Traffic State Estimation of Road Sections Without Detectors Based on Multisource Causal Interpretation Graph

Dongwei Xu, Yufu Tang, Hang Peng, Haifeng Guo, Qi Xuan

Abstract: Road traffic state estimation is an essential component of intelligent transportation systems (ITSs). However, some road sections lack fixed detectors, making it difficult to obtain complete traffic state data for the entire city. Thus, through the fusion of data collected from both the fixed and mobile detectors, we propose a novel model framework, a reasoning model based on the multisource causal interpretation graph (MS-CIG), to infer traffic state data for the road sections without detectors. First, a cross-layer random walk strategy is proposed to achieve fusion and embedding of road network topology graphs (constructed by the fixed detector data) and road network logic graphs (constructed by the mobile detector data). Second, the spatial similarity between road sections can be calculated by combining road sections static feature data, Point of Interest (PoI) distribution and embedding features; thereby, a weighted spatiotemporal graph of the road network is constructed. Finally, an interpretive module is combined to provide interpretability analysis for graph-based semi-supervised inference, thereby making the inferred data generated by graph-based semi-supervised more reasonable and accurate. Through the above processing, we are able to obtain the complete traffic state data for the entire city. Experimental evaluations on a real traffic data set demonstrate the superiority of the proposed method.