TY - GEN
T1 - Transportation Type Identification by using Machine Learning Algorithms with Cellular Information
AU - Lin, Yi Hao
AU - Chen, Jyh-Cheng
AU - Lin, Chih Yu
AU - Su, Bo Yue
AU - Lee, Pei Yu
PY - 2019/5/20
Y1 - 2019/5/20
N2 - 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%.
AB - 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%.
UR - http://www.scopus.com/inward/record.url?scp=85070207895&partnerID=8YFLogxK
U2 - 10.1109/ICC.2019.8761519
DO - 10.1109/ICC.2019.8761519
M3 - Conference contribution
AN - SCOPUS:85070207895
T3 - IEEE International Conference on Communications
BT - 2019 IEEE International Conference on Communications, ICC 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Conference on Communications, ICC 2019
Y2 - 20 May 2019 through 24 May 2019
ER -
