Resilience assessment and enhancement of urban road networks subject to traffic accidents: a network-scale optimization strategy

• Published in: Journal of intelligent transportation systems Vol. 28; no. 4; pp. 494 - 510
• Main Authors: Tao, Wang, Wang, Zhuang-Zhuang, Liu, Cong-Jian, Lu, Yi-Ning, Zhang, Yi, Jiang, Ze-Hao
• Format: Journal Article
• Language: English
• Published: Philadelphia Taylor & Francis 03.07.2024; Taylor & Francis Ltd
• Subjects: Constraints; Greedy algorithms; Linear programming; Natural disasters; network resilience; Nonlinear programming; Optimization; Optimization models; Peak hour traffic; Performance degradation; Recovery time; Resilience; Roads & highways; Sensitivity analysis; traffic accident; Traffic accidents; Traffic accidents & safety; Traffic capacity; Traffic congestion; Traffic control; Traffic management; traffic signal optimization; Traffic signals; Transportation networks; urban road network
• ISSN: 1547-2450; 1547-2442
• DOI: 10.1080/15472450.2022.2141119

This study is aimed at investigating the resilience degradation caused by traffic accidents and developing relevant resilience optimization strategies. A two-stage accident resilience triangle framework was proposed by comparing the differences between natural disasters and traffic accidents. To maximize system resilience, a network-wide traffic signal optimization model was presented. Spillback constraints and equilibrium constraints were established to enhance the capacity of urban-road networks to minimize congestion escalation, in addition to rapid recovery. A two-level algorithm based on greedy strategy and gradient descent was designed to solve the proposed non-linear programming model. In the experiment, a virtual road network was constructed based on the Simulation of Urban Mobility (SUMO) platform for validation and sensitivity analysis. The experimental results revealed that: (1) Compared to the traditional resilience framework, the proposed two-stage accident resilience framework can more reasonably describe the change mechanism of road network resilience under disturbance. (2) The proposed resilience-based traffic signal optimization model improved the system resilience under different conditions of traffic demand, accident severity, and rescue time in terms of the maximum performance degradation and recovery time. Precisely, the resilience loss is reduced by a maximum of 1.4%. Finally, the proposed model was further implemented with field data. The resilience improvement was significant during the evening rush hour. The results of this study contribute toward transportation resilience research and accident rescue strategies with respect to traffic management and control.