The objective of this project is to use data science to understand and predict traffic related injuries in Chapel Hill. The Town of Chapel Hill has expressed concerns over increasing instances of serious traffic crashes. In order to start addressing the problem, it is imperative to understand what makes some intersections and regions more dangerous than others. If we can find patterns in the data that shed light on what conditions make crashes more likely, it’s possible to take measures to prevent them.
This project can be divided into two parts: analysis and visualization. Analysis will consist of using data about traffic crashes to create a simple model for predicting which regions of Chapel Hill are more likely to see crashes within them. On the Town of Chapel Hill Data Portal, there are already some relevant data for this like pedestrian/bicycle crash information and traffic signal locations. We still need data that helps us better understand the conditions that make crashes possible. Having data about the traffic flow, speed limits, types of cars, etc. would be helpful here. Visualization will be about using the created model to cleanly present the likelihood of crashes by region on a map of Chapel Hill. Ultimately, the presentation should include visualizations pertaining to specific road segments and intersections and give week by week predictions about where accidents are most likely to happen.