In the field of Cooperative Intelligent Transport Systems (C-ITSs), the traditional approach to testing often emphasizes technological parameters, leaving the validation of message content insufficiently addressed. Since the content of these messages is crucial for the correct functioning of C-ITS, this article demonstrates the potential for automated evaluation of C-ITS message content against relevant standards. It leverages our novel tools, Karlos and C-ITS SIM, to facilitate this process. Through detailed laboratory testing and data analysis, the study showcases the effectiveness of these automated solutions in enhancing the accuracy and reliability of message content validation.

This paper reviews current approaches to cybersecurity risk analysis in critical transport infrastructure, with particular focus on their applicability to complex cyber-physical environments such as tunnel control systems. Modern transport infrastructures are characterised by strong interdependencies between digital control technologies and physical operational processes, which increase cybersecurity risks and the potential for cascading failures. The review shows that existing methodologies are based on standards-driven and governance-oriented frameworks. While these approaches provide structured procedures for risk identification and regulatory compliance, they remain largely qualitative, static, and limited in their ability to represent uncertainty, system interdependencies, and dynamic cyber-physical interactions. To address these limitations, the paper discusses the potential of probabilistic modelling approaches capable of supporting decision-making under uncertainty. Probabilistic graphical models offer mechanisms for modelling complex dependencies, integrating expert knowledge with available data, and improving analytical support for cybersecurity risk management. The findings highlight the need to complement compliance-oriented methodologies with probabilistic and decision-oriented approaches to enhance cybersecurity resilience in critical transport infrastructure.

Urban traffic management increasingly relies on accurate, timely and robust traffic detection to support safe, efficient and sustainable mobility in complex urban environments. While a wide range of traffic detection technologies is currently available, many limitations persist due to fragmented data utilization, limited interoperability and sensor-centric system architectures. This paper provides a structured review of commonly used urban traffic detection technologies and analyses their technical and system-level limitations. Based on this analysis, a data-centric reference architecture is proposed, which decouples traffic detection capabilities from individual sensing technologies and enables the integration of heterogeneous infrastructure-based sensors, mobile data and C-ITS sources through harmonization and AI-based multi-source fusion. Rather than presenting a concrete system implementation, the proposed architecture serves as a conceptual framework supporting resilient, scalable and future-ready traffic detection. Its relevance is illustrated using representative use cases focusing on vulnerable road user detection, predictive traffic management and incident handling. The paper argues that use case-driven, data-centric architectural design represents a key enabler for next-generation urban traffic management in smart city environments.

Autonomous public transport is becoming an integral component of the Smart City paradigm and, concurrently, a key element of critical urban infrastructure. Besides operational benefits, increasing automation, connectivity, and digitalization introduce updated requirements for security, reliability, and resilience against cyberattacks and unintended system behaviors. This article presents a methodological and technological framework for deploying autonomous public transport management in urban environments, with a special emphasis on rail-based modes and their safe integration into a city's critical infrastructure. The proposed approach combines dynamic HD maps, a digital twin of the urban environment, and cooperative intelligent transport systems (C-ITS) to achieve precise localization, enhanced situational awareness, and real-time data exchange among vehicles, infrastructure, and dispatch centers. Vehicle and infrastructure sensors feed a unified data platform that supports monitoring, diagnostics, and operational control. The architecture prioritizes secure data transmission, network segmentation, access control, and cyber-threat mitigation to ensure business continuity and high service availability. Functionality was verified on a pilot section of a real tram line instrumented with infrastructure and onboard sensors; testing occurred under normal operating conditions with driver supervision for maximum safety. The results indicate the feasibility of gradual autonomy introduction while maintaining high levels of safety and compliance with critical-infrastructure requirements. The combination of HD maps, a digital twin, and security-by-design principles significantly improves robustness, interoperability, and resilience to operational and cyber risks.