Indicator-based framework to evaluate the resilience of transport infrastructure systems

Abstract

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.