<p>Data modeling and decision support engineering are increasingly central to the management of smart urban infrastructure, yet their integration into transport critical infrastructure monitoring remains fragmented, largely due to the absence of standardized risk data models. This article addresses that gap by proposing a dynamic, risk-oriented data model and an associated database-driven decision support tool—RiskHub—designed to embed risk assessment outputs directly into operational and managerial workflows for urban transport networks. Grounded in a knowledge synthesis combining the COSO framework, ISO/IEC 27005 risk management standard, and Operational Risk Management (ORM) ontology, the model extends the Risk Data Open Standard (RDOS) by introducing explicit functions/actions and specializing mitigation tools into operational control, contract, and insured risk entities. The framework supports multi-criteria risk classification across safety, security, operational, and hybrid risk categories and enables timely data integration from operational sources to inform evidence-based response strategies. A six-phase bottom-up business process re-engineering approach guided the model’s development and implementation in the risk management operations of a suburban rail operator in northern Italy (ItaRail), enabling the transition from conventional practices to a data-driven solution. RiskHub is evaluated through a prototype deployment in a real-life operational case study and through exploratory expert elicitation involving 12 professionals in risk management and data governance. Evaluation results confirm that the data model achieves high scores across completeness, accuracy, adaptability, and extensibility, providing a robust semantic backbone for risk-informed performance analysis. The results indicate that the proposed data model provides a structured semantic backbone for risk-informed performance analysis and that the prototype dashboard supports data integration from operational sources, laying a conceptual foundation for dashboard-based decision support tools capable of managing cascading risks across interconnected networks. This work contributes a reusable, domain-adaptable data modeling and decision support framework for risk-aware operation management in railway transport, with potential adaptability to other critical infrastructure domains in smart urban environment and direct implications for sustainable mobility planning, security and emergency response, and intelligent transportation ecosystems.</p>