The Online Ride-Hailing Problem with Fairness

Shachaf Ben-Gal - M.Sc. student at the Dept. of Industrial Engineering

30 November 2021, 14:00 
Wolfson Building, Tel-Aviv University 
The Online Ride-Hailing Problem with Fairness


Ride-hailing is a prevalent transportation service that facilitates mobility in urban areas. A ride-hailing service system encompasses several research problems, among them is the operational assignment of on-demand ride requests to vehicles in real-time. The literature suggests various approaches to address similar systems, mainly optimizing the system efficiency, but recent studies pointed out that these systems are likely to cause geographical unfairness among passengers. Such unfairness may imply, for example, that requests whose origin or destination are far from centralized locations, may suffer from excessive service rejections.
In this work, we suggest an assignment mechanism that aims to overcome this phenomenon. We formulate the Online Ride-hailing Problem with Fairness that aims to maximize both efficiency and fairness in the system, while achieving an adequate balance between them. We find good assignment solutions from mixed-integer linear programming (MILP) formulations that are solved off-line, but are applicable to the online setting. Then we develop a new policy to assign ride requests to vehicles, leveraging data-driven techniques that make use of good assignment solutions, found by the MILP. With a simulation study, we examine the performance of our new approach relative to commonly used dispatching rules, as well as some more sophisticated ones, using synthetic random data that represent a real city layout and movement. Our results demonstrate that a good system performance is achieved using this approach



Shachaf Ben-Gal is an M.Sc. student at the Department of Industrial Engineering in Tel Aviv University, under the supervision of Prof. Michal Tzur. She holds a B.Sc. in Industrial Engineering from Tel Aviv University. Her research interests are in the area of transportation, operations research and data science.

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