Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting

Rose Yu · Cyrus Shahabi · Yan Liu

3,639 citations0/0 files storedarXiv (Cornell University)

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Traffic Prediction and Management Techniques Traffic control and management Air Quality Monitoring and Forecasting

Abstract

Spatiotemporal forecasting has various applications in neuroscience, climate and transportation domain. Traffic forecasting is one canonical example of such learning task. The task is challenging due to (1) complex spatial dependency on road networks, (2) non-linear temporal dynamics with changing road conditions and (3) inherent difficulty of long-term forecasting. To address these challenges, we propose to model the traffic flow as a diffusion process on a directed graph and introduce Diffusion Convolutional Recurrent Neural Network (DCRNN), a deep learning framework for traffic forecasting that incorporates both spatial and temporal dependency in the traffic flow. Specifically, DCRNN captures the spatial dependency using bidirectional random walks on the graph, and the temporal dependency using the encoder-decoder architecture with scheduled sampling. We evaluate the framework on two real-world large scale road network traffic datasets and observe consistent improvement of 12% - 15% over state-of-the-art baselines.

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References

2018Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting 2017Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction 2017Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction

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2018Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting 2019Graph WaveNet for Deep Spatial-Temporal Graph Modeling 2019T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction 2020GMAN: A Graph Multi-Attention Network for Traffic Prediction 2020Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting 2022Graph neural network for traffic forecasting: A survey 2020Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting 2021Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting

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