A Short-Term Traffic Flow Prediction Method Based on Asynchronous Temporal and Spatial Correlation

Abstract

The existing short-term traffic flow prediction model fails to consider the analysis of the asynchronous temporal and spatial characteristics of the traffic flow, which leads to the prediction accuracy is difficult to meet the requirements of refined traffic control. Thus, a short-term traffic flow prediction method based on the asynchronous temporal and spatial correlation was proposed in this paper. Firstly, based on Pearson-dynamic time warping method, the trend correlation and distance correlation were calculated respectively in predicted link and associated link, then the results of dynamic time warping were standardized, and discrete degree analysis was carried out on the two kinds of correlation value, evaluation index was designed based on variance of road traffic flow correlation, through the SVM method realizing the closed-loop optimization in the selection process of associated links; Secondly, a traffic flow prediction model based on TPD-LSTM was constructed by long and short term memory neural network. Finally, a link of Yusha Road of Xinhua Avenue in Chengdu was selected as the target link for example analysis, the results show that the prediction accuracy of TPD-LSTM improves to 89.62%.