Deep Learning on Traffic Prediction: Methods, Analysis, and Future Directions

TLDR

Traffic prediction is crucial for intelligent transportation systems, enabling route planning, vehicle dispatching, and congestion mitigation, yet it remains difficult due to complex spatio‑temporal dependencies across road networks, a challenge that recent deep‑learning research has begun to address. This paper surveys deep‑learning approaches to traffic prediction from multiple perspectives. The authors taxonomize existing methods, catalogue state‑of‑the‑art techniques and public datasets, and conduct extensive experiments on a real‑world dataset to compare performance. The study highlights remaining challenges in the field.

Abstract

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.