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基于低频定点检测数据的交叉口交通状态估计
引用本文:唐克双.基于低频定点检测数据的交叉口交通状态估计[J].同济大学学报(自然科学版),2017,45(5):0705-0713.
作者姓名:唐克双
作者单位:同济大学 道路与交通工程教育部重点实验室,上海 201804,同济大学 道路与交通工程教育部重点实验室,上海 201804,同济大学 道路与交通工程教育部重点实验室,上海 201804,同济大学 道路与交通工程教育部重点实验室,上海 201804
基金项目:国家自然科学基金(61673302)
摘    要:针对我国中小城市数据现状,提出了一种基于路中定点线圈低频(1/60Hz)检测数据的交叉口交通状态估计方法.该方法基于仿真数据,分析了不同环境变量组合条件下占有率、流量和交通状态的关系,并提出了基于线性拟合的交通状态分界线建立方法;又利用多元线性回归拟合出分界曲线各系数与环境变量的函数关系,用其估计一般条件下的交通状态.经过验证,本方法仿真环境和实证环境下的平均估计准确率分别达到80%和75%以上,且严重错误率均低于2.1%.

关 键 词:交通状态估计  信号控制交叉口  定点检测器  低频检测数据
收稿时间:2016/7/2 0:00:00
修稿时间:2017/3/24 0:00:00

Traffic State Estimation based on Low Frequency Detection Data at Signalized Intersections
TANG Keshuang,XU Tianxiang,DONG Keran and LI Keping.Traffic State Estimation based on Low Frequency Detection Data at Signalized Intersections[J].Journal of Tongji University(Natural Science),2017,45(5):0705-0713.
Authors:TANG Keshuang  XU Tianxiang  DONG Keran and LI Keping
Institution:Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China,Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China,Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China and Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China
Abstract:A traffic state identification method for intersections was proposed based on detection data with a low frequency of 1/60 Hz from detection on the mid of the urban roads which was applied for urban interrupted flow in medium and small cities of our country. At first, the relationship among occupation, volume and traffic state was analyzed under different parameters of circumstances based on simulation data and a method of curve fitting was proposed to build the boundaries of different traffic state. Then the functional relationship of coefficients of boundaries functions with environment variables was fit out which was later applied to general ones. The methods above were verified by simulation data with the identification rate of over 80%, and empirical data with the identification rate of over 75% , with severe mistake rates less than 2.1%.
Keywords:traffic state estimation  signalized intersection  point detector  low frequency detection data
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