Deep Learning on Traffic Prediction: Methods, Analysis and Future Directions
It addresses the problem of traffic prediction for intelligent transportation systems, but it is incremental as it focuses on reviewing and analyzing existing methods rather than introducing new ones.
This paper provides a comprehensive survey of deep learning methods for traffic prediction, summarizing existing approaches, listing state-of-the-art applications, and evaluating performance through experiments on a real-world dataset.
Traffic prediction plays an essential role in intelligent transportation system. Accurate traffic prediction can assist route planing, guide vehicle dispatching, and mitigate traffic congestion. This problem is challenging due to the complicated and dynamic spatio-temporal dependencies between different regions in the road network. Recently, a significant amount of research efforts have been devoted to this area, especially deep learning method, greatly advancing traffic prediction abilities. The purpose of this paper is to provide a comprehensive survey on deep learning-based approaches in traffic prediction from multiple perspectives. Specifically, we first summarize the existing traffic prediction methods, and give a taxonomy. Second, we list the state-of-the-art approaches in different traffic prediction applications. Third, we comprehensively collect and organize widely used public datasets in the existing literature to facilitate other researchers. Furthermore, we give an evaluation and analysis by conducting extensive experiments to compare the performance of different methods on a real-world public dataset. Finally, we discuss open challenges in this field.