Abstract:

The swift expansion of urban mobility requires smart, data-oriented solutions that can improve transportation efficiency, safety, and sustainability. This document suggests a combined architecture for Intelligent Transportation Systems (ITS) that utilizes IoT and AI, incorporating diverse sensing, instant communication, and sophisticated analytics to enhance smart mobility solutions. The framework includes multi-tier IoT sensing, Vehicle-to-Everything (V2X) communication, edge–cloud cooperative processing, and integrated AI models for traffic forecasting, incident identification, and adaptive management. Real-time data feeds from vehicles, roadside devices, and environmental sensors are combined and analyzed using lightweight edge AI to minimize latency, while cloud intelligence facilitates extensive analytics and long-term optimization. The suggested system improves operational resilience by utilizing dynamic resource distribution, context-sensitive decision-making, and secure data handling. Experimental studies and simulations show enhancements in congestion reduction, response times, and predictive accuracy when contrasted with conventional ITS systems. The research emphasizes the possibility of IoT–AI integration to facilitate scalable, resilient, and self-sufficient transportation systems for future smart cities