Computationally efficient dynamic assignment for on-demand ridesharing in congested networks

Ze Zhou*, Claudio Roncoli

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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Abstract

On-demand ridesharing service has been recognized as an effective way to meet travel needs while significantly reducing the number of required vehicles. However, most previous studies investigating dynamic assignment for ridesharing systems overlook the effects on travel times due to the assignment of requests to vehicles and their routes. To better assign the ridesharing vehicles while considering network traffic, we propose a framework that incorporates time-dependent link travel time into the request-vehicle assignment. Furthermore, we formulate an optimal assignment problem that considers multiple path options and that accounts for the congestion potentially caused by assigned routes. A set of simulations reveals that using an appropriate congestion avoidance ridesharing strategy can remarkably reduce passenger average travel and waiting time by alleviating traffic congestion in the network.
Original languageEnglish
Title of host publication2021 7th International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2021
PublisherIEEE
Number of pages6
ISBN (Electronic)978-1-7281-8995-6
DOIs
Publication statusPublished - 16 Jun 2021
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Models and Technologies for Intelligent Transportation Systems - Online, Heraklion, Greece
Duration: 16 Jun 202117 Jun 2021
Conference number: 7
https://ieeexplore.ieee.org/xpl/conhome/9529260/proceeding
https://www.mt-its2021.tse.bgu.tum.de/

Conference

ConferenceIEEE International Conference on Models and Technologies for Intelligent Transportation Systems
Abbreviated titleMT-ITS
Country/TerritoryGreece
City Heraklion
Period16/06/202117/06/2021
Internet address

Keywords

  • dynamic ridesharing
  • traffic control

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