Review on Adaptive Traffic Signal Control Based on Intelligent Computing

Adaptive traffic signal control is an innovative way to deal with traffic congestion. Its ability to detect real-time traffic and, on that basis, optimize parameters according to demand and fluctuations results in system improvement. Improvements in computer technology, autonomous driving, vehicle-to-vehicle communication, and other related technologies have resulted in better traffic data collection techniques and a better knowledge of traffic flow scenarios. This research examined the most widely used adaptive traffic signal control systems, their technical properties, the current state of adaptive control research, and signal control approaches. The study revealed that multi-agent reinforcement learning outperforms other intelligent computing approaches due to its self-learning and model-free characteristics, making it well-suited to the enormous traffic data.