As modern societies increasingly rely on transport infrastructure, ensuring its resilience is essential, particularly under climate change. Traditional simulation-based methods are often complex and resource-intensive, limiting their widespread use. In contrast, indicator-based approaches offer a practical alternative; however, a comprehensive and multidimensional indicator set remains underdeveloped. This paper proposes an indicator-based framework for assessing the resilience of transport infrastructure systems across physical, operational, and social dimensions. The framework enables a structured evaluation of how systems withstand, adapt to, and recover from disruptions while maintaining essential functions. A thorough literature review was conducted to identify and categorize a robust set of indicators. These indicators are adaptable and may be integrated with advanced techniques such as Machine Learning, Bayesian Networks, and Fuzzy Logic to strengthen resilience analysis. A case study demonstrates the framework’s applicability and highlights how combining indicators with analytical tools can enhance the assessment and management of infrastructure resilience.

As modern societies become increasingly dependent on infrastructure systems, ensuring their functionality is paramount. Current simulation-based approaches for evaluating infrastructure wellbeing and resilience are known to be complex and time-consuming, making them unfeasible for practical applications. Indicators-based methods have been proposed as a promising alternative to simulations. However, a comprehensive set of indicators that cover all aspects of infrastructure systems has yet to be established. In this study, we performed an extensive literature review on wellbeing and resilience indicators specific to the transport infrastructure system. We filtered out duplications among the indicators and categorized them under distinct components and dimensions. These indicators can be tailored to fit specific circumstances and employed for/alongside advanced techniques such as Machine Learning, Bayesian Networks, and Fuzzy Logic. Acquiring a comprehensive set of wellbeing and resilience indicators can significantly improve stakeholder communication, empower communities in decision-making processes and adaptive management, and support resilience-strengthening strategies.