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.
Scholars
Bikis Muhammed - Accelerated Master's Student
Karl Frank - Undergraduate Scholar
Jake Mason - Undergraduate Scholar
Jeremy Thomas - Undergraduate Scholar
Owen Miller-Fast - Undergraduate Scholar
Repositories
Traffic Prediction
This repository is the main repository for the project. This repository is not open to the public.
