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A Machine Learning Approach for Predicting 2D Aircraft Position Coordinates

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Advances in Networked-Based Information Systems (NBiS 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 313))

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Abstract

The prediction of arrival time for buses, trains and other transportation systems using Machine Learning (ML) and Deep Neural Network (DNN) approaches are attracting attention. For daily operation the data can be collected and used as training data for ML and DNNs. In this paper, we present a ML-based system for predicting two-dimensional aircraft position coordinates by using a part of the received data from ADS-B. The evaluation results show that our proposed system can predict the aircraft two dimensional path and the accuracy is about 94%.

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Correspondence to Makoto Ikeda .

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Matsuo, K., Ikeda, M., Barolli, L. (2022). A Machine Learning Approach for Predicting 2D Aircraft Position Coordinates. In: Barolli, L., Chen, HC., Enokido, T. (eds) Advances in Networked-Based Information Systems. NBiS 2021. Lecture Notes in Networks and Systems, vol 313. Springer, Cham. https://doi.org/10.1007/978-3-030-84913-9_30

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