Clustering Traffic Flow Patterns by Fuzzy C-Means Method: Some Preliminary Findings

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• 关键词：Vehicular traffic flow ; Flow pattern ; Clustering ; Fuzzy C ; Means
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9520
• 期：1
• 页码：756-764
• 全文大小：389 KB
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• 作者单位：Mehmet Ali Silgu (16)
  Hilmi Berk Celikoglu (16)
  
  16. Department of Civil Engineering, Technical University of Istanbul (ITU), Istanbul, Turkey
  
• 丛书名：Computer Aided Systems Theory ᾿EUROCAST 2015
• ISBN：978-3-319-27340-2
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

In this paper, performance of fuzzy c-means clustering method in specifying flow patterns, which are reconstructed by a macroscopic flow model, is sought using microwave radar data on fundamental variables of traffic flow. Traffic flow is simulated by the cell transmission model adopting a two-phase triangular fundamental diagram. Flow dynamics specific to the selected freeway test stretch are used to determine prevailing traffic conditions. The performance of fuzzy c-means clustering is evaluated in two cases, with two assumptions. The procedure fuzzy clustering method follows is systematically dynamic that enables the clustering, and hence partitions, over the fundamental diagram specific to selected temporal resolution. It is seen that clustering simulation with dynamic pattern boundary assumption performs better for almost all the steps of data expansion when considered to simulation with the corresponding static case.