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Reducing Traffic Mortality in the USA

How can we find a good strategy for reducing traffic-related deaths?

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  • 12 tasks
  • 1,817 participants
  • 1,500 XP

Project Description

While the rate of fatal road accidents has been decreasing steadily since the 80s, the past ten years have seen a stagnation in this reduction. Coupled with the increase in number of miles driven in the nation, the total number of traffic related-fatalities has now reached a ten year high and is rapidly increasing.

By looking at the demographics of traffic accident victims for each US state, we find that there is a lot of variation between states. Now we want to understand if there are patterns in this variation in order to derive suggestions for a policy action plan. In particular, instead of implementing a costly nation-wide plan we want to focus on groups of states with similar profiles. How can we find such groups in a statistically sound way and communicate the result effectively?

This project lets you apply skills from:

We recommend that you review the appropriate sections of those courses before starting this project.

Project Tasks

  • 1The raw data files and their format
  • 2Read in and get an overview of the data
  • 3Create a textual and a graphical summary of the data
  • 4Quantify the association of features and accidents
  • 5Fit a multivariate linear regression
  • 6Perform PCA on standardized data
  • 7Visualize the first two principal components
  • 8Find clusters of similar states in the data
  • 9KMeans to visualize clusters in the PCA scatter plot
  • 10Visualize the feature differences between the clusters
  • 11Compute the number of accidents within each cluster
  • 12Make a decision when there is no clear right choice
Joel Östblom

PhD Candidate at University of Toronto

Joel is a PhD student in Biomedical Engineering at the University of Toronto, where he uses computational and experimental approaches to better understand fundamental stem cell decisions. Outside school, he enjoys playing ice hockey, eating and making food, being in nature, and figuring out how he can maximize the time he spends inside vim.

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Rene Niehus

Research Fellow at University of Oxford

Rene is a public-health epidemiologist studying the spread of antibiotic resistance within and between human microbiomes using mathematical and statistical modeling. He has a background in biomedical research (Georg-August-University in Göttingen, Germany and Karolinska Institute in Stockholm, Sweden) and in computational systems biology (University of Oxford, UK). Rene now works as a postdoc with the Oxford Tropical Network. His interests include time series analysis, Bayesian models, teaching, rock climbing, and Lindy Hop.

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  • Python LogoPython
  • Topics

    Data ManipulationData VisualizationMachine LearningImporting & Cleaning Data