The Identified Method of Accident-Prone Section Based on Principal Component-Gray Clustering Analysis

Abstract:

In order to study the rapid and efficient identified method of accident-prone section in montane highway, the method of principal component - gray clustering analysis has been proposed. By deep analysis of the characteristics of accident-prone section, the identified indexes of accident-prone section have been screened out, the reducing dimensionality of principal component analysis and incomplete information processing of gray clustering analysis have been organically integrated, and the clustering weight coefficients are creatively determined based on the information content. Based on data investigation and treatment, using the identified method of principal components - gray clustering analysis, the security level of sections is achieved by programming. The results show that this identified method has high precision and convenience in aspects of aggregative indicators selected and clustering value calculated. The identified method can effectively identify the security level of accident-prone section, and divide the section security level into 4-grade. Aiming at the identified results, the security measures are further researched. So the identified method has practical value.

• Periodical: Applied Mechanics and Materials (Volumes 135-136)
• Pages: 1060-1066
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.135-136.1060
• Online since: October 2011
• Authors: Jin Jiang Liu, Mei He, Hong Ke Xu, Qi Wang, Yong Mei Yang
• Keywords: Gray Clustering, Identified Method, Principal Component Analysis (PCA), Security Level, Traffic Accident

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