交通运输系统工程与信息

交通运输系统工程与信息 ›› 2024, Vol. 24 ›› Issue (4): 212-222.DOI: 10.16097/j.cnki.1009-6744.2024.04.020

• 系统工程理论与方法 • 上一篇    下一篇

轨道交通远郊区段计划性停运对常乘客的转移影响

李洪运1a,1b，江志彬*1a,1b，谷金晶2，刘伟3，王炳勋1a,1b   

  1. 1. 同济大学，a.道路与交通工程教育部重点实验室，b.上海市轨道交通结构耐久与系统安全重点实验室，上海201804； 2. 云南大学，信息学院，昆明650500；3.上海申通地铁集团有限公司技术中心，上海201103
  • 收稿日期:2024-03-31 修回日期:2024-06-12 接受日期:2024-06-17 出版日期:2024-08-25 发布日期:2024-08-22
  • 作者简介:李洪运(1998- )，男，江西吉安人，博士生。
  • 基金资助:
    国家自然科学基金 (52372332, 52102382)。

Impact of Planned Shutdown of Suburban Rail Transit on Travel Transfer of Frequent Passengers

LI Hongyun1a,1b,JIANG Zhibin*1a,1b,GU Jinjing2,LIU Wei3,WANG Bingxun1a,1b   

  1. 1a. Key Laboratory of Road and Traffic Engineering of the Ministry of Education, 1b. Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety, Tongji University, Shanghai 201804, China; 2. School of Information Science and Engineering, Yunnan University, Kunming 650500, China; 3. Technical Center of Shanghai Shentong Metro Group Co Ltd, Shanghai 201103, China
  • Received:2024-03-31 Revised:2024-06-12 Accepted:2024-06-17 Online:2024-08-25 Published:2024-08-22
  • Supported by:
    NationalNaturalScienceFoundation of China (52372332, 52102382)。

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摘要： 轨道交通网络中乘客的出行受网络结构和运营状况变化的影响，个体出行偏好对这些变化的响应也各异。为分析轨道交通远郊区段计划性停运对常乘客的出行转移影响，本文提出考虑转移类型和转移比例的乘客出行特征刻画方法，结合时段属性生成乘客特征—时序(Feature Temporal, F-T)矩阵；通过改进的欧氏距离计算F-T矩阵间的相似性，实现F-T矩阵的相似性度量；提出一种基于相似度矩阵的K-Means聚类和层次聚类相结合的两步聚类方法(Two-step Clustering of K-Means Clustering and Hierarchical Clustering, KMHC)划分乘客影响群体，分析影响乘客出行转移的因素；以新冠肺炎疫情期间上海轨道交通11号线昆山段停运作为实例，对本文方法进行验证。研究结果表明：昆山段停运后，常乘客呈现出5种主要的出行转移影响群体，占常乘客总数的94.4%；各影响群体的转移距离、通勤时间和出行频率差异明显，是影响区段停运后常乘客出行选择的重要因素。本文方法可为其他计划性停运场景提供借鉴和参考，也可为区段停运后的网络客流变化预测，行车和客运组织方案优化提供支撑。

关键词: 城市交通, 出行转移, KMHC聚类, 区段计划性停运, F-T矩阵

Abstract: Passenger travel in rail transit networks is affected by changes in network structure and operating conditions, and individual travel preferences respond differently to these changes. In order to analyze the impact of a planned shutdown of a suburban rail line on the travel transfer of frequent passengers, a passenger travel feature characterization method considering transfer types and transfer ratios was proposed, and the passenger's feature temporal (F-T) matrix was generated by combining time period attributes. The similarity between F-T matrices was calculated by an improved Euclidean distance to achieve the similarity measurement of F-T matrices. A two-step clustering method of K-Means clustering and hierarchical clustering (KMHC) based on the similarity matrix was proposed to partition the affected passenger groups, and the factors affecting passenger transfer were analyzed. The Kunshan section of Shanghai Rail Transit Line 11 during COVID-19 was taken as an example to verify the method. The research results show that after the shutdown of the Kunshan section, there are five main groups of travel transfer impacts of frequent passengers, accounting for 94.4% of the total number of frequent passengers. The transfer distance, commuting time and travel frequency of the affected groups are obviously different, which are important factors influencing the travel choices of frequent passengers after the section shutdown. The method can serve as a reference for other planned shutdown scenarios, and can also provide support for predicting changes in network passenger flow, and optimizing driving and passenger transportation organization plans after the section shutdown.

Key words: urban traffic, travel transfer, KMHC clustering, section planned shutdown, F-T matrix

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