Review on short-term traffic flow prediction methods under big data

Abstract

The goal of traffic forecast is to predict the related traffic situation in the future according to the historical concept. The predicted angle can be divided into short-term prediction and long-term prediction. This method can be used to solve the increasingly serious urban traffic congestion problem, and researchers have proposed a deep learning model to help decision makers in the field of traffic control. It has made great contributions to improving future road capacity and optimizing intelligent transportation services. In this paper, short-term traffic forecast related documents under big data are helpful after sorting out, and the traffic flow data characteristics of intelligent transportation system and related correction methods are analysed. Secondly, it then classifies the applications involved by big data algorithm calculation according to various related principles, and summarizes the prediction accuracy, computational complexity, applicable interval and computation time of each type of algorithm in an overview. According to the relevant data, the combination forecasting model is effectively diversified, and combined with the related combination forecasting, I hope to improve the forecasting accuracy of the future development prospect.