建成环境对公交接驳地铁客流的异质性影响

doi:10.16097/j.cnki.1009-6744.2025.02.032

交通运输系统工程与信息 ›› 2025, Vol. 25 ›› Issue (2): 352-363.DOI: 10.16097/j.cnki.1009-6744.2025.02.032

建成环境对公交接驳地铁客流的异质性影响

许奇^1a，秦贝宁^1b，任澎^1b，陈越^1b,2，赖瑾璇^*3

1. 北京交通大学，a.综合交通运输大数据应用技术交通运输行业重点实验室，b.交通运输学院，北京100044； 2. 利兹大学，交通研究院，利兹LS29JT，英国；3.北京城建设计发展集团股份有限公司，北京100037

收稿日期:2024-11-28 修回日期:2025-02-17 接受日期:2025-02-26 出版日期:2025-04-25 发布日期:2025-04-20
作者简介:许奇(1982—)，男，云南普洱人，副教授，博士。
基金资助:
国家自然科学基金(72471024)。

Heterogeneous Effects of Built Environment on Ridership of Integrated Use of Bus and Metro

XU Qi^1a,QIN Beining^1b,REN Peng^1b,CHEN Yue^1b,2,LAI Jinxuan^*3

1a. Key Laboratory of' Transport Industry of Big Data Application Technologies for Comprehensive Transport, 1b. School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China; 2. Institute for Transport Studies, University of Leeds, Leeds LS2 9JT, UK; 3. Beijing Urban Construction Design & Development Group, Beijing 100037, China

Received:2024-11-28 Revised:2025-02-17 Accepted:2025-02-26 Online:2025-04-25 Published:2025-04-20
Supported by:
National Natural Science Foundation of China(72471024)。

摘要/Abstract

摘要： 地铁和公交作为城市公共交通系统的重要组成部分，明晰两者接驳影响因素的机理，对公共交通一体化具有重要意义。本文根据北京市一卡通刷卡数据，识别早晚高峰时段下区分接驳模式的4类客流，基于5D原则构建建成环境指标体系刻画地铁车站特征，基于多尺度地理加权回归(MultiscaleGeographically Weighted Regression, MGWR)模型对比分析建成环境的影响效果差异。研究表明：MGWR模型能够较好地反映建成环境对不同接驳情况的影响。车站区位对接驳量的总体影响程度最大；居住POI密度和土地利用混合熵对时间敏感，晚高峰时段两者的影响效果更为显著；公交站点数量对公交接驳模式敏感；公共交通可达性和接近中心性对时间和接驳模式均敏感，且两者对所敏感的接驳情况在城市中心区域多呈现出抑制作用，在城市外围区域多呈现出促进作用。因此，在考虑优化公交与地铁系统接驳目标时，需要充分考虑建成环境在空间、时间以及对不同接驳模式三个层面的异质性影响，因地制宜、因时制宜的制定策略，促进公共交通一体化发展。

关键词: 城市交通, 异质性影响, 多尺度地理加权回归模型, 公交接驳地铁, 空间特征

Abstract: Metro and buses both play important roles in urban public transportation system. It is of great significance to investigate the bus and metro connections and the influencing mechanism to enhance an integrated public transit system. This paper uses the smart card data of Beijing to analyze four types of bus connecting metro ridership during morning and evening peak hours. Based on the 5D principle, the built environment index system is constructed to describe the characteristics of metro stations. The Multiscale Geographically Weighted Regression (MGWR) model is used to compare and analyze the differences in the impact of the built environment. The results show that the MGWR model can well reflect the impact of the built environment on different connection conditions. The distance from the city center has the greatest impact on the total number of connections. The residential point of interest (POI) density and land use mixed entropy are sensitive to time, and the effect of the two is more significant in the evening peak period. The number of bus stops is sensitive to the connection mode. The public transport accessibility and closeness centrality are sensitive to time and connection mode. Both show a restraining effect on the sensitive ridership in the central area of the city. In the peripheral area of the city, there is a promoting effect. Therefore, when considering the optimization of the connection target of the bus and metro system, it is necessary to fully consider the heterogeneous effects of the built environment on spatial, temporal and connection mode, and formulate strategies according to local conditions and time to promote the integrated development of public transportation.

Key words: urban traffic, heterogeneous effect, multi-scale geographically weighted regression model, bus connecting metro, spatial characteristics

中图分类号:

U121

许奇, 秦贝宁, 任澎, 陈越, 赖瑾璇. 建成环境对公交接驳地铁客流的异质性影响[J]. 交通运输系统工程与信息, 2025, 25(2): 352-363.

XU Qi, QIN Beining, REN Peng, CHEN Yue, LAI Jinxuan. Heterogeneous Effects of Built Environment on Ridership of Integrated Use of Bus and Metro[J]. Journal of Transportation Systems Engineering and Information Technology, 2025, 25(2): 352-363.

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http://www.tseit.org.cn/CN/Y2025/V25/I2/352

参考文献

[1] GAN Z, YANG M, FENG T, et al. Examining the relationship between built environment and metro ridership at station-to-station level[J]. Transportation Research Part D: Transport and Environment, 2020, 82, 102332.

[2] ZUO Y, LIU Z, FU X. Measuring accessibility of bus system based on multi-source traffic data[J]. Geo-spatial Information Science, 2020, 23(3): 248-257.

[3] MARTÍNEZ M J, CORNEJO J. Value of the facilities and attributes of new heavy rail and bus rapid transit projects in a developing city: The case of Lima, Peru[J]. Transportation Research Record: Journal of the Transportation Research Board, 2003, 1835(1): 50-58.

[4] HERNANDEZ S, MONZON A. Key factors for defining an efficient urban transport interchange: Users' perceptions[J]. Cities, 2016, 50: 158-167.

[5] ZHAO P, LI S. Bicycle-metro integration in a growing city: The determinants of cycling as a transfer mode in metro station areas in Beijing[J]. Transportation Research Part A: Policy and Practice, 2017, 99: 46-60.

[6] 杨敏,吴静娴,赵静瑶,等.城市轨道交通多方式组合出行与接驳设施优化[J].城市交通,2017, 15(5): 64 69, 77. [YANG M, WU J X, ZHAO J Y, et al. Optimizing multimodal transfer facility design for urban rail transit service[J]. Urban Transport of China, 2017, 15(5): 64 69, 77.]

[7] 郑乐,高良鹏,陈学武.基于多源数据的地铁公交换乘量影响因素与空间异质性分析[J].交通运输系统工程与信息,2023, 23(2): 128-138. [ZHENG Y, GAO L P, CHEN X W. Investigating influencing factors on metro bus transfer demand incorporating spatial heterogeneity based on multi-source data[J]. Journal of Transportation Systems Engineering and Information Technology, 2023, 23(2): 128-138.]

[8] WU P, XU L, ZHONG L, et al. Revealing the determinants of the intermodal transfer ratio between metro and bus systems considering spatial variations[J]. Journal of Transport Geography, 2022, 104, 103415.

[9] ZHANG Y, LI H, XU X, et al. Analyzing the influencing factors on bus-metro transfer ridership incorporating spatial heterogeneity based on multi-source data[C]. 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), Bilbao, Spain: IEEE, 2023: 3034-3039.

[10] LI X, YAN Q, MA Y, et al. Spatially varying impacts of built environment on transfer ridership of metro and bus systems[J]. Sustainability, 2023, 15(10): 7891.

[11] LIU D, RONG W, ZHANG J, et al. Exploring the nonlinear effects of built environment on bus-transfer ridership: Take Shanghai as an example[J]. Applied Sciences, 2022, 12(11): 5755.

[12] CHEN E, YE Z, WU H. Nonlinear effects of built environment on intermodal transit trips considering spatial heterogeneity[J]. Transportation Research Part D: Transport and Environment, 2021, 90: 102677.

[13] 许奇, 李雯茜,陈越,等.建成环境对城市轨道交通起讫点客流的非线性影响及阈值效应[J].交通运输系统工程与信息, 2023, 23(4): 290-297. [XU Q, LI W X, CHEN Y, et al. Nonlinear and threshold effects of built environment on origin-destination flows of urban rail transit[J]. Journal of Transportation Systems Engineering and Information Technology, 2023, 23(4): 290-297.]

[14] ZHAO D, WANG W, WOODBURN A, et al. Isolating high-priority metro and feeder bus transfers using smart card data[J]. Transportation, 2017, 44(6): 1535-1554.

[15] XI Y, HOU Q, DUAN Y, et al. Exploring the spatiotemporal effects of the built environment on the nonlinear impacts of metro ridership: Evidence from Xi'an, China[J]. ISPRS International Journal of Geo Information, 2024, 13(3): 105.

建成环境对公交接驳地铁客流的异质性影响

Heterogeneous Effects of Built Environment on Ridership of Integrated Use of Bus and Metro

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