Combining Local Search into Genetic Algorithm for Bus Schedule Coordination through Small Timetable Modifications

Synchronizing bus arrival and departure times at transfer stations could reduce excess transfer times. This paper addresses the planning level of bus schedule coordination problem through small timetable modifications to given an initial timetable. Timetable modifications consist of shifts in initia...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:International journal of ITS research Jg. 17; H. 2; S. 102 - 113
1. Verfasser: Wu, Yinghui
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 15.05.2019
Springer Nature B.V
Schlagworte:
Automotive Engineering
Civil Engineering
Computer Imaging
Electrical Engineering
Engineering
Enumeration
Genetic algorithms
Mathematical models
Mixed integer
Pattern Recognition and Graphics
Production planning
Robotics and Automation
Schedules
Synchronism
Timetables
Transfer stations
Travel demand
User Interfaces and Human Computer Interaction
Vision
Local search
Nonlinear mixed integer-programming model
Bus schedule coordination
Genetic algorithm
ISSN:1348-8503, 1868-8659
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Synchronizing bus arrival and departure times at transfer stations could reduce excess transfer times. This paper addresses the planning level of bus schedule coordination problem through small timetable modifications to given an initial timetable. Timetable modifications consist of shifts in initial departure times of vehicles from the depot. Headway-sensitive passenger demand is also considered in this problem. A nonlinear mixed integer-programming model is proposed for the problem to maximize the number of total transferring passengers with smalll excess transfer times. Based on the analysis of the proposed model, a genetic algorithm combining local search (GACLS) is designed to solve this model. Several numerical experiments are performed to show the utility and performance of GACLS. For small case, the proposed GACLS is highly effective, and can obtain the optimal solution with less time than enumeration method. For large real case, the GACLS also has good performances.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1348-8503
1868-8659
DOI:10.1007/s13177-018-0165-7