Two-Step Coordinated Optimization Model of Mixed Demand Responsive Feeder Transit
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 146, Issue 3
Abstract
It is necessary to consider both the reservation and real-time travel demand in order to coordinate and optimize vehicle routing and scheduling. In this paper, a two-step coordinated optimization methodology is proposed for a multivehicle responsive transit route and scheduling under mixed demand conditions. At first, a coordinated optimization model of vehicle running route and schedule is constructed to compute the initial route and departure time for each feeder bus service under the reservation demand. The second step builds up a rule-based insertion method to determine whether to respond to the current real-time application according to the passenger’s real-time application. By obtaining the response status of the real-time application, the remaining route for the current bus fleet is determined by using the optimization method, and then the residual is determined through inserting the real-time application. In this study, two experiments will be conducted and tested in order to verify our theories. The first one with fixed and varied departure intervals is verified while simultaneously optimizing the determination of the running route and departure time for each feeder bus under mixed demands. On the other hand, the second one is mainly focused on vehicle ownership for the system, which proves this method can optimize the vehicle inventory to determine the system demand.
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Data Availability Statement
All data, models, and code generated or used during the study appear in the published article.
Acknowledgments
This research was funded by the National Natural Science Foundation of China (Nos. 51678075, 51808057, 51338002, and 71861023), the Special Funds for the Construction of Innovative Provinces in Hunan, China (No. 2019SK2171), and the Program of Humanities and Social Science of Education Ministry of China (No. 18YJC630118). In addition, this work was also supported by the Hunan Provincial Natural Science Foundation of China (No. 2019JJ30026), Project Nos. 2018RS3074 and kq1801056 in Hunan Province, the Innovation team project for Transportation Engineering in the Changsha Univ. of Science and Technology (CSUST), and the Ministry of Science project with Montenegro (3-2).
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Journal of Transportation Engineering, Part A: Systems
Volume 146 • Issue 3 • March 2020
Copyright
©2019 American Society of Civil Engineers.
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Received: Feb 5, 2019
Accepted: Aug 9, 2019
Published online: Dec 30, 2019
Published in print: Mar 1, 2020
Discussion open until: May 30, 2020
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