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A time-dependent logit-based taxi customer-search model

Author

Listed:
  • Wai Yuen Szeto
  • Ryan Cheuk Pong Wong
  • Sze Chun Wong
  • Hai Yang

Abstract

In this study, global positioning system data from 460 urban taxis are used to develop a time-dependent logit model. The rate of return (ROR, also known as profit per unit time) is used as a factor underlying taxi drivers' searching behaviour for customers. The data also reveal that the search behaviour across districts as well as the decisions towards a particular district in customer-search is strongly related to the daily profile of passenger demand, and that when the overall passenger demand is high, vacant taxi drivers tend to circulate within or wait at the area where their preceding customers got off to find their next customer. The results also show that the ROR is a significant factor that affects the customer-searching strategies of vacant taxi drivers over a day, and is inversely related to the percentage of taxi idling time. More importantly, this paper illustrates that there is a change in searching behaviour over time of day.

Suggested Citation

  • Wai Yuen Szeto & Ryan Cheuk Pong Wong & Sze Chun Wong & Hai Yang, 2013. "A time-dependent logit-based taxi customer-search model," International Journal of Urban Sciences, Taylor & Francis Journals, vol. 17(2), pages 184-198, July.
    • Handle: RePEc:taf:rjusxx:v:17:y:2013:i:2:p:184-198
    DOI: 10.1080/12265934.2013.776292
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    Citations

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    Cited by:

    1. Wong, R.C.P. & Szeto, W.Y. & Wong, S.C., 2014. "Bi-level decisions of vacant taxi drivers traveling towards taxi stands in customer-search: Modeling methodology and policy implications," Transport Policy, Elsevier, vol. 33(C), pages 73-81.
    2. Szeto, W.Y. & Wong, R.C.P. & Yang, W.H., 2019. "Guiding vacant taxi drivers to demand locations by taxi-calling signals: A sequential binary logistic regression modeling approach and policy implications," Transport Policy, Elsevier, vol. 76(C), pages 100-110.
    3. 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.
    4. García-Almeida, Desiderio Juan & Klassen, Norbert, 2017. "The influence of knowledge-based factors on taxi competitiveness at island destinations: An analysis on tips," Tourism Management, Elsevier, vol. 59(C), pages 110-122.
    5. Yuebing Liang & Zhan Zhao & Xiaohu Zhang, 2024. "Modeling taxi cruising time based on multi-source data: a case study in Shanghai," Transportation, Springer, vol. 51(3), pages 761-790, June.
    6. Yu, Xinlian & Gao, Song & Hu, Xianbiao & Park, Hyoshin, 2019. "A Markov decision process approach to vacant taxi routing with e-hailing," Transportation Research Part B: Methodological, Elsevier, vol. 121(C), pages 114-134.
    7. Wenbo Zhang & Satish V. Ukkusuri & Chao Yang, 2018. "Modeling the Taxi Drivers’ Customer-Searching Behaviors outside Downtown Areas," Sustainability, MDPI, vol. 10(9), pages 1-23, August.
    8. Wong, R.C.P. & Szeto, W.Y., 2022. "The effects of peak hour and congested area taxi surcharges on customers’ travel decisions: Empirical evidence and policy implications," Transport Policy, Elsevier, vol. 121(C), pages 78-89.
    9. Anil Yazici, M. & Kamga, Camille & Singhal, Abhishek, 2016. "Modeling taxi drivers’ decisions for improving airport ground access: John F. Kennedy airport case," Transportation Research Part A: Policy and Practice, Elsevier, vol. 91(C), pages 48-60.
    10. Di, Xuan & Ban, Xuegang Jeff, 2019. "A unified equilibrium framework of new shared mobility systems," Transportation Research Part B: Methodological, Elsevier, vol. 129(C), pages 50-78.

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