Optimized Graph Convolution Recurrent Neural Network for Traffic Prediction

Kan Guo · Yongli Hu · Sean Qian · Hao Liu · Ke Zhang · Yanfeng Sun +2 more

384 citations0/0 files storedIEEE Transactions on Intelligent Transportation Systems

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Traffic Prediction and Management Techniques Traffic control and management Advanced Graph Neural Networks

Abstract

Traffic prediction is a core problem in the intelligent transportation system and has broad applications in the transportation management and planning, and the main challenge of this field is how to efficiently explore the spatial and temporal information of traffic data. Recently, various deep learning methods, such as convolution neural network (CNN), have shown promising performance in traffic prediction. However, it samples traffic data in regular grids as the input of CNN, thus it destroys the spatial structure of the road network. In this paper, we introduce a graph network and propose an optimized graph convolution recurrent neural network for traffic prediction, in which the spatial information of the road network is represented as a graph. Additionally, distinguishing with most current methods using a simple and empirical spatial graph, the proposed method learns an optimized graph through a data-driven way in the training phase, which reveals the latent relationship among the road segments from the traffic data. Lastly, the proposed method is evaluated on three real-world case studies, and the experimental results show that the proposed method outperforms state-of-the-art traffic prediction methods.

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References

2018Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting 2017Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting 2019Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting 2014Traffic Flow Prediction With Big Data: A Deep Learning Approach 2017Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction 2004Travel-Time Prediction With Support Vector Regression 2014Short-term traffic forecasting: Where we are and where we’re going 2002Comparison of parametric and nonparametric models for traffic flow forecasting

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2022Traffic prediction using artificial intelligence: Review of recent advances and emerging opportunities 2020How to Build a Graph-Based Deep Learning Architecture in Traffic Domain: A Survey

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