Prediction of estimated time of arrival for multi-airport systems via "Bubble" mechanism. (April 2023)

• Record Type: Journal Article
• Authors: Wang, Lechen; Mao, Jianfeng; Li, Lishuai; Li, Xuechun; Tu, Yilei
• Is Part Of: Transportation research. Volume 149(2023)
• Publication Date: 2023-04
• Subjects: Multi-airport systems -- Flight estimated time of arrival -- Medium-term prediction -- Trajectory pattern clustering -- Sequence-to-sequence model -- Spatio-temporal features; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2023.104065
• Languages: English
• ISSNs: 0968-090X

Abstract

Abstract: 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. Highlights: A "bubble" mechanism is proposed to predict medium-term ETA in multi-airport systems. Trajectory uncertainties and network effects in shared TMAs of an MAS are analyzed. A data-driven hybrid polar sampling is developed to capture spatiotemporal pattern. A seq2seq model, MSB-LSTM, is proposed to predict multistep ahead in-MAS travel time. The method exhibits superior performance to 4 SOTA methods in a typical MAS, GHM-GBA.