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

Abstract. 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 such as bridges, tunnels, pumps or storage tanks can translate not only in direct losses but also in significant indirect losses at 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 modelling the natural hazards implications at full transportation network scale, we developed a new applicable framework relying on (i) GIS to define, geo-spatially analyze and represent transportation networks; (ii) methods for determining the probability of network segments to fail due to natural hazard effects; (iii) MonteCarlo simulation for multiple scenario generation; (iv) methods (using Dijkstra algorithm) to analyze the implications of connectivity loss on emergency intervention times and transit disruption, (v) correlations with other vulnerability and risk indicators. Currently, the framework is integrated in ArcGIS Desktop as a toolbox (entitled Network-risk ) – making use of the Model Builder functions and being free for download and customize. 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, aiding in risk evaluation and mitigation planning. In this article we present and test Network-risk at full urban scale, for the entire road network of Bucharest – one of the world's most exposed capitals due to earthquakes, with high seismic hazard values and a vulnerable building stock, but also significant traffic congestion problems not yet quantified in risk analyses.