지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- Korean Institute of Information Scientists and Engineers Journal of Computing Science and Engineering Journal of Computing Science and Engineering Vol.14 No.2
- 발행연도
- 2020.6
- 수록면
- 52 - 65 (14page)
- DOI
- 10.5626/JCSE.2020.14.2.52
이용수
초록· 키워드
오류제보하기
Many modern portable devices, especially smartphones, are equipped with positioning functionality. The rapid growth in the use of such devices has allowed for the accumulation of a vast amount of positioning data. Combined with deep learning methods, these data may be used for many novel applications. Herein, a trajectory pattern tree generation method via deep learning is proposed. The convolutional neural network (CNN) and recurrent neural network (RNN) model of deep learning were applied for trajectory generation and prediction. Several volunteers provided their raw positioning data. The trajectory generation and prediction are for individual mobility patterns and were performed for every volunteer. We present the results obtained from seven volunteers. The preciseness of prediction can be measured both for CNN and RNN. Consequently, we can predict an individual’s location with 32.98% accuracy, and predict the top-five up to 69.22% for unit area size of 0.030 km².
목차
Abstract
I. INTRODUCTION
II. RELATED WORKS
III. BACKGROUNDS
IV. PREDICTION OF OBJECT’S TRAJECTORY AND CREATION OF TRAJECTORY PATTERN
V. ACCURACY MEASUREMENT OF PREDICTED TRAJECTORY PATTERN
VI. PREPARATION OF EXPERIMENT
VII. RESULTS
VII. CONCLUSIONS
References
참고문헌 (33)
참고문헌 신청
Y. Lee and H. Ko, “Efficient STMPM (spatio-temporal moving pattern mining) using moving sequence tree,” in Proceedings of 2008 4th International Conference on Networked Computing and Advanced Information Management, Gyeongju, South Korea, 2008, pp. 432-437.
H. Y. Song, “Probabilistic space-time analysis of human mobility patterns,” WSEAS Transactions on Computers, vol. 12, pp. 222-238, 2016.
M. Gorawski and P. Jureczek, “Continuous pattern mining using the FCPGrowth algorithm in trajectory data warehouses,” in Hybrid Artificial Intelligence Systems. Heidelberg: Springer, 2010, pp. 187-195.
J. W. Lee, O. H. Paek, and K. H. Ryu, “Temporal moving pattern mining for location-based service,” Journal of Systems and Software, vol. 73, no. 3, pp. 481-490, 2004.
A. Monreale, F. Pinelli, R. Trasarti, and F. Giannotti, “Wherenext: a location predictor on trajectory pattern mining,” in Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France, 2009, pp. 637-646.
H. Y. Song, “Probabilistic space-time analysis of human mobility patterns,” WSEAS Transactions on Computers, vol. 12, pp. 222-238, 2016.
M. Gorawski and P. Jureczek, “Continuous pattern mining using the FCPGrowth algorithm in trajectory data warehouses,” in Hybrid Artificial Intelligence Systems. Heidelberg: Springer, 2010, pp. 187-195.
J. W. Lee, O. H. Paek, and K. H. Ryu, “Temporal moving pattern mining for location-based service,” Journal of Systems and Software, vol. 73, no. 3, pp. 481-490, 2004.
A. Monreale, F. Pinelli, R. Trasarti, and F. Giannotti, “Wherenext: a location predictor on trajectory pattern mining,” in Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France, 2009, pp. 637-646.
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