Optimizing vehicle distributions and fleet sizes for shared mobility-on-demand

Alex Wallar, Javier Alonso-Mora, Daniela Rus

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)
31 Downloads (Pure)

Abstract

Mobility-on-demand (MoD) systems are revolutionizing urban transit with the introduction of ride-sharing. Such systems have the potential to reduce vehicle congestion and improve accessibility of a city's transportation infrastructure. Recently developed algorithms can compute routes for vehicles in real-time for a city-scale volume of requests while allowing vehicles to carry multiple passengers at the same time. However, these algorithms focus on optimizing the performance for a given fleet of vehicles and do not tell us how many vehicles are needed to service all the requests. In this paper, we present an offline method to optimize the vehicle distributions and fleet sizes on historical demand data for MoD systems that allow passengers to share vehicles. We present an algorithm to determine how many vehicles are needed, where they should be initialized, and how they should be routed to service all the travel demand for a given period of time. Evaluation using 23,529,740 historical taxi requests from one month in Manhattan shows that on average 2864 four passenger vehicles are needed to service all of the taxi demand in a day with an average added travel delay of 2.8 mins.

Original languageEnglish
Title of host publication2019 International Conference on Robotics and Automation, ICRA 2019
PublisherIEEE
Pages3853-3859
Number of pages7
ISBN (Electronic)978-1-5386-6027-0
ISBN (Print)978-1-5386-8176-3
DOIs
Publication statusPublished - 2019
Event2019 International Conference on Robotics and Automation, ICRA 2019 - Montreal, Canada
Duration: 20 May 201924 May 2019

Conference

Conference2019 International Conference on Robotics and Automation, ICRA 2019
CountryCanada
CityMontreal
Period20/05/1924/05/19

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