# High-Resolution Road Vehicle Collision Prediction for the City of Montreal

## TL;DR

This study develops high-resolution, city-specific vehicle collision prediction models using open data and machine learning, achieving 85% detection with 13% false positives, to aid accident prevention and policy-making.

## Contribution

It introduces high-resolution accident prediction models for Montreal using big data analytics and evaluates machine learning methods, including Balanced Random Forest, for imbalanced data.

## Key findings

- 85% of collisions detected by the model
- False positive rate of 13%
- Key predictors include past accidents, temperature, and visibility

## Abstract

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.