Short-term Traffic Flow Prediction based on Adaptive Time Slice and KNN

Short-term traffic flow prediction with high accuracy and efficiency plays an important role in Intelligent Transportation Systems, which is a prerequisite for traffic guidance, management, and control. Due to the time-varying and non-stationary characteristics of the dynamic change of traffic flow, it is difficult to predict traffic flow with high accuracy, which needs to be resolved urgently in the transportation field. In order to improve the accuracy and efficiency of short-term traffic flow prediction, the paper develops a short-term traffic flow predicting algorithm based on adaptive time slice and the improved KNN model (A-TS-KNN), which is then implemented successfully in short-term traffic flow predicting experiments. In the first, the Dynamic Time Warping (DTW) algorithm is used to dynamically slice the daytime sequence of traffic flow into different traffic patterns. Secondly, the mutual information method is used to solve the maximum threshold of the time delays of traffic flow at each time in different traffic patterns. Then the traffic flow state vectors of different time delays is constructed, which generates a history database of traffic flow. Thirdly, the method of ten times ten-fold cross-validation is used to solve the orthogonal error distribution of different time delays and K values of traffic flow at each time. The orthogonal result with the smallest error is selected, and the parameters combination of adaptive time delay and K value are obtained. In the end, the weighted value of the reciprocal Euclidean distance of the K most similar neighbors is used for predicting traffic flow of next time. The forecasting accuracies of the improved A-TS-KNN and other four models including K-Nearest Neighbors (KNN) model, Support Vector Regression (SVR) model, Long-Short Term Memory (LSTM) neural networks, and Gate Recurrent Unit (GRU) neural networks are compared. The experimental results indicate that the improved A-TS-KNN model is more appropriate for short-term traffic flow forecasting than the other models. In addition, the A-TS-KNN algorithm is used for short-term traffic flow predicting at other four different intersections in the urban road network of Fuzhou, which has been shown good generalization ability. © 2022, Science Press. All right reserved.