Metro scheduling to minimize travel time and operating cost considering spatial and temporal constraints on passenger boarding
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Author(s)
Type
Journal Article
Abstract
Passengers on metro platforms can board a train only when the train has surplus capacity and
the dwell time is sufficient, while the latter condition is omitted in previous studies. Taking into account
the impacts of train capacity and dwell time on passengers boarding, this study develops a model on
optimizing metro timetable to reduce passenger travel time and metro operating cost, through regulating
trains’ inter-station run-time, dwell time and headway. The NSGA-II algorithm is employed to obtain
the near-optimal Pareto Frontier of the proposed model. To address insufficient dwell time scheduled in
the timetable, three operating strategies are proposed and compared: a. sticking to nominal timetable; b.
extending dwell time only; c. extending dwell time and recovering delay as soon as possible by compressing
train inter-station run-time. Case studies on real-life metro line prove that some passengers cannot board
the train during peak hours due to insufficient dwell time. In this context, strategy a brings low-quality
service because passengers are stranded at platform even though the train has surplus capacity. In contrast,
more passengers can board the train with strategies b and c because dwell time is extended for passengers’
boarding when train has surplus capacity. Compared to strategy b, strategy c reduces the average in-vehicle
time of passengers by 2.5% through compressing inter-station run-time to recover the delay. The timetable
optimized based on strategy c saves total travel time of passengers by 3.1% without increasing operating
cost when compared to the practical timetable.
the dwell time is sufficient, while the latter condition is omitted in previous studies. Taking into account
the impacts of train capacity and dwell time on passengers boarding, this study develops a model on
optimizing metro timetable to reduce passenger travel time and metro operating cost, through regulating
trains’ inter-station run-time, dwell time and headway. The NSGA-II algorithm is employed to obtain
the near-optimal Pareto Frontier of the proposed model. To address insufficient dwell time scheduled in
the timetable, three operating strategies are proposed and compared: a. sticking to nominal timetable; b.
extending dwell time only; c. extending dwell time and recovering delay as soon as possible by compressing
train inter-station run-time. Case studies on real-life metro line prove that some passengers cannot board
the train during peak hours due to insufficient dwell time. In this context, strategy a brings low-quality
service because passengers are stranded at platform even though the train has surplus capacity. In contrast,
more passengers can board the train with strategies b and c because dwell time is extended for passengers’
boarding when train has surplus capacity. Compared to strategy b, strategy c reduces the average in-vehicle
time of passengers by 2.5% through compressing inter-station run-time to recover the delay. The timetable
optimized based on strategy c saves total travel time of passengers by 3.1% without increasing operating
cost when compared to the practical timetable.
Date Issued
2020-06-22
Date Acceptance
2020-06-19
Citation
IEEE Access, 2020, 8, pp.114190-114210
ISSN
2169-3536
Publisher
Institute of Electrical and Electronics Engineers
Start Page
114190
End Page
114210
Journal / Book Title
IEEE Access
Volume
8
Copyright Statement
© 2020 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
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http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000549104400001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Subjects
Science & Technology
Technology
Computer Science, Information Systems
Engineering, Electrical & Electronic
Telecommunications
Computer Science
Engineering
Public transportation
urban railway
train scheduling
heuristic algorithms
operating cost
passenger travel time
TIMETABLE DESIGN
OPTIMIZATION
DEMAND
MODEL
ALGORITHM
Publication Status
Published
Date Publish Online
2020-06-22