Data‐driven flight time prediction for arrival aircraft within the terminal area

With the rapid development of the air transport industry, the problem of airspace congestion and flight delay in the terminal area (TMA) becomes more and more serious. In order to improve the efficiency of flight operations in TMA, point merge procedure had been devised. This paper takes the approach routes in TMA as the research object, taking into account such conditions as obstacle clearance, flight interval, and procedure area. Based on the flight time, fuel consumption, pollutant emission, and noise impact, an optimization model of point merge procedure is constructed. Genetic algorithm is used to optimize the structure of procedure. The Shanghai Hongqiao International Airport is selected for simulation verification, and the actual flow distribution of the airport is analyzed as an example. The results show that the average flight time was reduced by 0.26 min, the average fuel consumption was reduced by 1,240.64 kg, the average NOx emissions were reduced by 1.09 kg, and the noise impact range was contracted by 55 km2 after optimization. The point merge procedure optimization method can be expected to reduce the flight time, fuel consumption, and environmental impact of flights in TMA, so as to optimize the aircraft approach trajectory.

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A Data-Driven Approach for Taxi-Time Prediction: A Case Study of Singapore Changi Airport

Lecture Notes in Electrical Engineering - Air Traffic Management and Systems IV ◽

10.1007/978-981-33-4669-7_7 ◽

2021 ◽

pp. 113-129

Author(s):

D. T. Pham ◽

M. Ngo ◽

N. Tran ◽

S. Alam ◽

V. Duong

Keyword(s):

Data Driven ◽

Time Prediction ◽

Data Driven Approach

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Patient scheduling based on a service-time prediction model: a data-driven study for a radiotherapy center

Health Care Management Science ◽

10.1007/s10729-018-9459-1 ◽

2018 ◽

Vol 22 (4) ◽

pp. 768-782 ◽

Cited By ~ 4

Author(s):

Dina Bentayeb ◽

Nadia Lahrichi ◽

Louis-Martin Rousseau

Keyword(s):

Prediction Model ◽

Service Time ◽

Data Driven ◽

Patient Scheduling ◽

Time Prediction

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Flight time prediction for fuel loading decisions with a deep learning approach

Transportation Research Part C Emerging Technologies ◽

10.1016/j.trc.2021.103179 ◽

2021 ◽

Vol 128 ◽

pp. 103179

Author(s):

Xinting Zhu ◽

Lishuai Li

Keyword(s):

Deep Learning ◽

Flight Time ◽

Learning Approach ◽

Fuel Loading ◽

Time Prediction

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Data-Driven Simulation for Evaluating the Impact of Lower Arrival Aircraft Separation on Available Airspace and Runway Capacity at Tokyo International Airport

Aerospace ◽

10.3390/aerospace8060165 ◽

2021 ◽

Vol 8 (6) ◽

pp. 165

Author(s):

Katsuhiro Sekine ◽

Furuto Kato ◽

Kota Kageyama ◽

Eri Itoh

Keyword(s):

Simulation Models ◽

Weather Conditions ◽

Air Traffic ◽

Data Driven ◽

Wake Turbulence ◽

Automation Systems ◽

Delay Times ◽

Simulation Results ◽

The Impact ◽

Terminal Area

Although the application of new wake turbulence categories, the so-called “RECAT (wake turbulence category re-categorization)”, will realize lower aircraft separation minima and directly increase runway throughput, the impacts of increasing arrival traffic on the surrounding airspace and arrival traffic flow as a whole have not yet been discussed. This paper proposes a data-driven simulation approach and evaluates the effectiveness of the lower aircraft separation in the arrival traffic at the target airport. The maximum runway capacity was clarified using statistics on aircraft types, stochastic distributions of inter-aircraft time and runway occupancy time, and the levels of the automation systems that supported air traffic controllers’ separation work. Based on the estimated available runway capacity, simulation models were proposed by analyzing actual radar track and flight plan data during the 6 months between September 2019 and February 2020, under actual operational constraints and weather conditions. The simulation results showed that the application of RECAT would reduce vectoring time in the terminal area by 7% to 10% under the current airspace and runway capacity when following a first-come first-served arrival sequence. In addition, increasing airspace capacity by 10% in the terminal area could dramatically reduce en-route and takeoff delay times while keeping vectoring time the same as under the current operation in the terminal area. These findings clarified that applying RECAT would contribute to mitigating air traffic congestion close to the airport, and to reducing delay times in arrival traffic as a whole while increasing runway throughput. The simulation results demonstrated the relevance of the theoretical results given by queue-based approaches in the authors’ past studies.

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