Journal of Jilin University(Engineering and Technology Edition) ›› 2025, Vol. 55 ›› Issue (4): 1328-1335.doi: 10.13229/j.cnki.jdxbgxb.20240313

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Urban passenger transport planning algorithm based on location potential energy and multi source data

Yi GUO1,2(),Shu-wei WEI3,Tao JIANG4()   

  1. 1.School of Human Settlements and Civil Engineering，Xi'an Jiaotong University，Xi'an 710049，China
    2.School of Civil Engineering and Architecture，Henan University of Science and Technology，Luoyang 471000，China
    3.Xi'an University of Architecture and Technology Urban Planning and Design Research Institute，Xi'an 710000，China
    4.School of Art，Xi'an University of Architecture and Technology，Xi'an 710055，China
  • Received:2024-03-07 Online:2025-04-01 Published:2025-06-19
  • Contact: Tao JIANG E-mail:18638355566@163.com;jiangtao@xauat.edu.cn

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Abstract:

A research on urban passenger transportation planning algorithm based on location potential energy and multi-source data was proposed to address the problems of traffic congestion and resource waste caused by unreasonable layout and inappropriate scale of passenger transportation in some cities. Firstly， an appropriate method was selected to integrate urban passenger transportation multi-source data from the fusion of the original data level， feature data level， and transportation theory level. Then， based on the probability model of traffic distribution and the Furness model， a traffic distribution prediction model was constructed， introducing location potential energy to predict the distribution of passenger transportation trips between traffic analysis communities. Finally， a dual level planning model for urban passenger transportation was constructed， and an improved quantum particle swarm optimization algorithm was used to solve it， in order to achieve urban passenger transportation planning. The experimental results show that the proposed method has higher scores than the comparison method in various evaluation indicators， with the highest score reaching 50.492. The highest congestion rate of vehicles is only 29%， which is practical.

Key words: engineering of commuications and transportation system, location potential energy, multi source data fusion, urban passenger transportation, transportation planning, quantum particle swarm optimization algorithm

CLC Number: 

  • U292

Fig.1

Improved quantum particle swarmoptimization algorithm"

Fig.2

Main process of solving bilevelprogramming model"

Fig.3

Analysis of urban passenger transport andcommunity division"

Table 1

Experimental environment parameters"

实验环境算法参数数值
硬件部分-处理器Intel Core i5-2450M CPU @ 2.50 GHz
RAM8 GB
GPUNvidia GeForce RTX 3080
操作系统Windows10 64bit
模拟环境Matlab 2021b
软件部分改进的量子粒子群优化算法粒子群数目50
粒子维度5
最大迭代次数100
收缩-扩张因子最大值1.0
收缩-扩张因子最小值0.5

Table 2

Score detection results of various indicatorsusing different methods"

评级指标本文方法文献［5］方法文献［6］方法
协调性指标得分17.96512.84114.058
可达性指标得分12.6348.9427.541
换乘度指标得分50.49232.71344.713

Fig.4

Performance comparison of different methods insolving bilevel programming models"

Fig.5

Comparison of vehicle congestion levels afterapplication of different methods"

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