• Corpus ID: 238259542

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

  title={Efficiency, Fairness, and Stability in Non-Commercial Peer-to-Peer Ridesharing},
  author={Hoon Oh and Yanhan Tang and Zong Zhang and Alexandre Jacquillat and Fei Fang},
  • Hoon Oh, Yanhan Tang, +2 authors Fei Fang
  • Published 4 October 2021
  • Computer Science, Economics
  • 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… 

Figures from this paper


A Real-Time Algorithm to Solve the Peer-to-Peer Ride-Matching Problem in a Flexible Ridesharing System
Real-time peer-to-peer ridesharing is a promising mode of transportation that has gained popularity during the recent years, thanks to the wide-spread use of smart phones, mobile application
Recommending Fair Payments for Large-Scale Social Ridesharing
This work proposes the first approach that can compute fair coalitional payments that are also stable according to the game-theoretic concept of the kernel for systems with thousands of agents in real-world scenarios.
An analysis of peer-to-peer networks with altruistic peers
We develop a new model of the interaction of rational peers in a Peer-to-Peer (P2P) network that has at its heart altruism, an intrinsic parameter reflecting peers’ inherent willingness to
Stable Matching for Dynamic Ride-Sharing Systems
This paper considers a notion of stability for ride-sharing matches and presents several mathematical programming methods to establish stable or nearly-stable matches, where it is noted that ride-share matching optimization is performed over time with incomplete information.
The Price of Fairness
The price of fairness is introduced and studied, which is the relative system efficiency loss under a “fair” allocation assuming that a fully efficient allocation is one that maximizes the sum of player utilities.
The price of stability for network design with fair cost allocation
It is established that the fair cost allocation protocol is in fact a useful mechanism for inducing strategic behavior to form near-optimal equilibria, and its results are extended to cases in which users are seeking to balance network design costs with latencies in the constructed network.
On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment
A more general mathematical model for real-time high-capacity ride-sharing that scales to large numbers of passengers and trips and dynamically generates optimal routes with respect to online demand and vehicle locations is presented.
Variable neighborhood search for the dial-a-ride problem
This paper proposes a competitive variable neighborhood search-based heuristic, using three classes of neighborhoods, based on the ejection chain idea, which exploits the existence of arcs where the vehicle load is zero, giving rise to natural sequences of requests.
Spatial Pricing in Ride-Sharing Networks
In “Spatial Pricing in Ride-Sharing Networks,” Bimpikis, Candogan, and Saban explore the impact of the demand pattern for rides across a network of ride-sharing platforms.
Neural Approximate Dynamic Programming for On-Demand Ride-Pooling
This work provides a general ADP method that can learn from ILP-based assignments, handles the extra combinatorial complexity from combinations of passenger requests by using a Neural Network based approximate value function and shows a connection to Deep Reinforcement Learning that allows it to learn this value-function with increased stability and sample-efficiency.