Paper Info
Title
A Data-Driven Timetable Optimization of Urban Bus Line Based on Multi-Objective Genetic Algorithm
Abstract
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.
Year
DOI
Venue
2021
10.1109/TITS.2020.3025031
IEEE Transactions on Intelligent Transportation Systems
Keywords
DocType
Volume
Urban transit,bus timetable,multi-objective,data-driven method,non-dominated sorting genetic algorithm-II (NSGA-II)
Journal
22
Issue
ISSN
Citations 
4
1524-9050
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Jinjun Tang1504.63
Yifan Yang26511.28
Wei Hao384.26
Fang Liu4373.59
Yinhai Wang529239.37