International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume XLII-3/W10
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W10, 451–456, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W10-451-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W10, 451–456, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W10-451-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  07 Feb 2020

07 Feb 2020

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

J. Li1,2 and B. Wei1,2 J. Li and B. Wei
  • 1Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin University of Technology, Jiangan Road, Guilin, China
  • 2College of Geomatics and Geoinformation, Guilin University of Technology, Jiangan Road, Guilin, China

Keywords: Ant Colony Optimization Algorithm, Urban-Rural Public Transport, Path Planning, Analytic Hierarchy Process, Pheromone, Judgment Matrix, Space Shrinkage Transformation

Abstract. 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.