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基于社会力的信号交叉口施工区交通流建模
引用本文:邱小平,孙若晓,马丽娜,杨达.基于社会力的信号交叉口施工区交通流建模[J].交通运输系统工程与信息,2016,16(1):99-104.
作者姓名:邱小平  孙若晓  马丽娜  杨达
作者单位:1. 西南交通大学交通运输与物流学院,成都610031;2. 西南交通大学综合交通运输智能化国家地方联合工程 实验室,成都610031;3. 综合运输四川省重点实验室,成都610031)
基金项目:国家自然科学基金/National Natural Science Foundation of China (51408509, 51278429);四川省科技厅项目/ Foundation of Science and Technology Department of Sichuan Province (2013GZX0167, 2014ZR0091);成都市科技局软科学项 目/ Soft Science Foundation of Science and Technology Department of Chengdu (2014RK0000056ZF, 2014RK0000072ZF).
摘    要:由于施工区的存在,城市道路信号交叉口通行能力下降,车流运行混乱.本文为 了从微观层面研究信号交叉口施工区交通流运行特性,以初始社会力模型为基础,首次提 出一种新的适用于交叉口交通流的社会力模型;通过对影响岛式施工区通行能力的因素 进行分析,设计出一套数据采集方案;利用实测数据并结合遗传算法对提出的模型进行标 定,使用统计学指标对标定结果展开评价.结果表明,提出的新模型仿真得到的交通量与 实测值平均绝对相对误差仅为0.028,能为信号交叉口施工区交通流模拟提供参考.

关 键 词:交通工程  交通流建模  仿真  施工区  社会力模型  遗传算法标定  
收稿时间:2015-05-07

Modeling and Analyzing of Traffic Flow on the Work Zone of Urban Signalized Intersection Based on Social Force
QIU Xiao-ping,SUN Ruo-xiao,MALi-na,YANG Da.Modeling and Analyzing of Traffic Flow on the Work Zone of Urban Signalized Intersection Based on Social Force[J].Transportation Systems Engineering and Information,2016,16(1):99-104.
Authors:QIU Xiao-ping  SUN Ruo-xiao  MALi-na  YANG Da
Institution:1. School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, China; 2. Comprehensive Intelligent Transportation National and Local Joint Engineering Laboratory, Southwest Jiaotong University, Chengdu 610031, China; 3. Comprehensive Transportation Key Laboratory of Sichuan Province, Chengdu 610031, China)
Abstract:The capacity of urban signalized intersection will decrease and traffic flow will run chaotically because of a work zone sets in the intersection. In order to study urban signal intersection work zone traffic flow at a micro level, it puts forward a new applicable social force model of intersection traffic flow for the first time, based on initial social force model. Factors affecting the capacity of island work zone are analyzed. A data collection program is designed to obtain data of work zone intersection. The measured data and genetic algorithm (GA) are used to do the presented model calibration, and then it is evaluated with several statistical indicators. The results show that the average absolute relative error of simulation traffic data and real data is 0.028, so it can make a reference to analyze the traffic flow of urban signal intersection work zone.
Keywords:traffic engineering  traffic flow modeling  simulation  work zone  social force model  genetic algorithm calibration  
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