Quantifying the benefits of vehicle pooling with shareability networks (1310.2963v2)

Abstract: 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.

• The paper introduces a shareability network framework that models each taxi trip as a node, using graph algorithms to quantify ride-sharing benefits.
• It demonstrates that optimal matching in NYC taxi data cuts cumulative trip distance by over 40% with only minimal additional delays.
• The study highlights practical scalability, suggesting that shareability networks can efficiently optimize urban transport in diverse city settings.

An Analytical Framework for Quantifying Vehicle Pooling Benefits with Shareability Networks

The paper "Quantifying the benefits of vehicle pooling with shareability networks" presents a rigorous mathematical approach to understanding and optimizing taxi-sharing services. The research introduces the concept of a shareability network to systematically quantify the benefits of taxi pooling, addressing both collective advantages like reduced traffic and emissions, and individual passenger discomfort such as increased travel time. The authors apply advanced graph-theoretic methods to evaluate shared vehicle services using a substantial dataset of taxi trips in New York City.

The Shareability Network Framework

The shareability network is a pivotal innovation in this paper, translating the problem of vehicle sharing into a graph-theoretic framework. Each trip is represented as a node, and potential for shared trips is denoted by links between these nodes, conditional on a maximum allowable passenger delay (denoted by the service quality parameter, Δ). This model enables efficient computation of optimal sharing strategies via maximum matching and weighted maximum matching algorithms.

Findings and Numerical Outcomes

Applying this framework to a vast dataset comprising over 150 million trips, the authors demonstrate that substantial reductions in cumulative trip distance (by over 40%) can be achieved with minimal passenger discomfort. This result is significant, showing that even with modest increases in allowable delay per passenger, there is a tangible impact on identifying shareable rides.

Another noteworthy outcome is the saturation of shareability relative to trip density. The analysis indicates that even cities with lower densities than New York City or low passenger willingness to share can benefit significantly from taxi pooling.

Practical Implications and Theoretical Contributions

Practically, this paper argues for the feasibility of large-scale taxi-sharing systems, supported by simulations of an online real-time scheduling system. The theoretical contribution is the substantiation of shareability networks as an efficient method to tackle ride-sharing issues, bypassing more computationally expensive dynamic approaches.

Speculation on Future Developments

Considering the scalability and flexibility of the approach, future research could explore applications beyond taxis, such as in ride-sharing apps for private vehicles or even public transport systems. Extending this model to incorporate machine learning could enhance predictive capabilities for dynamic routing in real-time scenarios, further optimizing urban transport logistics.

In conclusion, the paper provides a well-structured, empirically validated framework for assessing and implementing vehicle pooling services. This research lays the groundwork for practical implementations in smart cities aiming to enhance mobility while reducing the environmental footprint.