mahshidjabari / accident-prediction-montreal

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

This repository contains the source code developed for a study of road vehicle collisions in the city of Montreal. Three datasets provided by the city of Montreal and the Government of Canada were used: a dataset containing road vehicle collisions, a dataset describing the Canadian road network, and a dataset containing historical weather information. These datasets have been fused to generate examples corresponding to an hour period and a road segment delimited by intersections. A binary classification has been performed with positive examples, corresponding to the occurrence of a collision, and negative examples, corresponding to the non-occurrence of a collision. Four models have been built and compared, a first basic model using only the count of accident during previous years on the road segment, a model built using random forest with under-sampling of the majority class, a model using balanced random forest and a model using XGBoost. The best performances were obtained by the balanced random forest model. It identifies as positives the 13% most dangerous examples which correspond to 85% of vehicle collisions.

For more information read the corresponding scientific paper.

Folder Structure

• mains: contains the scripts for the generation of the dataset, the hyperparameter tuning, the training and the evaluation of the models
• notebooks: Jupyter notebooks used during development for interactive exploration of the data and experimentations
• results: results of the four models

License

MIT