Report with description of dynamic risk-based predictive models developed
The scope of this deliverable is to provide a framework for dynamic risk-based predictive models for transportation networks. First, an overview of the most common predictive models for forecasting the future condition of transportation infrastructures is presented. These models were built upon performance indicators available in large databases. Then, the possibility of updating the predictive models based on new collected information from different sources of data was introduced into the framework through Bayesian inference procedures. Moreover, it was addressed how the occurrence of sudden events, i.e. natural and human-made events, affect the transportation network performance. In this way, a risk-based framework which enables the understanding of the effect of hazards on infrastructure assets and the associated consequences for the network was adopted. The framework is also endorsed by local and real time data to refine the risk estimation. Accordingly, return periods of extreme events and its magnitude were used to assess the direct impacts on road and railway infrastructures at both asset and network level. Additionally, two approaches were presented to facilitate the quantification of indirect impacts arising from the disruption of the transportation service and its duration, together with a review of all the socioeconomic costs associated to both road and railway disruptions. Finally, the time-dependent factors influencing the risk assessment of transportation networks were analysed. Essentially, expected changes in the return period of hazards due to climate change were examined, with focus on floods and wildfires. Thus, direct impacts for future scenarios were assessed. Additionally, time variant socioeconomic attributes which affect the indirect impacts such as the status of a population and traffic demands were reviewed for the development of consequence models. Consequently, the proposed framework introduced in the present deliverable enables a dynamic risk assessment of transportation networks.