交通运输系统工程与信息

交通运输系统工程与信息 ›› 2025, Vol. 25 ›› Issue (4): 24-33.DOI: 10.16097/j.cnki.1009-6744.2025.04.003

• 综合交通运输体系论坛 • 上一篇    下一篇

不同时期下城市轨道交通客流的时空影响机制研究

张鹏羽1 ，李正中*1 ，张翕然1 ，岳晓辉2   

  1. 1. 天津市交通科学研究院，天津300074；2.天津轨道交通线网管理有限公司，天津300380
  • 收稿日期:2025-04-12 修回日期:2025-06-07 接受日期:2025-06-10 出版日期:2025-08-25 发布日期:2025-08-25
  • 作者简介:张鹏羽(1999—)，女，河北石家庄人，助理工程师。
  • 基金资助:
    天津市交通运输科技发展计划项目 (2024-B12)。

Spatiotemporal Impact Mechanism of Urban Rail Transit Passenger Flow in Different Periods

ZHANG Pengyu1, LI Zhengzhong*1, ZHANG Xiran1, YUE Xiaohui2   

  1. 1. Tianjin Transportation Research Institute, Tianjin 300074, China; 2. Tianjin Rail Transit Network Management Co Ltd, Tianjin 300380, China
  • Received:2025-04-12 Revised:2025-06-07 Accepted:2025-06-10 Online:2025-08-25 Published:2025-08-25
  • Supported by:
    Transportation Technology Development Plan Project of Tianjin (2024-B12)。

PDF

摘要： 研究工作日、双休日和法定节假日轨道交通客流的时空影响机制，对于制定侧重性开发策略和优化时空资源配置等方面具有重要意义。既有研究多针对工作日客流，未充分考虑不同时期下客流的关键影响因素及其作用差异。本文基于3种时期客流量与“5Ds+C”(密度、混合度、设计、目的地可达性、公交邻近性和网络中心性)影响因素变量数据，实现机器学习回归模型调参训练与评价筛选，运用极限梯度提升回归树-沙普利加法解释模型(XGBoost-SHAP)从整体特征重要性、交互效应和局部时空异质性这3个层面分析客流所受时空影响的差异。针对天津地铁的案例表明：XGBoost相比随机森林(RF)和梯度提升决策树(GBDT)具备更优的解释能力，拟合系数达0.7以上。就整体特征重要性分析而言，工作日、双休日和节假日的关键影响因素、影响重要性和影响模式存在显著差异，双休日和节假日多元化出行因素重要性达59.8%和61.3%。就交互效应分析而言，居住类型用地分别与办公类型用地、购物休闲用地、旅游景点用地对不同时期客流具有显著的交互影响作用。就局部时空异质性分析而言，工作日、双休日和节假日时段应分别注重土地利用程度低的站点域内居住用地，成熟居住区配套设施建设水平和商业旅游用地客流集聚效应，休闲旅游设施完善的站点，节假日客流量显著提升，SHAP影响值涨幅约5000。

关键词: 城市交通, 轨道交通客流, 时空影响机制差异, 极限梯度提升回归树, 沙普利加法解释模型

Abstract: Researches on the spatiotemporal impact mechanism of rail transit passenger flow on workdays, weekends, and statutory holidays are much crucial to formulate targeted development strategies and optimize spatiotemporal resource allocation. Previous studies have mainly focused on weekday passenger flow, but have not fully considered the key influencing factors and differences about their effects on passenger flow during different periods. This article implements machine learning regression model through tuning, training, and evaluation screening based on the data from three different periods of passenger flow and the variable of "5Ds+C" (Density, Diversity, Design, Destination Accessibility, Distance and Centrality) influencing factors. The XGBoost-SHAP model is used to analyze the differences from the spatiotemporal impact on passenger flow at three levels: overall feature importance, interaction effects, and local spatiotemporal heterogeneity. The case study of Tianjin Metro shows that the XGBoost has better explanatory power compared to the Random Forest (RF) and Gradient Boosting Decision Tree (GBDT), with a fitting coefficient of over 0.7. There are significant differences in key influencing factors, importance, and mode of influence between workdays, weekends, and holidays in terms of overall feature importance analysis. The importance of diversified travel factors on weekends and holidays reaching 59.8% and 61.3% . According to interaction effect analysis, residential land has significant interaction effects on passenger flow in office land, shopping and leisure land, and tourist attraction land over different periods. Attention should be paid to the residential land at low land use level, the mature residential areas with good facilities construction, and the commercial tourism land with passenger flow agglomeration respectively during weekdays, weekends, and holidays by the analysis of the local spatiotemporal heterogeneity. The stations with complete leisure tourism facilities have passenger flow increased significantly during holidays, with a SHAP impact value increasing by about 5000.

Key words: urban traffic, rail transit passenger flow, differences in spatiotemporal influence mechanisms, extreme gradient boosting regression tree, Shapley additive explanatory model

中图分类号: