https://github.com/big-data-lab-team/accident-prediction-montreal
https://github.com/big-data-lab-team/accident-prediction-montreal
Science Score: 10.0%
This score indicates how likely this project is to be science-related based on various indicators:
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○CITATION.cff file
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○codemeta.json file
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○.zenodo.json file
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○DOI references
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✓Academic publication links
Links to: arxiv.org -
○Academic email domains
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (8.6%) to scientific vocabulary
Keywords
Repository
Basic Info
- Host: GitHub
- Owner: big-data-lab-team
- License: mit
- Language: Jupyter Notebook
- Default Branch: master
- Size: 65 MB
Statistics
- Stars: 9
- Watchers: 6
- Forks: 7
- Open Issues: 7
- Releases: 0
Topics
Metadata Files
README.md
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
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