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Urban systems and public transportation networks are integral to modern societies, exhibiting intricate dynamics and vulnerabilities that impact daily life. We tackle these challenges using approaches rooted in complexity science. The first study leverages statistical modeling and deep learning to predict diverse urban phenomena. Analyzing data on the movements of car-sharing vehicles, the study not only identifies significant observables driving urban dynamics but also introduces a robust forecasting tool applicable to urban planning, transportation, and epidemic-spreading modeling. The framework reveals the relevance of statistical inference in understanding the fundamental forces shaping urban systems. Complementing this, the second study addresses the challenges of delay propagation in public railway systems. Recognizing that minor disruptions can lead to widespread delays, the study introduces a systemic risk-based approach for managing delay cascades nationally. By computing the systemic impact of each train in a detailed impact network, the study identifies structural weaknesses in railway networks. It proposes an optimal allocation of shared resources to minimize delays with cost-effective solutions. This systemic approach aligns with detailed agent-based simulations. It offers a practical and intuitive framework for delay management, emphasizing the importance of understanding the interplay between trains, infrastructure, and operational units. Together, these studies provide a comprehensive perspective on the dynamics of urban systems and public transportation networks, highlighting the importance of robust modeling and complex network approaches for a deeper understanding and effective management of these complex systems. This talk will focus on the two works below: - Daniotti, Simone, et al. "A maximum entropy approach for the modelling of car-sharing parking dynamics." Scientific Reports 13.1 (2023): 2993. - Daniotti, Simone, et al. "Systemic risk approach to mitigate delay cascading in railway networks." arXiv preprint arXiv:2310.13773 (2023).