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Analysis of Washington, DC taxi demand using GPS and land-use data

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  • Yang, Zhuo
  • Franz, Mark L.
  • Zhu, Shanjiang
  • Mahmoudi, Jina
  • Nasri, Arefeh
  • Zhang, Lei

Abstract

Taxis remain a key asset for urban mobility despite the tremendous growth of modern mobility-on-demand service providers such as Uber and Lyft. A fundamental understanding of the factors that impact the taxi demand is essential for planning an effective multi-modal transportation system, and can also shed lights on new on-demand services. This study addressed a gap in literature by investigating the correlation between demand for taxi, land use patterns, and accessibility to other modes using detailed GPS and GIS information collected from the Washington D.C. metropolitan area. The results of the models showed a strong link between demand for taxi, land use patterns, and accessibility to other modes. Mixed land use did not show a strong correlation with taxi demand. The study also found that the taxi mode is likely to complement metro trips, but compete with bus trips, although both of these modes of travel are considered public transit. Airport trips were found to be the most important source for taxi travel. These findings were further supported by the time-of-day and seasonality analysis.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:jotrge:v:66:y:2018:i:c:p:35-44
    DOI: 10.1016/j.jtrangeo.2017.10.021
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    as
    1. 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.
    2. John Rose & David Hensher, 2014. "Demand for taxi services: new elasticity evidence," Transportation, Springer, vol. 41(4), pages 717-743, July.
    3. Cervero, R. & Duncan, M., 2003. "Walking, Bicycling, and Urban Landscapes: Evidence from the San Francisco Bay Area," American Journal of Public Health, American Public Health Association, vol. 93(9), pages 1478-1483.
    4. Yao, Can-Zhong & Lin, Ji-Nan, 2016. "A study of human mobility behavior dynamics: A perspective of a single vehicle with taxi," Transportation Research Part A: Policy and Practice, Elsevier, vol. 87(C), pages 51-58.
    5. Mishra, Sabyasachee & Welch, Timothy F. & Jha, Manoj K., 2012. "Performance indicators for public transit connectivity in multi-modal transportation networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(7), pages 1066-1085.
    6. Jonathan V. Hall & Alan B. Krueger, 2015. "An Analysis of the Labor Market for Uber's Driver-Partners in the United States," Working Papers 587, Princeton University, Department of Economics, Industrial Relations Section..
    7. Judd Cramer & Alan B. Krueger, 2016. "Disruptive Change in the Taxi Business: The Case of Uber," American Economic Review, American Economic Association, vol. 106(5), pages 177-182, May.
    8. Jeremy Toner, 2010. "The welfare effects of taxicab regulation in English towns," Economic Analysis and Policy, Elsevier, vol. 40(3), pages 299-312, December.
    9. Hughes, Ryan & MacKenzie, Don, 2016. "Transportation network company wait times in Greater Seattle, and relationship to socioeconomic indicators," Journal of Transport Geography, Elsevier, vol. 56(C), pages 36-44.
    10. Wong, K. I. & Wong, S. C. & Yang, Hai, 2001. "Modeling urban taxi services in congested road networks with elastic demand," Transportation Research Part B: Methodological, Elsevier, vol. 35(9), pages 819-842, November.
    11. 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.
    12. Antonio M. Bento & Maureen L. Cropper & Ahmed Mushfiq Mobarak & Katja Vinha, 2005. "The Effects of Urban Spatial Structure on Travel Demand in the United States," The Review of Economics and Statistics, MIT Press, vol. 87(3), pages 466-478, August.
    13. Cervero, Robert & Duncan, Michael, 2003. "Walking, Bicycling, and Urban Landscapes: Evidence from the San Francisco Bay Area," University of California Transportation Center, Working Papers qt6zr1x95m, University of California Transportation Center.
    14. Bruce Schaller, 1999. "Elasticities for taxicab fares and service availability," Transportation, Springer, vol. 26(3), pages 283-297, August.
    15. Zhang, Lei & Hong, Jin Hyun & Nasri, Arefeh & Shen, Qing, 2012. "How built environment affects travel behavior: A comparative analysis of the connections between land use and vehicle miles traveled in US cities," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 5(3), pages 40-52.
    16. 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.
    Full references (including those not matched with items on IDEAS)

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