Add PEMS-BAY traffic prediction dataset with Parquet files

README.md

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+---
+license: mit
+task_categories:
+- time-series-forecasting
+- tabular-regression
+tags:
+- traffic-prediction
+- time-series
+- graph-neural-networks
+- transportation
+size_categories:
+- 10M<n<100M
+---
+
+# PEMS-BAY Traffic Dataset
+
+## Dataset Description
+
+This dataset contains traffic flow data for time series forecasting tasks, commonly used with Graph Neural Networks and specifically the Diffusion Convolutional Recurrent Neural Network (DCRNN) model.
+
+## Dataset Structure
+
+### Data Format
+- **Format**: Parquet files for efficient loading and analysis
+- **Splits**: train (70%), validation (10%), test (20%) - **temporal splits** preserving chronological order
+- **Features**: Time series traffic flow data with temporal and spatial dimensions
+
+### Split Strategy
+- **Temporal splitting**: Data is split chronologically to prevent data leakage
+- **All sensors included**: Each split contains data for all sensors at each time step
+- **Training period**: Earliest 70% of time samples across all sensors
+- **Validation period**: Next 10% of time samples across all sensors  
+- **Test period**: Latest 20% of time samples across all sensors
+- **Graph structure preserved**: Spatial relationships maintained in all splits
+
+### Data Schema
+- `node_id`: Sensor/node identifier (0-206 for METR-LA, 0-324 for PEMS-BAY)
+- `x_t*_d*`: Input features at different time offsets and dimensions
+  - `x_t-11_d0` to `x_t+0_d0`: Traffic flow values at 12 historical time steps
+  - `x_t-11_d1` to `x_t+0_d1`: Time-of-day features (normalized 0-1)
+- `y_t*_d*`: Target values at future time steps and dimensions
+  - `y_t+1_d0` to `y_t+12_d0`: Traffic flow predictions for next 12 time steps
+  - `y_t+1_d1` to `y_t+12_d1`: Time-of-day features for prediction horizon
+
+### Dataset Statistics
+- **Total time series samples**: ~34K (METR-LA) / ~52K (PEMS-BAY)
+- **Total records**: ~7M (METR-LA) / ~17M (PEMS-BAY) 
+- **Records per sample**: 207 (METR-LA) / 325 (PEMS-BAY) sensors
+- **Temporal resolution**: 5-minute intervals
+- **Prediction horizon**: 1 hour (12 time steps)
+
+## Usage
+
+```python
+from datasets import load_dataset
+import pandas as pd
+
+# Load from Hugging Face Hub
+dataset = load_dataset("witgaw/PEMS-BAY")
+
+# Or load locally
+train_df = pd.read_parquet("train.parquet")
+val_df = pd.read_parquet("val.parquet") 
+test_df = pd.read_parquet("test.parquet")
+
+print(f"Train samples: {len(train_df) // 207:,}")  # Divide by number of sensors
+print(f"Total records: {len(train_df):,}")
+print(f"Features per record: {len(train_df.columns)}")
+
+# Example: Get data for first time sample
+first_sample = train_df[train_df.index < 207]  # First 207 records (all sensors)
+print(f"Shape for one time sample: {first_sample.shape}")
+```
+
+## Citation
+
+If you use this dataset, please cite the original DCRNN paper:
+
+```bibtex
+@inproceedings{li2018dcrnn,
+  title={Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting},
+  author={Li, Yaguang and Yu, Rose and Shahabi, Cyrus and Liu, Yan},
+  booktitle={International Conference on Learning Representations},
+  year={2018}
+}
+```
+
+## Dataset Generation
+
+The code used to generate this Hugging Face-compatible dataset can be found at [witgaw/DCRNN](https://github.com/witgaw/DCRNN), a fork of the original DCRNN repository with enhanced data processing capabilities.
+
+## Original Data Source
+
+This dataset is derived from the original PEMS-BAY dataset used in the DCRNN paper.
+
+## License
+
+MIT License - See LICENSE file for details.

sensor_graph/README.md

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+# Sensor Graph Data for PEMS-BAY
+
+This directory contains spatial information about the traffic sensors used in the PEMS-BAY dataset.
+
+## Files
+
+- `graph_sensor_locations_bay.csv`: Sensor coordinates (latitude, longitude) for 325 sensors  
+- `distances_bay_2017.csv`: Pairwise distances between sensors in meters
+- `adj_mx_bay.npy`: Pre-computed adjacency matrix (325×325) for graph neural networks
+- `adj_mx_bay_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/graph_sensor_locations_bay.csv')
+print(f"Dataset has {len(locations)} sensors")
+
+# Load distances (for custom graph construction)
+distances = pd.read_csv('sensor_graph/distances_bay_2017.csv')
+
+# Load pre-computed adjacency matrix
+adj_matrix = np.load('sensor_graph/adj_mx_bay.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.

sensor_graph/adj_mx_bay.npy

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+size 422628

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