| Summary: |
Transportation networks are fundamental for the development of the economy of each country, and assume a pivotal role after destructive earthquakes, not only during emergency but also in the recovery and reconstruction phases. Understanding disaster risk and building resilient infrastructure is therefore of critical importance, as highlighted by the Sendai Framework for Disaster Risk Reduction and the United Nations 17 Sustainable Development Goals.
However, there are several obstacles to the incorporation of transportation networks' damage in risk assessment. One of the main challenges in these studies is the lack of reliable and detailed data, especially regarding the structural properties of the network assets, and the fact that the vast majority of these analysis assume each element in isolation, neglecting that one damaged element might affect the performance of other components, or even of the entire system. Another important aspect that is frequently neglected is the incorporation of population dynamics throughout the day and year within the risk assessment framework, due to daily (work) commutes and seasonal tourism, which will have a great impact on the traffic flow of the network. Finally, the existing tools focus mostly on damage on bridges due to ground shaking, and disregard other possible sources of disruption such as blockages due to building damage, liquefaction phenomena and landslides. It is thus urgent to tackle these issues and advance the field of network seismic risk analysis, to better inform decision makers and other stakeholders in the development and implementation of disaster risk measures at various geographical scales.
At the urban level, the assessment of systemic risk can provide critical information regarding the impact in the transportation network, which is typically due to road blockages from collapsed buildings and damages in structures such as bridges, viaducts or tunnels. Considering not only specific seismic scenario  |
Summary
Transportation networks are fundamental for the development of the economy of each country, and assume a pivotal role after destructive earthquakes, not only during emergency but also in the recovery and reconstruction phases. Understanding disaster risk and building resilient infrastructure is therefore of critical importance, as highlighted by the Sendai Framework for Disaster Risk Reduction and the United Nations 17 Sustainable Development Goals.
However, there are several obstacles to the incorporation of transportation networks' damage in risk assessment. One of the main challenges in these studies is the lack of reliable and detailed data, especially regarding the structural properties of the network assets, and the fact that the vast majority of these analysis assume each element in isolation, neglecting that one damaged element might affect the performance of other components, or even of the entire system. Another important aspect that is frequently neglected is the incorporation of population dynamics throughout the day and year within the risk assessment framework, due to daily (work) commutes and seasonal tourism, which will have a great impact on the traffic flow of the network. Finally, the existing tools focus mostly on damage on bridges due to ground shaking, and disregard other possible sources of disruption such as blockages due to building damage, liquefaction phenomena and landslides. It is thus urgent to tackle these issues and advance the field of network seismic risk analysis, to better inform decision makers and other stakeholders in the development and implementation of disaster risk measures at various geographical scales.
At the urban level, the assessment of systemic risk can provide critical information regarding the impact in the transportation network, which is typically due to road blockages from collapsed buildings and damages in structures such as bridges, viaducts or tunnels. Considering not only specific seismic scenarios but also probabilistic seismic hazard, it is possible to identify the most vulnerable segments of the network and evaluate the accessibility of each neighbourhood to the closest health centre. Furthermore, incorporating traffic data in a detailed traffic simulation allows the estimation of traffic delays in daily commutes and consequently an evaluation of economic losses. When these analyses are expanded to wider geographic scales, network disruptions derive mainly from damage in specific components, not only due to ground shaking, but also from liquefaction and landslide phenomena. Identifying vulnerable segments of the network at this scale may highlight regions where risk mitigation measures should be prioritized, and identify eventual disconnections between major urban centres or locations of strategic importance for emergency operations. The analysis at this scale can also have specific applications in the estimation of losses for industries that depend on the network to import raw materials or distribute their products.
In this project we propose to develop a novel framework to tackle some of the main challenges in network analysis, employing state-of-the-art machine learning techniques and big data. These methods will be integrated into a dynamic platform to assess the impact of earthquakes in transportation networks and in the surrounding building stock. The development of the platform will follow an open-source philosophy, and will be co-designed with relevant national and international stakeholders to ensure that the outcomes are usable and useful for risk reduction activities. The developed methods and platform will be built upon openly accessible seismic risk analysis and traffic simulation tools to avoid duplication of efforts, and involve well-established communities around these resources. The characterization of the elements comprising the transportation network and the building stock will benefit from the rise of artificial intelligence methods, in combination with open-data from OpenStreetMap. The population dynamics will be modelled using big data from mobile towers, which can be used as a proxy for population density at different times of the day or throughout the year. To demonstrate the applicability and usefulness of this platform, two case studies will be explored considering the district of Lisbon (for a risk evaluation at the urban scale) and the Southern region of Portugal (regional level). This project has the support of relevant national stakeholders that will steer the development of the tools and case studies, and international experts from the Humanitarian OpenStreetMap Team and the SimCenter artificial intelligence group in California (Berkeley and Stanford University), that will ensure the scientific quality and credibility of the outcomes. |