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Traffic accidents are one of the biggest challenges in a society wherecommuting is so important. What triggers an accident can be dependent onseveral subjective parameters and varies within each region, city, or country.In the same way, it is important to understand those parameters in order toprovide a knowledge basis to support decisions regarding future casesprevention. The literature presents several works where machine learningalgorithms are used for prediction of accidents or severity of accidents, inwhich city-level datasets were used as evaluation studies. This work attemptsto add to the diversity of research, by focusing mainly on concentration ofaccidents and how machine learning can be used to predict hotspots. Thisapproach demonstrated to be a useful technique for authorities to understandnuances of accident concentration behavior. For the first time, data from theFederal District of Brazil collected from forensic traffic accident analystswere used and combined with data from local weather conditions to predicthotspots of collisions. Out of the five algorithms we considered, two had goodperformance: Multi-layer Perceptron and Random Forest, with the latter beingthe best one at 98% accuracy. As a result, we identify that weather parametersare not as important as the accident location, demonstrating that localintervention is important to reduce the number of accidents.

Hotspot Prediction of Severe Traffic Accidents in the Federal District of Brazil