The Analysis of BP Network Traffic Accident Serious Level Model Based on Principal Component

Abstract:

This paper analyzes the relevant factor which influences the accident severity, and puts forward BP neural network traffic accident serious level model based on principal component analysis, it selects the main causes and accident serious level of 344 traffic accidents in Harbin in 2003 on the main road as the training and binding of the model, make sure that there is a good correspondence between the related main causes and the serious level. The model provides reference for the main control factors in order to determine the serious level of road traffic accidents of Nonlinear Systems.

• Periodical: Applied Mechanics and Materials (Volumes 641-642)
• Pages: 910-915
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.641-642.910
• Online since: September 2014
• Authors: Yue Jing Lv, Bao Sheng Ying, Li Qiong Zou, Guan Ming Qing, Xing Lin Zhou
• Keywords: BP Neural Network (BPNN), Principal Component Analysis (PCA), Traffic Accident Serious Level, Urban Road

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