Dynamic Spatial-Temporal Graph Convolutional Neural Networks for Traffic Forecasting

Zulong Diao · Xin Wang · Dafang Zhang · Yingru Liu · Kun Xie · Shaoyao He

351 citations0/0 files storedProceedings of the AAAI Conference on Artificial Intelligence

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Traffic Prediction and Management Techniques Air Quality Monitoring and Forecasting Advanced Graph Neural Networks

Abstract

Graph convolutional neural networks (GCNN) have become an increasingly active field of research. It models the spatial dependencies of nodes in a graph with a pre-defined Laplacian matrix based on node distances. However, in many application scenarios, spatial dependencies change over time, and the use of fixed Laplacian matrix cannot capture the change. To track the spatial dependencies among traffic data, we propose a dynamic spatio-temporal GCNN for accurate traffic forecasting. The core of our deep learning framework is the finding of the change of Laplacian matrix with a dynamic Laplacian matrix estimator. To enable timely learning with a low complexity, we creatively incorporate tensor decomposition into the deep learning framework, where real-time traffic data are decomposed into a global component that is stable and depends on long-term temporal-spatial traffic relationship and a local component that captures the traffic fluctuations. We propose a novel design to estimate the dynamic Laplacian matrix of the graph with above two components based on our theoretical derivation, and introduce our design basis. The forecasting performance is evaluated with two realtime traffic datasets. Experiment results demonstrate that our network can achieve up to 25% accuracy improvement.

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References

2017Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting 2017Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction 2003Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results 2017Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction 2016DNN-based prediction model for spatio-temporal data 2018Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction 2016Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework 2014Forecasting holiday daily tourist flow based on seasonal support vector regression with adaptive genetic algorithm

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2023Spatio-Temporal Meta-Graph Learning for Traffic Forecasting 2020How to Build a Graph-Based Deep Learning Architecture in Traffic Domain: A Survey 2021Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network 2022Adaptive Spatio-temporal Graph Neural Network for traffic forecasting

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