Applying Artificial Intelligence and Deep Belief Network to predict traffic congestion evacuation performance in smart cities

This work is developed to discuss the feasibility and efficiency of adopting Artificial Intelligence (AI) Deep Learning in smart city scenarios. A traffic flow prediction model is constructed based on the Deep Belief Network (DBN) algorithm. The target road section and its historical traffic flow data in Tianjin are collected and pre-processed. Then, several Restricted Boltzmann Machines (RBM) are stacked together to form a DBN, which is trained as a generative model. Finally, its performance is analyzed by the simulation experiment. The algorithm model proposed is compared with Neuro Fuzzy C-Means (FCM) model, Deep Learning Architecture (DLA), and Convolutional Neural Network (CNN) model. The results show that the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) of the algorithm model proposed are 4.42%, 6.21%, and 8.03%, respectively. Its prediction accuracy is significantly higher than that of the other three algorithms. In addition, the algorithm can effectively suppress the spread of congestion in the smart city, achieving timely evacuation of traffic congestion. In short, the constructed Deep Learning-based traffic flow prediction model has a high-precision prediction effect and traffic congestion evacuation performance, which can provide experimental references for the later construction of smart cities. (c) 2022 Elsevier B.V. All rights reserved.

Automated summary

This study discusses the feasibility and efficiency of adopting Artificial Intelligence (AI) Deep Learning in smart city scenarios. A traffic flow prediction model based on the Deep Belief Network (DBN) algorithm is constructed and compared with other models. The results show that the proposed algorithm has higher prediction accuracy and better performance in traffic congestion evacuation, providing experimental references for the construction of smart cities.