The car pooling problem: Heuristic algorithms based on savings functions

Ferrari, Emilio; Manzini, Riccardo; Pareschi, Arrigo; Persona, Alessandro; Regattieri, Alberto

doi:10.1002/atr.5670370302

Cited by 42 publications

(20 citation statements)

References 17 publications

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“…There have been a few studies regarding the carpool optimization problem. Ferrari et al [1] designed several different automatic and heuristic data processing routines based on savings functions to support efficient matching in carpool schemes. The problem involved locating users at their origin locations who needed to reach the same destination point given temporal and topographical constraints.…”

Section: Introductionmentioning

confidence: 99%

A Car Pooling Model and Solution Method With Stochastic Vehicle Travel Times

Yan

Chen

Chang

2014

IEEE Trans. Intell. Transport. Syst.

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Carpooling is one method that can be easily instituted and can help resolve a variety of problems that continue to plague urban areas, ranging from energy demands and traffic congestion to environmental pollution. However, most carpooling organizations currently use a trial-and-error process, in accordance with the projected vehicle travel times, for carpooling, which is neither effective nor efficient. In other words, stochastic disturbances arising from variations in vehicle travel times in actual operations are neglected. In the worst case scenario, where vehicle travel times fluctuate wildly during operations, the planned schedule could be disturbed enough to lose its optimality. Therefore, we constructed a stochastic carpooling model that considers the influence of stochastic travel times. The model is formulated as an integer multiple commodity network flow problem. Since real problem sizes can be large, it could be difficult to find optimal solutions within a reasonable period of time. Therefore, we develop a solution algorithm to solve the model. To test how well the model and the solution algorithm can be applied to the real world, we also developed a simulation-based evaluation method. To test the model and the solution algorithm, a case study is performed based upon data reported from a past study carried out in northern Taiwan. The results show that the model and solution algorithm are good and could be useful for carpooling practices.

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Section: Introductionmentioning

confidence: 99%

A Car Pooling Model and Solution Method With Stochastic Vehicle Travel Times

Yan

Chen

Chang

2014

IEEE Trans. Intell. Transport. Syst.

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“…One attempt to solve the LTCPP is based on saving functions [13]. Automatic and heuristic data processing routines were developed by the authors to allow efficient matching of rides for passengers and riders.…”

Section: Introductionmentioning

confidence: 99%

An intelligent carpooling app for a green social solution to traffic and parking congestions

Dakroub

Boukhater

Lahoud

et al. 2013

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)

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Because existing public transportation infrastructure cannot be adapted in a timely manner to address the daunting traffic and parking congestion in urban environments, researchers are investigating social solutions, such as carpooling, where a driver and one or more passengers having semi-common routes share a private vehicle. Although many carpooling systems have been proposed, most of them lack various levels of automation, functionality, practicality, and solution quality. While Genetic Algorithms (GAs) have been successfully adopted for solving combinatorial optimization problems, their use is highly uncommon in carpooling problems. Motivated to propose a solution for the many to many carpooling scenario, we present in this paper a GA with a customized fitness function that searches for the solution with minimal travel distance, efficient ride matching, timely arrival, and maximum fairness while taking into account the riding preferences of the carpoolers. The computational results and simulations based on real user data show the merits of the proposed method and motivate follow up research.

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“…In (Guo et al, 2012) a "Genetic Adaptive algorithm" was used, while in (Calvo et al, 2004) a system is presented using web GIS and SMS where the problem of carpooling is solved with a heuristic algorithm. Also in (Ferrari et al, 2003) a "Heuristic algorithms based on savings functions" was proposed. Finally (Santi et al, 2014) has been showed and quantified "the benefits of vehicle pooling with shareability networks".…”

Section: Introductionmentioning

confidence: 99%

A Method for Managing Transportation Requests and Subdivision Costs in Shared Mobility Systems

Siano¹,

Gallo²,

Glielmo³

2015

Proceedings of the 1st International Conference on Vehicle Technology and Intelligent Transport Systems

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This paper presents an algorithm for managing the demand and supply in a shared transportation system. In particular we present a method, independent from the Geographic Information System (GIS), which processes drivers and passengers requests and ranks them in order to encourage matching and to propose the solution profitable for all. The basic idea is to give priority to the requests of passengers with more common route and avoid those with greater excess path. In the end, we propose a solution for the distribution of costs among the participants of shared travel based on the application of the Shapley value.

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The car pooling problem: Heuristic algorithms based on savings functions

Cited by 42 publications

References 17 publications

A Car Pooling Model and Solution Method With Stochastic Vehicle Travel Times

A Car Pooling Model and Solution Method With Stochastic Vehicle Travel Times

An intelligent carpooling app for a green social solution to traffic and parking congestions

A Method for Managing Transportation Requests and Subdivision Costs in Shared Mobility Systems

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