Abstract (EN):
In order to efficiently manage emergency situations in cities, it is crucial to have a well-distributed network of emergency response centres that can quickly initiate mitigation actions. Conversely, such infrastructure can also be leveraged when designing emergency management systems in smart cities. To enable this in any city, we have developed a risk classification system that uses open geospatial data to preprocess urban areas. For that, we exploited the previous concepts of Area of Interest (AoI) and Points of Interest (PoI), extending them to also incorporate the concept of Area of Mitigation (AoM) for any regular or irregular polygonal AoI, considerably enhancing the definition of risk perceptions in a city. Additionally, we have proposed a new algorithm that finds realistic positions for Event Detection Units (EDUs) based on the streets in a city, avoiding prohibited deployment areas, which was in fact a deficiency of previous algorithms for this problem. By potentially reducing the time required to detect and notify emergency events in more critical urban areas, we want to improve the performance of emergency management systems as a whole, bringing important contributions for smart city applications in this area. © 2023 IEEE.
Language:
English
Type (Professor's evaluation):
Scientific