A Model for the Stop Planning and Timetables of Customized Buses

• Published in: PloS one Vol. 12; no. 1; p. e0168762
• Main Authors: Ma, Jihui, Zhao, Yanqing, Yang, Yang, Liu, Tao, Guan, Wei, Wang, Jiao, Song, Cuiying
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 05.01.2017; Public Library of Science (PLoS)
• Subjects: Algorithms; Biology and Life Sciences; Buses; Demand analysis; Design; Empirical analysis; Engineering and Technology; Fitness; Genetic algorithms; Laboratories; Mathematical models; Medicine and Health Sciences; Models, Theoretical; Motor Vehicles; Mutation; Network management systems; Passengers; Physical Sciences; Planning; Public transportation; Reproductive fitness; Research and Analysis Methods; Schedules; Services; Social Sciences; System theory; Timetables; Traffic congestion; Transportation; Travel time; Traveltime; Vehicle customizing; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0168762

Customized buses (CBs) are a new mode of public transportation and an important part of diversified public transportation, providing advanced, attractive and user-led service. The operational activity of a CB is planned by aggregating space-time demand and similar passenger travel demands. Based on an analysis of domestic and international research and the current development of CBs in China and considering passenger travel data, this paper studies the problems associated with the operation of CBs, such as stop selection, line planning and timetables, and establishes a model for the stop planning and timetables of CBs. The improved immune genetic algorithm (IIGA) is used to solve the model with regard to the following: 1) multiple population design and transport operator design, 2) memory library design, 3) mutation probability design and crossover probability design, and 4) the fitness calculation of the gene segment. Finally, a real-world example in Beijing is calculated, and the model and solution results are verified and analyzed. The results illustrate that the IIGA solves the model and is superior to the basic genetic algorithm in terms of the number of passengers, travel time, average passenger travel time, average passenger arrival time ahead of schedule and total line revenue. This study covers the key issues involving operational systems of CBs, combines theoretical research and empirical analysis, and provides a theoretical foundation for the planning and operation of CBs.