CAJ | 학술논문

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。
수착기차보유량적신속증가，성시도로교통옹도변득우위엄중，정학지검측교통태세가이방조완해교통문제。위차，제출일충기우차량자조직망락（vehicular Ad hoc networks，VANETs）적교통태세검측방법---TraSD-VANET（traffic situation detection method based on VANETs）。재해방법중，차량자동취족，연후주동향족두회보당전자신적위치화속도신식；족두근거수도적신식계산족내적차량밀도화로면상적가권평균속도，지후기우모호라집판단족내적교통태세。방진결과표명，재사충차량장경하，TraSD-VANET검측준학정도비협작검측방법 CoTEC （cooperative traffic congestion detection）평균고16％。해방법재도로교통태세검측중유중요적응용개치。
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 situa-tion.In this method,vehicles constituted clusters autonomously and then sent their speed and locations to cluster heads.After re-ceiving 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 1 6%compared with the CoTEC in four traffic scenari-os.This method has important practical value in traffic congestion detection on road.