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Latest revision as of 01:22, 2 February 2021
Abstract
International audience; Recent air traffic management aims to provide a safety-first operation to support the aircraft approaching and landing procedures. Due to the complexity of air traffic in the terminal control area (also known as the terminal maneuvering area or TMA), simultaneous consideration of aviation economics, environmental concerns, and safety operations in decision makings can be challenging. To improve air traffic controllers' work efficiency and reduce the adverse environmental impact, it is crucial to establish a robust arrival strategy that incorporates weather conditions and flight trajectory configuration. The current state-of-the-art solutions for arrival sequencing and scheduling problem focus more on the operation research aspect, which neglects the airway configuration. Also, no wind condition is assumed to simplify the weather condition. Furthermore, many research efforts have not properly considered practical phenomenon such as holding patterns in their arrival sequencing model, which affects the accuracy of fuel burnt consumption. In this work, we will construct a study on aircraft arrival flow based on historical data at Hong Kong International Airport (HKIA). By extracting features from the data, our results include the spatiotemporal pattern recognition for aircraft arrival transit time and congestion inside HKIA TMA. Besides delivering the statistical analysis on the HKIA aircraft arrival flow, an arrival transit time prediction based on random forest regression is also converted. Results show that our methodologies are not only advantageous in extracting crucial hidden information from historical data for air traffic controllers but also can increase the accuracy of arrival transit time prediction under most of the circumstances.
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Published on 01/01/2020
Volume 2020, 2020
DOI: 10.2514/6.2020-2869
Licence: CC BY-NC-SA license
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