基于车辆自组织网络的交通态势检测方法

发布时间：2018-05-12 04:21

  本文选题：智能交通系统 + 车辆自组织网络　； 参考：《计算机应用研究》2014年11期

【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——TraSDVANET(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。
[Abstract]:With the rapid increase of vehicle ownership, urban road traffic congestion becomes particularly serious, accurate detection of traffic situation can help alleviate traffic problems. Therefore, a traffic situation detection method based on vehicle Ad hoc Networks (Ad hoc Networks) is proposed. In this method, the vehicle clusters automatically, and then reports the current position and speed information to the cluster head, which calculates the vehicle density in the cluster and the weighted average speed on the road according to the information received. Then the traffic situation in the cluster is judged based on fuzzy logic. Simulation results show that the accuracy of TraSD-VANET detection in four vehicle scenarios is 16 times higher than that of collaborative detection method (CoTEC(cooperative traffic congestion detection). This method has important application value in road traffic situation detection.
【作者单位】： 电子科技大学移动互联实验室;
【基金】：国家自然科学基金资助项目(61071099)
【分类号】：TN929.5
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本文编号：1877077