Traffic Flow Prediction Using Deep Learning Techniques

In the "Intelligent Transportation System (ITS)", accurate and real-time traffic flow prediction is crucial, particularly for traffic control. To develop a smart city, data related to traffic flow is essential. Many Intelligent Transportation Systems now employ the ongoing technology to predict the traffic flow, reduce road accidents, and anticipate vehicle speed, and so on. However, the prediction that considers some other factors as environmental and weather conditions are considered to be more accurate. Predicting traffic flow is a fascinating research area. To forecast traffic, several different data mining approaches are used. Existing traffic flow forecast approaches are mostly based on shallow traffic prediction methods, which are insufficient for many real-world applications. Since traffic flow shows both spatial and temporal dependency features, as well as being affected by weather, social event data, and other factors, therefore, a new deep-learning-based traffic flow prediction technique such as "Stacked Auto-Encoder (SAE) Convolutional Neural Network (CNN), Long- and Short-Term Memory Neural Network (LSTM)" is proposed in this paper, which considers both "Spatial and Temporal Correlations". The results of the experiments showed the efficiency of suggested approach and compare its performance with several deep learning techniques on a real-world public dataset of Predicting in a complex traffic situation with its accuracy rate.