Short-term Traffic Flow Prediction Based on Deep Learning Model

China Automotive Technology & Research Center Co., Ltd. Tianjin, China

^a e-mail: rennver@catarc.ac.cn
^b e-mail: tanglanwen@catarc.ac.cn
^c e-mail: yinyuehua@adcsoft.cn
^d e-mail: wangyaodong@catarc.ac.cn

Abstract

In order to improve the prediction accuracy of the intelligent transportation system and provide effective support for the dynamic control and guidance of the highway management department, with the goal of minimizing the short-term traffic flow prediction error, the long-term short-term memory (LSTM) model is trained, fitted and adjusted based on the deep learning framework. In addition, the established model is used to predict the short-term traffic flow of the expressway during holidays and working days. At the same time, the traffic flow was simulated by microscopic simulation software to further verify the feasibility of the LSTM algorithm.