brianpinto91/atfm-delay-prediction

Machine learning approach to predict ATFM delay (in min) and delayed flights in the European Air Traffic Management Network (EATMN) with only capacity regulations as input

ATFM delay predictions from Capacity Regulations using Machine Learning

This project is about demonstrating the use of Machine Learning instead of complex mathematical modelling of the Air Transportation Network to predict the ATFM delays.

To be completed!

Table of Contents

• Introduction
• License

Introduction

In Europe, the Air Traffic Management Network comprises of more than forty member states and a large number of stakeholders like airlines, airports, Air Navigation Service Providers (ANSPs) who are involved in the decision making or the planning process to manage the congestions in the network. The Air Traffic Flow Management (ATFM) delay and the number of delayed traffic (flights) are important key performance metrics and are an indication of efficiency of planning. Capacity regulations are one of the methods used to regulate the air traffic and manage the congestion. Tools that assist in evaluating the impact of these regulations would be very beneficial in the planning process. Owing to the complexity of accurately modelling the interactions in the network, Machine Learning models can be seen as promising alternatives and is explored in this project.

License

Copyright 2021 Brian Pinto