# A Decomposition Algorithm to Solve the Multi-Hop Peer-to-Peer Ride-Matching Problem

arXiv: https://arxiv.org/abs/1704.06838 · HTML: https://mlsanity.com/paper/1704.06838/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Apr 2017.

## Built on by (most cited first, 4 of 4)

- [The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learning](https://mlsanity.com/paper/2111.05326/) (2021)
- [Coordinated Multi-Agent Pathfinding for Drones and Trucks over Road Networks](https://mlsanity.com/paper/2110.08802/) (2021)
- [A Survey of Machine Learning-Based Ride-Hailing Planning](https://mlsanity.com/paper/2303.14646/) (2023)
- [Research on Multi-hop Inference Optimization of LLM Based on MQUAKE Framework](https://mlsanity.com/paper/2509.04770/) (2025)

## Abstract (arXiv)

In this paper, we mathematically model the multi-hop Peer-to-Peer (P2P) ride-matching problem as a binary program. We formulate this problem as a many-to-many problem in which a rider can travel by transferring between multiple drivers, and a driver can carry multiple riders. We propose a pre-processing procedure to reduce the size of the problem, and devise a decomposition algorithm to solve the original ride-matching problem to optimality by means of solving multiple smaller problems. We conduct extensive numerical experiments to demonstrate the computational efficiency of the proposed algorithm and show its practical applicability to reasonably-sized dynamic ride-matching contexts. Finally, in the interest of even lower solution times, we propose heuristic solution methods, and investigate the trade-offs between solution time and accuracy.