IWAC2022 Program & Abstracts
Day 3 (Thursday, 27 October) 11:15 - 12:45, Hall C

Technical Session 12

Air Traffic Management 4

T12-1-I
Free Route Airspace Implementation Efforts in the Latin America and Caribbean Region throughCollaboration
Midori Tanino (Federal Aviation Administration)

The Federal Aviation Administration (FAA) and the Civil Air Navigation Services Organization (CANSO) organized theCANSO Air Traffic Flow Management (ATFM) Data Exchange Network for Americas (CADENA) Regional Implementation
Group (RIG) in 2016. Since its establishment, CADENA has delivered many improvements that have led to the Free
Route Airspace (FRA) implementation efforts in the Latin America and Caribbean (LAC) region. This presentation willshow the background of CADENA and its foundation where many Air Navigation Service Providers (ANSPs), airlines, andother stakeholders work collaboratively. It presents the accomplishments made thus far. Accomplishments includes
implementation of the Planned Airway System Alternatives (PASA) reroute database and end-to-end route request
capability. It also shows two types of route optimization trials and their results, PASA route optimization 90-day trialsand user preferred route (UPR) trials. The trials started in 2021 and CADENA will continue both types of trials during2022 and 2023.

T12-2-A
Demand Assessment for Climate Optimal Aircraft Trajectories at Network Scale

Fateme Baneshi, Manuel Soler, Abolfazl Simorgh, Ivan Martinez (Universidad Carlos III de Madrid)
Aviation contributes to global warming through the emission of carbon dioxide (CO$_2$) and other non-CO$_2$
effects. The climate impact associated with non-CO$_2$ species highly depends on atmospheric location and time ofthe emissions. Hence, they can be mitigated by efficient climate-aware trajectory planning to avoid climate-sensitive
regions. However, the increase in demand around climate hotspots caused by adopting individually optimized
trajectories in a climate-friendly manner may not be practical due to the limited capacity of airspace. Consequently, theactual mitigation potential of climate impact needs to be analyzed at the network level to assess how the adoption ofclimate optimized trajectories affects Air Traffic Management (ATM) system performance. In this paper, we aim to study
the effects of employing climate optimized trajectories on traffic demand. In this regard, taking climate impacts intoaccount, aircraft trajectory optimization is performed for a scenario with 1006 flights in free-route airspace. Uncertainty
in the meteorological conditions, as an essential factor affecting aircraft trajectories and estimated climate impacts, isaddressed by performing ensemble trajectory prediction.
The traffic demand for optimized aircraft trajectories considering different routing options (ranging from cost-optimal toclimate optimal) is then assessed. The results show that as we move toward trajectories with lower climate impacts,
in addition to the increase in the operational cost, the demand is considerably increased in the sectors adjacent toclimate hotspots.

T12-3-AA Deep Neural Network Approach for Prediction of Aircraft Top of Descent
Hao Jie Ang, Qing Cai, Sameer Alam (Nanyang Technological University)

An arrival flight starts to transit from the cruise phase to the descent phase at the top of descent (TOD). Pilots get toknow the TOD locations via on-board devices, while controllers can estimate the TOD locations with the help of radar
surveillance and simple rules. In order to help controllers to get a better situation awareness of the traffic surroundingan aerodrome, it is of great operational importance to get an accurate prediction of the TOD locations for arrival flights.
In this paper, we propose to apply deep learning for TOD location prediction for arrival flights. To do so, a TOD-specificfeature engineering is suggested and applied to historical flight trajectories. Then the simple yet effective multilayer
perceptron neural network model is adopted for TOD prediction. A case study on the arrival flights to Singapore Changi
airport with respect to one-month historical trajectory data is carried out. Experiments demonstrate that the adopted
deep learning method is effective for TOD location prediction. When compared against several typical machine leaningmodels for regression, the adopted model yields a mean square error of 0.0039, which is smaller than the error
achieved by the comparison models. Meanwhile, the adopted deep learning model yields TOD location prediction
errors of 0.29 nautical miles (NM) on average with a standard deviation of 46.88 NM.

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