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The impact of rainfall on the temporal and spatial distribution of taxi passengers

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  • Dandan Chen
  • Yong Zhang
  • Liangpeng Gao
  • Nana Geng
  • Xuefeng Li

Abstract

This paper focuses on the impact of rainfall on the temporal and spatial distribution of taxi passengers. The main objective is to provide guidance for taxi scheduling on rainy days. To this end, we take the occupied and empty states of taxis as units of analysis. By matching a taxi's GPS data to its taximeter data, we can obtain the taxi's operational time and the taxi driver's income from every unit of analysis. The ratio of taxi operation time to taxi drivers' income is used to measure the quality of taxi passengers. The research results show that the spatio-temporal evolution of urban taxi service demand differs based on rainfall conditions and hours of operation. During non-rush hours, taxi demand in peripheral areas is significantly reduced under increasing precipitation conditions, whereas during rush hours, the demand for highly profitable taxi services steadily increases. Thus, as an intelligent response for taxi operations and dispatching, taxi services should guide cruising taxis to high-demand regions to increase their service time and ride opportunities.

Suggested Citation

  • Dandan Chen & Yong Zhang & Liangpeng Gao & Nana Geng & Xuefeng Li, 2017. "The impact of rainfall on the temporal and spatial distribution of taxi passengers," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-16, September.
    • Handle: RePEc:plo:pone00:0183574
    DOI: 10.1371/journal.pone.0183574
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    References listed on IDEAS

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    1. Colin Camerer & Linda Babcock & George Loewenstein & Richard Thaler, 1997. "Labor Supply of New York City Cabdrivers: One Day at a Time," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 112(2), pages 407-441.
    2. Singhal, Abhishek & Kamga, Camille & Yazici, Anil, 2014. "Impact of weather on urban transit ridership," Transportation Research Part A: Policy and Practice, Elsevier, vol. 69(C), pages 379-391.
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    Cited by:

    1. Dokuz, Yesim & Dokuz, Ahmet Sakir, 2023. "Time-persistent regions discovery of taxi trajectory big datasets based on regional spatio-temporal velocity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 623(C).
    2. Dokuz, Ahmet Sakir, 2022. "Weighted spatio-temporal taxi trajectory big data mining for regional traffic estimation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 589(C).

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