DeepTSP: Deep traffic state prediction model based on large-scale empirical data

Real-time traffic state (e.g., speed) prediction is an essential component for traffic control and management in an urban road network. How to build an effective large-scale traffic state prediction system is a challenging but highly valuable problem. This study focuses on the construction of an effective solution designed for spatiotemporal data to predict the traffic state of large-scale traffic systems. In this study, we first summarize the three challenges faced by large-scale traffic state prediction, i.e., scale, granularity, and sparsity. Based on the domain knowledge of traffic engineering, the propagation of traffic states along the road network is theoretically analyzed, which are elaborated in aspects of the temporal and spatial propagation of traffic state, traffic state experience replay, and multi-source data fusion. A deep learning architecture, termed as Deep Traffic State Prediction (DeepTSP), is therefore proposed to address the current challenges in traffic state prediction. Experiments demonstrate that the proposed DeepTSP model can effectively predict large-scale traffic states.

Automated summary

This study focuses on building an effective solution for spatiotemporal data to predict the traffic state of large-scale traffic systems. The research analyzes the propagation of traffic states along the road network and proposes a deep learning architecture called Deep Traffic State Prediction (DeepTSP) to address the challenges in traffic state prediction, which is proven to effectively predict large-scale traffic states.