Publication Type

Journal Article

Version

publishedVersion

Publication Date

6-2026

Abstract

The rapid expansion of ride-sourcing platforms has enabled freelance drivers to flexibly determine both their participation and working hours. Understanding this flexible labor supply behavior is essential for managing platform capacity and evaluating the impacts of pricing and incentive policies on driver welfare. This study develops a labor supply model in which drivers optimally choose whether to participate (extensive margin) and how long to work (intensive margin) to maximize their utility from consumption and leisure. The model incorporates heterogeneity in drivers’ other income, idle time, and participation costs, allowing us to analytically characterize equilibrium labor supply decisions. The results show that participation occurs only when the income rate exceeds a threshold. Participating drivers work all of their idle time when the income rate lies within an intermediate range, but allocate less time to work when the rate is very low or very high. Lower other income or higher participation costs increase the optimal working hours. Moreover, the working-hour elasticity becomes negative when the income rate is sufficiently high. Using real-world operational data, we estimate key model parameters and find that most drivers exhibit positive working-hour elasticity, whereas a smaller but non-negligible group show negative elasticity. These findings suggest that an increase in the income rate always expands platform capacity through greater participation but may either augment or reduce capacity through changes in working hours, depending on driver heterogeneity.

Keywords

Ride-sourcing platform, Labor supply, Extensive margin, Intensive margin, Structural modeling, Inverse optimization

Discipline

Operations Research, Systems Engineering and Industrial Engineering | Theory and Algorithms | Transportation

Research Areas

Intelligent Systems and Optimization

Publication

Transportation Research Part C: Emerging Technologies

Volume

187

First Page

1

Last Page

25

ISSN

0968-090X

Identifier

10.1016/j.trc.2026.105635

Publisher

Elsevier

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1016/j.trc.2026.105635

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