---
title: "Real-Time Pricing Optimization for Ride-Hailing Quality of Service"
authors:
  - "Enpeng Yuan"
  - "Pascal Van Hentenryck"
doi: "10.24963/ijcai.2021/515"
url: "https://ijcai.org/proceedings/2021/515"
pdf: "https://ijcai.org/proceedings/2021/0515.pdf"
venue: "Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence"
year: 2021
pages: "3742-3748"
track: "Main Track"
keywords:
  - "Multidisciplinary Topics and Applications: Real-Time Systems"
  - "Multidisciplinary Topics and Applications: Transportation"
---

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

Enpeng Yuan, Pascal Van Hentenryck

*Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence*, pages 3742-3748. DOI: [10.24963/ijcai.2021/515](https://doi.org/10.24963/ijcai.2021/515)

## 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.