Denver Traffic Accidents: A Case Study

Project Overview

• Motivation: This analysis attempts to uncover trends and patterns among traffic accidents in the Denver Metro area over the last 10 years.
• Agenda: Attempt to understand general accident behavior by asking these questions
  • Where are the accidents usually?
  • Which locations are the most dangerous?
  • How do the accidents look over time?
  • What is the common cause of these accidents?

Data Details

• Source: Denver Open Data Catalog
  • Run by the city and county of Denver, it is a public access website for various data sets involving transportation, public safety, education, etc.
  • The dataset was first published in 2020, and last updated in June 2024
• Data collection period: January 2013 to June 2024
• Tools, skills, and methodologies:
  • Python was utilized for exploratory data analysis, data cleaning, and manipulation.
  • Libraries used included pandas, NumPy, matplotlib, and seaborn.
  • Created Tableau visuals to help communicate findings.
  • GitHub repository for scripts and other analytical details can be found here.

Exploratory Data Analysis

• I started with a time series analysis to determine how traffic accidents have (or haven’t) changed since 2013.
• The line graph below shows gradual increases from 2013 to 2019 (reflecting population growth), followed by a stark dropoff in 2020.
• What happened in 2020? Oh yeah, COVID-19. The world shut down for a period of time, and remote work took off.

Further Analysis and Next Steps

• I derived a “Fatality-Injury Index” (FII) which is the summation of fatalities and serious injuries from each accident.
• From there, I plotted all major accidents (with an FII of at least 3) to visualize which areas in the city see the most serious accidents.
• The more serious the accident, the bigger and darker the blue dot is.
• As you can see, and perhaps unsurprisingly, a lot of the worst accidents are located on or near major highways (I70, I25, or HWY 6).
• Highway driving = faster speeds= faster collisions = higher possibility for serious injury or death.

Conclusion and Takeaways

• This analysis provided me with a few useful pieces of information that can help inform city drivers
• I determined the safest time of day to drive, the safest days of the week, the most dangerous roadways, a ranking of the city’s most and least dangerous neighborhoods for accidents, and more.
• For the full story, please refer to the presentation below (and check out my blog post on this project).