| # Sensor Graph Data for METR-LA |
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| This directory contains spatial information about the traffic sensors used in the METR-LA dataset. |
|
|
| ## Files |
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| - `sensor_locations.csv`: Sensor coordinates (latitude, longitude) for 207 sensors |
| - `distances.csv`: Pairwise distances between sensors in meters |
| - `adj_mx.npy`: Pre-computed adjacency matrix (207×207) for graph neural networks |
| - `adj_mx_mapping.json`: Metadata and parameters used to generate the adjacency matrix |
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| ## Usage |
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|
| ```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}") |
| ``` |
|
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| ## Coordinate System |
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| - Coordinates are in WGS84 (latitude, longitude) |
| - Distances are in meters |
| - Use this data to construct the adjacency matrix for graph neural networks |
|
|
| ## Citation |
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| This spatial data is part of the original dataset used in: |
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| > Li, Y., Yu, R., Shahabi, C., & Liu, Y. (2018). Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting. ICLR 2018. |
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