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【背景】城市郊区化发展所引起的职住分离现象，加剧了低出行密度地区公共交通的运营负担和服务供给难度。【目标】基于居民出行分布特征提出一种公交服务区域设计方法，并进一步构建响应式公交路径优化模型，以提高公交系统的集约化运营水平和经济效益。【方法】首先构建了一个基于出行密度的通道服务区域设计模型，以此确定公交服务区域宽度；然后以系统总成本最小化为目标，构建带时间窗和同时取送客约束的通道型响应式公交路径优化模型，并针对性地设计一种改进的人工蜂群算法进行求解。【数据】陆丰市客流OD数据在中小规模城市职住分离情况下，具有通道出行的特点，为问题分析和模型的训练提供数据支撑。【结论】在中小规模城市的低出行密度区域，通道型响应式公交路径优化模型能够使综合成本降低43.0%，乘客出行时间节省39.8%，显著提高了公交的经济效益和服务质量。
Abstract：[Background] The phenomenon of “separation of work and residence” caused by the development of urban suburbanization has intensified the operational burden and service supply difficulty of public transit in low-density areas. [Objective] Therefore, a design method for public transportation service areas based on the distribution characteristics of residents' travel is proposed, and a responsive public transportation path optimization model is further constructed to improve the intensive operation level and economic benefits of the public transportation system. [Method] Firstly, a channel service area design model based on travel density was constructed to determine the width of the public transportation service area; Then, with the goal of minimizing the total system cost, a channel-based responsive bus route optimization model with time windows and simultaneous passenger pick-up and drop-off constraints was constructed, and an improved artificial bee colony algorithm was designed for targeted solution. [Data] Based on the OD data of passenger flow in Lufeng City, it is found that under the phenomenon of “separation of work and residence” in small and mediumsized cities, passenger flow has the characteristic of “channel travel”, which provides data support for problem analysis and model training. [Conclusion] Compared with the traditional responsive bus model, it is found that in low travel density areas of small and medium-sized cities, the channel based responsive public transportation path optimization model can reduce comprehensive costs by43.0%, save passenger travel time by 39.8%, and significantly improve the economic benefits and service quality of public transportation.
[1]吴焕,卢顺达.中山市居民出行特征变化规律和趋势研判[J].交通与港航, 2023, 10(6):100-106.WU Huan, LU Shunda. Analysis on trip characteristics change law and development trend prediction of Zhongshan City[J]. Communication&Shipping, 2023, 10(6):100-106.
[2]毕晓萤,罗崴.中小城市居民出行特征分析及交通改善策略研究[J].交通科技与经济, 2018, 20(3):28-31, 71.BI Xiaoying, LUO Wei. Analysis of travel characteristics and transportation improvement strategies of small and medium-sized cities[J]. Technology&Economy in Areas of Communications, 2018, 20(3):28-31, 71.
[3]陈俊励.城市居民公交出行特征研究[D].北京:北京交通大学, 2008.CHEN Junli. Travel behavior analysis of urban residents in public transport[D]. Beijing:Beijing Jiaotong University, 2008.
[4]VELAGA N R, NELSON J D, WRIGHT S D, et al. The potential role of flexible transport services in enhancing rural public transport provision[J]. Journal of Public Transportation, 2012, 15(1):111-131.
[5]詹静.低需求区域发展需求响应式公交运营模式及适应性研究[D].重庆：重庆交通大学, 2016.ZHAN Jing. Study on the operating mode and adaptability of demand responsive transport develop in low demand area[D]. Chongqing:Chongqing Jiaotong University,2016.
[6]DYTCKOV S, PERSSON J A, LORIG F, et al. Potential benefits of demand responsive transport in rural areas:a simulation study in Lolland, Denmark[J]. Sustainability,2022, 14(6):3252.
[7]PAPANIKOLAOU A, BASBAS S. Analytical models for comparing demand responsive transport with bus services in low demand interurban areas[J]. Transportation Letters , 2020,13(4):255-262.
[8]WANG Z, YU J, HAO W, et al. Joint optimization of running route and scheduling for the mixed demand responsive feeder transit with time-dependent travel times[J].IEEE Transactions on Intelligent Transportation Systems,2021, 22(4):2498-2509.
[9]任婧璇,常孝亭,巫威眺,等.考虑候选站点和全服务过程的需求响应接驳公交调度[J].交通运输系统工程与信息, 2023, 23(5):202-214.REN Jingxuan, CHANG Xiaoting, WU Weitiao, et al. Demand responsive feeder transit scheduling considering candidate stops and full-service process[J]. Journal of Transportation Systems Engineering and Information Technology, 2023, 23(5):202-214.
[10]PEI M, LIN P, LIU R, et al. Flexible transit routing model considering passengers’willingness to pay[J]. IET Intelligent Transport Systems, 2019, 13(5):841-850.
[11]AMIRGHOLY M, GONZALES E J. Demand responsive transit systems with time-dependent demand:User equilibrium, system optimum, and management strategy[J].Transportation Research Part B:Methodological, 2016,92:234-252.
[12]SUN B, WEI M, ZHU S. Optimal design of demand-responsive feeder transit services with passengers’multiple time windows and satisfaction[J]. Future Internet,2018, 10(3):30.
[13]RAHIMI M, AMIRGHOLY M, GONZALES E J. System modeling of demand responsive transportation services:Evaluating cost efficiency of service and coordinated taxi usage[J]. Transportation Research Part E:Logistics and Transportation Review, 2018, 112:66-83.
[14]范文博,王曙光.考虑需求不确定的响应式接驳公交自适应发车策略优化[J].交通运输工程与信息学报,2024, 22(2):34-47.FAN Wenbo, WANG Shuguang. Optimization of adaptive dispatch strategies for on-demand feeder transit considering uncertain demand[J]. Journal of Transportation Engineering and Information, 2024, 22(2):34-47.
[15]龙雲,周剑峰,方侃,等.旅行时间不确定的灵活线路公交调度优化[J].交通运输工程与信息学报, 2024, 22(2):48-62.LONG Yun, ZHOU Jianfeng, FANG Kan, et al. Robust flexible-route bus optimization model considering travel time uncertainty[J]. Journal of Transportation Engineering and Information, 2024, 22(2):48-62.
[16]KIM M E, ROCHE A. Optimal service zone and headways for flexible-route bus services for multiple periods[J]. Transportation Planning and Technology, 2021, 44(2):194-207.
[17]孙继洋,黄建玲,陈艳艳,等.面向多目标站的灵活型公交路径优化调度模型[J].交通运输系统工程与信息, 2019, 19(6):105-111.SUN Jiyang, HUANG Jianling, CHEN Yanyan, et al.Flexible bus route optimization scheduling model for multi-target stations[J]. Journal of Transportation Systems Engineering and Information Technology, 2019, 19(6):105-111.
[18]LEFFLER D, BURGHOUT W, CATS O, et al. An adaptive route choice model for integrated fixed and flexible transit systems[J]. Transportmetrica B:Transport Dynamics, 2024, 12(1):2303047.
[19]郭梅雪,靳文舟,巫威眺.考虑充换电的模块化需求响应公交路径优化[J].交通运输工程与信息学报, 2024,22(3):34-51.GUO Meixue, JIN Wenzhou, WU Weitiao. Optimization of modular demand-responsive transit routes considering charging and battery swapping[J]. Journal of Transportation Engineering and Information, 2024, 22(3):34-51.
[20]张凯,康厚萍,龚莉莉.人员稀疏地区柔性公交区域特性的研究[J].科学技术与工程, 2013, 13(12):3331-3336.ZHANG Kai, KANG Houping, GONG Lili. The study of flexible route transit service area characteristics in low demand areas[J]. Science Technology and Engineering, 2013, 13(12):3331-3336.
[21]韩艳,果林峰,赵昊.低客流公交运营模式优化方法[J].重庆交通大学学报(自然科学版), 2024, 43(4):60-66.HAN Yan, GUO Linfeng, ZHAO Hao. Operation mode optimization method of low passenger flow bus[J]. Journal of Chongqing Jiaotong University(Natural Science),2024, 43(4):60-66.
[22]郭杉.城郊区域典型出行场景下的需求响应式公交线路优化研究[D].北京：北京交通大学, 2020.GUO Shan. Research on demand response bus route optimization in typical travel scenarios in suburban areas[D]. Beijing:Beijing Jiaotong University, 2020.
[23]PAN S, YU J, YANG X, et al. Designing a flexible feeder transit system serving irregularly shaped and gated communities:determining service area and feeder route planning[J]. Journal of Urban Planning and Development, 2015, 141(3):4014028.
[24]TUNG C T, CHU C H, HUNG K C, et al. Improved analytic model of the optimum dimensions designated for transit bus service zones[J]. Journal of Transportation Engineering, 2015, 141(5):4014095.
[25]姚尹杰.通道型需求响应公交的调度优化研究[D].广州:华南理工大学, 2021.YAO Yinjie. Research on Optimization of scheduling based on corridor demand responsive transit[D]. Guangzhou:South China University of Technology, 2021.
[26]BANSAL M, KIANFAR K. Planar maximum coverage location problem with partial coverage and rectangular demand and service zones[J]. INFORMS Journal on Computing, 2017, 29(1):152-169.
[27]CHEN P W, NIE Y M. Optimal design of demand adaptive paired-line hybrid transit:Case of radial route structure[J]. Transportation Research Part E:Logistics and Transportation Review, 2018, 110:71-89.
[28]肖延成.同时接送模式下需求响应式接驳公交路径优化研究[D].长春:吉林大学, 2020.XIAO Yancheng. Research on routing optimization model of demand responsive connector under simultaneous pickup and delivery mode[D]. Changchun:Jilin University, 2020.
[29]毛声,谢文俊,张建业,等.车辆路径问题的双重进化蜂群算法求解研究[J].计算机工程与应用, 2016, 52(7):35-42, 78.MAO Sheng, XIE Wenjun, ZHANG Jianye, et al. Double evolutional artificial bee colony algorithm for solving vehicle routing problem[J]. Computer Engineering and Applications, 2016, 52(7):35-42, 78.
[30]靳文舟,邓钦原,郝小妮,等.改进人工蜂群算法的农村DRT路径优化研究[J].郑州大学学报(工学版),2021, 42(4):84-90.JIN Wenzhou, DENG Qinyuan, HAO Xiaoni, et al. Research on route optimization of rural DRT based on improved ABC algorithm[J]. Journal of Zhengzhou University(Engineering Science), 2021, 42(4):84-90.
[31]CHEN M. Improved artificial bee colony algorithm based on escaped foraging strategy[J]. Journal of the Chinese Institute of Engineers, 2019, 42(6):516-524.
[32]邓钦原.考虑需求集中程度的需求响应公交调度优化研究[D].广州:华南理工大学, 2021.DENG Qinyuan. Research on demand responsive transit scheduling optimization considering demand concentration[D]. Guangzhou:South China University of Technology, 2021.
[33]WU W, ZHU Y, LIU R. Dynamic scheduling of flexible bus services with hybrid requests and fairness:Heuristics-guided multi-agent reinforcement learning with imitation learning[J]. Transportation Research Part B:Methodological, 2024, 190:103069.
[34]WU W, ZOU H, LIU R. Prediction-failure-risk-aware online dial-a-ride scheduling considering spatial demand correlation via approximate dynamic programming and scenario approach[J]. Transportation Research Part C:Emerging Technologies, 2024, 169:104801.
基本信息:
DOI：10.19961/j.cnki.1672-4747.2024.12.017
中图分类号:U491.17
引用信息:
[1]邓钦原,秦雅琴,钱正富.低出行密度下的通道型响应式公交路径优化[J].交通运输工程与信息学报,2026,24(02):11-21.DOI:10.19961/j.cnki.1672-4747.2024.12.017.
基金信息:
国家自然科学基金项目（71861016）
2025-03-24
2025-03-24
2025-03-24