sensor_graph/README.md

# Sensor Graph Data for PEMS-BAY

This directory contains spatial information about the traffic sensors used in the PEMS-BAY dataset.

## Files

- `sensor_locations.csv`: Sensor coordinates (latitude, longitude) for 325 sensors
- `distances.csv`: Pairwise distances between sensors in meters
- `adj_mx.npy`: Pre-computed adjacency matrix (325×325) for graph neural networks
- `adj_mx_mapping.json`: Metadata and parameters used to generate the adjacency matrix

## Usage

```python
import pandas as pd
import numpy as np

# Load sensor locations
locations = pd.read_csv('sensor_graph/sensor_locations.csv')
print(f"Dataset has {len(locations)} sensors")

# Load distances (for custom graph construction)
distances = pd.read_csv('sensor_graph/distances.csv')

# Load pre-computed adjacency matrix
adj_matrix = np.load('sensor_graph/adj_mx.npy')
print(f"Adjacency matrix shape: {adj_matrix.shape}")
```

## Coordinate System

- Coordinates are in WGS84 (latitude, longitude)
- Distances are in meters
- Use this data to construct the adjacency matrix for graph neural networks

## Citation

This spatial data is part of the original dataset used in:

> Li, Y., Yu, R., Shahabi, C., & Liu, Y. (2018). Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting. ICLR 2018.