Traffic Prediction

Traffic Prediction Overview

Our Goal

The Traffic Prediction group is working to create an application that predicts dense road segments by using information that can be gained from a cell phone or other routing device, such as heading, destination, and location. Our prediction method attempts to be as accurate as possible by accounting for predicted dense areas when predicting dense areas further in the future. We also attempt to be as computationally efficient as possible by reducing the areas that are searched for dense roads by using dynamically sized cells. Having the knowledge of where roads are going to be busy and how busy they will be will allow drivers to plan their routes accordingly and avoid traffic saving both time and money. The phases of the traffic prediction algorithm can be found below.


  • Bikis Muhammed - Accelerated Master's Student
  • Karl Frank - Undergraduate Scholar
  • Jake Mason - Undergraduate Scholar
  • Jeremy Thomas - Undergraduate Scholar
  • Owen Miller-Fast - Undergraduate Scholar


Traffic Prediction
This repository is the main repository for the project. This repository is not open to the public.