# Quantifying the benefits of vehicle pooling with shareability networks

arXiv: https://arxiv.org/abs/1310.2963 · HTML: https://mlsanity.com/paper/1310.2963/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Oct 2013.

## Built on by (most cited first, 12 of 12)

- [Human-AI Coevolution](https://mlsanity.com/paper/2306.13723/) (2023)
- [One Step is Enough: Multi-Agent Reinforcement Learning based on One-Step Policy Optimization for Order Dispatch on Ride-Sharing Platforms](https://mlsanity.com/paper/2507.15351/) (2025)
- [Future Aware Pricing and Matching for Sustainable On-demand Ride Pooling](https://mlsanity.com/paper/2302.10510/) (2023)
- [Mutual Information as Intrinsic Reward of Reinforcement Learning Agents for On-demand Ride Pooling](https://mlsanity.com/paper/2312.15195/) (2023)
- [Large-scale Online Ridesharing: The Effect of Assignment Optimality on System Performance](https://mlsanity.com/paper/2305.02209/) (2023)
- [Large-scale Ridesharing DARP Instances Based on Real Travel Demand](https://mlsanity.com/paper/2305.18859/) (2023)
- [Efficiency, Fairness, and Stability in Non-Commercial Peer-to-Peer Ridesharing](https://mlsanity.com/paper/2110.01152/) (2021)
- [Optimal Chaining of Vehicle Plans with Time Windows](https://mlsanity.com/paper/2401.02873/) (2024)
- [Ride-pool Assignment Algorithms: Modern Implementation and Swapping Heuristics](https://mlsanity.com/paper/2504.10649/) (2025)
- [Adaptive Optimisation of Ride-Pooling Personalised Fares in a Stochastic Framework](https://mlsanity.com/paper/2508.20723/) (2025)
- [Non-myopic Matching and Rebalancing in Large-Scale On-Demand Ride-Pooling Systems Using Simulation-Informed Reinforcement Learning](https://mlsanity.com/paper/2510.25796/) (2025)
- [JointMatch: A Unified Heterogeneous Graph Neural Solver for Large-Scale Ride-Sharing Matching](https://mlsanity.com/paper/2609.20200/) (2026)

## Abstract (arXiv)

Taxi services are a vital part of urban transportation, and a considerable contributor to traffic congestion and air pollution causing substantial adverse effects on human health. Sharing taxi trips is a possible way of reducing the negative impact of taxi services on cities, but this comes at the expense of passenger discomfort quantifiable in terms of a longer travel time. Due to computational challenges, taxi sharing has traditionally been approached on small scales, such as within airport perimeters, or with dynamical ad-hoc heuristics. However, a mathematical framework for the systematic understanding of the tradeoff between collective benefits of sharing and individual passenger discomfort is lacking. Here we introduce the notion of shareability network which allows us to model the collective benefits of sharing as a function of passenger inconvenience, and to efficiently compute optimal sharing strategies on massive datasets. We apply this framework to a dataset of millions of taxi trips taken in New York City, showing that with increasing but still relatively low passenger discomfort, cumulative trip length can be cut by 40% or more. This benefit comes with reductions in service cost, emissions, and with split fares, hinting towards a wide passenger acceptance of such a shared service. Simulation of a realistic online system demonstrates the feasibility of a shareable taxi service in New York City. Shareability as a function of trip density saturates fast, suggesting effectiveness of the taxi sharing system also in cities with much sparser taxi fleets or when willingness to share is low.