Internet of Things and Artificial Intelligence for Secure and Sustainable Green Mobility: A Multimodal Data Fusion Approach to Enhance Efficiency and Security

The increasing complexity of urban mobility systems demands innovative solutions to address challenges such as traffic congestion, energy inefficiency, and environmental sustainability. This paper proposes an IoT and AI-driven framework for secure and sustainable green mobility, leveraging multimodal data fusion to enhance traffic management, energy efficiency, and emissions reduction. Using publicly available datasets, including METR-LA for traffic flow and OpenWeatherMap for environmental context, the framework integrates machine learning models for congestion prediction and reinforcement learning for dynamic route optimization. Simulation results demonstrate a 20% reduction in travel time, 15% energy savings per kilometer, and a 10% decrease in CO2 emissions compared to baseline methods. The modular architecture of the framework allows for scalability and adaptability across various smart city applications, including traffic management, energy grid optimization, and public transit coordination. These findings underscore the potential of IoT and AI technologies to revolutionize urban transportation, contributing to more efficient, secure, and sustainable mobility systems.