《计算机应用研究》|Application Research of Computers

基于车辆自组织网络的交通态势检测方法

Road traffic situation detection method based on VANETs

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作者 张恩展,邝育军
机构 电子科技大学 移动互联实验室,成都 611731
统计 摘要被查看 次,已被下载
文章编号 1001-3695(2014)11-3257-04
DOI 10.3969/j.issn.1001-3695.2014.11.014
摘要 随着汽车保有量的迅速增加,城市道路交通拥堵变得尤为严重,精确地检测交通态势可以帮助缓解交通问题。为此,提出一种基于车辆自组织网络(vehicular Ad hoc networks,VANETs)的交通态势检测方法——TraSD-VANET(traffic situation detection method based on VANETs)。在该方法中,车辆自动聚簇,然后主动向簇头汇报当前自身的位置和速度信息;簇头根据收到的信息计算簇内的车辆密度和路面上的加权平均速度,之后基于模糊逻辑判断簇内的交通态势。仿真结果表明,在四种车辆场景下,TraSD-VANET检测准确程度比协作检测方法CoTEC(cooperative traffic congestion detection)平均高16%。该方法在道路交通态势检测中有重要的应用价值。
关键词 智能交通系统;车辆自组织网络;交通态势检测
基金项目 国家自然科学基金资助项目(61071099)
本文URL http://www.arocmag.com/article/01-2014-11-014.html
英文标题 Road traffic situation detection method based on VANETs
作者英文名 ZHANG En-zhan, KUANG Yu-jun
机构英文名 Mobile Link Laboratory, University of Electronic Science & Technology of China, Chengdu 611731, China
英文摘要 Road traffic congestion pressure becomes more serious as the number of vehicles increases. Accurate traffic situation detection methods are needed to help in alleviating them. This paper proposed a TraSD-VANET method to detect the traffic situation. In this method, vehicles constituted clusters autonomously and then sent their speed and locations to cluster heads. After receiving data from cluster members, the cluster head calculated the lane weighted average speed and the traffic density adaptively. Afterwards, it estimated the traffic situation in its cluster according to the above calculation results by using fuzzy theory. The simulation results show that the average precision of estimation is improved 16% compared with the CoTEC in four traffic scenarios. This method has important practical value in traffic congestion detection on road.
英文关键词 intelligent transportation systems; VANETs(traffic situation detection method based on VANETs) ; traffic situation detection
参考文献 查看稿件参考文献
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收稿日期 2013/10/30
修回日期 2013/12/28
页码 3257-3260
中图分类号 TN929.5;TP391
文献标志码 A