基于MOP模型的ITS对城市交通运输效率的优化研究
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摘要
智能交通系统(ITS)作为协调各种交通方式和改善运输效率的主要途径,受众多因素影响导致应用效益折减,如何根据交通实际需求,凝练ITS优化运输效率的方案是ITS发展面临的重大任务.通过明确ITS对交通运输效率优化的目标和影响因素,以不同交通方式客运周转量为主要指标,构建ITS优化城市交通运行效率的多目标规划模型(MOP模型),并以北京市为例进行实证.研究结果显示,ITS是不同交通方式达到预期目标的支撑,并基于此提出了在保证交通运行效率最优时各交通方式完成的目标客运周转量.
Intelligent Transportation System( ITS) has become the inevitable trend and the main ways to improve the efficiency of road network traffic. However,ITS application in traffic is influenced by many factors,which discounted the benefit to a great extent. Therefore,how to refine the optimization scheme of ITS on transport efficiency is a major task that the urban traffic faced,according to the actual needs of urban traffic. Through analyzing,the optimization goal and influencing factors are firstly cleared. Then the passenger person-kilometer of different traffic modes is selected as the main indicator. Moreover,the multi-objective planning method is selected and the MOP model of ITS to urban road network traffic efficiency is built. Furthermore,Beijing is selected as an example for empirical study. Research results show that the application of ITS is the support of different transportation to achieve the expected goal. Then the transportation turnover is put forward to ensure the optimal efficiency of network operation.
Intelligent Transportation System( ITS) has become the inevitable trend and the main ways to improve the efficiency of road network traffic. However,ITS application in traffic is influenced by many factors,which discounted the benefit to a great extent. Therefore,how to refine the optimization scheme of ITS on transport efficiency is a major task that the urban traffic faced,according to the actual needs of urban traffic. Through analyzing,the optimization goal and influencing factors are firstly cleared. Then the passenger person-kilometer of different traffic modes is selected as the main indicator. Moreover,the multi-objective planning method is selected and the MOP model of ITS to urban road network traffic efficiency is built. Furthermore,Beijing is selected as an example for empirical study. Research results show that the application of ITS is the support of different transportation to achieve the expected goal. Then the transportation turnover is put forward to ensure the optimal efficiency of network operation.
引文
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[2] CHEN C L,YUAN T W,LEE W C. Multi-criteria fuzzy optimization for locating warehouses and distribution centers in a supply chain network[J]. Journal of the Chinese Institute of Chemical Engineers,2007,38(5/6):393-407.
[3] CARDONA-VALDES Y,ALVAREZ A,PACHECO J. Metaheuristic procedure for a bi-objective supply chain design problem with uncertainty[J]. Transportation Research Part B-methodological,2014,60(C):66-84.
[4] CHE Z H. A particle swarm optimization algorithm for solving unbalanced supply chain planning problems[J]. Applied Soft Computing,2012,12(4):1279-1287.
[5] CINTRON A,RAVINDRAN A R,VENTURA J A. Multi-criteria mathematical model for designing the distribution network of a consumer goods company[J]. Computers&Industrial Engineering,2010,58(4):584-593.
[6] KAMALI A,GHOMI S M T,FATEMI J F. A multi-objective quantity discount and joint optimization model for coordination of a single-buyer multi-vendor supply chain[J]. Computers&Mathematics with Applications,2011,62(8):3251-3269.
[7] SADJADY H,DAVOUDPOUR H. Two-echelon,multi-commodity supply chain network design with mode selection,leadtimes and inventory costs[J]. Computers&Operations Research,2012,39(7):1345-1354.
[8] BOUDAHRI F,SARI Z,BENNEKROUF M. Renovation of a distribution network of poultry products:Application city of Tlemcen[J]. International Conference on Logistics,2011,9(11):115-121.
[9] BEVILACQUA V,COSTANTINO N,DOTOLI M,et al. Strategic design and multi-objective optimisation of distribution networks based on genetic algorithms[J]. International Journal of Computer Integrated Manufacturing,2012,25(12):1139-1150.
[10]林晓辉.车路协同下基于交通密度的交叉口交通信号控制方法与仿真[J].工业工程,2014,17(4):123-128.LIN Xiaohui. Traffic signal control method and simulation based on traffic density in cooperative vehicle infrastructure system[J]. Industrial Engineering Journal,2014,17(4):123-128.
[11] BECK L F,DELLINGER A M,O’NEIL M E. Motor vehicle crash injury rates by mode of travel,United States:using exposure-based methods to quantify differences[J]. American Journal of Epidemiology,2007,166(2):212-218.
[12]吴超.基于GIS的南通市公路交通网络可达性研究[J].信阳师范学院学报(自然科学版),2014,27(1):57-60,83.WU Chao. Road transportation network’s accessibility in nantong city based on GIS[J]. Journal of Xinyang Normal University(Natural Science Edition),2014,27(1):57-60,83.
[13]吕慎,田锋,李旭宏.大城市客运交通结构优化模型研究[J].公路交通科技,2007,24(7):117-120.LYU Shen,TIAN Feng,LI Xuhong. Study on the optimized model of urban passenger traffic structure[J]. Journal of Highway and Transportation Research and Development,2007,24(7):117-120.
[14]刘文宇.北京市发展低碳交通的前景分析[J].综合运输,2010(9):37-39.LIU Wenyu. Analysis on the prospect of developing low carbon transportation in Beijing[J]. Integrated Transportation,2010(9):37-39.
[15] CARLSSON-KANYAMA A. Food and life cycle energy inputs:consequences of diet and ways to increase efficiency[J]. Ecological Economics,2003,44(2):293-307.
[16]王海林,何建坤.交通部门CO2排放、能源消费和交通服务量达峰规律研究[J].中国人口·资源与环境,2018,28(2):59-65.WANG Hailin,HE Jiankun. Peaking rule of CO2emissions,energy consumption and transport volume in transportation sector[J]. China Population,Resources and Environment,2018,28(2):59-65.