Prediction of Terminal-Area Weather Penetration on the Basis of Operational Factors

Lin, Yi‐Hsin; Balakrishnan, Hamsa

doi:10.3141/2400-06

Cited by 2 publications

(2 citation statements)

References 3 publications

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“…An additional uncertainty source could be the one in the pilots' avoidance strategy. However, the influence of non-weather related factors on pilots' maneuvers in a scenario of meteorological hazard is not clear according to the relevant literature [25][26][27][28][29], and the only agreement seems to be that operational factors-e.g., type and density of traffic, available airspace capacity, leader/follower roles and flight delays-are less important than weather-related factors. Therefore, we decided not to include it in our study.…”

Section: Uncertainty Characterizationmentioning

confidence: 99%

Predicting Air Traffic Congestion under Uncertain Adverse Weather

Nunez-Portillo,

Valenzuela,

Franco

et al. 2024

Aerospace

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This paper presents an approach for integrating uncertainty information in air traffic flow management at the tactical phase. In particular, probabilistic methodologies to predict sector demand and sector congestion under adverse weather in a time horizon of 1.5 h are developed. Two sources of uncertainty are considered: the meteorological uncertainty inherent to the forecasting process and the uncertainty in the take-off time. An ensemble approach is adopted to characterize both uncertainty sources. The methodologies rely on a trajectory predictor able to generate an ensemble of 4D trajectories that provides a measure of the trajectory uncertainty, each trajectory avoiding the storm cells encountered along the way. The core of the approach is the statistical processing of the ensemble of trajectories to obtain probabilistic entry and occupancy counts of each sector and their congestion status when the counts are compared to weather-dependent capacity values. A new criterion to assess the risk of sector overload, which takes into account the uncertainty, is also defined. The results are presented for a historical situation over the Austrian airspace on a day with significant convection.

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Section: Uncertainty Characterizationmentioning

confidence: 99%

Predicting Air Traffic Congestion under Uncertain Adverse Weather

Nunez-Portillo,

Valenzuela,

Franco

et al. 2024

Aerospace

View full text Add to dashboard Cite

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“…Many scholars are interested in researching the impact of convective weather on flight operations [3][4][5]. This paper focuses on flight path optimisation in the terminal area under convective weather, aiming to enhance airspace utilisation, reduce flight delays and improve the operational efficiency of the air traffic system.…”

Section: Introductionmentioning

confidence: 99%

Multi-Flight Rerouting Optimisation Based on Typical Flight Paths Under Convective Weather in the Terminal Area

Wang¹,

Duan²,

Chu³

et al. 2022

PROMTT

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With the rapid growth of flight volume, the impact of convective weather on flight operations in the terminal area has become more and more serious. In this paper, the typical flight paths (TFPs) are used to replace flight procedures as the routine flight paths in the terminal area, and the TFP of each flight is predicted by Random Forest (RF), Boosting Tree (BT) and K-Nearest Neighbour (KNN) algorithms based on the weather and flight plan characteristics. A multi-flight rerouting optimisation model by bi-level programming is established, which contains a flight flow optimisation model in the upper layer and a single flight path optimisation model in the lower layer. The simulated annealing algorithm and the bidirectional A* algorithm are used to solve the upper and lower models. This paper uses the terminal area of Guangzhou Baiyun Airport (ZGGG) and Wuhan Tianhe Airport (ZHHH) for case analysis. The RF algorithm has better performance in predicting TFPs compared with the BT and KNN algorithms. Compared to the historical radar trajectory, the flight path optimisation results show that for the Guangzhou terminal area, while meeting the Terminal Airspace Availability (TAA) as constraint, the flight flow increases and the flight distance reduces, effectively improving the operational efficiency within the terminal.

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Prediction of Terminal-Area Weather Penetration on the Basis of Operational Factors

Cited by 2 publications

References 3 publications

Predicting Air Traffic Congestion under Uncertain Adverse Weather

Predicting Air Traffic Congestion under Uncertain Adverse Weather

Multi-Flight Rerouting Optimisation Based on Typical Flight Paths Under Convective Weather in the Terminal Area

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