Data-driven evacuation and rescue traffic optimization with rescue contraflow control

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外文摘要：In response to local sudden disasters,e.g.,high-rise office or residential building fire disasters,road occupation can cause conflicts,and traffic directions may be opposite between evacuation vehicles and rescue vehicles;moreover,lane contraflow can be adopted to meet these surge traffic demands.However,lane contraflow that provides more roads for rescue vehicles reduces the traffic supply in the evacuation direction.It is unclear how to control the number of contraflow roads used by rescue vehicles to coordinate evacuation and rescue traffic operations.Here,we adjust the critical rescue traffic volume of reversing the normal road traffic direction to control rescue contraflow.Additionally,we propose a multiobjective mixed integer linear programming formulation for evacuation and rescue traffic optimization.Additionally,considering that the upper limit of the critical rescue traffic volume is unknown and that the proposed formulation includes multiple objectives and multi-priority vehicle classes,a three-stage solving algorithm is developed.Next,a large-scale evacuation and rescue traffic optimization result dataset is obtained for the Nguyen-Dupuis road network,and the impact of different rescue contraflow control plans on evacuation and rescue traffic is studied based on data-driven sta-tistical analysis.The results show that by adjusting the optimal rescue traffic route,the critical rescue traffic volume for reversing the normal road traffic direction can reduce the interference of rescue traffic to evacuation traffic operation performance without reducing rescue traffic operation performance,and can be used to coor-dinate evacuation and rescue traffic operation under rescue contraflow.

外文关键词：

Evacuation and rescue traffic optimizationRescue contraflow controlMultiobjective mixed integer linear programmingThree-stage solving algorithmData-driven statistical analysis

作者：

Zheng Liu、Jialin Liu、Xuecheng Shang、Xingang Li

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作者单位：

School of Management Engineering,Zhengzhou University of Aeronautics,Zhengzhou 450046,China

Collaborative Innovation Center for Aviation Economy Development of Henan Province,Zhengzhou University of Aeronautics,Zhengzhou 450046,China

School of Systems Science,Beijing Jiaotong University,Beijing 100044,China

Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport,Beijing Jiaotong University,Beijing 100044,China

School of Transportation Engineering,Shandong Jianzhu University,Jinan 250101,China

School of Systems science,Beijing Jiaotong University,Beijing 100044,China

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出版年：

2024

DOI：

10.1016/j.jnlssr.2023.11.002

安全科学与韧性(英文)

ISSN：

年,卷(期)：2024.(1)