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This paper aims to present an improved traffic risk predictor, applying a fuzzy version of Harmonic Systems. The topic has many contributions from many diverse fields. From the practical perspective, there are a few tools that may be successfully used for a driver, pedestrian and cyclist risk prediction at the same time, with no extra devices. The proposed predictor can be used in any mobile device,...
Traffic produces not only pollution but also many incidents producing material lost and human injuries or even persons dead. But not all the incidents involve two cars, it may be pedestrian and any type of cycles (motorcycles, bicycles, tricycles, etc). Most of the approaches try to model traffic accidents using traditional information and avoiding some other like environment elements, driver profile,...
Vehicle and pedestrian risks can be modelled in order to advice drivers and persons. A good model requires the ability to adapt itself to several environmental variations and to preserve essential information about the area under scope. This paper aims to present a proposal based on a Machine Learning extension for timing named Harmonic Systems. A global description of the problem, its relevance and...
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