摘要This research studies ways to coordinate timetables in a regional bus transit with multiple transport modes to satisfy numerous realistic constraints, such as maximum and minimum departure intervals, by considering transfer among buses, subways, and passenger special lines with intersecting routes. The primary objective is to minimize the total waiting time for non-transferring and transferring passengers in all stops, while the secondary objective is to maximize the number of berths for all vehicles arriving at stations during a certain period. The constraint method converts the problem into a single objective programming problem. Based on the characteristic of the model, this study proposes an improved bacterial foraging optimization (BFO) design, which defines solution coding, redesigns the heuristic procedure to initialize chromosomes randomly, and uses the "ladder" concept to enhance bacterial foraging operation, to resolve the problem. Finally, a numerical example is provided to reveal the differences in schedule coordination between single and multiple transport modes as well as to analyze the influence of the best schemes of station capacity. Furthermore, the improved BFO is compared with other intelligent algorithms to verify the model as well as the accuracy and effectiveness of the algorithm.
Abstract:This research studies ways to coordinate timetables in a regional bus transit with multiple transport modes to satisfy numerous realistic constraints, such as maximum and minimum departure intervals, by considering transfer among buses, subways, and passenger special lines with intersecting routes. The primary objective is to minimize the total waiting time for non-transferring and transferring passengers in all stops, while the secondary objective is to maximize the number of berths for all vehicles arriving at stations during a certain period. The constraint method converts the problem into a single objective programming problem. Based on the characteristic of the model, this study proposes an improved bacterial foraging optimization (BFO) design, which defines solution coding, redesigns the heuristic procedure to initialize chromosomes randomly, and uses the "ladder" concept to enhance bacterial foraging operation, to resolve the problem. Finally, a numerical example is provided to reveal the differences in schedule coordination between single and multiple transport modes as well as to analyze the influence of the best schemes of station capacity. Furthermore, the improved BFO is compared with other intelligent algorithms to verify the model as well as the accuracy and effectiveness of the algorithm.
基金资助:Supported by the National Program on Key Basic Research Project (973 Program) (No.2012 CB725402); the National Natural Science Foundation of China (No.51338003, No.51178109); the China Postdoctoral Science Foundation (No.2013M540408); the Natural Science Foundation of Jiangsu High Education (No.13KJB580008); the Nantong Science and Technology Program (No.BK2014059); the Chongqing Key Lab of Traffic System & Safety in Mountain Cities Open Program (No.KTSS201303); and the Jiangsu Construction Systems Technology Project (No.2013ZD38)
魏明, 陈学武, 孙博. 多模式区域公交协调调度模型和算法[J]. Journal of Highway and Transportation Research and Development, 2015, 9(3): 78-84.
WEI Ming, CHEN Xue-wu, SUN Bo. Model and Algorithm of Schedule Coordination in Regional Bus Transit with Multiple Transport Modes. Journal of Highway and Transportation Research and Development, 2015, 9(3): 78-84.
[1] DADUNA J R, VOSS S. Practical Experiences in Schedule Synchronization[J]. Computer-Aided Transit Scheduling, 1995, 430:39-55.
[2] KWAN C M, CHANG C S. Timetable Synchronization of Mass Rapid Transit System Using Multi-objective Evolutionary Approach[J]. IEEE Transactions on System,Man and Cybernetics, Part C:Applications and Reviews, 2008, 38(5):636-648.
[3] CHANG C S, XU D Y, QUEK H B. Pareto-optimal Set Based Multi-objective Tuning of Fuzzy Automatic Train Operation for Mass Transit System[J]. IEE Proceedings-Electric Power Application, 1999, 146(5):577-581.
[4] KWAN C M, CHANG C S. Application of Evolutionary Algorithm on a Transportation Scheduling Problem:The Mass Rapid Transit[C]//The 2005 IEEE Congress on Evolutionary Computation. Edinburgh:IEEE, 2005:987-994.
[5] CEDER A. Creating Bus Timetables with Maximal Synchronization[J]. Transportation Research, 2001, 35(10):456-471.
[6] ZOU Ying. Study on Bus Regional Scheduling Travel Plan Organizing Method[J]. Journal of Transportation Systems Engineering and Information Technology,2007, 7(3):123-126. (in Chinese)
[7] LIU Zhi-gang, SHEN Jin-sheng, WANG Hai-xing. Regional Public Transportation Timetabling Model with Synchronization[J]. Journal of Transportation Systems Engineering and Information Technology,2007,7(2):345-361.(in Chinese)
[8] SHI Qin, QIN Yun-mei, HUANG Zhi-peng. Maximal Synchronous Transfer Model of Bus Regional Dispatching[J]. China Journal of Highway and Transport, 2007, 20(6):453-558. (in Chinese)
[9] SITU Bing-qiang, JIN Wen-zhou. Research on Timetable Optimization Model for Transit Network in Cooperation and Competition[J]. Journal of Highway and Transportation Research and Development, 2010, 27(6):122-128. (in Chinese)
[10] CHEN Xu-mei, LIN Guo-xin, YU Lei. Modeling Operation Scheduling Coordination between Urban Rail System and Bus System[J]. Systems Engineering-Theory & Practice, 2009, 29(10):165-173.(in Chinese)
[11] ZONG Fang, WANG Lin-hong, JIA Hong-fei. Coordinated Scheduling Model of Traffic Modes in Comprehensive Passenger Transport Hub[J]. Journal of South China University of Technology:Natural Science Edition, 2010, 38(3):53-58. (in Chinese)
[12] LIU Xiao-long. Improvement and Application of Bacterial Foraging Optimization Algorithm[D]. Guangzhou:South China University of Technology Press, 2011. (in Chinese)
[13] Editorial Board of Modern Applied Mathematics Editorial Handbook. Modern Applied Mathematics Handbook:Operations Research and Optimization Theory[M]. Beijing:Tsinghua University Press, 2004. (in Chinese)
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