Research On Targeted Dissemination Of Traffic Incident Information

The efficient and accurate dissemination of traffic incident information enables drivers to know the incident information in advance and rechoose the route,alleviating the negative impacts caused by the road congestion after the traffic incident to a certain extent.The existing researches on incident information dissemination mainly focused on the communication performance of non-targeted dissemination in the 4G Internet of Vehicles(Io V)scenario.Subsequently,the research began to turn to the semi-targeted dissemination strategy,namely,selecting influential nodes as relay forwarding nodes to disseminate information to all vehicle terminals in order to reduce the link competition pressure and improve communication performance.However,there are some unsolved hidden dangers in non-targeted and semi-targeted dissemination strategies: Without considering the impacts of over-dissemination of traffic incident information on transport systems,congestion transfer may result in delays,additional fuel consumption and increased carbon dioxide emissions;In the 4G Io V scenario,non-targeted or semi-targeted dissemination of information to a large and growing number of Internet vehicles still faces the problems of fierce channel competition and reducing communication performance;The channel competition caused by massive non-incident information in the future 5G NR(New Radio)autonomous driving environment will seriously affect the transmission performance of two different business information,making it difficult to guarantee low delay and high reliability.Because of the above problems,this thesis will look at the following two aspects.In view of the problem that excessive information dissemination may cause congestion transfer,resulting in negative impacts on fuel consumption,emissions and other aspects,and the increase in the number of connected vehicles makes it difficult to guarantee the communication performance in the 4G Io V scenario,this thesis comprehensively considers the impact of information dissemination on traffic and the dissemination performance,and proposes a joint traffic-communication targeted-dissemination strategy that comprehensively traffic consumption and communication efficiency.By minimizing the total consumption of the system,the optimal targeted-dissemination matrix is obtained,and the incident information is selectively released to some connected vehicles,which reduces the additional fuel consumption and emissions caused by the excessive dissemination of incident information,and further alleviates the huge challenges caused by the increase in the number of connected vehicles and the limited wireless resources.The simulation results verify the superiority of the strategy in ensuring communication performance,reducing system total cost and carbon dioxide emission rate.The huge demand for information in the future 5G NR autonomous driving scene makes other non-incident and incident information have a large wireless resource competition,which will seriously affect the dissemination performance of the two kinds of information,and resulting in the performance of targeted dissemination strategy being difficult to guarantee.Based on the above research,the incident and non-incident information are classified into Ultra-Reliable Low-Latency Communications(URLLC)service and enhanced Mobile Broad Band(e MBB)service with the high transmission rate,respectively.An information publishing strategy based on e MBB-URLLC dynamic resource scheduling is proposed to maximize the transmission rate of e MBB service.The performance of low latency and high reliability of URLLC business is guaranteed,so as to ensure the superior performance of the targeted dissemination strategy while facing fierce resource competition.The simulation results verify the ability of the dynamic resource scheduling strategy to guarantee the performance requirements of URLLC and e MBB services,and the effectiveness of the targeted-dissemination strategy based on the scheduling strategy in saving traffic fuel consumption.