吉林大学学报(工学版) ›› 2026, Vol. 56 ›› Issue (3): 711-724.doi: 10.13229/j.cnki.jdxbgxb.20240908

• 交通运输工程·土木工程 • 上一篇    

地铁站周边常规公交换乘站点布局多目标优化模型

程国柱1(),吕岩峰1,冯天军2()   

  1. 1.东北林业大学 土木与交通学院,哈尔滨 150040
    2.吉林建筑大学 交通科学与工程学院,长春 130118
  • 收稿日期:2024-08-17 出版日期:2026-03-01 发布日期:2026-03-31
  • 通讯作者: 冯天军 E-mail:guozhucheng@126.com;fengtianjun@ jlju.edu.cn
  • 作者简介:程国柱(1977-),男,教授,博士.研究方向:交通安全,智能交通系统.E-mail:guozhucheng@126.com
  • 基金资助:
    中央高校基本科研业务费专项资金项目(2572023CT21);吉林省科技发展计划项目(20220402030GH)

Multi⁃objective optimization model for bus transfer station layouts around metro stations

Guo-zhu CHENG1(),Yan-feng LYU1,Tian-jun FENG2()   

  1. 1.School of Civil Engineering & Transportation,Northeast Forestry University,Harbin 150040,China
    2.School of Transportation Science & Engineering,Jilin University of Architecture,Changchun 130118,China
  • Received:2024-08-17 Online:2026-03-01 Published:2026-03-31
  • Contact: Tian-jun FENG E-mail:guozhucheng@126.com;fengtianjun@ jlju.edu.cn

摘要:

为提升公共交通换乘效率,开展了地铁站周边常规公交换乘站点布局多目标协同优化研究。构建了考虑乘客换乘便捷性、出行时间成本和公交站点负荷均衡性3个主要目标的多目标协同优化模型,给出了基于社交蜘蛛优化算法(SSOA)的模型求解方法,引入动态学习率调整方法和帕累托最优解集对算法进行优化。案例分析结果表明,优化后最短路径平均缩小28.98%,最短路径时间平均缩小34.22%,站点平均负荷均衡性提升6.5%,说明该模型站点布局优化效果较好。

关键词: 城市交通, 地铁站, 公交换乘站点布局, 多目标协同优化, 社交蜘蛛优化算法

Abstract:

To enhance the efficiency of public transit transfers, a multi-objective collaborative optimization study on the layout of regular bus transfer stations around subway stations was conducted.A multi-objective collaborative optimization model considering three main objectives:passenger transfer convenience,travel time cost,and bus station load balance,was constructed.A solution method based on the social spider optimization algorithm was provided,and dynamic learning rate adjustment methods and Pareto optimal solution sets were introduced to optimize the algorithm.The case analysis results show that after optimization,the average shortest path is reduced by 28.98%,the average shortest path time is reduced by 34.22%,and the average station load balance is improved by 6.5%,indicating that the model has a good optimization effect on station layout.

Key words: urban traffic, metro stations, bus transfer station layout, multi-objective collaborative optimization, social spider optimization algorithm(SSOA)

中图分类号: 

  • U491

图1

社交蜘蛛优化算法(SSOA)计算流程"

图2

案例站点位置关系"

图3

地铁换乘站A 2023年5月逐日分时段进出站客流"

图4

地铁换乘站A周边公交站点2023年5月逐日分时段进出站客流"

表1

地铁换乘站A周边公交站点刷卡高峰时段及刷卡数"

公交站点刷卡高峰时段高峰时段刷卡数/(人次·h-1
B18~9214
B216~171 695
B37~8648
B47~8964
B517~18184
B616~171 797
B77~81 400
B816~17916
B97~8546

表2

地铁换乘站A进出站高峰时段及最大进出站客流量"

时间进站高峰时段进站最大客流/(人次·h-1出站高峰时段出站最大客流/(人次·h-1
工作日16~171 4827~82 397
非工作日16~171 3467~82 301

图5

地铁换乘站A周边公交站点客流分布"

图6

优化后公交站点位置"

表3

优化前、后各目标节点最短路径"

公交站点优化前最短路径/m优化后最短路径/m最短路径缩小比例/%
B12622398.70
B227520724.83
B335827822.40
B436220842.65
B5120143-18.78
B636518649.12
B736221141.62
B822012941.22
B930815749.05

表4

优化前、后各目标节点最短路径时间"

目标节点优化前最短路径时间/min优化后最短路径时间/min最短路径时间缩小比例/%
B15.194.856.49
B212.848.6232.82
B38.436.0827.92
B421.327.6963.93
B52.432.62-7.86
B69.184.0755.73
B711.885.9150.23
B84.613.4724.83
B96.733.1053.88

表5

优化前、后各公交站点负荷标准差和变异系数"

时间

初始

标准差

初始

变异系数

优化后

标准差

优化后

变异系数

早高峰23.880.5222.240.49
晚高峰31.300.6624.370.51

表6

优化前、后各公交站点负荷(高峰时段)"

公交站点早高峰晚高峰
初始负荷/人

优化后

负荷/人

初始负荷/人

优化后

负荷/人

B11830916
B273698877
B344474042
B452553139
B59101521
B674719382
B772706060
B837336865
B943432628

图7

公交站点负荷分布"

图8

不同算法站点位置优化比对"

图9

不同算法收敛曲线对比"

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