Proceedings

Real-Time Pricing Optimization for Ride-Hailing Quality of Service

Enpeng Yuan, Pascal Van Hentenryck

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Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Main Track. Pages 3742-3748.

https://doi.org/10.24963/ijcai.2021/515

Keywords

  • Multidisciplinary Topics and Applications: Real-Time Systems
  • Multidisciplinary Topics and Applications: Transportation

Abstract

When demand increases beyond the system capacity, riders in ride-hailing/ride-sharing systems often experience long waiting time, resulting in poor customer satisfaction. This paper proposes a spatio-temporal pricing framework (AP-RTRS) to alleviate this challenge and shows how it naturally complements state-of-the-art dispatching and routing algorithms. Specifically, the pricing optimization model regulates demand to ensure that every rider opting to use the system is served within reason-able time: it does so either by reducing demand to meet the capacity constraints or by prompting potential riders to postpone service to a later time. The pricing model is a model-predictive control algorithm that works at a coarser temporal and spatial granularity compared to the real-time dispatching and routing, and naturally integrates vehicle relocations. Simulation experiments indicate that the pricing optimization model achieves short waiting times without sacrificing revenues and geographical fairness.

Enpeng Yuan, and Pascal Van Hentenryck. 2021. Real-Time Pricing Optimization for Ride-Hailing Quality of Service. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. IJCAI, 3742–3748. https://doi.org/10.24963/ijcai.2021/515
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