Transportation Type Identification by using Machine Learning Algorithms with Cellular Information

Yi Hao Lin, Jyh-Cheng Chen, Chih Yu Lin, Bo Yue Su, Pei Yu Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

It is crucial for future 5G networks to intelligently understand how users move so that the networks can allocate different resources efficiently. In this paper, we try to find practical features to identify four common types of motorized transportations, including High-Speed Rail (HSR), subway, railway, and highway. We propose a system architecture that can provide accurate, real-time, and adaptive solution by using cellular information only. Because we do not use GPS as that in most of the prior studies, we can reduce energy consumption, size of log data, and computational time. Around 500-hour data are collected for performance evaluation. Experimental results confirm the effectiveness of the proposed algorithm, which can improve well-known machine learning algorithms to approximately 98% classification accuracy. The results also show that battery consumption can be reduced about 37%.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Communications, ICC 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538680889
DOIs
StatePublished - 1 May 2019
Event2019 IEEE International Conference on Communications, ICC 2019 - Shanghai, China
Duration: 20 May 201924 May 2019

Publication series

NameIEEE International Conference on Communications
Volume2019-May
ISSN (Print)1550-3607

Conference

Conference2019 IEEE International Conference on Communications, ICC 2019
CountryChina
CityShanghai
Period20/05/1924/05/19

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  • Cite this

    Lin, Y. H., Chen, J-C., Lin, C. Y., Su, B. Y., & Lee, P. Y. (2019). Transportation Type Identification by using Machine Learning Algorithms with Cellular Information. In 2019 IEEE International Conference on Communications, ICC 2019 - Proceedings [8761519] (IEEE International Conference on Communications; Vol. 2019-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICC.2019.8761519