A Method of Planning Disaster Emergency Rescue Paths in Road-Free Environment

Abstract

The occurrence of natural disasters has caused great loss of lives and properties that affected many people. To improve the efficiency of the emergency rescue efforts, the problem of road planning for emergency workers, vehicles, and other mobile objects should be overcome. Due to the characteristics of the emergency planning for rescue paths, timely, effective, and rapid efforts should be taken into effect based on geographical data in the road-free environment. This paper analyzes trafficability, researches both access ability and traffic efficiency, and uses traffic efficiency concerning the road-free environment as an influential weight and timeliness to improve the A∗ algorithm, which realizes the rapid planning of the mobile objects’ emergency rescue paths in the road-free environment, providing decision-making services for the rescue path planning of disaster emergencies in the environment of no road network. Finally, the experiments were carried out using available data for Shuozhou City, Shanxi Province. The results show that the research can provide a reference for the rescue path planning of disaster emergencies and has contributed to theoretical and applicable aspects of the research.

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“…Tang et al established the shortest path calculation model based on GIS network analysis, analyzed and calculated the railway network data set, and obtained the shortest distance from emergency rescue point to disaster point, which provided a reference for the selection of emergency rescue path of railway emergencies (Tang et al, 2019). Xu et al proposed an improved A-star algorithm to realize the rapid planning of the emergency rescue path of moving objects in a roadless environment (Xu et al, 2022). Wen * Xuedong Zhang, zhangxuedong@bucea.edu.cn et al proposed a co-evolutionary optimization algorithm to solve the low efficiency of urban road congestion emergency rescue, which realized the co-evolution of sub-path weight function and future environmental dynamics (Wen et al, 2020).…”