Efficiency, Fairness, and Stability in Non-Commercial Peer-to-Peer Ridesharing

TL;DR

This paper introduces new algorithms for non-commercial P2P ridesharing that prioritize user preferences, fairness, and stability, demonstrating that such solutions are computationally feasible and improve upon purely efficiency-based methods.

Contribution

It presents novel notions of fairness and stability in P2P ridesharing and algorithms that incorporate user preferences for improved matching outcomes.

Findings

Fair and stable solutions are computationally feasible.

User-centric matching improves baseline efficiency.

Algorithms effectively balance fairness, stability, and efficiency.

Abstract

Unlike commercial ridesharing, non-commercial peer-to-peer (P2P) ridesharing has been subject to limited research -- although it can promote viable solutions in non-urban communities. This paper focuses on the core problem in P2P ridesharing: the matching of riders and drivers. We elevate users' preferences as a first-order concern and introduce novel notions of fairness and stability in P2P ridesharing. We propose algorithms for efficient matching while considering user-centric factors, including users' preferred departure time, fairness, and stability. Results suggest that fair and stable solutions can be obtained in reasonable computational times and can improve baseline outcomes based on system-wide efficiency exclusively.