Network-risk: an open GIS toolbox for estimating the implications of transportation network damage due to natural hazards, tested for Bucharest, Romania

Due to their widespread and continuous expansion, transportation networks are considerably exposed to natural hazards such as earthquakes, floods, landslides or hurricanes. The vulnerability of specific segments and structures among bridges, tunnels, pumps or storage tanks can translate not only into direct losses but also into significant indirect losses at the systemic level. Cascading effects such as post-event traffic congestion, building debris or tsunamis can contribute to an even greater level of risk. To support the effort of modeling the natural hazards’ implications at the full transportation network scale, we developed a new applicable framework, relying on (i) GIS to define, analyze and represent transportation networks; (ii) methods for determining the probability of network segments to fail due to natural-hazard effects; (iii) Monte Carlo simulation for multiple scenario generation; (iv) methods to analyze the implications of connectivity loss on emergency intervention times and transit disruption; and (v) correlations with other vulnerability and risk indicators. Currently, the framework is integrated into ArcGIS Desktop as a toolbox entitled “Networkrisk”, which makes use of the ModelBuilder functions and is free to download and modify. Network-risk is an attempt to bring together interdisciplinary research with the goal of creating an automated solution to deliver insights on how a transportation network can be affected by natural hazards, directly and indirectly, assisting in risk evaluation and mitigation planning. In this article we present and test Network-risk at the full urban scale for the road network of Bucharest. This city is one of Europe’s most exposed capitals to earthquakes, with high seismic-hazard values and a vulnerable building stock but also significant traffic congestion problems not yet accounted for in risk analyses and risk reduction strategies