A macroscopic and hierarchical location model of regional road traffic disaster relief material repository

Abstract

Since the disaster point of road traffic emergency and the emergency demand were uncertain, the demand weighting model and the hierarchical location model are suitable for the characteristics of road traffic emergency. According to the requirements for coverage area of the macroscopic-location of the large area of disaster relief material repository, the demand weighting model and the hierarchical location model were established in this article. Among them, the demand weight model was solved by modeling, and the demand weight of each disaster point was obtained; the location model was combined with immune algorithm and ant colony algorithm to get the hierarchical location scheme. Finally, Jing-jin-ji that represented China's ''capital circle'' was taken as an example, the model was solved using MATLAB, the mathematical software, and provided scientific and reasonable decision-making support for location selection. Moreover, it also provided a basis for the classification of the road traffic disaster relief material repository.

Emergency Response Resource Allocation in Sparse Network Using Improved Particle Swarm Optimization

“…The inertia weight is adjusted in Equations ( 7) and ( 8), and a constriction factor is introduced [32][33][34][35]. All particles in PSO iteratively update their speed and location according to Equations ( 7) and (8).…”

“…The hierarchical model weighted demand for response services, and the route optimization model consisted of an immune response model coupled with an ant colony algorithm. They made various simplifying assumptions and idealizations, and used locations in Beijing-Tianjin-Hebei region to validate the model and establish its feasibility [ 8 ]. Wang et al analyzed traffic congestion, weather and road conditions to develop a multi-objective model that took account of road network reliability [ 9 ].…”

Geo-Economic Analysis Based on an Improved Ant Colony Optimization

“…Additionally, some studies have improved the ACO for different application areas. For example, one study focused on relief channel selection and the improvement of the algorithm parameters [39]. Wei et al [40] proposed an improved ACO for urban bus network optimization based on existing bus routes, and another group of researchers [41] proposed a path planning algorithm for unmanned surface vehicles based on the ACO.…”