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

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openAccess
Journal Title
Journal ISSN
Volume Title
Conference article in proceedings
Date
2021-06-16
Major/Subject
Mcode
Degree programme
Language
en
Pages
6
Series
2021 7th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)
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.
Description
| openaire: EC/H2020/856602/EU//FINEST TWINS
Keywords
dynamic ridesharing, traffic control
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Citation
Zhou , Z & Roncoli , C 2021 , Computationally efficient dynamic assignment for on-demand ridesharing in congested networks . in 2021 7th International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2021 . IEEE , IEEE International Conference on Models and Technologies for Intelligent Transportation Systems , Heraklion , Greece , 16/06/2021 . https://doi.org/10.1109/MT-ITS49943.2021.9529283