The Share-A-Ride Problem with Integrated Routing and Design Decisions

The Case of Mixed-Purpose Shared Autonomous Vehicles

Conference Paper (2021)
Author(s)

Max van der Tholen (Student TU Delft)

Breno A. Beirigo (TU Delft - Transport Engineering and Logistics)

Jovana Jovanova (TU Delft - Transport Engineering and Logistics)

Frederik Schulte (TU Delft - Transport Engineering and Logistics)

Research Group
Transport Engineering and Logistics
Copyright
© 2021 Max van der Tholen, B. Alves Beirigo, J. Jovanova, F. Schulte
DOI related publication
https://doi.org/10.1007/978-3-030-87672-2_23
More Info
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Publication Year
2021
Language
English
Copyright
© 2021 Max van der Tholen, B. Alves Beirigo, J. Jovanova, F. Schulte
Research Group
Transport Engineering and Logistics
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.@en
Pages (from-to)
347-361
ISBN (print)
978-3-030-87671-5
ISBN (electronic)
978-3-030-87672-2
Reuse Rights

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Abstract

The shared autonomous vehicle (SAV) is a new concept that meets the upcoming trends of autonomous driving and changing demands in urban transportation. SAVs can carry passengers and parcels simultaneously, making use of dedicated passenger and parcel modules on board. A fleet of SAVs could partly take over private transport, taxi, and last-mile delivery services. A reduced fleet size compared to conventional transportation modes would lead to less traffic congestion in urban centres. This paper presents a method to estimate the optimal capacity for the passenger and parcel compartments of SAVs. The problem is presented as a vehicle routing problem and is named variable capacity share-a-ride-problem (VCSARP). The model has a MILP formulation and is solved using a commercial solver. It seeks to create the optimal routing schedule between a randomly generated set of pick-up and drop-off requests of passengers and parcels. The objective function aims to minimize the total energy costs of each schedule, which is a trade-off between travelled distance and vehicle capacity. Different scenarios are composed by altering parameters, representing travel demand at different times of the day. The model results show the optimized cost of each simulation along with associated routes and vehicle capacities.

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