WANG Zheng-wu, CHEN Tao, SONG Ming-qun. Coordinated optimization of operation routes and schedules for responsive feeder transit under simultaneous pick-up and delivery mode[J]. Journal of Traffic and Transportation Engineering, 2019, 19(5): 139-149. doi: 10.19818/j.cnki.1671-1637.2019.05.014
Citation: WANG Zheng-wu, CHEN Tao, SONG Ming-qun. Coordinated optimization of operation routes and schedules for responsive feeder transit under simultaneous pick-up and delivery mode[J]. Journal of Traffic and Transportation Engineering, 2019, 19(5): 139-149. doi: 10.19818/j.cnki.1671-1637.2019.05.014

Coordinated optimization of operation routes and schedules for responsive feeder transit under simultaneous pick-up and delivery mode

doi: 10.19818/j.cnki.1671-1637.2019.05.014
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  • Author Bio:

    WANG Zheng-wu(1973-), male, professor, PhD, zhengwu.wang@csust.edu.cn

  • Received Date: 2019-04-14
  • Publish Date: 2019-10-25
  • The coordinated optimization problem of operation routes and vehicle schedules for responsive feeder transit under the simultaneous pick-up and delivery mode was studied. The vehicle route representation method based on passengers rather than demand points was devised by considering the personalization of passenger travel time window. The objective function that represents system benefit was constructed by combining the costs of vehicle departure and travel, penalty costs of vehicle early and late arrival, and ticket fares. The vehicle capacity, passenger time window, vehicle running time, vehicle holding quantity and departure time were all taken as constraints, and the integrated optimization model of departure interval, vehicle type and running route was constructed. The double genetic algorithm was designed to solve the integrated optimization model. In this algorithm, the chromosome was coded by multi-chain coding structure, and the chromosome chiasma included two ways of inter-individual and intra-individual. In order to validate the superiority of the simultaneous pick-up and delivery mode, and the effectiveness of the integrated optimization model and the algorithm, several examples were designed to compare the calculation results of the simultaneous pick-up and delivery mode and the separate pick-up and deliver mode. The effects of vehicle speed, single trip running time limit and vehicle composition on the operation efficiency of responsive feeder transit were analyzed. Calculation result shows that under the same passenger demand, compared with the separate pick-up and deliver mode, under the simultaneous pick-up and delivery mode, the departure times reduce by 1, the number of required vehicles reduce by 2, the average seat utilization rate increases by 8.3%, the average vehicle distance required to transport unit passenger reduces by 11.0%, and the operation cost reduces by 15.9%. Therefore, the simultaneous pick-up and delivery mode can effectively improve the operation efficiency. At the same time, under the simultaneous pick-up and delivery mode, when vehicle speed, single trip running time limit, and small vehicle ratio fluctuate by 15.0%, 15.0%, and 12.5% near the reference values, respectively, the maximum change rate of the departure times, the average seat utilization rate, and the objective value reach 20.0%, 15.7%, and 27.1%, respectively, therefore, these parameters have a significant impact on the system operating efficiency.

     

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  • [1]
    PAN Shu-liang, YU Jie, LU Xiao-lin, et al. A review of flexible transit service[J]. Urban Transport of China, 2014, 12(2): 62-68, 58. (in Chinese). doi: 10.3969/j.issn.1672-5328.2014.02.009
    [2]
    CHANDRA S, QUADRIFOGLIO L. A new street connectivity indicator to predict performance for feeder transit services[J]. Transportation Research Part C: Emerging Technologies, 2013, 30: 67-80. doi: 10.1016/j.trc.2013.02.004
    [3]
    LI Xiu-gang, QUADRIFOGLIO L. Feeder transit services: choosing between fixed and demand responsive policy[J]. Transportation Research Part C: Emerging Technologies, 2010, 18(5): 770-780. doi: 10.1016/j.trc.2009.05.015
    [4]
    QUADRIFOGLIO L, LI Xiu-gang. A methodology to derive the critical demand density for designing and operating feeder transit services[J]. Transportation Research Part B: Methodological, 2009, 43(10): 922-935. doi: 10.1016/j.trb.2009.04.003
    [5]
    QUADRIFOGLIO L, DDESSOUKY M M, ORDÓÑEZ F. Asimulation study of demand responsive transit system design[J]. Transportation Research Part A: Policy and Practice, 2008, 42(4): 718-737. doi: 10.1016/j.tra.2008.01.018
    [6]
    EDWARDS D, WATKINS K. Comparing fixed-route and demand responsive feeder transit systems in real-world settings[J]. Transportation Research Record, 2013(2352): 128-135.
    [7]
    LI Xiu-gang, QUADRIFOGLIO L. Optimal zone design for feeder transit services[J]. Transportation Research Record, 2009(2111): 100-108.
    [8]
    MIAO Yi-di. A decision-making model for determining the service area of a flexible-route bus[D]. Dalian: Dalian University of Technology, 2011. (in Chinese).
    [9]
    MANG Lie. Scheduling method of railway transit station oriented demand responsive connector system[D]. Changchun: Jilin University, 2017. (in Chinese).
    [10]
    PAN Shu-liang, YU Jie, YANG Xian-feng, et al. Designing a flexible feeder transit system serving irregular shaped and gated communities: service area and feeder route planning[J]. Journal of Urban Planning and Development, 2015, 141(3): 04014028-1-9.
    [11]
    FAN Wen-hao. Research on routing optimization model of demand-responsive connector[D]. Nanjing: Southeast University, 2017. (in Chinese).
    [12]
    XIONG Jie, GUAN Wei, HUANG Ai-ling. Research on optimal routing of community shuttle connect rail transit line[J]. Journal of Transportation Systems Engineering and Information Technology, 2014, 14(1): 166-173. (in Chinese). doi: 10.3969/j.issn.1009-6744.2014.01.026
    [13]
    YU Yao, MACHEMEHL R B, XIE Chi. Demand-responsive transit circulator service network design[J]. Transportation Research Part E: Logistics and Transportation Review, 2015, 76: 160-175. doi: 10.1016/j.tre.2015.02.009
    [14]
    DESSOUKY M, RAHIMI M, WEIDNER M. Jointly optimizing cost, service, and environmental performance in demandresponsive transit scheduling[J]. Transportation Research Part D: Transport and Environment, 2003, 8(6): 433-465. doi: 10.1016/S1361-9209(03)00043-9
    [15]
    NOURBAKHSH S M, OUYANG Yan-feng. A structured flexible transit system for low demand areas[J]. Transportation Research Part B: Methodological, 2012, 46(1): 204-216. doi: 10.1016/j.trb.2011.07.014
    [16]
    NÚNEZ A, CORTÉSC E, SÁEZ D, et al. Multiobjective model predictive control for dynamic pickup and delivery problems[J]. Control Engineering Practice, 2014, 32: 73-86. doi: 10.1016/j.conengprac.2014.07.004
    [17]
    KIRCHLER D, CALVO R W. A granular tabu search algorithm for the dial-a-ride problem[J]. Transportation Research Part B: Methodological, 2013, 56: 120-135. doi: 10.1016/j.trb.2013.07.014
    [18]
    SCHILDE M, DOERNER K F, HARTL R F. Integrating stochastic time-dependent travel speed in solution methods for the dynamic dial-a-ride problem[J]. European Journal of Operational Research, 2014, 238(1): 18-30. doi: 10.1016/j.ejor.2014.03.005
    [19]
    PAN Shu-liang, YU Jie, ZOU Nan, et al. Service area and route selection choice model for flexible feeder transit with special demands[J]. Journal of Northeastern University(Natural Science), 2014, 35(11): 1650-1654. (in Chinese). doi: 10.3969/j.issn.1005-3026.2014.11.029
    [20]
    SHEU J B. A fuzzy clustering approach to real-time demandresponsive bus dispatching control[J]. Fuzzy Sets and Systems, 2005, 150(3): 437-455. doi: 10.1016/j.fss.2004.05.006
    [21]
    QIU Feng, LI Wen-quan, SHEN Jin-xing. Two-stage model for flex-route transit scheduling[J]. Journal of Southeast University(Natural Science Edition), 2014, 44(5): 1078-1084. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-DNDX201405036.htm
    [22]
    GAO Xu-ming. Resaerch on dispatching system of demandresponsive connector with on-demand stations[D]. Nanjing: Southeast University, 2015. (in Chinese).
    [23]
    ZHAO Wei-zhong. The research on route generation model of real-time custom bus based on random user's demand[D]. Xi'an: Chang'an University, 2017. (in Chinese).
    [24]
    CHEN Jian, LI Wu, WU Dan, et al. Scheduling optimization model of intelligent public transport system based on MAST[J]. Journal of Chongqing Jiaotong University(Natural Science), 2016, 35(5): 140-145. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-CQJT201605027.htm
    [25]
    GHANNADPOUR S F, NOORI S, TAVAKKOLI-MOGHADDAM R, et al. A multi-objective dynamic vehicle routing problem with fuzzy time windows: model, solution and application[J]. Applied Soft Computing Journal, 2014, 14(Part C): 504-527.
    [26]
    YI Xing. Research on urban public transport scheduling algorithm based on user needs[J]. Intelligent Computer and Applications, 2018, 8(4): 129-131, 135. (in Chinese). doi: 10.3969/j.issn.2095-2163.2018.04.027
    [27]
    ZHANG Peng-hao. Research on scheduling model of regional flexible feeder bus for high-speed railway stations[D]. Beijing: Beijing Jiaotong University, 2018. (in Chinese).
    [28]
    WANG Zheng-wu, YI Tong-xiang, GAO Zhi-bo. Coordinated optimization of running route and vehicle scheduling for responsive feeder transit[J]. Journal of Transport Science and Engineering, 2018, 34(1): 68-73. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-CSJX201801012.htm
    [29]
    WANG Zheng-wu, YUAN Yuan, GAO Zhi-bo. Coordination optimization for partition path and scheduling with high degree of freedom demand response transit[J]. Journal of Changsha University of Science and Technology(Natural Science), 2018, 15(1): 41-48. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-HNQG201801007.htm
    [30]
    WANG Zheng-wu, LIU An-qi, TAN Kang-kang. DRC bus operation cycle optimization corresponding to passenger demand characters[J]. Journal of Changsha University of Science and Technology(Natural Science), 2016, 13(2): 19-25. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-HNQG201602005.htm
    [31]
    DENG Lian-bo, GAO Wei, ZHOU Wen-liang, et al. Optimal design of feeder-bus network related to urban rail line based on transfer system[J]. Procedia-Social and Behavioral Sciences, 2013, 96: 2383-2394.
    [32]
    CHEN Cheng-pin, HAN Sheng-jun, LU Jian-sha, et al. A multi-chromosome genetic algorithm for multi-depot and multi-type vehicle routing problems[J]. China Mechanical Engineering, 2018, 29(2): 218-223. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-ZGJX201802014.htm

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