Altruistic Ride Sharing: A Community-Driven Approach to Short-Distance Mobility
- URL: http://arxiv.org/abs/2510.13227v1
- Date: Wed, 15 Oct 2025 07:24:48 GMT
- Title: Altruistic Ride Sharing: A Community-Driven Approach to Short-Distance Mobility
- Authors: Divyanshu Singh, Ashman Mehra, Snehanshu Saha, Santonu Sarkar,
- Abstract summary: Altruistic Ride-Sharing (ARS) is a decentralized, peer-to-peer mobility framework where participants alternate between driver and rider roles based on altruism points rather than monetary incentives.<n>ARS reduces travel distance and emissions, increases vehicle utilization, and promotes equitable participation compared to both no-sharing and optimization-based baselines.<n>Results establish ARS as a scalable, community-driven alternative to conventional ride-sharing, aligning individual behavior with collective urban sustainability goals.
- Score: 3.936187569159195
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Urban mobility faces persistent challenges of congestion and fuel consumption, specifically when people choose a private, point-to-point commute option. Profit-driven ride-sharing platforms prioritize revenue over fairness and sustainability. This paper introduces Altruistic Ride-Sharing (ARS), a decentralized, peer-to-peer mobility framework where participants alternate between driver and rider roles based on altruism points rather than monetary incentives. The system integrates multi-agent reinforcement learning (MADDPG) for dynamic ride-matching, game-theoretic equilibrium guarantees for fairness, and a population model to sustain long-term balance. Using real-world New York City taxi data, we demonstrate that ARS reduces travel distance and emissions, increases vehicle utilization, and promotes equitable participation compared to both no-sharing and optimization-based baselines. These results establish ARS as a scalable, community-driven alternative to conventional ride-sharing, aligning individual behavior with collective urban sustainability goals.
Related papers
- Transit for All: Mapping Equitable Bike2Subway Connection using Region Representation Learning [4.403122905236942]
Bike-sharing systems (BSS) can bridge equity gaps by providing affordable first- and last-mile connections.<n>We introduce Transit for All (TFA), a spatial computing framework designed to guide the equitable expansion of BSS.<n>Using NYC as a case study, we identify transit accessibility gaps that disproportionately impact low-income and minority communities.
arXiv Detail & Related papers (2025-06-18T03:31:07Z) - Fairness-Enhancing Vehicle Rebalancing in the Ride-hailing System [7.531863938542706]
The rapid growth of the ride-hailing industry has revolutionized urban transportation worldwide.
Despite its benefits, equity concerns arise as underserved communities face limited accessibility to affordable ride-hailing services.
This paper focuses on enhancing both algorithmic and rider fairness through a novel vehicle rebalancing method.
arXiv Detail & Related papers (2023-12-29T23:02:34Z) - Studying the Impact of Semi-Cooperative Drivers on Overall Highway Flow [76.38515853201116]
Semi-cooperative behaviors are intrinsic properties of human drivers and should be considered for autonomous driving.
New autonomous planners can consider the social value orientation (SVO) of human drivers to generate socially-compliant trajectories.
We present study of implicit semi-cooperative driving where agents deploy a game-theoretic version of iterative best response.
arXiv Detail & Related papers (2023-04-23T16:01:36Z) - HumanLight: Incentivizing Ridesharing via Human-centric Deep
Reinforcement Learning in Traffic Signal Control [3.402002554852499]
We present HumanLight, a novel decentralized adaptive traffic signal control algorithm.
Our proposed controller is founded on reinforcement learning with the reward function embedding the transportation-inspired concept of pressure at the person-level.
By rewarding HOV commuters with travel time savings for their efforts to merge into a single ride, HumanLight achieves equitable allocation of green times.
arXiv Detail & Related papers (2023-04-05T17:42:30Z) - How Bad is Selfish Driving? Bounding the Inefficiency of Equilibria in
Urban Driving Games [64.71476526716668]
We study the (in)efficiency of any equilibrium players might agree to play.
We obtain guarantees that refine existing bounds on the Price of Anarchy.
Although the obtained guarantees concern open-loop trajectories, we observe efficient equilibria even when agents employ closed-loop policies.
arXiv Detail & Related papers (2022-10-24T09:32:40Z) - Play&Go Corporate: An End-to-End Solution for Facilitating Urban
Cyclability [9.61441029601318]
Municipalities are increasingly facing problems of traffic congestion, road safety, energy dependency and air pollution.
We present an end-to-end solution, called Play&Go Corporate, for enabling urban cyclability and its concrete exploitation in the realization of a home-to-work sustainable mobility campaign.
arXiv Detail & Related papers (2022-09-06T18:21:06Z) - Efficiency, Fairness, and Stability in Non-Commercial Peer-to-Peer
Ridesharing [84.47891614815325]
This paper focuses on the core problem in P2P ridesharing: the matching of riders and drivers.
We introduce novel notions of fairness and stability in P2P ridesharing.
Results suggest that fair and stable solutions can be obtained in reasonable computational times.
arXiv Detail & Related papers (2021-10-04T02:14:49Z) - Euro-PVI: Pedestrian Vehicle Interactions in Dense Urban Centers [126.81938540470847]
We propose Euro-PVI, a dataset of pedestrian and bicyclist trajectories.
In this work, we develop a joint inference model that learns an expressive multi-modal shared latent space across agents in the urban scene.
We achieve state of the art results on the nuScenes and Euro-PVI datasets demonstrating the importance of capturing interactions between ego-vehicle and pedestrians (bicyclists) for accurate predictions.
arXiv Detail & Related papers (2021-06-22T15:40:21Z) - Value Function is All You Need: A Unified Learning Framework for Ride
Hailing Platforms [57.21078336887961]
Large ride-hailing platforms, such as DiDi, Uber and Lyft, connect tens of thousands of vehicles in a city to millions of ride demands throughout the day.
We propose a unified value-based dynamic learning framework (V1D3) for tackling both tasks.
arXiv Detail & Related papers (2021-05-18T19:22:24Z) - Flatland Competition 2020: MAPF and MARL for Efficient Train
Coordination on a Grid World [49.80905654161763]
The Flatland competition aimed at finding novel approaches to solve the vehicle re-scheduling problem (VRSP)
The VRSP is concerned with scheduling trips in traffic networks and the re-scheduling of vehicles when disruptions occur.
The ever-growing complexity of modern railway networks makes dynamic real-time scheduling of traffic virtually impossible.
arXiv Detail & Related papers (2021-03-30T17:13:29Z) - FlexPool: A Distributed Model-Free Deep Reinforcement Learning Algorithm
for Joint Passengers & Goods Transportation [36.989179280016586]
This paper considers combining passenger transportation with goods delivery to improve vehicle-based transportation.
We propose FlexPool, a distributed model-free deep reinforcement learning algorithm that jointly serves passengers & goods workloads.
We show that FlexPool achieves 30% higher fleet utilization and 35% higher fuel efficiency in comparison to model-free approaches.
arXiv Detail & Related papers (2020-07-27T17:25:58Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.