Data Analysis on Vehicle Collision in Alberta

A project written at

HackED Beta 2022,

a 24-hour hackathon hosted by

University of Alberta Computer Engineering Club

Website Link: http://kdhminime.pythonanywhere.com/

Our goal was to raise awareness on road safety, specifically in Alberta.

To do so, we compiled news data, analyzed government-provided datasets, and displayed them in a website.

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Funtionality (C.A.D.)

• Compiles information through web scraping and pdf parsing

  • Scrapes news report data from a website whose approach is one of the most systematic and thorough in recording locations of collisions:
    • https://www.navbug.com/alberta_traffic.htm
  • Extracts table from pdf files:
    • https://open.alberta.ca/publications/0844-7985

• Analyzes government data on car collisions through machine learning

  • Uses two training models (ARIMA and Prophet) to analyze trends and to predict future occurrences

• Displays above data in a website

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Our noteworthy achievements

• Originality:
  • Currently, there is no publicly accessible compiled information on car collision locations
  • Nobody else analyzed the trends in car collision using machine learning in Canada
• Complexity:
  • Quickly familiarized ourselves with techniques required to compile, analyze, and display data
    • Web scraping
    • Machine learning
    • Web designing
• Execution and Polishness
  • Completed the working project within 24 hours
• Utility
  • Our project raises awareness on road safety and can be extended to encourage the government authorities to implement our system through showcasing its importance.

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Steps to run the program

• Scraping website data
  1. Move to "webscraping" directory
  2. To start the program and to create an output file, run:

  python3 web_scraper.py > scraped_data.txt
  

• Accessing Jupyter notebook
  1. Select machine learning model folder
  2. Click on ipython file(.ipynb)
  3. Enjoy the model!

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Background information of contents

• "ARIMA_Prediction_Model" directory
• "Prophet_Prediction_Model" directory
• "datasets" directory contains required datas for prediction that is parsed from pdf file
• "src" directory contains html files
• "webscraping" directory contains files that scrape website data

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Team members:

• Min Joh
• Dohyun Kim
• Yongbin Kim
• Jamie Lee
• Taekwan Yoon