The road traffic safety situation is severe worldwide and exploring driving behavior is a research hotspot since it is the main factor causing road accidents. However, there are few studies investigating how to evaluate real-time traffic safety of driving behavior and the number of driving behavior safety levels has not yet been thoroughly explored. This paper aims to propose a framework of real-time driving behavior safety level classification and evaluation, which was validated by a case study of driving simulation experiments. The proposed methodology focuses on determining the optimal aggregation time interval, finding the optimal number of safety levels for driving behavior, classifying the safety levels, and evaluating the driving safety levels in real time. An improved cross-validation mean square error model based on driver behavior vectors was proposed to determine the optimal aggregation time interval, which was found to be 1s. Three clustering techniques were applied, i.e., k-means clustering, hierarchical clustering and model-based clustering. The optimal number of clusters was found to be three. Support vector machines, decision trees and naïve Bayes classifiers were then developed as classification models. The accuracy of the combination of k-means clustering and decision trees proved to be the best with three clusters.

中文翻译：

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驾驶行为安全等级的分类与评估：驾驶模拟研究

世界范围内道路交通安全形势严峻，探索驾驶行为是导致道路交通事故的主要因素，因此成为研究热点。然而，很少有研究探讨如何评估驾驶行为的实时交通安全性，驾驶行为安全等级的数量尚未深入探讨。本文旨在提出一个实时驾驶行为安全等级分类和评估的框架，并通过驾驶模拟实验的案例研究进行了验证。所提出的方法侧重于确定最佳聚合时间间隔，找到驾驶行为的最佳安全级别数，对安全级别进行分类，并实时评估驾驶安全级别。提出了一种改进的基于驾驶员行为向量的交叉验证均方误差模型来确定最佳聚合时间间隔，发现该时间间隔为1s。应用了三种聚类技术，即k-means聚类、层次聚类和基于模型的聚类。发现最佳聚类数为三个。支持向量机、决策树和朴素贝叶斯分类器随后被开发为分类模型。k-means 聚类和决策树组合的准确性被证明是三个聚类中最好的。支持向量机、决策树和朴素贝叶斯分类器随后被开发为分类模型。k-means 聚类和决策树组合的准确性被证明是三个聚类中最好的。支持向量机、决策树和朴素贝叶斯分类器随后被开发为分类模型。k-means 聚类和决策树组合的准确性被证明是三个聚类中最好的。