Identification of critical transport infrastructures

Since 1990 the annual number of extreme weather and climate related events has doubled in comparison to the previous decade. These events account for about 80% of all economic losses caused by catastrophic events (European Environment Agency (2004), 70). In the course of climate change they are projected to happen even more often. Therefore economic losses due to weather and climate related events will increase, if efforts for prevention and disaster management are stagnating. Critical infrastructures are especially sensitive to catastrophic events. The Deutsches Bundesamt für Sicherheit in der Informationstechnik (2004) defines critical infrastructures as "organizations and institutions that are important to public welfare; such that failure or disruption of them will result in long-lasting supply bottlenecks, significant disturbances in public security or have other dramatic consequences”. Critical infrastructures include among others energy supply, telecommunications and information technology as well as transportation systems. Due to their societal importance, it is crucial to understand how their functionality might be impacted by disruptions. The paper on hand focuses on transportation infrastructures. In 2005 more than 3500 million tons and nearly 70 billion passengers were transported on German networks. Thus, transportation networks can be considered an intermediate input for satisfying the demand for goods and accomplishing nonmarket activities. Therefore a disrupted transportation system immediately affects a large spectrum of human activities. The locations most critical in a network “show the most severe (socio-economic) consequences resulting from network failure at those locations“ (Taylor, M. A. P. and D'Este, G. M. (2007), 10). These should be the points of interest for possible disaster mitigation measures. The paper at hand aims to identify these locations. Two approaches are employed for this purpose. Approach 1 is a very straight forward way using data on traffic load. Approach 2 is more complicated using data on traffic load and detouring costs. The following chapters explain their methodologies. At the example of the German state of Baden-Württemberg the approaches are tested for the road network and their results are compared.