Time density approach to managing en-route airspace
With the rapid growth in air travel and advancement in Air Traffic Management (ATM) technologies, the airspace is increasingly more complex and dynamic. These have adverse impact on Air Traffic Controllers’ (ATCO) monitoring performances and intensifying their workload. To cope with the challenging...
Saved in:
Main Author: | |
---|---|
Other Authors: | |
Format: | Final Year Project |
Language: | English |
Published: |
Nanyang Technological University
2020
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/141169 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Summary: | With the rapid growth in air travel and advancement in Air Traffic Management (ATM) technologies, the airspace is increasingly more complex and dynamic. These have adverse impact on Air Traffic Controllers’ (ATCO) monitoring performances and intensifying their workload. To cope with the challenging environment, the usage of models has been developed to aid in the identification of sectors with high workload. Presently, ATM systems using capacity modelling, such as the Monitor Alert Parameter, have limitations and are oversimplified in their approach. Moreover, not many studies have been done on improving the current system. Thus, the models are unable to fully capture the complexity at individual crossing points and the entire workload of the ATCOs. This study aims to investigate the relation of overall flight time within the sector and the number of aircraft around the crossing point to create a model to identify the risk of collision around the crossing point. ATCOs’ response time was used as the performance indicator in the study. It was found that the increase in flight time and the number of aircraft around the crossing point lead to an increased risk of collision. Factors like the number of aircraft in the entire sector and the headings of the aircraft around the crossing point were factors affecting the overall flight time and the number of aircraft around crossing point respectively. Using thresholds on the flight time and the number of aircraft, the model created was able to characterise the crossing points into 3 levels of risk. This helps to distinguish crossing points with higher chances of conflicts being missed out. |
---|