Route Planning of Freeway Rescue Vehicles Based on Spatial–Temporal Analysis

Abstract

Timely accident rescue on freeways is critical to reducing the loss of lives and property. However, current cost models lack specificity, and there are no proven algorithms for solving them. Because there are distinct distribution characteristics of different freeway accidents, a spatial–temporal analysis driven route planning model of rescue vehicles is presented. By simultaneously analyzing the spatial–temporal characteristics of freeways and urban roads involved in the rescue process, five main impedance parameters were determined to build the route planning model. To efficiently and accurately solve the model, a dynamic ant colony optimization (ACO) algorithm integrating the merits of adaptive A* algorithm was proposed. Based on the established evaluation criteria, the validity and feasibility of the proposed model were verified by the case studies in Dongying City. Experimental results revealed that the proposed algorithm was capable of comprehensively searching the road networks for route planning within a reasonable range, solving the problem of easily falling into the local optimum and accelerating the convergence speed and being practically applied in rescue vehicle scheduling.