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

Abstract

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.

Keywords:

• network resilience
• traffic accident
• traffic signal optimization
• urban road network

Acknowledgements

The authors would like to thank every anonymous reviewer for their constructive advice and comments, and the staff of the traffic management department for their assistance.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the [The National Natural Science Foundation of China] under Grant [52102377]; [China Postdoctoral Science Foundation] under Grant [2021M701312]; [The Science and Technology Commission of Shanghai Municipality] under Grant [19DZ1208800]; and [The Key Laboratory of Urban Traffic Management Integration and Optimization] under Grant [2017KFKT02].