Abstract: Safety is a relevant issue in many domains. Unfortunately, in the real life the implementation of programs devoted to achieve the safety is impeded by the budget of public administrations which is in constant contraction, especially in periods of crisis as those we are living, since, many years. The study 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 (e.g., a region) with respect to the exposure to the landslide hazard. The knowledge of the Hotspots as meant in this study 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. The method we propose is simple to understand and to implement. As counterpart, it may return false positives from which originates the denomination of rough method. Nevertheless, our method is suitable to implement a “Do more with less” strategy with respect to the case where the railway lines have to be inspected in full.
Paolino Di Felice and Antonello Di Felice, 2018. A Rough Method for the Detection of Hotspots along Railway Lines. Asian Journal of Information Technology, 17: 287-297.