Review of Deep Learning Traffic Congestion Control in Intelligent Transportation Systems

Problem of managing and controlling traffic congestion in Intelligent Transportation System (ITS). Classical, statistical and model-driven methods usually fail to take into account the nonlinear dynamic properties of congestion especially for that which concern abrupt transitions between free-flowing and congested states. Deep Learning (DL) has recently shown effective results as it is able to process large scale, high resolution data and elaborate on complex patterns that traditional methods tend to miss. To this end, a comprehensive survey is presented to investigate the most recent literature on DL-based methods for congestion prediction, detection and control, focusing on methodological advances, model diversity and performance improvements with respect to accuracy, scalability and computational complexity. At the same time, it highlights remaining gaps such as lack of robustness support in real-world scenarios, haphazard metrics interpretations and trade-offs between predictive accuracy and scalability. Through a critical review of the literature, this paper. In reviewing the available literature, this work underscores the potential of DL to improve congestion management and provides important pointers for future development in order to make it more applicable to sustainable and intelligent transportation systems.