[1] Sun Lu, Zhang Huimin, Gao Rong, et al. Gaussian mixture models for clustering and classifying trafficflow in real-time for traffic operation and management [J]. Journal of Southeast University (English Edition), 2011, 27 (2): 174-179. [doi:10.3969/j.issn.1003-7985.2011.02.012]

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Gaussian mixture models for clustering and classifying trafficflow in real-time for traffic operation and management()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

27

Issue:

2011 2

Page:

174-179

Research Field:

Traffic and Transportation Engineering

Publishing date:

2011-06-30

Info

Title:

Gaussian mixture models for clustering and classifying trafficflow in real-time for traffic operation and management

Author(s):

Sun Lu¹;  2;  Zhang Huimin³;  Gao Rong⁴;  Gu Wenjun¹;  Xu Bing¹;  Chen Liliang¹

¹School of Transportation, Southeast University, Nanjing 210096, China
²Department of Civil Engineering, The Catholic University of America, Washington DC 20064, USA
³Jinzhong Bureau of Highway Administration of Shanxi Province, Jinzhong 030600, China
⁴Xinzhou Bureau of Highway Administration of Shanxi Province, Xinzhou 034000, China

Keywords:

traffic flow patterns;  Gaussian mixture model;  level of service;  data mining;  cluster analysis;  classifier

PACS:

U491

DOI:

10.3969/j.issn.1003-7985.2011.02.012

Abstract:

Based on Gaussian mixture models(GMM), speed, flow and occupancy are used together in the cluster analysis of traffic flow data. Compared with other clustering and sorting techniques, as a structural model, the GMM is suitable for various kinds of traffic flow parameters. Gap statistics and domain knowledge of traffic flow are used to determine a proper number of clusters. The expectation-maximization(E-M)algorithm is used to estimate parameters of the GMM model. The clustered traffic flow patterns are then analyzed statistically and utilized for designing maximum likelihood classifiers for grouping real-time traffic flow data when new observations become available.Clustering analysis and pattern recognition can also be used to cluster and classify dynamic traffic flow patterns for freeway on-ramp and off-ramp weaving sections as well as for other facilities or things involving the concept of level of service, such as airports, parking lots, intersections, interrupted-flow pedestrian facilities, etc.

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Memo

Memo:

Biography: Sun Lu(1972—), male, doctor, professor, sunl@cua.edu.
Foundation items: The US National Science Foundation(No.CMMI-0408390, CMMI-0644552), the American Chemical Society Petroleum Research Foundation(No.PRF-44468-G9), the Research Fellowship for International Young Scientists(No.51050110143), the Fok Ying-Tong Education Foundation(No.114024), the Natural Science Foundation of Jiangsu Province(No.BK2009015), the Postdoctoral Science Foundation of Jiangsu Province(No.0901005C).
Citation: Sun Lu, Zhang Huimin, Gao Rong, et al. Gaussian mixture models for clustering and classifying traffic flow in real-time for traffic operation and management[J].Journal of Southeast University(English Edition), 2011, 27(2):174-179.[doi:10.3969/j.issn.1003-7985.2011.02.012]

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