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논문 기본 정보

자료유형
학술저널
저자정보
Swe Sw Aung (University of the Ryukyus) Itaru Nagayama (University of the Ryukyus) Shiro Tamaki (University of the Ryukyus)
저널정보
대한전자공학회 IEIE Transactions on Smart Processing & Computing IEIE Transactions on Smart Processing & Computing Vol.5 No.6
발행연도
2016.12
수록면
430 - 439 (10page)

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초록· 키워드

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Estimation and analysis of traffic jams plays a vital role in an intelligent transportation system and advances safety in the transportation system as well as mobility and optimization of environmental impact. For these reasons, many researchers currently mainly focus on the brilliant machine learning-based prediction approaches for traffic prediction systems. This paper primarily addresses the analysis and comparison of prediction accuracy between two machine learning algorithms: Naïve Bayes and K-Nearest Neighbor (K-NN). Based on the fact that optimized estimation accuracy of these methods mainly depends on a large amount of recounted data and that they require much time to compute the same function heuristically for each action, we propose an approach that applies multi-threading to these heuristic methods. It is obvious that the greater the amount of historical data, the more processing time is necessary. For a real-time system, operational response time is vital, and the proposed system also focuses on the time complexity cost as well as computational complexity. It is experimentally confirmed that K-NN does much better than Naïve Bayes, not only in prediction accuracy but also in processing time. Multithreading- based K-NN could compute four times faster than classical K-NN, whereas multithreading - based Naïve Bayes could process only twice as fast as classical Bayes.

목차

Abstract
1. Introduction
2. Related Work
3. Data Collection and Representation
4. Traffic Prediction Model
5. Analytical Model
6. Conclusion
References

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