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

TL;DR

This paper presents a machine learning approach to predict traffic accident hotspots in Brazil's Federal District, highlighting the importance of location over weather factors for targeted interventions.

Contribution

It introduces a novel application of machine learning to accident hotspot prediction using local forensic data and weather conditions, with high accuracy results.

Findings

Random Forest achieved 98% accuracy in hotspot prediction

Location is more influential than weather in accident concentration

Machine learning can assist authorities in targeted accident prevention

Abstract

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.