URBAN-RURAL BUS PATH PLANNING BASED ON ANT COLONY OPTIMIZATION ALGORITHM

• Published in: International archives of the photogrammetry, remote sensing and spatial information sciences. Vol. XLII-3/W10; pp. 451 - 456
• Main Authors: Li, J., Wei, B.
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Gottingen Copernicus GmbH 07.02.2020; Copernicus Publications
• Subjects: Algorithms; Analytic hierarchy process; Ant colony optimization; Buses; Buses (vehicles); Computer simulation; Economic analysis; Economic benefits; Optimization; Path planning; Pheromones; Public transportation; Rural areas; Simulation; Urban areas; Urban development; Urbanization
• ISSN: 2194-9034; 1682-1750; 1682-1777; 2194-9034
• DOI: 10.5194/isprs-archives-XLII-3-W10-451-2020

With the advancement of urbanization, urban-rural public transport issues have become one of the most critical issues in urban development. The paper makes a detailed study on the optimization of urban-rural bus routes in Erqi District of Zhengzhou, China. The factors of bus stop selection are analyzed, and the three categories, including traffic road condition factors, economic benefit factors and waiting number factors, are mainly considered. The analytic hierarchy process is used to determine 35 specific objectives of urban-rural bus stop optimization, 20 of which are selected for simulation experiment with large weight. Then the ant colony optimization (ACO) algorithm in path optimization is analyzed, which has the following two advantages. First, the global pheromone update is combined with the local pheromone update to enhance the algorithm's optimization ability and convergence speed. Second, through the method of spatial contraction transformation, the ant constructs a solution to reduce the number of construction steps and speed up the operation. Based on the actual analysis of urban-rural public transportation in the Erqi District of Zhengzhou, a simulation experiment is executed to show that the ACO algorithm is able to find out the optimal path, which is 15.1% shorter than the ant colony system (ACS) algorithm. The ACO algorithm improved path planning has good time effectiveness and path practicability.