On-Demand Ride-Matching in a Spatial Model with Abandonment and Cancellation

Guangju Wang (), Hailun Zhang () and Jiheng Zhang ()
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Guangju Wang: Shanghai Qi Zhi Institute, Shanghai, 200232 China
Hailun Zhang: School of Data Science, Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong, Shenzhen, 518172 China
Jiheng Zhang: Department of Industrial Engineering & Decision Analytics, The Hong Kong University of Science and Technology, Hong Kong

Operations Research, 2024, vol. 72, issue 3, 1278-1297

Abstract: Ride-hailing platforms, such as Uber, Lyft, and DiDi, coordinate supply and demand by matching passengers and drivers. The platform has to promptly dispatch drivers when receiving requests because, otherwise, passengers may lose patience and abandon the service by switching to alternative transportation methods. However, having fewer idle drivers results in a possible lengthy pickup time, which is a waste of system capacity and may cause passengers to cancel the service after they are matched. Because of the complex spatial and queueing dynamics, analysis of the matching decision is challenging. In this paper, we propose a spatial model to approximate the pickup time based on the number of waiting passengers and idle drivers. We analyze the dynamics of passengers and drivers in a queueing model in which the platform can control the matching process by setting a threshold on the expected pickup time. Applying fluid approximations, we obtain accurate performance evaluations and an elegant optimality condition, based on which we propose a policy that adapts to time-varying demand.

Keywords: Stochastic Models; on-demand matching; ride-hailing platform; fluid approximation (search for similar items in EconPapers)
Date: 2024
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