Short-Term Traffic Prediction considering Spatial-Temporal Characteristics of Freeway Flow

This paper presents a short-term traffic prediction method, which takes the historical data of upstream points and prediction point itself and their spatial-temporal characteristics into consideration. First, the Gaussian mixture model (GMM) based on Kullback-Leibler divergence and Grey relation analysis coefficient calculated by the data in the corresponding period is proposed. It can select upstream points that have a great impact on prediction point to reduce computation and increase accuracy in the next prediction work. Second, the hybrid model constructed by long short-term memory and K-nearest neighbor (LSTM-KNN) algorithm using transformed grey wolf optimization is discussed. Parallel computing is used in this part to reduce complexity. Third, some meaningful experiments are carried out using real data with different upstream points, time steps, and prediction model structures. The results show that GMM can improve the accuracy of the multifactor models, such as the support vector machines, the KNN, and the multi-LSTM. Compared with other conventional models, the TGWO-LSTM-KNN prediction model has better accuracy and stability. Since the proposed method is able to export the prediction dataset of upstream and prediction points simultaneously, it can be applied to collaborative management and also has good potential prospects for application in freeway networks.