Abstract:
Traffic congestion in semiurban areas presents unique challenges due to limited road capacity, growing vehicle volumes, school zone peak demand, and environmental disruptions such as flooding. Traditional fixed-time traffic control systems often fail to adapt to dynamic traffic conditions, leading to prolonged delays, safety risks, and reduced mobility efficiency. This article investigates the potential of artificial intelligence (AI) and Internet of Things (IoT) technologies to improve traffic management in semiurban environments, with a case study focusing on school zones and flood-prone road segments. The proposed framework integrates AI-adaptive traffic signal control, IoT-based flood detection and road condition monitoring, and AI-driven school zone traffic management to enable real-time sensing, predictive decision making, and dynamic traffic optimization. By leveraging sensor networks, machine learning models, and privacy-preserving traffic concepts, the system enhances congestion mitigation, pedestrian safety, and operational responsiveness.