Understanding working time and relocation choices of ridehailing drivers
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We identified four types of ridehailing drivers and jointly modeled driver working time and relocation choices using a stated preference survey of 200 drivers in Seattle, US. Based on the results, we simulated the impact of surge price on drivers’ both working time and relocation choices.
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The paper is under major revision in one of the top journals in Transportation.
Team
Yuanjie (Tukey) Tu, PhD, University of Washington
Moein Khaloei, PhD, University of Washington
Natalia Zuniga-Garcia, PhD, Argonne National Lab
Don MacKenzie, PhD, University of Washington
Main takeaways
Driver types
We identified four types of drivers: more than half of drivers (57% of the sample) have both a working time target and an earnings target, with the remainder of the sample split fairly evenly among no-target drivers (14%), time-target drivers (17%), and earnings-target drivers (13%).
Working time choice
Our findings mainly echo the neo-classic theory of driver labor supply: all types of drivers choose to continue working as their earning rate increases. We see little evidence of an earnings threshold effect that the reference dependent theory postulates, even when the driver reported having an earnings target in the survey.
Time-target drivers and both-target drivers are less likely to continue working after hitting their working time targets.
Relocation choice
Higher surge price in the current neighborhood encourages the driver to stay, while higher surge price in a nearby neighborhood attracts the driver to relocate.
Drivers are more likely to stay in a neighborhood where the average trip waiting time is low.
Longer relocation time discourages drivers to relocate.
Drivers are more likely to stay in the same place, everything else being equal.
Future directions
Future studies might consider including drivers from other channels (e.g., hailing rides) to provide a more representative view of ridehailing drivers.
Future efforts should identify and consider other important factors such as pay rates of different cities to have a better understanding of the relationship between trip features and drivers’ behaviors.
Acknowledgment
The work was supported by the U.S. Department of Energy (DOE) Vehicle Technologies Office (VTO) under the Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Laboratory Consortium, an initiative of the Energy Efficient Mobility Systems (EEMS) Program. The submitted manuscript has been created by University of Washington and the UChicago Argonne, LLC, Operator of Argonne National Laboratory (Argonne). Argonne, a U.S. Department of Energy Office of Science laboratory, is operated under Contract No. DE-AC02-06CH11357. The U.S. Government retains for itself, and others acting on its behalf, a paid-up nonexclusive, irrevocable worldwide license in said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Government.
Contributing
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