Ride-Sharing Matching under Travel Time Uncertainty through A Data-Driven Robust Optimization Approach

Conference paper (2021)

Authors

Xiaoming Li Ericsson Inc., Shenyang Aerospace University, Concordia University
J. Gao Ericsson Inc., Concordia University
Chun Wang Concordia University
Xiao Huang Concordia University
Yimin Nie Ericsson Inc.
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Published Date

2021

Language

English

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Abstract

In this paper, we propose a data-driven robust optimization model to reduce total travel cost in ride-sharing systems under travel time uncertainty. Instead of using a pre-defined uncertainty set, we study a data-driven robust optimization approach that integrates gated recurrent units (GRUs) predictions with a one-stage robust optimization model. The proposed approach has the ability to compute high quality solutions by leveraging the large-scale historical data to derive the uncertainty set for the designed robust optimization model. To evaluate the proposed approach, we conduct a group of simulations based on the New York taxi trip record data sets. The validation results show that our data-driven robust optimization approach outperforms the robust optimization approach with a pre-defined uncertainty set in terms of travellers' total travel cost. Most importantly, the total travel cost under the proposed approach is reduced by up to 31.7%, and by 26.7% on average compared with the robust model with a pre-defined uncertainty set.