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|Title:||Pre-tactical airspace collision risk model for air traffic controller||Authors:||Ngoi, Kai Ling||Keywords:||DRNTU::Engineering||Issue Date:||2017||Abstract:||Air traffic is projected to grow in the next few years due to the persistently increasing demand for air travel by the tourism sector. The national airspace is likely to get complex and the amount of expected workload for an air traffic controller will increase, affecting their performance during operations. Hence, it is likely that the workload and performance of an enroute air traffic controller will increase as well. This will potentially lead to mid-air collision. With the increasing workload, more errors will be made by ATCOs that contributes to mid-air collision. Thus, there’s a need to have a safety indicator in an airspace sector to let the air traffic controller know of any pending conflicts. The vital indicator for airspace will be the collision risk estimates which are used as a target safety level to offer a quantifiable source to determine the level of safety operation within an airspace. In this project, a collision risk model will be used to identify the different level of collision risk in a given airspace in the pre- tactical phase in Air Traffic Flow Capacity Management (ATFCM). This model will take into account the different complexity of an airspace sector. The model will then be converted into visual graphics on the radar screen. This visual graphics will adopt the collision risk model and systematically identifies airspace collision risk hot spot in the pre-tactical phase of ATFCM. With these visual graphics, the air traffic controller will be able to identify the different level collision risk hot spot in the given sector beforehand. This would better able them to better prepare and focus their tasks on hand. The methodology was tested in Singapore Airspace region to check on the robustness of this pre-tactical collision assessment tool. Results show that one is able to identify collision hot spot in the given sector physically and dynamically.||URI:||http://hdl.handle.net/10356/73056||Rights:||Nanyang Technological University||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||MAE Student Reports (FYP/IA/PA/PI)|
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