This project focuses on employing Automatic Dependent Surveillance-Broadcast (ADS-B) data combined with machine learning techniques to enhance the prediction of aircraft arrival times at major airports. The main goal is to optimize Air Traffic Management by offering more accurate estimates of landing times. The system will utilize a mix of historical and real-time data, including weather conditions, to train predictive models. These models will integrate data received from a receiver and antenna that capture both ADS-B information by utilizing advanced algorithms such as k-means clustering. The system also features a user interface for displaying these predictions alongside pertinent aircraft and weather data.

Team Members:

Dasol Ahn
Miguel González
Sarthak Gupta
Mahek Kakkar
Vikram Padmanaban
Amaia Rotaeche
Randy Tan