Traffic Congestion Inhibition Strategy And Algorithm Based On Group Decision-making In Intelligent Connected Environment

With the rapid growth of automobile ownership and the relative limitation of urban roads,traffic congestion in urban road environment has become a prominent problem in intelligent transportation system.At the same time,with the continuous development of intelligent transportation and artificial intelligence technology,cities are endowed with higher requirements,which brings new development opportunities to the construction of smart cities.With the rapid development of the Internet of Things and the continuous investment of the government in the intelligent transportation system,the development of intelligent transportation technology under the intelligent network environment has ushered in a new opportunity.In particular,the research on traffic congestion suppression under the intelligent network environment has gradually attracted the attention of many scholars.Therefore,for the problem of traffic congestion suppression in intelligent connected vehicle environment,this paper proposes a new class of iterative Reverse Top-K algorithm based on multi-attribute group decision making,and designs a traffic congestion suppression system scheme.Finally,numerical experiments and actual road condition tests verify the advantages and effectiveness of the proposed method.The main research contents of this paper include:1)For the multi-attribute group decision making problem,an iterative Rtop-k decision-making algorithm based on attribute dominance is proposed in this paper.Firstly,from the perspective of attribute indexin to researched and mined the "similarity" feature information among attributes indexin,and then the dominance degree of each attribute is determined.In order to avoid the result distortion caused by the unreasonable value of k,designed a class of iterative rtop-k operators,and the calculation formula of attribute similarity degree and the definition of dominance degree are given,and the special case where similarity degree is 0 is rationalized and corrected.Finally,the advantages and effectiveness of the algorithm are verified by a specific experimental case.2)Aiming at the problem of traffic congestion suppression in the environment of intelligent connected vehicles,a traffic congestion control scheme based on the iterative Rtop-k decision algorithm of attribute dominance is proposed in this paper.Firstly,the data of road network traffic are collected and preprocessed,and give the calculation formula of evaluation index about road network.Furthermore,considering the heterogeneity of decision information,an average filling method is proposed.Finally,a new index forward method suitable for multi-attribute group decision making problem is proposed in the decision information.This method can effectively avoid the special case that the evaluation value is 0 after the transformation,so as to realize a traffic congestion suppression scheme in the environment of intelligent connected vehicles.This paper conducted an experimental analysis on the application of the traffic congestion suppression scheme based on the actual scene of a traffic network in Changsha,and the results verified the effectiveness of the proposed scheme.3)In order to solve the problem that the linguistic road network evaluation expert information and its evaluation indexes are fuzzy,an improved iterative Rtop-k method based on interval type two fuzzy sets is proposed in this paper.The core idea of this method is to improve the iterative Rtop-k operator and mine the similarity between attributes,which is based on the fuzzy computing method of interval type two fuzzy sets,so as to effectively carry out group decision processing.