A Data-Driven Timetable Optimization of Urban Bus Line Based on Multi-Objective Genetic Algorithm

Reasonable bus timetable can reduce the operating costs of bus company and improve the quality of bus services. A data-driven method is proposed to optimize bus timetable in this study. Firstly, a bi-objective optimization model is constructed considering minimize the total waiting time of passenger...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on intelligent transportation systems Jg. 22; H. 4; S. 2417 - 2429
Hauptverfasser: Tang, Jinjun, Yang, Yifan, Hao, Wei, Liu, Fang, Wang, Yinhai
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.04.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
bus timetable
Companies
data-driven method
Dwell time
Encoding
Genetic algorithms
Global Positioning System
Global positioning systems
GPS
multi-objective
non-dominated sorting genetic algorithm-II (NSGA-II)
Optimization
Passengers
Scheduling
Smart cards
Sorting algorithms
Time dependence
Timetables
Travel time
Urban transit
Urban transportation
ISSN:1524-9050, 1558-0016
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Reasonable bus timetable can reduce the operating costs of bus company and improve the quality of bus services. A data-driven method is proposed to optimize bus timetable in this study. Firstly, a bi-objective optimization model is constructed considering minimize the total waiting time of passengers and the departure times of bus company. Then, Global Positioning System (GPS) trajectories of buses and passenger information collected from Smart Card are fused and applied to calculate the key parameters or variables in optimization model, including time-dependent travel time, bus dwell time and passenger volume. Finally, by adopting a specific coding scheme, an improved Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is designed to quickly search Pareto optimal solutions. Furthermore, an experiment is conducted in Beijing city from one bus line to validate the effectiveness of the proposed method. Comparing with empirical scheduling method and traditional single-objective optimization base on GA, the results show that the proposed model could quickly provide high-quality and reasonable timetable schemes for the administrator in urban transit system.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2020.3025031