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Taxi vacancy duration: a regression analysis

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  • Won Kyung Lee
  • So Young Sohn

Abstract

Taxi vacancy duration is a major efficiency measure for taxi services. A clear understanding of the various factors and their effect on vacancy duration is necessary for the optimal operational management of taxis. Previous research has only dealt with vacancy duration by assuming probability distributions and has not investigated heterogeneity in the data caused by various factors. We develop a parametric duration model using not only new operational characteristics but also variables associated with taxi demand, such as weather, land use, demographics, socioeconomic variables, and accessibility of public transportation. The model is applied to a large-scale New York City (NYC) taxi trip dataset that covers operations for 2013. The results show that all the attributes have significant associations with vacancy duration that follows a log-normal distribution. Our study is expected to help improve the efficiency of taxi operations by decreasing the time spent in vacant states.

Suggested Citation

  • Won Kyung Lee & So Young Sohn, 2017. "Taxi vacancy duration: a regression analysis," Transportation Planning and Technology, Taylor & Francis Journals, vol. 40(7), pages 771-795, October.
    • Handle: RePEc:taf:transp:v:40:y:2017:i:7:p:771-795
    DOI: 10.1080/03081060.2017.1340025
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    Cited by:

    1. Park, Chung & Lee, Jungpyo & Sohn, So Young, 2019. "Recommendation of feeder bus routes using neural network embedding-based optimization," Transportation Research Part A: Policy and Practice, Elsevier, vol. 126(C), pages 329-341.
    2. Park, Chung & Sohn, So Young, 2017. "An optimization approach for the placement of bicycle-sharing stations to reduce short car trips: An application to the city of Seoul," Transportation Research Part A: Policy and Practice, Elsevier, vol. 105(C), pages 154-166.

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