Uncovering the spatiotemporal patterns of traffic congestion from large-scale trajectory data: A complex network approach

Understanding the spatiotemporal characteristics of traffic congestion is the cornerstone of generating traffic management and control strategies. Based on the large-scale taxi trajectory data in Shenzhen, China, this study designs an effective framework to explore the spatiotemporal patterns of traffic congestions. To bridge trajectory data with urban road networks, we develop a two-stage map-matching method from the aspects of distance and angle. Then, the free-flow speed of each road segment is extracted and employed to identify traffic congestion. In this way, a novel complex network method, named chronological network (chronnet), is utilized for traffic congestion modeling, and we employ an overlapping community detection algorithm to identify regionlevel bottlenecks. According to the network properties, we explore the influence scope of traffic congestions and uncover the role of each road segment in the propagation process. Meanwhile, community detection results indicate that there are typical local clustering structures in traffic congestions, and each community also has its unique traffic characteristics. Overall, these findings reveal that the complex network can effectively mine the consecutive patterns of traffic congestion. (C) 2022 Elsevier B.V. All rights reserved.

Automated summary

Understanding the spatiotemporal characteristics of traffic congestion is crucial for generating effective traffic management and control strategies. This study uses large-scale taxi trajectory data to explore the patterns of traffic congestion. By combining map-matching techniques and complex network analysis, the study identifies traffic congestion, analyzes the influence of congestion, and reveals the role of each road segment in the propagation process. The findings suggest that traffic congestion exhibits typical local clustering structures and each community has unique traffic characteristics.