Fusion of deep belief network and SVM regression for intelligence of urban traffic control system

• Published in: The Journal of supercomputing 2024-11, Vol.80 (17), p.25685-25709
• Main Authors: Soleimani, Alireza, Farhang, Yousef, Sangar, Amin Babazadeh
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial neural networks; Belief networks; Compilers; Computer Science; Control systems; Internet of Things; Interpreters; Machine learning; Processor Architectures; Programming Languages; Regression; Support vector machines; Traffic congestion; Traffic control; Traffic flow; Traffic management; Traffic signals
• ISSN: 0920-8542; 1573-0484
• DOI: 10.1007/s11227-024-06386-1

Increasing urban traffic and congestion have led to significant issues such as rising air pollution and wasted time, highlighting the need for an intelligent traffic light control (TLC) system to minimize vehicle waiting times. This paper presents a novel TLC system that leverages the Internet of Things (IoT) for data collection and employs the random forest algorithm for preprocessing and feature extraction. A deep belief network predicts future traffic conditions, and a support vector regression network is integrated to enhance prediction accuracy. Additionally, the traffic light control strategy is optimized using reinforcement learning. The proposed method is evaluated through two different scenarios. The first scenario is compared with fixed-time control and the double dueling deep neural network (3DQN) methods. The second scenario compares it with the SVM, KNN, and MAADAC approaches. Simulation results demonstrate that the proposed method significantly outperforms these alternative approaches, showing substantial improvements in average vehicle waiting times by more than 20%, 32%, and 45%, respectively. Using a deep belief network, supplemented by support vector regression, ensures high precision in forecasting traffic patterns. Furthermore, the reinforcement learning-based optimization of the traffic light control strategy effectively adapts to changing traffic conditions, providing superior traffic flow management. The results indicate that the proposed system can substantially reduce traffic congestion and improve urban traffic flow.