IDEAS home Printed from https://ideas.repec.org/a/kap/transp/v51y2024i4d10.1007_s11116-023-10372-6.html
   My bibliography  Save this article

Analysis of mobility patterns for urban taxi ridership: the role of the built environment

Author

Listed:
  • Zhitao Li

    (Central South University)

  • Xiaolu Wang

    (Central South University)

  • Fan Gao

    (Central South University)

  • Jinjun Tang

    (Central South University)

  • Hanmeng Xu

    (Central South University)

Abstract

Understanding mobility patterns of taxi ridership is important for transport planning. However, there is still room for a thorough understanding of the role of the built environment for taxi ridership across different spatial–temporal patterns. This paper proposes an analytical framework that combines non-negative CANDECOMP/PARAFAC (NCP) decomposition for pattern extraction and the light gradient boosting machine (LightGBM) for modeling the relationship between the built environment and taxi ridership. The case study was conducted in Shenzhen, China. We identified four spatial–temporal patterns by evaluating the root mean square error of the decomposition result and the representativeness of patterns. Based on the LightGBM method, we examined the nonlinear associations between taxi ridership for different patterns and the built environment. The results show that demographic characteristics are important across space. Housing-price is mainly associated with taxi ridership in western Shenzhen. Among all types of POIs, finance and entertainment are the most prominent, affecting taxi ridership in southern Shenzhen. The effects of influencing factors exhibit a high degree of localization, and mixed effects may result from localization. All variables show significant nonlinear and threshold effects on taxi ridership, and these effects could guide transport planning in different areas of the city.

Suggested Citation

  • Zhitao Li & Xiaolu Wang & Fan Gao & Jinjun Tang & Hanmeng Xu, 2024. "Analysis of mobility patterns for urban taxi ridership: the role of the built environment," Transportation, Springer, vol. 51(4), pages 1409-1431, August.
    • Handle: RePEc:kap:transp:v:51:y:2024:i:4:d:10.1007_s11116-023-10372-6
    DOI: 10.1007/s11116-023-10372-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11116-023-10372-6
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11116-023-10372-6?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Yang, Zhuo & Franz, Mark L. & Zhu, Shanjiang & Mahmoudi, Jina & Nasri, Arefeh & Zhang, Lei, 2018. "Analysis of Washington, DC taxi demand using GPS and land-use data," Journal of Transport Geography, Elsevier, vol. 66(C), pages 35-44.
    2. Tang, Jinjun & Liu, Fang & Wang, Yinhai & Wang, Hua, 2015. "Uncovering urban human mobility from large scale taxi GPS data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 140-153.
    3. Ruben Cordera & Pierluigi Coppola & Luigi dell’Olio & Ángel Ibeas, 2017. "Is accessibility relevant in trip generation? Modelling the interaction between trip generation and accessibility taking into account spatial effects," Transportation, Springer, vol. 44(6), pages 1577-1603, November.
    4. Kim, Kyoungok, 2018. "Exploring the difference between ridership patterns of subway and taxi: Case study in Seoul," Journal of Transport Geography, Elsevier, vol. 66(C), pages 213-223.
    5. Miaoyi Li & Lei Dong & Zhenjiang Shen & Wei Lang & Xinyue Ye, 2017. "Examining the Interaction of Taxi and Subway Ridership for Sustainable Urbanization," Sustainability, MDPI, vol. 9(2), pages 1-12, February.
    6. Tang, Jinjun & Bi, Wei & Liu, Fang & Zhang, Wenhui, 2021. "Exploring urban travel patterns using density-based clustering with multi-attributes from large-scaled vehicle trajectories," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 561(C).
    7. Cervero, Robert B., 2013. "Linking urban transport and land use in developing countries," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 6(1), pages 7-24.
    8. Xu, Yiming & Yan, Xiang & Liu, Xinyu & Zhao, Xilei, 2021. "Identifying key factors associated with ridesplitting adoption rate and modeling their nonlinear relationships," Transportation Research Part A: Policy and Practice, Elsevier, vol. 144(C), pages 170-188.
    9. Yang, Chen & Zhao, Shuqing, 2022. "Urban vertical profiles of three most urbanized Chinese cities and the spatial coupling with horizontal urban expansion," Land Use Policy, Elsevier, vol. 113(C).
    10. Jiaoe Wang & Jie Huang & Fangye Du, 2020. "Estimating spatial patterns of commute mode preference in Beijing," Regional Studies, Regional Science, Taylor & Francis Journals, vol. 7(1), pages 382-386, January.
    11. Weiming Kuang & Shi An & Huifu Jiang, 2015. "Detecting Traffic Anomalies in Urban Areas Using Taxi GPS Data," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-13, September.
    12. Shao, Qifan & Zhang, Wenjia & Cao, Xinyu & Yang, Jiawen & Yin, Jie, 2020. "Threshold and moderating effects of land use on metro ridership in Shenzhen: Implications for TOD planning," Journal of Transport Geography, Elsevier, vol. 89(C).
    13. Fan Gao & Jinjun Tang & Zhitao Li, 2022. "Effects of spatial units and travel modes on urban commuting demand modeling," Transportation, Springer, vol. 49(6), pages 1549-1575, December.
    14. Chen, Chao & Feng, Tao & Ding, Chuan & Yu, Bin & Yao, Baozhen, 2021. "Examining the spatial-temporal relationship between urban built environment and taxi ridership: Results of a semi-parametric GWPR model," Journal of Transport Geography, Elsevier, vol. 96(C).
    15. Liu, Xi & Gong, Li & Gong, Yongxi & Liu, Yu, 2015. "Revealing travel patterns and city structure with taxi trip data," Journal of Transport Geography, Elsevier, vol. 43(C), pages 78-90.
    16. Yeran Sun & Yunyan Du & Yu Wang & Liyuan Zhuang, 2017. "Examining Associations of Environmental Characteristics with Recreational Cycling Behaviour by Street-Level Strava Data," IJERPH, MDPI, vol. 14(6), pages 1-12, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ying Ni & Jiaqi Chen, 2020. "Exploring the Effects of the Built Environment on Two Transfer Modes for Metros: Dockless Bike Sharing and Taxis," Sustainability, MDPI, vol. 12(5), pages 1-20, March.
    2. Apantri Peungnumsai & Apichon Witayangkurn & Masahiko Nagai & Hiroyuki Miyazaki, 2018. "A Taxi Zoning Analysis Using Large-Scale Probe Data: A Case Study for Metropolitan Bangkok," The Review of Socionetwork Strategies, Springer, vol. 12(1), pages 21-45, June.
    3. Tong Zhou & Xintao Liu & Zhen Qian & Haoxuan Chen & Fei Tao, 2019. "Dynamic Update and Monitoring of AOI Entrance via Spatiotemporal Clustering of Drop-Off Points," Sustainability, MDPI, vol. 11(23), pages 1-20, December.
    4. Chen, Chao & Feng, Tao & Ding, Chuan & Yu, Bin & Yao, Baozhen, 2021. "Examining the spatial-temporal relationship between urban built environment and taxi ridership: Results of a semi-parametric GWPR model," Journal of Transport Geography, Elsevier, vol. 96(C).
    5. Mepparambath, Rakhi Manohar & Soh, Yong Sheng & Jayaraman, Vasundhara & Tan, Hong En & Ramli, Muhamad Azfar, 2023. "A novel modelling approach of integrated taxi and transit mode and route choice using city-scale emerging mobility data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 170(C).
    6. Song Li & Fei Xue & Chuyu Xia & Jian Zhang & Ao Bian & Yuexi Lang & Jun Zhou, 2022. "A Big Data-Based Commuting Carbon Emissions Accounting Method—A Case of Hangzhou," Land, MDPI, vol. 11(6), pages 1-18, June.
    7. Roya Etminani-Ghasrodashti & Shima Hamidi, 2019. "Individuals’ Demand for Ride-hailing Services: Investigating the Combined Effects of Attitudinal Factors, Land Use, and Travel Attributes on Demand for App-based Taxis in Tehran, Iran," Sustainability, MDPI, vol. 11(20), pages 1-19, October.
    8. Yu, Haitao & Peng, Zhong-Ren, 2019. "Exploring the spatial variation of ridesourcing demand and its relationship to built environment and socioeconomic factors with the geographically weighted Poisson regression," Journal of Transport Geography, Elsevier, vol. 75(C), pages 147-163.
    9. Kirtonia, Sajeeb & Sun, Yanshuo, 2022. "Evaluating rail transit's comparative advantages in travel cost and time over taxi with open data in two U.S. cities," Transport Policy, Elsevier, vol. 115(C), pages 75-87.
    10. Zhou, Xiaolu & Wang, Mingshu & Li, Dongying, 2019. "Bike-sharing or taxi? Modeling the choices of travel mode in Chicago using machine learning," Journal of Transport Geography, Elsevier, vol. 79(C), pages 1-1.
    11. Changhee Kim & Soo Wook Kim & Hee Jay Kang & Seung-Min Song, 2017. "What Makes Urban Transportation Efficient? Evidence from Subway Transfer Stations in Korea," Sustainability, MDPI, vol. 9(11), pages 1-18, November.
    12. Shao, Qifan & Zhang, Wenjia & Cao, Xinyu (Jason) & Yang, Jiawen, 2023. "Built environment interventions for emission mitigation: A machine learning analysis of travel-related CO2 in a developing city," Journal of Transport Geography, Elsevier, vol. 110(C).
    13. Zhang, Shen & Liu, Xin & Tang, Jinjun & Cheng, Shaowu & Qi, Yong & Wang, Yinhai, 2018. "Spatio-temporal modeling of destination choice behavior through the Bayesian hierarchical approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 537-551.
    14. Yang, Xiping & Fang, Zhixiang & Xu, Yang & Yin, Ling & Li, Junyi & Lu, Shiwei, 2019. "Spatial heterogeneity in spatial interaction of human movements—Insights from large-scale mobile positioning data," Journal of Transport Geography, Elsevier, vol. 78(C), pages 29-40.
    15. Hu, Songhua & Xiong, Chenfeng & Chen, Peng & Schonfeld, Paul, 2023. "Examining nonlinearity in population inflow estimation using big data: An empirical comparison of explainable machine learning models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 174(C).
    16. Yang, Zhuo & Franz, Mark L. & Zhu, Shanjiang & Mahmoudi, Jina & Nasri, Arefeh & Zhang, Lei, 2018. "Analysis of Washington, DC taxi demand using GPS and land-use data," Journal of Transport Geography, Elsevier, vol. 66(C), pages 35-44.
    17. Tang, Jinjun & Zhang, Shen & Zhang, Wenhui & Liu, Fang & Zhang, Weibin & Wang, Yinhai, 2016. "Statistical properties of urban mobility from location-based travel networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 694-707.
    18. Yang, Hongtai & Zheng, Rong & Li, Xuan & Huo, Jinghai & Yang, Linchuan & Zhu, Tong, 2022. "Nonlinear and threshold effects of the built environment on e-scooter sharing ridership," Journal of Transport Geography, Elsevier, vol. 104(C).
    19. Zhao, Pengxiang & Kwan, Mei-Po & Qin, Kun, 2017. "Uncovering the spatiotemporal patterns of CO2 emissions by taxis based on Individuals' daily travel," Journal of Transport Geography, Elsevier, vol. 62(C), pages 122-135.
    20. Zhenbao Wang & Xin Gong & Yuchen Zhang & Shuyue Liu & Ning Chen, 2023. "Multi-Scale Geographically Weighted Elasticity Regression Model to Explore the Elastic Effects of the Built Environment on Ride-Hailing Ridership," Sustainability, MDPI, vol. 15(6), pages 1-22, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:transp:v:51:y:2024:i:4:d:10.1007_s11116-023-10372-6. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.