A Multi-Bus Dispatching Strategy Based on Boarding Control

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A Multi-Bus Dispatching Strategy Based on Boarding Control
Title:
A Multi-Bus Dispatching Strategy Based on Boarding Control
Other Titles:
IEEE Transactions on Intelligent Transportation Systems
Publication Date:
15 January 2021
Citation:
Zhang, Y., Su, R., Zhang, Y., & Guruge, N. S. G. (2021). A Multi-Bus Dispatching Strategy Based on Boarding Control. IEEE Transactions on Intelligent Transportation Systems, 1–15. doi:10.1109/tits.2020.3046285
Abstract:
A multi-bus dispatching strategy is proposed for a ring-shaped road bus transport system, which allows dispatching single bus or multiple buses and incorporates volume dynamics on both buses and stations. Also, the passengers’ perceived waiting time is firstly formulated as one part of the cost function to take passengers’ anxiety into account, and thereby improving the bus quality of service of bus operations. At upstream stations, as many passengers as possible will board the bus, which leads to the less space remaining on the bus and thus the enlongated wait for passengers at downstream stations.With the aim to avoid such phenomenon, the bus boarding control is implemented in the passengers’ boarding process captured by a simultaneous loading model to provide boarding opportunities for the waiting passengers at downstream bus stations. The formulated problem is tackled in two different scenarios, i.e., either with a linear cost or with a nonlinear cost. The linear cost, incorporating the passengers’ actual waiting time and the bus utilization, is firstly converted into a Mixed Integer Linear Programming (MILP) problem, and is solved by the commercial solver Gurobi. With the computational complexity as a concern, two different evolutionary algorithms, Genetic Algorithm (GA) and Harmony Search algorithm (HS), are also adopted to solve the problem in real time. In Scenario 2, the nonlinear cost, integrating the passengers’ perceived waiting time and the bus utilization, is directly solved by both GA and HS. Finally, case studies are provided to illustrate the efficiency of our proposed strategy by comparing with the traditional bus schedule strategies, as well as analyzing the different impacts of the bus loading process when either passengers’ actual waiting time or passengers’ perceived waiting time are taken into account.
License type:
Publisher Copyright
Funding Info:
There was no specific funding for the research done
Description:
© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
1524-9050
1558-0016
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