Department of Civil Engineering, University of Porto, Porto, 4200-465, Portugal.
Accid Anal Prev. 2021 Feb;150:105938. doi: 10.1016/j.aap.2020.105938. Epub 2020 Dec 17.
Several studies have often cited crash occurrences as a motivation to perform a driving simulator experiment and test driver behavior to understand their causal relations. However, decisions regarding the simulated scenario and participants' requirements do not often rely directly on traffic crash data. To fill the gap between simulation and real data, we have proposed a new framework based on Clustering Analysis (K-medoids) to support the definition of driving simulator experiments when the purpose is to investigate the driver behavior under real risky road conditions to improve road safety. The suggested approach was tested with data of three years of police records regarding loss-of-control crashes and information on three Brazilian rural highways' geometry and traffic volume. The results showed the good suitability of the method to compile the data's diversity into four clusters, representing and summarizing the crashes' main characteristics in the region of study. Drivers' attributes (age and gender) were initially intended to integrate the clustering analysis; however, due to the sample's homogeneity of these characteristics, they did not contribute to the cluster definition. Hence, they were used simply to identify the target population for all scenarios. Therefore, we concluded that driving simulator experiments could benefit from the new approach since it identifies scenarios characterized by many variables connected to real risky situations and orients participants' recruitment leading to efficient safety analysis.
许多研究经常将事故发生作为进行驾驶模拟器实验和测试驾驶员行为以了解其因果关系的动机。然而，关于模拟场景和参与者要求的决策并不总是直接依赖于交通碰撞数据。为了填补模拟与真实数据之间的差距，我们提出了一个基于聚类分析（K-medoids）的新框架，以支持在目的是研究驾驶员在真实危险道路条件下的行为以提高道路安全的情况下定义驾驶模拟器实验。该方法使用三年的警察记录数据进行了测试，这些数据涉及失控事故以及巴西三条农村公路的几何形状和交通量信息。结果表明，该方法非常适合将数据的多样性汇总为四个聚类，代表并总结了研究区域内事故的主要特征。驾驶员的属性（年龄和性别）最初旨在整合聚类分析；然而，由于这些特征的样本同质性，它们对聚类定义没有贡献。因此，它们仅用于识别所有场景的目标人群。因此，我们得出结论，驾驶模拟器实验可以受益于新方法，因为它可以识别与真实危险情况相关的许多变量特征的场景，并为参与者的招募提供方向，从而进行有效的安全分析。
