SMART TRANSPORTATION SYSTEMS: AI-BASED TRAFFIC CONTROL, SMART SIGNALS, IMPACT ON CONGESTION

Abstract: Traffic congestion remains a serious issue in cities. It causes delays, higher emissions, and economic losses. This paper reviews recent advancements in data-driven methods for detecting, predicting, and reducing congestion. We examine eight recent studies that include deep-learning surveys, deep- reinforcement methods for traffic-signal control, Q-learning, and DQN approaches. We also look at trajectory-based congestion prediction and combined micro and macro state representations. We summarize common architectures, such as cnns, lstms, gnns, and multi-agent DRL. We also cover input representations, including images, probe and trajectory data, and sensor flows. Additionally, we discuss control strategies like DQN, D3QN, DDPG/KS-DDPG, and joint phase and timing control. Overall, deep learning and DRL show consistent improvements in queue length and waiting time. They also adapt better than fixed-time methods. Meanwhile, trajectory-mining methods help address sudden events that historical flow models may miss. However, challenges remain. These include ensuring reliable performance during rare events, dealing with partial observability, managing state-space explosion, improving interpretability, and deploying solutions from simulation to real-world settings. We highlight specific research gaps and suggest future directions. These include combining data from multiple sources, using hybrid model-based and learning approaches, improving scalable multi-agent coordination, and making predictions that account for uncertainty.