Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting

TL;DR

This paper introduces TGC-LSTM, a deep learning framework that models traffic networks as graphs to improve the accuracy of network-scale traffic forecasting by capturing complex spatiotemporal dependencies.

Contribution

It proposes a novel traffic graph convolutional LSTM model that incorporates physical network topology and regularization for better interpretability and performance in traffic prediction.

Findings

Outperforms baseline methods on real-world datasets

Recognizes influential road segments through weight visualization

Enhances model interpretability with regularization

Abstract

Traffic forecasting is a particularly challenging application of spatiotemporal forecasting, due to the time-varying traffic patterns and the complicated spatial dependencies on road networks. To address this challenge, we learn the traffic network as a graph and propose a novel deep learning framework, Traffic Graph Convolutional Long Short-Term Memory Neural Network (TGC-LSTM), to learn the interactions between roadways in the traffic network and forecast the network-wide traffic state. We define the traffic graph convolution based on the physical network topology. The relationship between the proposed traffic graph convolution and the spectral graph convolution is also discussed. An L1-norm on graph convolution weights and an L2-norm on graph convolution features are added to the model's loss function to enhance the interpretability of the proposed model. Experimental results show that the proposed model outperforms baseline methods on two real-world traffic state datasets. The visualization of the graph convolution weights indicates that the proposed framework can recognize the most influential road segments in real-world traffic networks.