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

The Case of Mixed-Purpose Shared Autonomous Vehicles

Conference paper (2021)

Authors

B. Alves Beirigo Transport Engineering and Logistics - Mechanical, Maritime and Materials Engineering
J. Jovanova Transport Engineering and Logistics - Mechanical, Maritime and Materials Engineering
F. Schulte Transport Engineering and Logistics - Mechanical, Maritime and Materials Engineering
Research Group
Transport Engineering and Logistics (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2021 Max van der Tholen, B. Alves Beirigo, J. Jovanova, F. Schulte
DOI: https://doi.org/10.1007/978-3-030-87672-2_23
Shared autonomous vehicles Vehicle routing problem Capacity optimization
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Published Date

2021

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Marine and Transport Technology

Research Group

Transport Engineering and Logistics

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.