Modeling Anger and Aggressive Driving Behavior in a Dynamic Choice- Latent Variable Model

摘要

The state-trait anger theory has been extensively used in order to describe anger, its causes, and manifestations. This theory was extended in order to be applied in the context of driving, whereby trait driving anger was introduced. The theory states that anger expression is dependent on state anger, which is a temporary emotional state, and trait anger, which is a chronic tendency to experience state anger more frequently and intensely. This paper develops a hybrid choice – latent variable model combined with a Hidden Markov model in order to express the state-trait anger theory mathematically. Trait anger is treated as a latent variable that is expressed as a function of individual characteristics, or as an agent effect, while state anger is expressed 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.). The model is then applied to a dataset obtained from a driving simulator experiment performed at the American University of Beirut. The results 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. This model may be used in order to test measures aimed at reducing aggressive driving behavior.