Turn-level network traffic bottleneck identification using vehicle trajectory data. (July 2022)

• Record Type: Journal Article
• Authors: Wei, Lei; Chen, Peng; Mei, Yu; Wang, Yunpeng
• Is Part Of: Transportation research. Volume 140(2022)
• Publication Date: 2022-07
• Subjects: Turn-level bottleneck identification -- Vehicle trajectories -- Directional congestion correlation -- Correlation probability; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2022.103707
• Languages: English
• ISSNs: 0968-090X

Abstract

Abstract: Identifying traffic bottlenecks is a prerequisite to alleviate traffic congestion in urban networks. However, the state-of-the-art methods for bottleneck identification stay at the segment level which assume bottlenecks only result from congested road segments (CRSs). The possibility that a turning direction in a road segment could be a bottleneck has not been thoroughly investigated. In addition, most existing techniques only focus on the congestion degree of road segments, however ignoring the correlations between CRSs and their underlying congestion propagations. This study proposed a framework for turn-level bottleneck identification in large-scale road networks using vehicle trajectory data. First, filtered GPS trajectory data was used to identify CRSs and their congestion start time. Then, congestion correlation graphs and corresponding spanning trees were constructed by investigating forward and backward correlations between CRSs. These directional correlations were later modeled via a Bayesian inference approach. Last, congestion correlation cost models were built up to identify turn-level bottlenecks considering the probability of congestion correlation. Both simulation and field experiments were conducted to evaluate the performance of the proposed framework and compare with the state-of-the-art methods. Results reveal that our framework can effectively capture directional correlations between CRSs, and successfully identify turn-level bottlenecks in large-scale networks.