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

arXiv: https://arxiv.org/abs/2110.01152 · HTML: https://mlsanity.com/paper/2110.01152/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 25 references that are in the map.
- First posted on arXiv 4 Oct 2021.

## Builds on (most cited first, 2 of 2)

- [Neural Approximate Dynamic Programming for On-Demand Ride-Pooling](https://mlsanity.com/paper/1911.08842/) (2019)
- [Quantifying the benefits of vehicle pooling with shareability networks](https://mlsanity.com/paper/1310.2963/) (2013)

## Abstract (arXiv)

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.