  Road accidents are an important issue of our modern societies, responsible
for millions of deaths and injuries every year in the world. In Quebec only, in
2018, road accidents are responsible for 359 deaths and 33 thousands of
injuries. In this paper, we show how one can leverage open datasets of a city
like Montreal, Canada, to create high-resolution accident prediction models,
using big data analytics. Compared to other studies in road accident
prediction, we have a much higher prediction resolution, i.e., our models
predict the occurrence of an accident within an hour, on road segments defined
by intersections. Such models could be used in the context of road accident
prevention, but also to identify key factors that can lead to a road accident,
and consequently, help elaborate new policies.
  We tested various machine learning methods to deal with the severe class
imbalance inherent to accident prediction problems. In particular, we
implemented the Balanced Random Forest algorithm, a variant of the Random
Forest machine learning algorithm in Apache Spark. Interestingly, we found that
in our case, Balanced Random Forest does not perform significantly better than
Random Forest.
  Experimental results show that 85% of road vehicle collisions are detected by
our model with a false positive rate of 13%. The examples identified as
positive are likely to correspond to high-risk situations. In addition, we
identify the most important predictors of vehicle collisions for the area of
Montreal: the count of accidents on the same road segment during previous
years, the temperature, the day of the year, the hour and the visibility.
