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Airport Cluster Delay Prediction Based on TS-BiLSTM-Attention

Authors :
Xing, Xiujie Wei
Yinfeng Li
Ranran Shang
Chang Ruan
Jingzhang
Source :
Aerospace; Volume 10; Issue 7; Pages: 580
Publication Year :
2023
Publisher :
Multidisciplinary Digital Publishing Institute, 2023.

Abstract

To conduct an accurate and reliable airport delay prediction will provide an important basis for the macro control of an airspace delay situation and the dynamic allocation of airspace system capacity balance. Accordingly, a method of delay prediction for target airports based on the spatio-temporal delay variables of adjacent airports is proposed in this paper. First, by combining the complex network theory, we first extract the topology of the airport network and create airport clusters with comparable network properties. Second, we develop the TS-BiLSTM-Attention mode to predict the delay per hour for airports in the cluster. As the spatio-temporal feature variables, the arrival delay of airport cluster-associated airports and the delay time series of landing airports are utilized to reach the conclusion. The experimental results indicate that the delay prediction predicated on clusters is superior to that based on data from a single airport. This demonstrates that the delay propagation law derived from cluster data based on spatio-temporal feature extraction can generalize the delay propagation characteristics of airports within clusters.

Details

Language :
English
ISSN :
22264310
Database :
OpenAIRE
Journal :
Aerospace; Volume 10; Issue 7; Pages: 580
Accession number :
edsair.multidiscipl..c6cc31fe8df4b5105caac8648497bb45
Full Text :
https://doi.org/10.3390/aerospace10070580