Reforming mixed operation schedule for electric buses and traditional fuel buses by an optimal framework

Abstract Bus scheduling plays a significant role in public transportation and supports the sustainable development of transportation systems. Challenges are beginning to appear with the newly emerging electric buses (EBs), as scheduling changes due to fleet composition make traditional fixed timetab...

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Main Authors: Mengyuan Duan, Geqi Qi, Wei Guan, Chaoru Lu, Congcong Gong
Format: Article
Language:English
Published: Wiley 2021-10-01
Series:IET Intelligent Transport Systems
Online Access:https://doi.org/10.1049/itr2.12098
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spelling doaj-f2a47eddffa64ddca5519760db3f60922021-09-01T12:36:08ZengWileyIET Intelligent Transport Systems1751-956X1751-95782021-10-0115101287130310.1049/itr2.12098Reforming mixed operation schedule for electric buses and traditional fuel buses by an optimal frameworkMengyuan Duan0Geqi Qi1Wei Guan2Chaoru Lu3Congcong Gong4Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Ministry of Transport Beijing Jiaotong University Beijing ChinaKey Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Ministry of Transport Beijing Jiaotong University Beijing ChinaKey Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Ministry of Transport Beijing Jiaotong University Beijing ChinaTraffic Engineering Research Center Department of Civil and Environmental Engineering Norwegian University of Science and Technology Trondheim 7491 NorwayState Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing ChinaAbstract Bus scheduling plays a significant role in public transportation and supports the sustainable development of transportation systems. Challenges are beginning to appear with the newly emerging electric buses (EBs), as scheduling changes due to fleet composition make traditional fixed timetables no longer able to satisfy operational needs. Moreover, the fixed‐trip time hypothesis has been inappropriate for large cities due to the variety of urban traffic statuses. This paper proposes an optimal framework for reforming the mixed operation schedule for electric buses and traditional fuel buses under stochastic trip times. Based on the primary grouping genetic algorithm (GGA), a straightforward framework with a Monte Carlo simulation is presented to optimize the scheduling scheme. Case studies based on the operating environment and service trips of real bus lines in Beijing are conducted to verify the effectiveness of the proposed model by considering both the composition of fleet types and time stochasticity. Additionally, the impacts of stochasticity, fleet composition, government subsidies and cost factors on operational costs are investigated. Considering stochastic trip times, the achieved scheduling strategies can provide the optimal proportion of electric and traditional fuel buses and make a crucial impact on operational costs.https://doi.org/10.1049/itr2.12098
collection DOAJ
language English
format Article
sources DOAJ
author Mengyuan Duan
Geqi Qi
Wei Guan
Chaoru Lu
Congcong Gong
spellingShingle Mengyuan Duan
Geqi Qi
Wei Guan
Chaoru Lu
Congcong Gong
Reforming mixed operation schedule for electric buses and traditional fuel buses by an optimal framework
IET Intelligent Transport Systems
author_facet Mengyuan Duan
Geqi Qi
Wei Guan
Chaoru Lu
Congcong Gong
author_sort Mengyuan Duan
title Reforming mixed operation schedule for electric buses and traditional fuel buses by an optimal framework
title_short Reforming mixed operation schedule for electric buses and traditional fuel buses by an optimal framework
title_full Reforming mixed operation schedule for electric buses and traditional fuel buses by an optimal framework
title_fullStr Reforming mixed operation schedule for electric buses and traditional fuel buses by an optimal framework
title_full_unstemmed Reforming mixed operation schedule for electric buses and traditional fuel buses by an optimal framework
title_sort reforming mixed operation schedule for electric buses and traditional fuel buses by an optimal framework
publisher Wiley
series IET Intelligent Transport Systems
issn 1751-956X
1751-9578
publishDate 2021-10-01
description Abstract Bus scheduling plays a significant role in public transportation and supports the sustainable development of transportation systems. Challenges are beginning to appear with the newly emerging electric buses (EBs), as scheduling changes due to fleet composition make traditional fixed timetables no longer able to satisfy operational needs. Moreover, the fixed‐trip time hypothesis has been inappropriate for large cities due to the variety of urban traffic statuses. This paper proposes an optimal framework for reforming the mixed operation schedule for electric buses and traditional fuel buses under stochastic trip times. Based on the primary grouping genetic algorithm (GGA), a straightforward framework with a Monte Carlo simulation is presented to optimize the scheduling scheme. Case studies based on the operating environment and service trips of real bus lines in Beijing are conducted to verify the effectiveness of the proposed model by considering both the composition of fleet types and time stochasticity. Additionally, the impacts of stochasticity, fleet composition, government subsidies and cost factors on operational costs are investigated. Considering stochastic trip times, the achieved scheduling strategies can provide the optimal proportion of electric and traditional fuel buses and make a crucial impact on operational costs.
url https://doi.org/10.1049/itr2.12098
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AT geqiqi reformingmixedoperationscheduleforelectricbusesandtraditionalfuelbusesbyanoptimalframework
AT weiguan reformingmixedoperationscheduleforelectricbusesandtraditionalfuelbusesbyanoptimalframework
AT chaorulu reformingmixedoperationscheduleforelectricbusesandtraditionalfuelbusesbyanoptimalframework
AT congconggong reformingmixedoperationscheduleforelectricbusesandtraditionalfuelbusesbyanoptimalframework
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