节点文献

区域交通流的时空预测与分析

Forecasting and Analysis of Regional Traffic Flow in Space and Time

  • 推荐 CAJ下载
  • PDF下载
  • 不支持迅雷等下载工具,请取消加速工具后下载。

【作者】 韩卫国王劲峰高一鸽胡建军

【Author】 HAN Wei-guo1,WANG Jin-feng1,GAO Yi-ge1,2,HU Jian-jun3(1.State Key Laboratory of Resources & Environmental Information System,Institute of Geographic Sciences & Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China;2.Graduate School of Chinese Academy of Sciences,Beijing 100039,China;3.Science Research Institute,Beijing Public Security Bureau of Traffic Management,Beijing 100061,China)

【机构】 中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室北京市公安交通管理局科研所 北京100101北京100101中国科学院研究生院北京100039北京100061

【摘要】 论述了短时交通流预测模型的分类、特点和适用条件。通过历史交通流量记录运用最优抽样间隔数据分析发现,在城市道路网络中,路口自身和近邻路口的交通流数据之间存在紧密的时空关系。利用时空自回归移动平均模型来建立路口间交通流的时空关联关系,用于区域交通流的短时预测和时空分析,并详细介绍了该模型的数学描述和建模过程。采用长安街及其沿线路口的区域交通流量作为试验数据,验证了该模型在交通流的短时预测和时空分析中的可行性。该模型在考虑预测值所在位置时间序列的同时,也考虑到了空间上相邻位置的时间序列,大大提高了短时交通流预测的准确性。

【Abstract】 Classification,features and applicability of short term traffic flow forecast models are reviewed at first.Based on data analysis of historical traffic volume datasets using the optimized sampling interval method,close spatial and temporal associations exist between traffic flow of the intersection and its neighbors in the urban traffic network.Space-time autoregressive moving average model is utilized to develop this relationship in space and time between traffic flow of each intersection,and to forecast and explore traffic flow in space and time.Mathematical description and development procedure of the model are also introduced and described specifically.Traffic volume datasets of the intersections located on the Chang’an Street and its related roads are used as the sample datasets to verify model’s applicability in short term forecast and spatial and temporal analysis.Not only traffic time series datasets of some intersection but also those of its spatial neighbors are used to estimate the short term traffic flow in the model,and the results show that the prediction accuracy is improved greatly.

【基金】 国家自然科学基金资助项目(40471111);北京市自然科学基金资助项目(8033015)
  • 【文献出处】 公路交通科技 ,Journal of Highway and Transportation Research and Development , 编辑部邮箱 ,2007年06期
  • 【分类号】U491.14
  • 【被引频次】44
  • 【下载频次】946
节点文献中: 

本文链接的文献网络图示:

本文的引文网络