IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i10p6045-d816823.html
   My bibliography  Save this article

Exploring the Spatiotemporal Impacts of the Built Environment on Taxi Ridership Using Multisource Data

Author

Listed:
  • Chen Xie

    (Department of Traffic Information and Control Engineering, Jilin University, Changchun 130022, China)

  • Dexin Yu

    (College of Jimei Navigation, Jimei University, Xiamen 361021, China)

  • Ciyun Lin

    (Department of Traffic Information and Control Engineering, Jilin University, Changchun 130022, China
    Jilin Engineering Research Center for ITS, Changchun 130022, China)

  • Xiaoyu Zheng

    (Department of Traffic Information and Control Engineering, Jilin University, Changchun 130022, China)

  • Bo Peng

    (Department of Traffic Information and Control Engineering, Jilin University, Changchun 130022, China)

Abstract

Taxis are an important component of the urban public transportation system, with wide geographical coverage and on-demand services characteristics. Thorough understanding of the built environment affecting taxi ridership can enable transportation authorities to develop targeted policies for transportation planning. Previous studies in this field had few data sources and did not consider the spatiotemporal variability. This study aims to develop an analytical framework for understanding the spatiotemporal correlation between the urban built environment and taxi ridership, which is empirically analyzed in New York City. The built environment is defined through multisource data in terms of density, design, diversity, and destination accessibility. Besides the exploration of travel patterns, the spatiotemporal heterogeneity of taxi ridership is modeled using geographically and temporally weighted regression (GTWR). The result shows that GTWR outperforms ordinary least squares (OLS), geographically weighted regression (GWR), and temporally weighted regression (TWR) in both goodness of fit and explanatory accuracy. More importantly, our study found that land use diversity is negatively correlated with taxi ridership, while transportation diversity is positively correlated with it. A highly accessible road network improves the people’s demand for taxis in the morning rush hours. Moreover, the density of railway stations is positively correlated with taxi ridership on weekdays but adversely on weekends. These findings provide practical insights for urban transportation policy development and taxicab regulation.

Suggested Citation

  • Chen Xie & Dexin Yu & Ciyun Lin & Xiaoyu Zheng & Bo Peng, 2022. "Exploring the Spatiotemporal Impacts of the Built Environment on Taxi Ridership Using Multisource Data," Sustainability, MDPI, vol. 14(10), pages 1-24, May.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:10:p:6045-:d:816823
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/10/6045/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/10/6045/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ma, Xinwei & Ji, Yanjie & Yuan, Yufei & Van Oort, Niels & Jin, Yuchuan & Hoogendoorn, Serge, 2020. "A comparison in travel patterns and determinants of user demand between docked and dockless bike-sharing systems using multi-sourced data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 139(C), pages 148-173.
    2. Reid Ewing & Robert Cervero, 2010. "Travel and the Built Environment," Journal of the American Planning Association, Taylor & Francis Journals, vol. 76(3), pages 265-294.
    3. Ahmed El-Geneidy & Michael Grimsrud & Rania Wasfi & Paul Tétreault & Julien Surprenant-Legault, 2014. "New evidence on walking distances to transit stops: identifying redundancies and gaps using variable service areas," Transportation, Springer, vol. 41(1), pages 193-210, January.
    4. 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.
    5. 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.
    6. A. Stewart Fotheringham & Wenbai Yang & Wei Kang, 2017. "Multiscale Geographically Weighted Regression (MGWR)," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 107(6), pages 1247-1265, November.
    7. Gollini, Isabella & Lu, Binbin & Charlton, Martin & Brunsdon, Christopher & Harris, Paul, 2015. "GWmodel: An R Package for Exploring Spatial Heterogeneity Using Geographically Weighted Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i17).
    8. Haitao Yu & Zhong-Ren Peng, 2020. "The impacts of built environment on ridesourcing demand: A neighbourhood level analysis in Austin, Texas," Urban Studies, Urban Studies Journal Limited, vol. 57(1), pages 152-175, January.
    9. 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).
    10. Ulak, Mehmet Baran & Yazici, Anil & Aljarrah, Mohammad, 2020. "Value of convenience for taxi trips in New York City," Transportation Research Part A: Policy and Practice, Elsevier, vol. 142(C), pages 85-100.
    11. Le Yu & Binglei Xie & Edwin H. W. Chan, 2018. "How does the Built Environment Influence Public Transit Choice in Urban Villages in China?," Sustainability, MDPI, vol. 11(1), pages 1-15, December.
    12. de Abreu e Silva, João & Morency, Catherine & Goulias, Konstadinos G., 2012. "Using structural equations modeling to unravel the influence of land use patterns on travel behavior of workers in Montreal," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(8), pages 1252-1264.
    13. Jinjun Tang & Fan Gao & Fang Liu & Wenhui Zhang & Yong Qi, 2019. "Understanding Spatio-Temporal Characteristics of Urban Travel Demand Based on the Combination of GWR and GLM," Sustainability, MDPI, vol. 11(19), pages 1-19, October.
    14. Zhang, Xiaohu & Xu, Yang & Tu, Wei & Ratti, Carlo, 2018. "Do different datasets tell the same story about urban mobility — A comparative study of public transit and taxi usage," Journal of Transport Geography, Elsevier, vol. 70(C), pages 78-90.
    15. Andrew Tracy & Peng Su & Adel Sadek & Qian Wang, 2011. "Assessing the impact of the built environment on travel behavior: a case study of Buffalo, New York," Transportation, Springer, vol. 38(4), pages 663-678, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Qiu Yuanhong & Zhang Ting & Yin Jian & Cao Yuequn & Xu Zetian, 2024. "Spatiotemporal evolution of efficiency and driving factors of Chinese herbal medicine industry," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(7), pages 17105-17129, July.

    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. Xiang Li & Qipeng Yan & Yafeng Ma & Chen Luo, 2023. "Spatially Varying Impacts of Built Environment on Transfer Ridership of Metro and Bus Systems," Sustainability, MDPI, vol. 15(10), pages 1-24, May.
    2. HUO, Zhengqi & YANG, Xiaobao & LIU, Xiaobing & YAN, Xuedong, 2024. "Spatio-temporal analysis on online designated driving based on empirical data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 183(C).
    3. Guanwei Zhao & Zhitao Li & Yuzhen Shang & Muzhuang Yang, 2022. "How Does the Urban Built Environment Affect Online Car-Hailing Ridership Intensity among Different Scales?," IJERPH, MDPI, vol. 19(9), pages 1-25, April.
    4. 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.
    5. Hosseinzadeh, Aryan & Algomaiah, Majeed & Kluger, Robert & Li, Zhixia, 2021. "Spatial analysis of shared e-scooter trips," Journal of Transport Geography, Elsevier, vol. 92(C).
    6. Du, Mingyang & Cheng, Lin & Li, Xuefeng & Liu, Qiyang & Yang, Jingzong, 2022. "Spatial variation of ridesplitting adoption rate in Chicago," Transportation Research Part A: Policy and Practice, Elsevier, vol. 164(C), pages 13-37.
    7. Zhang, Xiaohu, 2021. "Beyond expected regularity of aggregate urban mobility: A case study of ridesourcing service," Journal of Transport Geography, Elsevier, vol. 95(C).
    8. Zhan, Zilin & Guo, Yuanyuan & Noland, Robert B. & He, Sylvia Y. & Wang, Yacan, 2023. "Analysis of links between dockless bikeshare and metro trips in Beijing," Transportation Research Part A: Policy and Practice, Elsevier, vol. 175(C).
    9. Qiao, Si & Yeh, Anthony Gar-On, 2021. "Is ride-hailing a valuable means of transport in newly developed areas under TOD-oriented urbanization in China? Evidence from Chengdu City," Journal of Transport Geography, Elsevier, vol. 96(C).
    10. de Oliveira Souza, Camilla & Vitorino Guimarães, Gabriella & da Cruz Saldanha, Luiz Emerson & Almeida Corrêa do Nascimento, Filipe & Floriano dos Santos, Tálita & Vieira da Silva, Marcelino Aurélio, 2021. "Analysis of job accessibility promoted by ride hailing services: A proposed method," Journal of Transport Geography, Elsevier, vol. 93(C).
    11. Liao, Yuan, 2021. "Ride-sourcing compared to its public-transit alternative using big trip data," Journal of Transport Geography, Elsevier, vol. 95(C).
    12. Ding, Yu & Lu, Huapu, 2016. "Activity participation as a mediating variable to analyze the effect of land use on travel behavior: A structural equation modeling approach," Journal of Transport Geography, Elsevier, vol. 52(C), pages 23-28.
    13. He, Mingwei & He, Chengfeng & Shi, Zhuangbin & He, Min, 2022. "Spatiotemporal heterogeneous effects of socio-demographic and built environment on private car usage: An empirical study of Kunming, China," Journal of Transport Geography, Elsevier, vol. 101(C).
    14. Li, Mengya & Kwan, Mei-Po & Hu, Wenyan & Li, Rui & Wang, Jun, 2023. "Examining the effects of station-level factors on metro ridership using multiscale geographically weighted regression," Journal of Transport Geography, Elsevier, vol. 113(C).
    15. Mulley, Corinne & Ho, Chinh & Ho, Loan & Hensher, David & Rose, John, 2018. "Will bus travellers walk further for a more frequent service? An international study using a stated preference approach," Transport Policy, Elsevier, vol. 69(C), pages 88-97.
    16. Rodrigo Victoriano-Habit & Ahmed El-Geneidy, 2024. "Studying the Interrelationship between Telecommuting during COVID-19, residential local accessibility, and active travel: a panel study in Montréal, Canada," Transportation, Springer, vol. 51(3), pages 1149-1166, June.
    17. Yigong Hu & Binbin Lu & Yong Ge & Guanpeng Dong, 2022. "Uncovering spatial heterogeneity in real estate prices via combined hierarchical linear model and geographically weighted regression," Environment and Planning B, , vol. 49(6), pages 1715-1740, July.
    18. Vale, David S. & Viana, Cláudia M. & Pereira, Mauro, 2018. "The extended node-place model at the local scale: Evaluating the integration of land use and transport for Lisbon's subway network," Journal of Transport Geography, Elsevier, vol. 69(C), pages 282-293.
    19. Xiaoquan Wang & Weifeng Wang & Chaoying Yin, 2023. "Exploring the Relationships between Multilevel Built Environments and Commute Durations in Dual-Earner Households: Does Gender Matter?," IJERPH, MDPI, vol. 20(6), pages 1-17, March.
    20. Rémy Le Boennec & Julie Bulteau & Thierry Feuillet, 2022. "The role of commuter rail accessibility in the formation of residential land values: exploring spatial heterogeneity in peri-urban and remote areas," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 69(1), pages 163-186, August.

    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:gam:jsusta:v:14:y:2022:i:10:p:6045-:d:816823. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.