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A New Stochastic Model for Bus Rapid Transit Scheduling with Uncertainty

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Title: A New Stochastic Model for Bus Rapid Transit Scheduling with Uncertainty
Authors: Milad Dehghani Filabadi, Afshin Asadi, Ramin Giahi, Ali Tahanpour Ardakani, Ali Azadeh
Source: Future Transportation, Vol 2, Iss 1, Pp 165-183 (2022)
Publisher Information: MDPI AG, 2022.
Publication Year: 2022
Collection: LCC:Engineering (General). Civil engineering (General)
Subject Terms: bus rapid transit (BRT), waiting time, traveling time, mixed-integer nonlinear programming, based-scenario stochastic programming, uncertainty modeling, Engineering (General). Civil engineering (General), TA1-2040
Description: Nowadays, authorities of large cities in the world implement bus rapid transit (BRT) services to alleviate traffic problems caused by the significant development of urban areas. Therefore, a controller is required to control and dispatche buses in such BRT systems.. However, controllers are facing new challenges due to the inherent uncertainties of passenger parameters such as arrival times, demands, alighting fraction as well as running time of vehicles between stops. Such uncertainties may significantly increase the operational cost and the inefficiencies of BRT services. In this paper, we focus on the controller’s perspective and propose a stochastic mixed-integer nonlinear programming (MINLP) model for BRT scheduling to find the optimal departure time of buses under uncertainty. The objective function of the model consists of passenger waiting and traveling time and aims to minimize total time related to passengers at any stop. From the modeling perspective, we propose a new method to generate scenarios for the proposed stochastic MINLP model. Furthermore, from the computational point of view, we implement an outer approximation algorithm to solve the proposed stochastic MINLP model and demonstrate the merits of the proposed solution method in the numerical results. This paper accurately reflect the complexity of BRT scheduling problem and is the first study, to the best of our knowledge, that presents and solves a mixed-integer nonlinear programming model for BRT scheduling.
Document Type: article
File Description: electronic resource
Language: English
ISSN: 2673-7590
Relation: https://www.mdpi.com/2673-7590/2/1/9; https://doaj.org/toc/2673-7590
DOI: 10.3390/futuretransp2010009
Access URL: https://doaj.org/article/8ca1c93f3a0a49f7a3ca37d24c5561cc
Accession Number: edsdoj.8ca1c93f3a0a49f7a3ca37d24c5561cc
Database: Directory of Open Access Journals
Description
Abstract:Nowadays, authorities of large cities in the world implement bus rapid transit (BRT) services to alleviate traffic problems caused by the significant development of urban areas. Therefore, a controller is required to control and dispatche buses in such BRT systems.. However, controllers are facing new challenges due to the inherent uncertainties of passenger parameters such as arrival times, demands, alighting fraction as well as running time of vehicles between stops. Such uncertainties may significantly increase the operational cost and the inefficiencies of BRT services. In this paper, we focus on the controller’s perspective and propose a stochastic mixed-integer nonlinear programming (MINLP) model for BRT scheduling to find the optimal departure time of buses under uncertainty. The objective function of the model consists of passenger waiting and traveling time and aims to minimize total time related to passengers at any stop. From the modeling perspective, we propose a new method to generate scenarios for the proposed stochastic MINLP model. Furthermore, from the computational point of view, we implement an outer approximation algorithm to solve the proposed stochastic MINLP model and demonstrate the merits of the proposed solution method in the numerical results. This paper accurately reflect the complexity of BRT scheduling problem and is the first study, to the best of our knowledge, that presents and solves a mixed-integer nonlinear programming model for BRT scheduling.
ISSN:26737590
DOI:10.3390/futuretransp2010009