Ma Jihui, Zhao Yanqing, Yang Yang, Liu Tao, Guan Wei, Wang Jiao, Song Cuiying
MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing, China.
Department of Civil and Environmental Engineering, University of Auckland, Auckland, New Zealand.
PLoS One. 2017 Jan 5;12(1):e0168762. doi: 10.1371/journal.pone.0168762. eCollection 2017.
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.
定制公交是一种新型公共交通方式，也是多元化公共交通的重要组成部分，提供先进、有吸引力且以用户为导向的服务。定制公交的运营活动通过汇总时空需求和类似的乘客出行需求来规划。基于对国内外研究及中国定制公交当前发展情况的分析，并考虑乘客出行数据，本文研究了定制公交运营相关问题，如站点选择、线路规划和时刻表制定，并建立了定制公交站点规划和时刻表模型。采用改进的免疫遗传算法（IIGA）求解该模型，具体涉及以下方面：1）多种群设计和运输算子设计；2）记忆库设计；3）变异概率设计和交叉概率设计；4）基因片段的适应度计算。最后，对北京的一个实际案例进行了计算，并对模型及求解结果进行了验证与分析。结果表明，IIGA能够求解该模型，且在乘客数量、出行时间、乘客平均出行时间、乘客平均提前到达时间和线路总收入方面优于基本遗传算法。本研究涵盖了定制公交运营系统的关键问题，将理论研究与实证分析相结合，为定制公交的规划与运营提供了理论基础。
