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作 者:姚志洪[1,2] 蒋阳升[1,2] 王逸 陈彦如[3] YAO Zhihong;JIANG Yangsheng;WANG Yi;CHEN Yanru(School of Transportation and Logistics,Southwest Jiaotong University,Chengdu 610031,China;National Engineering Laboratory of ApplicationTechnology of Integrated Transportation Big Data,Southwest Jiaotong University,Chengdu 610031,China;School of Economics and Management,Southwest Jiaotong University,Chengdu 610031,China)
机构地区:[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大数据应用技术国家工程实验室,成都610031 [3]西南交通大学经济管理学院,成都610031
出 处:《北京交通大学学报》2019年第2期107-116,共10页JOURNAL OF BEIJING JIAOTONG UNIVERSITY
基 金:国家自然科学基金(51578465;71771190);重庆市交通运输工程重点实验室开放基金(2018TE01);西南交通大学优秀博士学位论文培育项目(D-YB201708)~~
摘 要:传统的异质交通流车队离散模型参数估计基于历史数据,不能很好地反映交通流的动态变化特征.为解决这一问题,构建车联网环境下的动态异质交通流车队离散模型.首先,考虑到车联网环境下,车辆行程时间数据易于获得,可对模型的分布参数进行动态估计;然后,基于动态分布参数,构建动态异质交通流车队模型;最后,通过实测数据分析下游交叉口到达流量分布与上游交叉口离去流量分布之间的关系.结果表明:与经典的Robertson车队离散模型相比,动态异质交通流车队离散模型对下游交叉口的到达车辆流量分布预测效果更好,平均预测均方根误差可减少26.51%.The discrete model parameters of the traditional heterogeneous platoon are based on historical data, which cannot achieve a good reflection for the dynamic characteristics of traffic flow. To solve this problem, a discrete model of dynamic heterogeneous traffic flow platoon in the Internet of vehicles is proposed. First, considering the influence of Internet of vehicles, the travel time of the vehicles can be easily obtained, and the distribution parameters of the model can be estimated in real time based on this. Then, a discrete model of dynamic heterogeneous traffic flow platoon is proposed. Finally, the relation between arrival traffic flow distribution at downstream intersection and departure traffic flow distribution at upstream intersection is analyzed by field collected data. Compared with Robertson’s model, the results show that the proposed model has better prediction performance, and the root mean square error is reduced by 26.51%.
关 键 词:交通工程 车队离散模型 混合正态分布 异质交通流 车联网 行程速度
分 类 号:U491.5[交通运输工程—交通运输规划与管理]
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