A Comprehensive Review on Deep Learning Based Algorithms for Traffic Control Systems

Abstract

Traffic congestion has emerged as one of the most critical challenges in modern urban transportation systems, leading to increased travel delays, fuel consumption, environmental pollution, and economic losses. Conventional traffic control methods often rely on fixed-time signal plans and rule-based approaches, which are inadequate for handling the dynamic and complex nature of real-world traffic conditions. Recent advances in deep learning have provided innovative solutions for intelligent traffic control by enabling real-time data analysis, traffic prediction, and adaptive decision-making. This review paper presents a comprehensive analysis of deep learning-based traffic control systems and their applications in intelligent transportation systems. The study examines various deep learning architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, Graph Neural Networks (GNNs), and Deep Reinforcement Learning (DRL) models, for traffic flow prediction, traffic signal optimization, vehicle detection, congestion management, and route planning. The review highlights the effectiveness of these models in improving traffic efficiency, reducing congestion, and supporting smart city initiatives. Furthermore, the paper discusses key challenges such as data heterogeneity, computational complexity, scalability, privacy concerns, and real-time implementation. Future research directions involving the integration of deep learning with Internet of Things (IoT), edge computing, connected vehicles, and autonomous transportation systems are also explored. The findings indicate that deep learning-based traffic control systems offer significant potential for developing intelligent, adaptive, and sustainable urban transportation networks.