Data-Driven Vehicle Rebalancing With Predictive Prescriptions in the Ride-Hailing System
Name
Data-Driven_Vehicle_Rebalancing_With_Predictive_Prescriptions_in_the_Ride-Hailing_System.pdf
Description
Published version
Size
2.37 MB
Format
Adobe PDF
Checksum (MD5)
57ef733f948a3bb0d7a8e620cf5244c6
Author(s)
Guo, Xiaotong
Wang, Qingyi
Zhao, Jinhua
Date Issued
2022
Journal
IEEE Open Journal of Intelligent Transportation Systems
Publisher
Institute of Electrical and Electronics Engineers
Citation
X. Guo, Q. Wang and J. Zhao, "Data-Driven Vehicle Rebalancing With Predictive Prescriptions in the Ride-Hailing System," in IEEE Open Journal of Intelligent Transportation Systems, vol. 3, pp. 251-266, 2022.
Version
Final published version
Abstract
Rebalancing vacant vehicles is one of the most critical strategies in ride-hailing operations. An effective rebalancing strategy can significantly reduce empty miles traveled and reduce customer wait times by better matching supply and demand. While the supply (vehicles) is usually known to the system, future passenger demand is uncertain. There are two ways to handle uncertainty. First, the point-prediction-driven optimization framework involves predicting the future demand and then producing rebalancing decisions based on the predicted demand. Second, the data-driven optimization approaches directly prescribe rebalancing decisions from data. In this study, a predictive prescription framework is introduced to this problem, where the benefits of predictive and data-driven optimization models are combined. Based on a state-of-the-art vehicle rebalancing model, the matching-integrated vehicle rebalancing (MIVR) model, predictive prescriptions are introduced to handle demand uncertainty. Model performances are evaluated using real-world simulations with New York City (NYC) ride-hailing data under four demand scenarios. When demand can be accurately predicted, a point-prediction-driven optimization framework should be adapted. The proposed predictive prescription models achieve shorter customer wait times over the point-prediction-driven optimization models when future demand predictions are not so accurate, and achieve a competitive performance with respect to the cutting-edge robust optimization models.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Massachusetts Institute of Technology. Department of Urban Studies and Planning
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ojits.2022.3163180
