Short-Term Traffic Flow Prediction: A Long Short-Term Memory Model Enhanced by Temporal Information

Real-time and accurate short-term traffic flow prediction can effectively improve the efficiency and safety of the transportation system. However, complex traffic systems are highly nonlinear and random, which makes short-term traffic flow prediction a challenging issue. In recent years, deep-learning based methods have been widely applied in short-term traffic flow prediction. Particularly, the long short-term memory neural network (LSTM) model bears great potential for its capability in learning from temporal information. In this paper, an improved LSTM model is used to predict the short-term traffic flow of a target road section of the East 4th Ring Road of Beijing, and to analyze the influence of different input configuration on prediction accuracy as well. Experimental results demonstrate that feeding upstream flow and velocity information does improve its overall performance. Especially after traffic flow information is fed with corresponding temporal information, the accuracy of traffic flow prediction has been significantly improved.