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A composite traffic flow modeling approach for incident-responsive network traffic assignment

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  • Sheu, Jiuh-Biing

Abstract

This paper presents a hybrid traffic simulation-based model to address the network traffic route choice issue under conditions of lane-blocking incidents on surface streets. The proposed approach includes four sequential mechanisms: (1) link flow loading, (2) link traffic moving, (3) link cost calculation, and (4) searching the shortest path. To deal with the traffic flows moving on lane-blocking links, specific incident-induced link traffic flow models, which are extended from the Lighthill–Whitham (L–W for short) model, are formulated. A simulation-based approach is then proposed to determine the instantaneous shortest path associated with each vehicle approaching to each given intersection on the network. In addition, numerical examples associated with diverse incident scenarios are investigated. The numerical results demonstrate the competitiveness of the proposed simulation-based method by reducing the network-wide path travel time by 11.4% and the incident impact on link traffic flows by 66.7% in comparison with the Paramics traffic simulator. It is expected that this study can provide linkage between the fields of incident management and dynamic traffic assignment that will allow the development of such related technologies as real-time incident-responsive route guidance and incident management systems.

Suggested Citation

  • Sheu, Jiuh-Biing, 2006. "A composite traffic flow modeling approach for incident-responsive network traffic assignment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 367(C), pages 461-478.
    • Handle: RePEc:eee:phsmap:v:367:y:2006:i:c:p:461-478
    DOI: 10.1016/j.physa.2005.11.039
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    References listed on IDEAS

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    3. Xiao, Jianli, 2019. "SVM and KNN ensemble learning for traffic incident detection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 517(C), pages 29-35.

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