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

本研究使用机器学习方法，通过分析巴西联邦区交通事故数据并结合当地天气条件，成功预测事故热点区域，并表明事故位置比天气参数对事故发生的影响更为重要，强调地方干预对减少事故数量的重要性。