关键词: 智能交通系统, 短时车流量, 多因素分析, Long Short-Term Memory

Abstract:

With the development of traffic flow detection technology, massive traffic flow information can be obtained more easily and efficiently. Aiming at the accuracy requirement of short-term traffic flow prediction, an optimization method of short-term traffic flow prediction based on deep learning is proposed. The Long Short-Term Memory algorithm of neural network is used to process the data with the idea of multi-factor analysis. Through the multi-factor analysis of short-term traffic flow data, such as weather factors, holidays, etc., short-term traffic flow data are divided into multiple data sets according to factors, and different data sets are used as training sets to preview. The traffic flow in the next few days with the same factors as the training set is measured. Through this method, the obtained data are more pure, and the influence of various factors on traffic flow prediction is effectively solved. The results show that the optimization method can better reflect the characteristics of road traffic flow change and overcome the disadvantage that the factors of traffic flow data set are not single.

Key words: intelligent transportation system, short-term traffic flow, multivariate analysis, Long Short-Term Memory