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Development of a Daily Pattern Clustering Algorithm using Historical Profiles  

Cho, Jun-Han (한양대학교 공학기술연구소)
Kim, Bo-Sung (한양대학교 교통공학과)
Kim, Seong-Ho (한양대학교 교통공학과)
Kang, Weon-Eui (한국건설기술연구원 첨단교통연구실)
Publication Information
The Journal of The Korea Institute of Intelligent Transport Systems / v.10, no.4, 2011 , pp. 11-23 More about this Journal
Abstract
The objective of this paper is to develop a daily pattern clustering algorithm using historical traffic data that can reliably detect under various traffic flow conditions in urban streets. The developed algorithm in this paper is categorized into two major parts, that is to say a macroscopic and a microscopic points of view. First of all, a macroscopic analysis process deduces a daily peak/non-peak hour and emphasis analysis time zones based on the speed time-series. A microscopic analysis process clusters a daily pattern compared with a similarity between individuals or between individual and group. The name of the developed algorithm in microscopic analysis process is called "Two-step speed clustering (TSC) algorithm". TSC algorithm improves the accuracy of a daily pattern clustering based on the time-series speed variation data. The experiments of the algorithm have been conducted with point detector data, installed at a Ansan city, and verified through comparison with a clustering techniques using SPSS. Our efforts in this study are expected to contribute to developing pattern-based information processing, operations management of daily recurrent congestion, improvement of daily signal optimization based on TOD plans.
Keywords
정보가공;과거이력자료;요일분류;패턴기반 알고리즘;도시부도로;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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