Urban Traffic Management

Urban traffic management aims to optimize the flow of vehicles and pedestrians in cities, improving efficiency, safety, and sustainability. Current research focuses on leveraging advanced technologies like deep reinforcement learning (e.g., for adaptive traffic signal control), large language models (for real-time decision-making and incident response planning), and digital twin simulations (for risk assessment and proactive resource allocation), often incorporating data from various sources including LiDAR and crowdsourced sensors. These advancements offer the potential for significant improvements in traffic flow, reduced congestion, enhanced safety, and more informed urban planning decisions.