Abstract
Safety is a relevant issue in many context of the real life. The paper proposes a scientifically robust method for the identification of the top-N list of railway hotspots that can be used as input for the definition of a strategy of selective monitoring of the state of safety the railway network of an administrative unit (namely, a province, a region, a country, etc.), with respect to the exposure to the landslide hazard. The knowledge of the hotspots, as meant in this paper, is a conceptual tool for providing a rigorous analytical basis for narrowing down a global problem – train derailments – to smaller, highest risk, geographic areas where the management of the disaster risk is most crucial. To be applied, the method needs data about the railway lines to be kept under control and data about the geomorphology of the underlying terrain.
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Di Felice, P., Di Felice, A., Evangelista, M., Fraticelli, A., Venturoni, L. (2017). Identification of High-Risk Hotspots Along Railway Lines. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2017. ICCSA 2017. Lecture Notes in Computer Science(), vol 10407. Springer, Cham. https://doi.org/10.1007/978-3-319-62401-3_24
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DOI: https://doi.org/10.1007/978-3-319-62401-3_24
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