Research on Traffic Dynamic Shortest Path Allocation Model Based on Triangular Fuzzy Number Weight and K-Means Algorithm

Aiming at the traffic dynamic shortest path allocation problem with triangular fuzzy numbers as attribute values, a traffic dynamic shortest path allocation model method based on triangular fuzzy number weights is proposed. The weighted distance optimization model of triangular fuzzy number, ideal solution and negative ideal solution is established. The weight value of each attribute is obtained by solving and optimizing. The similarity difference between attribute information is determined longitudinally by using the idea of deviation maximization, and the evaluation uncertainty of each scheme under different attributes is described horizontally by entropy value. The attribute weights based on reliability are obtained by synthesizing the similarity difference index and the uncertainty index. Based on triangular fuzzy weights. A subset of vertex set V, R, is selected as the representative vertex, and the shortest path distance between all vertex pairs in R is calculated. At the same time, considering the spatial correlation of road traffic flow and the dynamic change process of traffic flow in each section of the road network, the time is discretized, and the abrupt point of road traffic state is taken as the node pair, and the road traffic flow is dynamically loaded. The prediction model of road section travel time under occasional congestion is established. Experiments show that when the error upper limit is small, the retrieval results of the approximation algorithm are relatively accurate. Then, an example analysis of traffic dynamic path allocation is provided, which shows the effectiveness and feasibility of this method in traffic dynamic shortest path allocation.