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Modeling the decision of ridesourcing drivers to park and wait at trip ends: a comparison between Perth, Australia and Kolkata, India

Author

Listed:
  • Jayita Chakraborty

    (Indian Institute of Technology
    Curtin University)

  • Debapratim Pandit

    (Indian Institute of Technology)

  • Jianhong Xia

    (Curtin University)

  • Felix Chan

    (Curtin University)

Abstract

It is often difficult for the ridesourcing drivers to get a trip immediately after dropping off a passenger. The main objective of the drivers is to increase their income by serving more trips. The most prominent options available to the drivers after reaching passengers’ destinations are: (a) park and wait in and around their drop-off location, (b) cruise in and around their drop-off location and (c) drive to another location to receive trip requests quickly. Previous studies were conducted to understand the driver behaviour in a taxi and other similar services. However, the perception of ridesourcing drivers on parking and waiting after dropping off passengers is yet to be explored. The drivers’ decision on waiting can affect users’ waiting time, the number of matched trips by the TNCs, and parking spaces in the city. Moreover, drivers’ waiting time tolerance can also impact other drivers’ total number of trips, total earnings, total distance travelled in the city, and fleet size. The aim of this study is to understand the influence of drivers’ characteristics on drivers’ decision to park and wait after dropping off a passenger. This study estimates and compares the waiting time tolerance of the ridesourcing drivers using a zero-inflated cox spline model between Perth and Kolkata. It is observed that drivers in Kolkata have higher waiting time tolerance than Perth drivers. Moreover, the drivers in both the cities are more likely to wait at high-demand areas urging the urban authorities to determine spatio-temporal parking demand to design the parking infrastructure for such areas.

Suggested Citation

  • Jayita Chakraborty & Debapratim Pandit & Jianhong Xia & Felix Chan, 2024. "Modeling the decision of ridesourcing drivers to park and wait at trip ends: a comparison between Perth, Australia and Kolkata, India," Transportation, Springer, vol. 51(3), pages 1089-1124, June.
    • Handle: RePEc:kap:transp:v:51:y:2024:i:3:d:10.1007_s11116-022-10367-9
    DOI: 10.1007/s11116-022-10367-9
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    References listed on IDEAS

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    1. Meng Liu & Erik Brynjolfsson & Jason Dowlatabadi, 2021. "Do Digital Platforms Reduce Moral Hazard? The Case of Uber and Taxis," Management Science, INFORMS, vol. 67(8), pages 4665-4685, August.
    2. 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..
    3. Yuan K. Chou, 2002. "Testing Alternative Models Of Labour Supply: Evidence From Taxi Drivers In Singapore," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 47(01), pages 17-47.
    4. Ke Xu & Luping Sun & Jingchen Liu & Hansheng Wang, 2018. "An empirical investigation of taxi driver response behavior to ride-hailing requests: A spatio-temporal perspective," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-17, June.
    5. Ronghua Wang & Jiangbi Hu & Xiaoqin Zhang, 2016. "Analysis of the Driver’s Behavior Characteristics in Low Volume Freeway Interchange," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-9, April.
    6. 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.
    7. Wang, Sicheng & Smart, Michael, 2020. "The disruptive effect of ridesourcing services on for-hire vehicle drivers’ income and employment," Transport Policy, Elsevier, vol. 89(C), pages 13-23.
    8. Goodspeed, Robert & Xie, Tian & Dillahunt, Tawanna R. & Lustig, Josh, 2019. "An alternative to slow transit, drunk driving, and walking in bad weather: An exploratory study of ridesourcing mode choice and demand," Journal of Transport Geography, Elsevier, vol. 79(C), pages 1-1.
    9. Sun, Hao & Wang, Hai & Wan, Zhixi, 2019. "Model and analysis of labor supply for ride-sharing platforms in the presence of sample self-selection and endogeneity," Transportation Research Part B: Methodological, Elsevier, vol. 125(C), pages 76-93.
    10. Gleb Romanyuk, 2017. "Ignorance is Strength: Improving the Performance of Matching Markets by Limiting Information (JOB MARKET PAPER)," Working Paper 460961, Harvard University OpenScholar.
    11. Henry S. Farber, 2005. "Is Tomorrow Another Day? The Labor Supply of New York City Cabdrivers," Journal of Political Economy, University of Chicago Press, vol. 113(1), pages 46-82, February.
    12. Kareem Haggag & Brian McManus & Giovanni Paci, 2017. "Learning by Driving: Productivity Improvements by New York City Taxi Drivers," American Economic Journal: Applied Economics, American Economic Association, vol. 9(1), pages 70-95, January.
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