Prediction of estimated time of arrival for multi-airport systems via “Bubble” mechanism

Prediction of estimated time of arrival for multi-airport systems via “Bubble” mechanism

Wang, Lechen (The Chinese University of Hong Kong, Shenzhen)
Mao, Jianfeng (The Chinese University of Hong Kong, Shenzhen)
Li, L. (TU Delft Air Transport & Operations; City University of Hong Kong)
Li, Xuechun (The Chinese University of Hong Kong, Shenzhen)
Tu, Yilei (The Chinese University of Hong Kong, Shenzhen)

2023

Predicting Estimated Time of Arrival (ETA) for a Multi-Airport System (MAS) is much more challenging than for a single airport system because of complex air route structure, dense air traffic volume and vagaries of traffic conditions in an MAS. In this work, we propose a novel “Bubble” mechanism to accurately predict medium-term ETA for a Multi-Airport System (MAS), in which the prediction of travel time of an origin–destination (OD) pair is decomposed into two stages, termed as out-MAS and in-MAS stages. For the out-MAS stage, Auto-Regressive Integrated Moving Average (ARIMA) is used to predict the travel time of a flight to reach the MAS boundary. For the in-MAS stage, we construct new spatio-temporal features based on clustering analysis of trajectory patterns facilitated by a novel data-driven hybrid polar sampling method. A sequence-to-sequence prediction model, Multi-variate Stacked Fully connected Bidirectional Long–Short Term Memory, is further developed to achieve multi-step-ahead predictions of in-MAS travel time for each trajectory pattern using the spatio-temporal features as input. Finally, the medium-term ETA prediction for an MAS is achieved by integrating the out-MAS and in-MAS prediction with the help of trajectory pattern prediction via random forest. A case study of predicting medium-term ETA for a typical MAS in China, Guangdong–Hong Kong–Macao Greater Bay Area, is conducted to demonstrate the usage and promising performance of the proposed method in comparison to several commonly used end-to-end learning methods.

Flight estimated time of arrival
Medium-term prediction
Multi-airport systems
Sequence-to-sequence model
Spatio-temporal features
Trajectory pattern clustering

http://resolver.tudelft.nl/uuid:358ecbae-1949-4a73-b6a3-870d8573eac0

https://doi.org/10.1016/j.trc.2023.104065

2023-09-04

0968-090X

Transportation Research. Part C: Emerging Technologies, 149

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2023 Lechen Wang, Jianfeng Mao, L. Li, Xuechun Li, Yilei Tu