Understanding user equilibrium states of road networks: Evidence from two Chinese mega-cities using taxi trajectory mining

User equilibrium (UE) has long been regarded as the cornerstone of transport planning studies. Despite its fundamental importance, our understanding of the actual UE state of road networks has remained surprisingly incomplete. Using big datasets of taxi trajectories, this study investigates the UE states of road networks in two Chinese mega-cities, i.e., Wuhan and Shenzhen. Effective indicators, namely relative gaps, are introduced to quantify how actual traffic states deviate from theoretical UE states. Advanced machine learning techniques, including XGBoost and SHAP values, are employed to analyze nonlinear relationships between network disequilibrium states and seven influencing factors extracted from trajectory data. The results in these two study areas reveal consistent and significant gaps between actual traffic states and the theoretical UE states at various times of the day during both weekdays and weekends. The XGBoost analysis shows that differences in travel distances, travel speeds, and signalized intersection numbers among alternative routes are the primary causes of road network disequilibrium. The results of this study could present several important methodological and policy implications for using the UE models in transport applications.

• Record URL:
  • https://doi.org/10.1016/j.tra.2024.103976
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0965856424000247
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/09658564
• Supplemental Notes:
  • © 2024 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Chen, Bi Yu
  • Chen, Xuan-Yan
  • Chen, Hui-Ping
  • Huang, Yan-Bin
  • Jia, Tao
  • Lam, William H K
• Publication Date: 2024-2

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References;
• Pagination: 103976
• Serial:
  • Transportation Research Part A: Policy and Practice
  • Volume: 180
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0965-8564
  • Serial URL: http://www.sciencedirect.com/science/journal/09658564

Subject/Index Terms

• TRT Terms: Data mining; Equilibrium (Systems); Machine learning; Route choice; Taxicabs; Trajectory control
• Geographic Terms: Shenzhen (China); Wuhan (China)
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Vehicles and Equipment;

Filing Info

• Accession Number: 01905729
• Record Type: Publication
• Files: TRIS
• Created Date: Jan 26 2024 10:02AM