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http://dx.doi.org/10.7470/jkst.2017.35.6.525

Development and Analysis of the Interchange Centrality Evaluation Index Using Network Analysis  

KIM, Suhyun (Department of System Design and Control, UNIST)
PARK, Seungtae (Department of System Design and Control, UNIST)
WOO, Sunhee (Department of System Design and Control, UNIST)
LEE, Seungchul (Department of System Design and Control, UNIST)
Publication Information
Journal of Korean Society of Transportation / v.35, no.6, 2017 , pp. 525-544 More about this Journal
Abstract
With the advent of the big data era, the interest in the development of land using traffic data has increased significantly. However, the current research on traffic big data lingers around organizing or calibrating the data only. In this research, a novel method for discovering the hidden values within the traffic data through data mining is proposed. Considering the fact that traffic data and network structures have similarities, network analysis algorithms are used to find valuable information in the actual traffic volume data. The PageRank and HITS algorithms are then employed to find the centralities. While conventional methods present centralities based on uncomplicated traffic volume data, the proposed method provides more reasonable centrality locations through network analysis. Since the centrality locations that we have found carry detailed spatiotemporal characteristics, such information can be used as an objective basis for making policy decisions.
Keywords
centrality; HITS; network analysis; PageRank; TCS traffic;
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Times Cited By KSCI : 1  (Citation Analysis)
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