Direction Prediction of Traffic Flow in Vissim Simulation Based on K Nearest Neighbor Algorithm

Abstract

In order to make short-term prediction of the direction of traffic flow in urban roads, a short-term prediction method of urban road travel time based on K nearest neighbor algorithm and vissim simulation is constructed. First, the intersection of Shiji Road and Yingbin Road was selected as the survey site, and the number of vehicles in each direction of each entrance lane of the intersection was investigated using manual counting, and the signal timing of each phase of the intersection was investigated. Input the survey data into the vissim simulation software to get the travel time of each entrance lane in each direction. Then build a vissim simulation traffic flow direction prediction model based on the KNN algorithm, including the construction of feature vectors, cross-validation methods to determine K values, and local estimation methods. The experimental results show that the average relative error between the predicted traffic flow direction and the actual traffic flow direction tends to 0.27. Due to the small amount of data, the prediction result is more accurate.