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Examining the spatial-temporal relationship between urban built environment and taxi ridership: Results of a semi-parametric GWPR model

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  • Chen, Chao
  • Feng, Tao
  • Ding, Chuan
  • Yu, Bin
  • Yao, Baozhen

Abstract

With the advance of intelligent transportation systems (ITSs) and data acquisition systems (DASs), it becomes possible in recent to explore the determinants of urban taxi ridership using multi-source heterogeneous data. This paper aims to use floating car data, points-of-interests (POIs) data and housing-price data to assess the influence of the built environment on taxi ridership. Within a scale of 0.5 km grid, critical indicators related to the economic aspect, intermodal connection, and land use factors were obtained using the multi-source data in Shanghai. To capture the spatial and temporal heterogeneity, Semi-parametric Geographically Weighted Poisson Regression (SGWPR) models are built over different time dimensions. It is found that SGWPR models result in higher goodness-of-fit than the generalized linear models. More importantly, the results show the impacts of built environment factors on taxi demand are highly heterogeneous, positive or negative in different city areas, reflected in the significant temporal variations of the effects. Overall, these findings suggest that the built environment factors have significant impacts on urban taxi demand, and the spatial context should not be ignored. Findings in this paper are expected to help better understand the relationship between urban taxi demand and built environment factors, improving the service level of the urban taxi system, and offering valuable insights into future urban and transportation planning.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:jotrge:v:96:y:2021:i:c:s0966692321002258
    DOI: 10.1016/j.jtrangeo.2021.103172
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    as
    1. Chen, Fangxi & Yin, Zhiwei & Ye, Yingwei & Sun, Daniel(Jian), 2020. "Taxi hailing choice behavior and economic benefit analysis of emission reduction based on multi-mode travel big data," Transport Policy, Elsevier, vol. 97(C), pages 73-84.
    2. 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.
    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. Jun, Myung-Jin & Choi, Keechoo & Jeong, Ji-Eun & Kwon, Ki-Hyun & Kim, Hee-Jae, 2015. "Land use characteristics of subway catchment areas and their influence on subway ridership in Seoul," Journal of Transport Geography, Elsevier, vol. 48(C), pages 30-40.
    5. Zhang, Bin & Chen, Shuyan & Ma, Yongfeng & Li, Tiezhu & Tang, Kun, 2020. "Analysis on spatiotemporal urban mobility based on online car-hailing data," Journal of Transport Geography, Elsevier, vol. 82(C).
    6. David Wheeler & Michael Tiefelsdorf, 2005. "Multicollinearity and correlation among local regression coefficients in geographically weighted regression," Journal of Geographical Systems, Springer, vol. 7(2), pages 161-187, June.
    7. 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.
    8. 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.
    9. Yadi Zhu & Feng Chen & Zijia Wang & Jin Deng, 2019. "Spatio-temporal analysis of rail station ridership determinants in the built environment," Transportation, Springer, vol. 46(6), pages 2269-2289, December.
    10. Hai Yang & Yan Lau & Sze Wong & Hong Lo, 2000. "A macroscopic taxi model for passenger demand, taxi utilization and level of services," Transportation, Springer, vol. 27(3), pages 317-340, June.
    11. 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.
    12. Sun, Daniel(Jian) & Ding, Xueqing, 2019. "Spatiotemporal evolution of ridesourcing markets under the new restriction policy: A case study in Shanghai," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 227-239.
    13. Wenbo Zhang & Satish V. Ukkusuri & Jian John Lu, 2017. "Impacts of urban built environment on empty taxi trips using limited geolocation data," Transportation, Springer, vol. 44(6), pages 1445-1473, November.
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    Cited by:

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    3. 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.
    4. Ying Huang & Yongli Zhang & Feifan Deng & Daiqing Zhao & Rong Wu, 2022. "Impacts of Built-Environment on Carbon Dioxide Emissions from Traffic: A Systematic Literature Review," IJERPH, MDPI, vol. 19(24), pages 1-17, December.

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