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Ride-hailing marketplaces like Uber and Lyft use dynamic pricing, often called surge, to balance the supply of available drivers with the demand for rides. We study driver-side payment mechanisms for such marketplaces, presenting the theoretical foundation that has informed the design of Uber’s new additive driver surge mechanism. We present a dynamic stochastic model to capture the impact of surge pricing on driver earnings and their strategies to maximize such earnings. In this setting, some time periods (surge) are more valuable than others (nonsurge), and therefore trips of different time lengths vary in the induced driver opportunity cost. First, we show that multiplicative surge, historically the standard on ride-hailing platforms, is not incentive compatible in a dynamic setting. We then propose a structured, incentive-compatible pricing mechanism. This closed-form mechanism has a simple form and is well approximated by Uber’s new additive surge mechanism. Finally, through both numerical analysis and real data from a ride-hailing marketplace, we show that additive surge is more incentive compatible in practice than is multiplicative surge. This paper was accepted by David Simchi-Levi, revenue management and market analytics.
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Inferring Trip Occupancies in the Rise of Ride-Hailing Services
The knowledge of all occupied and unoccupied trips made by selfemployed drivers are essential for optimized vehicle dispatch by ride-hailing services (e.g., Didi Dache, Uber, Lyft, Grab, etc.). However, vehicles’ occupancy status is not always known to service operators due to adoption of multiple ride-hailing apps. In this paper, we propose a novel framework, Learning to INfer Trips (LINT), to infer occupancy of car trips by exploring characteristics of observed occupied trips. Two main research steps, stop point classification and structural segmentation, are included in LINT. In the first step, we represent a vehicle trajectory as a sequence of stop points, and assign stop points with pick-up, drop-off, and intermediate labels thus producing a stop point label sequence. In the second step, for structural segmentation, we further propose several segmentation algorithms, including greedy segmentation (GS), efficient greedy segmentation (EGS), and dynamic programming-based segmentation (DP) to infer occupied trip from stop point label sequences. Our comprehensive experiments on real vehicle trajectories from self-employed drivers show that (1) the proposed stop point classifier predicts stop point labels with high accuracy, and (2) the proposed segmentation algorithm GS delivers the best accuracy performance with efficient running time.
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- Award ID(s):
- 1717084
- PAR ID:
- 10100137
- Date Published:
- Journal Name:
- Proceedings of the 27th ACM International Conference on Information and Knowledge Management
- Page Range / eLocation ID:
- 2097 to 2105
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3269206.3272025
