Estimated off-block time based on LSTM-TCN network

Zhiwei Xing; Ke Liu

doi:10.1117/12.2679159

15 May 2023 Estimated off-block time based on LSTM-TCN network

Zhiwei Xing, Ke Liu

Proceedings Volume 12699, Third International Conference on Sensors and Information Technology (ICSI 2023); 126990V (2023) https://doi.org/10.1117/12.2679159
Event: International Conference on Sensors and Information Technology (ICSI 2023), 2023, Xiamen, China

Abstract

In order to improve the accuracy of the Off-Block Time and incorporate uncertainties in the prediction process. Firstly, this paper considers the key influencing factors of AGS (Aviation Ground Support) and simplifies the complex Turn-Round process. Secondly, a method for estimating the time based on LSTM-TCN hybrid network model is proposed, and the uncertainty that may occur in the prediction process is quantified by using MC Dropout approximate Bayesian neural network. Select a single flight departure operation data of an airport in central China to verify. The results show that the accuracy of each node interval can reach 80 %, and the reliability is more than 90 %.

Citation Download Citation

Zhiwei Xing and Ke Liu "Estimated off-block time based on LSTM-TCN network", Proc. SPIE 12699, Third International Conference on Sensors and Information Technology (ICSI 2023), 126990V (15 May 2023); https://doi.org/10.1117/12.2679159

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