Integrated User Matching and Pricing in Round-Trip Car-Sharing

TL;DR

This paper introduces a novel N-user matching algorithm for round-trip car-sharing that enables one-way trips, optimizing pricing and matching to significantly increase demand fulfillment and system efficiency.

Contribution

It presents a two-stage ILP-based framework for joint pricing and user matching, allowing flexible trip options within traditional round-trip systems, which is a novel approach.

Findings

Approximately 35% increase in demand fulfillment.

Model robustness under uncertainty factors tested.

Effective joint optimization of pricing and matching.

Abstract

Traditional round-trip car rental systems mandate users to return vehicles to their point of origin, limiting the system adaptability to meet diverse mobility demands. This constraint often leads to fleet under-utilization and incurs high parking costs for idle vehicles. To address this inefficiency, we propose a N-user matching algorithm which is designed to facilitate one-way trips within the round-trip rental framework. Our algorithm addresses the joint problem of optimal pricing and user matching through a Two-Stage Integer Linear Programming (ILP)-based formulation. In the first stage, optimal rental prices are determined by setting a risk factor that governs the likelihood of matching a set of N-user. The second stage involves maximizing expected profit through a novel ILP-based user-matching formulation. Testing our algorithm on real-world scenarios demonstrates an approximate 35\% increase in demand fulfillment. Additionally, we assess the model robustness under uncertainty by varying factors such as the risk factor (probability of user ride acceptance at the offered price), cost factor (rental cost-to-fare ratio), and maximum chain length.