LS-TFP: A LSTM-Based Traffic Flow Prediction Method in Intelligent Internet of Things

Abstract

Intelligent transport system has been emerging as a crucial component of the smart city context, and traffic flow prediction plays an essential role in ITS. Recently, many studies have used algorithms based on time prediction and deep learning. However, their prediction accuracy is insufficient for the significant growth in IoT applications. To overcome this issue, we proposed a novel prediction model, namely the LSTM-based traffic flow prediction (LS-TFP), using the combination of the long short-term memory and recurrent neural network (LSTM-RNN). In our proposal, we stack two LSTM layers to produce a more in-depth model. In addition, as a consequence of the remembering ability of LSTM, the predicted value could achieve high accuracy. Our practical experiments on real datasets show that the LS-TFP accuracy is reached up to 98.1% and outperforms our competitors.