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.

翻译：交通事故是通勤至关重要的社会中最大的挑战之一。引发事故的因素可能依赖于多个主观参数，并因地区、城市或国家而异。同样，理解这些参数对于提供知识基础以支持未来事故预防决策至关重要。文献中已有多项研究使用机器学习算法预测交通事故或其严重性，这些研究通常采用城市级数据集作为评估依据。本研究旨在通过重点关注事故集中度以及机器学习如何用于预测热点，进一步丰富研究多样性。该方法被证明是一种有助于当局理解事故集中行为细微差别的实用技术。我们首次使用了巴西联邦区法医交通事故分析师收集的数据，并结合当地天气状况数据来预测碰撞热点。在考虑的五个算法中，有两个表现良好：多层感知器和随机森林，其中后者以98%的准确率成为最佳模型。研究结果表明，天气参数的重要性远低于事故地点，这说明当地干预措施对于减少事故数量至关重要。