A Review of Smart Traffic Operation System for Traffic Control Using Internet of effects & Reinforcement Learning

This review encapsulates the transformative potential of integrating the Internet of Things (IoT) and Reinforcement Learning (RL) in devising a Smart Traffic Operation System for urban traffic control. The contemporary urban landscape, marked by burgeoning vehicular populations and increasing instances of congestion, necessitates a paradigm shift in traffic management. Traditional systems, often static and reactive, are rendered inadequate to address the dynamism and unpredictability of modern traffic patterns. Enter the synergistic blend of IoT and RL. Through the pervasive data collection capabilities of IoT devices, the system gains real-time insights into traffic conditions, while RL facilitates adaptive and proactive decision-making. This fusion not only promises enhanced efficiency and safety but also offers environmental dividends by reducing vehicular emissions through minimized congestion. However, the successful implementation of this system hinges on addressing challenges related to data security, storage, and privacy concerns. This study comprehensively examines the intricacies of the integration under consideration, thoroughly analyzing its benefits, possible drawbacks, and the future trajectory for the management of urban traffic.