Research On Traffic State Perception And Induction Technology Based On Cloud Computing

Traffic state perception and induction technology is one of the most active research directions in the field of intelligent transportation,especially with the development of cloud computing technology,which promotes the progress of traffic state perception and induction technology.The main purpose of traffic state perception and induction technology based on cloud computing is to quickly and efficiently find the internal rules of traffic state information,and provide technical support for the design of intelligent traffic system.It is beneficial to alleviate traffic congestion,optimize traffic network and reduce pollution.The focus of current research on traffic state perception and induction based on cloud computing is how to design efficient parallel algorithm model,which has two main problems: on the one hand,due to the particularity of traffic state information,the existing data processing model can not be effectively implemented in the mass traffic state data.On the other hand,because the management and traffic network are highly sensitive to the time,the existing computing model can not meet the actual application requirements.In this paper,the problems faced by mass traffic state data processing are discussed in the following aspects,such as large data clustering algorithm,floating vehicle traffic parameter calculation,traffic state information estimation and the shortest path induction algorithm of mass traffic network.The main research work completed in this paper is summarized as follows:(1)The improved K-means algorithm based on Cloud ComputingIn order to verify the effectiveness of the big data processing platform,a CK-means clustering algorithm is designed in this paper.The algorithm is generated by two algorithms,Canopy algorithm and K-means.It solves the problem that the number of iterations increases faster and the time consuming increases when the local trap and cluster calculation are easily triggered during the data clustering process.The example analysis shows that the CK-means algorithm is better than the CBK-means algorithm in terms of clustering accuracy,expansion rate and speedup ratio.(2)Traffic parameters extraction based on floating car dataTraffic flow,speed and travel time are important parameters to reflect the traffic state.In this paper,a distributed computing framework is designed to calculate the traffic parameters with the data of the big floating car as the object.The computing framework can provide scalable,high-performance data intensive computing services for urban traffic monitoring.(3)The estimation of traffic state informationAccording to traffic flow,density and travel time information,the correlation between three traffic states at the same time is analyzed.The correlation between the traffic states of time t and(t-1).Under the same time t,the relationship between the state of the traffic infrastructure on the three traffic states and the general problem solving model are established.Secondly,The basic diagram,link queue model and basic traffic infrastructure state based on basic graph are given for three traffic state associations,and an extended Calman filter algorithm based on expectation optimization is proposed.The experimental results show that the algorithm is an efficient algorithm.(4)The shortest path algorithm for large scale road networkUnder the cloud computing framework,a shortest path induction algorithm based on MapReduce model is proposed to solve the shortest path problem of large-scale road network.The induction algorithm first subdivides the large scale road network,then calculates the GPS coordinates and determines the division positions to get the sub graph sets.Second,an iterative loop is used to solve the subgraph.Finally,the shortest global path is obtained by merging the shortest path of each sub graph.The algorithm is analyzed from the influence of the size of the subgraph on the calculation results,the influence of the size of the subgraph on the computational complexity,and so on.The example shows that the shortest path algorithm of the large-scale road network achieves good performance.