Quantifying Road Criticality through the Impact of Disruptions due to Natural Hazards
Road networks are highly exposed to natural hazard events, which can lead to significant economic and social consequences. In New Zealand, events such as the 2011 Christchurch earthquake, the 2016 Kaikōura earthquake, and the Cyclone Gabrielle in 2023 have demonstrated the severe consequences of road network disruptions. Traditional post event economic assessments often focus solely on clean-up and repair costs, neglecting the broader and more enduring impacts these events can have. Furthermore, business cases for resilience investments usually fail when quantifying the economic benefits of mitigation strategies, due to the underestimation of road disruption consequences. Importantly, not all road link disruptions contribute equally to these consequences, making the identification of critical road links a key step in resilience focused investment prioritization. Furthermore, traditional transportation asset management typically evaluates the life cycle of roads under normal conditions, such as traffic loads and standard environmental factors, while neglecting the influence of natural hazards. However, these events can significantly alter road deterioration and increase maintenance costs, emphasizing the need for integrating risk and resilience into transportation asset management approaches. This thesis presents a methodology to evaluate road criticality by assessing the economic consequences of road disruptions in combination with a hazard model in a prioritization index. Initially, the consequences are quantified through increased travel time, higher vehicle operating costs, and increased gas emissions. Thereafter, a new consequence model is introduced to estimate the increase in maintenance costs on alternative routes that absorb diverted traffic following a disruption. These consequence models are initially applied in a 'full-scan' analysis approach, where each road link is removed in turn to quantify its potential impact and, therefore, its criticality. Subsequently, a hazard model is integrated to develop a road prioritization index that combines the expected impacts of road disruptions, the individual road link criticality, and the probability of occurrence of natural hazard events. This index is designed to help road agencies in prioritizing mitigation strategies. Furthermore, the proposed methodology can also be applied to quantify the indirect economic impacts of natural hazard events. The methodology is demonstrated using New Zealand’s South Island inter-urban network as a case study, incorporating an earthquake-induced landslide model, with Python based simulations, providing road agencies a valuable tool to quantify the economic benefits of resilience investments.