Danaf Mazen, Abou-Zeid Maya, Kaysi Isam
American University of Beirut, Department of Civil and Environmental Engineering, Riad El-Solh, Beirut 1107 2020, Lebanon.
Accid Anal Prev. 2015 Feb;75:105-18. doi: 10.1016/j.aap.2014.11.012. Epub 2014 Nov 26.
This paper develops a hybrid choice-latent variable model combined with a Hidden Markov model in order to analyze the causes of aggressive driving and forecast its manifestations accordingly. The model is grounded in the state-trait anger theory; it treats trait driving anger as a latent variable that is expressed as a function of individual characteristics, or as an agent effect, and state anger as a dynamic latent variable that evolves over time and affects driving behavior, and that is expressed as a function of trait anger, frustrating events, and contextual variables (e.g., geometric roadway features, flow conditions, etc.). This model may be used in order to test measures aimed at reducing aggressive driving behavior and improving road safety, and can be incorporated into micro-simulation packages to represent aggressive driving. The paper also presents an application of this model to data obtained from a driving simulator experiment performed at the American University of Beirut. The results derived from this application indicate that state anger at a specific time period is significantly affected by the occurrence of frustrating events, trait anger, and the anger experienced at the previous time period. The proposed model exhibited a better goodness of fit compared to a similar simple joint model where driving behavior and decisions are expressed as a function of the experienced events explicitly and not the dynamic latent variable.
本文开发了一种结合隐马尔可夫模型的混合选择-潜变量模型，以分析攻击性驾驶的成因并据此预测其表现形式。该模型基于状态-特质愤怒理论；它将特质驾驶愤怒视为一个潜变量，该潜变量表示为个体特征的函数，或作为一种主体效应，而状态愤怒则视为一个动态潜变量，其随时间演变并影响驾驶行为，并且表示为特质愤怒、令人沮丧的事件和情境变量（例如，道路几何特征、交通流状况等）的函数。该模型可用于测试旨在减少攻击性驾驶行为和提高道路安全的措施，并可纳入微观模拟程序包以表示攻击性驾驶。本文还介绍了该模型在美国贝鲁特美国大学进行的驾驶模拟器实验所获数据中的应用。该应用得出的结果表明，特定时间段内的状态愤怒受令人沮丧的事件的发生、特质愤怒以及上一时间段所经历的愤怒的显著影响。与类似的简单联合模型相比，所提出的模型表现出更好的拟合优度，在简单联合模型中，驾驶行为和决策明确表示为所经历事件的函数，而非动态潜变量的函数。
