics_route_optimization

# Nigeria Transport & Logistics – Route Optimization

Planned vs optimized routes with constraints, distance/time savings, feasibility.

- **[category]** Route & Trip Data
- **[rows]** ~150,000
- **[formats]** CSV + Parquet (snappy)
- **[geography]** Nigeria (major cities, corridors, ports, airports)

## Schema

| column | dtype |

|---|---| | timestamp | object | | origin_city | object | | origin_lat | float64 | | origin_lon | float64 | | dest_city | object | | dest_lat | float64 | | dest_lon | float64 | | planned_distance_km | float64 | | planned_time_min | float64 | | constraints | object | | optimized_distance_km | float64 | | optimized_time_min | float64 | | savings_km | float64 | | savings_min | float64 | | feasible | bool |

## Usage

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

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