Entropy-Based City Tunnel Real-Time Traffic Incident Detection Clustering Algorithm

Xiao Feng Li; Wei Wei  Gao; Xue Mei Wang

doi:10.4028/www.scientific.net/AMM.380-384.753

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 380-384Entropy-Based City Tunnel Real-Time Traffic...

Entropy-Based City Tunnel Real-Time Traffic Incident Detection Clustering Algorithm

Abstract:

The use of spatial clustering technology has important practical significance to obtain useful information. According to the characteristics of city tunnel real-time traffic ,then, put forward ECRT (Entropy-based City Tunnel Real-time), the object associated with the city tunnel as real-time traffic properties to calculate the entropy of information between the city tunnel, based on information entropy change to achieve real-time traffic urban tunnel clustering. Algorithm used in the actual data set ECRT test. The results showed that the algorithm ECRT is effective.

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Periodical:

Applied Mechanics and Materials (Volumes 380-384)

Pages:

753-756

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.380-384.753

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Online since:

August 2013

Authors:

Xiao Feng Li, Wei Wei Gao, Xue Mei Wang

Keywords:

City Tunnel Real-Time, Clustering, Information Entropy, Spatial Data Mining

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References

[1] Liu chun , Huang meixian. Based on the data of the floating car city road tunnel real-time simulation DuoYuan linear regression [J]. Traffic and computer, 2008: 26-38.

Google Scholar

[2] Lin Chengru, Liu Kenhao, Chen Ming-syan. Dual Clustering: Integrating Data Clustering over Optimization and Constraint Domains[C]. Proceedings of the IEEE Transactions on Knowledge and Data Engineering[C]. Piscataway: IEEE Educational Activities Department, 2005: 628-637.

DOI: 10.1109/tkde.2005.75

Google Scholar

[3] Zhang Xueping, Wang Jiayao, Wu Fang, et al. A Novel Spatial Clustering with Obstacles Constraints Based on Genetic Algorithms and K-Medoids[C]. Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications (ISDA' 06), 2006, 1: 605-610.

DOI: 10.1109/isda.2006.75

Google Scholar

[4] Huang Ming, Bian Fuling. A Grid and Density Based Fast Spatial Clustering Algorithm. 2009 International Conference on Artificial Intelligence and Computational Intelligence, 2009, 4: 260-263.

DOI: 10.1109/aici.2009.228

Google Scholar

[5] Hu Caiping, Qin Xiaolin. A novel spatial cluster algorithm with sampling[C]. Berlin Heidelberg: Springer Verlag, 2007: 216-225.

Google Scholar

[6] M ao demei , Ding ruiguo In the data mining association rules algorithm was studied[J]. Wanxi college Journal 2006(10): 27-30.

Google Scholar

[7] Xiang Laisheng, Guo Yajun, Lan Tian. Topological cluster: a generalized view for density-based spatial clustering[C]. International Conference on Management Science& Engineering(14th). Harbin, China: IEEE Service Center, 2007: 422-428.

DOI: 10.1109/icmse.2007.4421884

Google Scholar