liyaguang/DCRNN

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1439 stars · 423 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A TensorFlow implementation of the Diffusion Convolutional Recurrent Neural Network (DCRNN) from the ICLR 2018 paper on data-driven traffic forecasting. It models road networks as graphs and forecasts traffic time series on benchmark datasets like METR-LA and PEMS-BAY.

Use cases

• forecast traffic speed on highway sensor networks
• reproduce DCRNN results from the ICLR 2018 paper
• train spatiotemporal graph neural networks on METR-LA or PEMS-BAY
• benchmark traffic prediction models
• learn graph-based time series forecasting with TensorFlow

When to choose

• you need a reference implementation of DCRNN for research or benchmarking
• you want to experiment with graph recurrent networks for traffic or sensor forecasting
• you are working with the METR-LA or PEMS-BAY datasets

When to avoid

• you need production-ready traffic forecasting with modern framework support
• you want PyTorch or newer TensorFlow 2.x compatibility out of the box
• your sensor network is not the Los Angeles road graph, since adjacency is precomputed for specific sensor ids

Facets

library · maturity maintenance

deep-learning machine-learning data-science streaming deep-learning machine-learning time-series python tensorflow graph-neural-networks traffic-forecasting spatiotemporal iclr-2018 research-code metr-la pems-bay transportation linux macos gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="liyaguang/DCRNN")