A new method for short-term traffic congestion forecasting based on LSTM

Abstract

Traffic congestion in metropolitan areas such as shenzhen, has become more and more serious. Over the past decades, many academic and industrial efforts have been made to alleviate this issue. In this paper, we propose a novel approach to predicting short-term traffic congestion. At first, we pre-process the data to get the speed, traffic, lane number of these parameters. Second, we carry out statistical data and create training samples. Third, We establish a hybrid neural network prediction model based on LSTM and substitute the generated samples into training. Finally, we use the model to predict the future congestion situation. The experimental results show that our model achieves good predictive results.