Data analytics and predictive models for sustainable multimodal mobility in future smart cities

This thesis investigates the integration of data analytics and predictive modeling to enhance multimodal urban mobility systems within the framework of smart cities. By leveraging diverse datasets—including Wi-Fi signals, mobile phone data, and vehicular sensor inputs—predictive models are developed to simulate and analyze pedestrian and vehicular mobility patterns. Two case studies are presented: the dynamics of pedestrian mobility in cultural heritage cities (Ferrara, ˇSibenik, and Dubrovnik) and vehicular traffic analysis in the Emilia-Romagna region. Advanced mobility algorithms and simulation tools, such as agent-based models and optimal velocity frameworks, are employed to optimize traffic flow and urban planning strategies. These methods provide actionable insights into infrastructure planning, pedestrian movement behaviors, and regional traffic management. The proposed models offer a robust framework for real-time decision-making and support sustainable urban mobility practices. The research underscores the transformative potential of data-driven models in urban mobility modeling and analysis, enabling the design of more efficient transportation systems and fostering urban sustainability. The methodologies developed in this work provide a scalable foundation for addressing complex challenges in mobility systems and advancing smart city infrastructures.