electricsheepafrica/nigerian_transport_and_logistics_engine_sensors

# Nigeria Transport & Logistics – Engine & Sensor Data

Coolant/oil/intake temperatures, manifold pressure, vibration, DTC codes, severity.

- **[category]** Vehicle & Fleet Data
- **[rows]** ~200,000
- **[formats]** CSV + Parquet (snappy)
- **[geography]** Nigeria (major cities, corridors, ports, airports)

## Schema

| column | dtype |

|---|---| | timestamp | object | | vehicle_id | object | | fleet | object | | vehicle_type | object | | coolant_temp_c | float64 | | oil_temp_c | float64 | | intake_temp_c | float64 | | manifold_press_kpa | float64 | | vibration_g | float64 | | dtc_code | object | | dtc_active | bool | | severity | int64 |

## Usage

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

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