关键词: 公路运输, 高速公路, 路段划分, 事故多发点鉴别, 诱发因素识别, 动态聚类, 自组织神经网络

Abstract: In order to improve the objectivity, fairness and automated identification level of black spot identification, dynamic clustering algorithm, which is used to divide initial assessment of sites along the road, is proposed firstly. Then, the self-organizing neural network model is established to identify black spot. Thirdly, black spot prominent accidents induced factors’ identification process based on discrete multi-variable algorithm combined with probability distribution is presented. The results show that the initial assessment based on dynamic clustering method can describe the accidents’ concentration and dispersion objectivity, while the neural network model can give an automatic classification to the initial assessment of the security situation and the results are more reasonable. In the master can meet the requirements of statistical analysis of accidents on the basis of the number of sample points, black spot prominent accidents inducing factor identification methods, which can be used to establish a set of evaluation criteria and identify prominent accidents inducing factor.

Key words: highway transportation, freeway, segment division, black spot identification, accident inducing factor identification, dynamic cluster analysis, self-organizing neural network