Robust matching-integrated vehicle rebalancing in ride-hailing system with uncertain demand

Guo, Xiaotong; Caros, Nicholas S.; Zhao, Jinhua

doi:10.1016/j.trb.2021.05.015

Cited by 53 publications

(39 citation statements)

References 47 publications

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“…Most recently, Guo et al [10] proposed a MIVR model, introducing driver-customer matching component into the vehicle rebalancing problem to produce better rebalancing decisions. Robust optimization was used to better protect rebalancing decisions against demand uncertainty.…”

Section: A Vehicle Rebalancingmentioning

confidence: 99%

“…Jinhua Zhao is with the Department of Urban Studies and Planning, Massachusetts Institute of Technology, Cambridge, MA 02139, USA (e-mail: jinhua@mit.edu), corresponding author. with anticipated high demand to reduce the discrepancy between spatial distributions of supply and demand during each time period, therefore reducing customer wait times [6]- [10].…”

Section: Introductionmentioning

confidence: 99%

“…Subsequently, the decisions are made according to either the nominal predicted demand, which is named point-predictiondriven optimization, or an uncertainty set around the prediction, which is termed robust optimization. Robust optimization has been used widely for decision-making under uncertainty and has been applied to the vehicle rebalancing problem in [10]. However, a good demand prediction does not necessarily lead to a good rebalancing decision.…”

Section: Introductionmentioning

confidence: 99%

“…When demand can be predicted accurately, point-predictiondriven optimization is a better approach to adopt. • Comparing predictive prescriptions with the robust matching-integrated vehicle rebalancing (MIVR) model proposed in [10]. Compared to the robust MIVR model, predictive prescriptions achieve competitive performances without relying on any additional information about the future demand.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Data-Driven Vehicle Rebalancing With Predictive Prescriptions in the Ride-Hailing System

Guo

Wang

Zhao

2022

IEEE Open J. Intell. Transp. Syst.

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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.

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Section: A Vehicle Rebalancingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Data-Driven Vehicle Rebalancing With Predictive Prescriptions in the Ride-Hailing System

Guo

Wang

Zhao

2022

IEEE Open J. Intell. Transp. Syst.

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“…Mo et al [19] utilized the robust optimization technique to solve the individual path recommendation problem under rail disruptions considering demand uncertainty. For shared mobility systems, Guo et al [20] formulated a robust matching-integrated vehicle rebalancing model to balance vacant vehicles in the ride-hailing operations given demand uncertainty. Guo et al [21] extended the matchingintegrated vehicle rebalancing model proposed by Guo et al [20] by introducing predictive prescriptions approach [22] to handle demand uncertainty, which is an advanced approach for handling data uncertainty based on the stochastic optimization framework.…”

Section: Stochastic Programming Robust Optimization and Applications ...mentioning

confidence: 99%

Transit Frequency Setting Problem with Demand Uncertainty

Guo¹,

Wang²

2022

Preprint

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Public transit systems are the backbone of efficient and sustainable urban mobility systems in the era of urbanization. This paper proposes a transit frequency setting model for a single transit line to generate transit schedules that could better serve passengers. The proposed model optimizes both service patterns and frequency for transit routes during a service period, and crowding levels on transit vehicles are also considered. To handle demand uncertainties when designing transit schedules, both stochastic programming and robust optimization techniques are introduced to protect transit schedules against inaccurate demand estimates. To address the computation complexity in both extended models under large-scale demand matrices, a Bender decomposition algorithm and two dimension reduction techniques are designed. The proposed models will be tested with real-world data from Chicago Transit Authority (CTA).

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Effective credit assignment deep policy gradient multi-agent reinforcement learning for vehicle dispatch

et al. 2023

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Robust matching-integrated vehicle rebalancing in ride-hailing system with uncertain demand

Cited by 53 publications

References 47 publications

Data-Driven Vehicle Rebalancing With Predictive Prescriptions in the Ride-Hailing System

Data-Driven Vehicle Rebalancing With Predictive Prescriptions in the Ride-Hailing System

Transit Frequency Setting Problem with Demand Uncertainty

Effective credit assignment deep policy gradient multi-agent reinforcement learning for vehicle dispatch

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