A Comprehensive Review on the Role of Deep Learning in Intelligent Traffic Management Toward Green Transportation

The escalating complexity of urban transportation systems, characterized by chronic congestion, significant energy consumption, and substantial environmental pollution, necessitates a paradigm shift toward Intelligent Traffic Management Systems (ITMS). Traditional traffic control methodologies often fall short in adapting to the highly dynamic and non-linear nature of urban traffic flow. This comprehensive review critically examines the transformative role of Deep Learning (DL) techniques in addressing these multifaceted challenges, with a specific focus on promoting Green Transportation. We delineate the theoretical foundations, prevalent DL architectures (including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Reinforcement Learning (RL)), and their targeted applications within traffic management—spanning traffic flow prediction, signal control optimization, anomaly detection, and eco-friendly routing. The integration of DL facilitates superior pattern recognition from vast streams of heterogeneous traffic data (sensor data, GPS traces, video feeds), leading to proactive and adaptive control strategies that minimize stop-and-go cycles, thereby reducing fuel consumption and greenhouse gas emissions. We synthesize current research findings, highlight key performance enhancements achieved by DL models over classical methods, and meticulously analyze the persistent challenges, such as data dependency, model interpretability, and real-time deployment hurdles. Finally, we outline promising future research trajectories aimed at achieving truly sustainable and intelligent urban mobility ecosystems.

کلیدواژه ها:

Deep Learning ، Intelligent Traffic Management Systems (ITMS) ، Green Transportation ، Traffic Flow Prediction ، Adaptive Signal Control ، Reinforcement Learning ، Sustainable Mobility