Modeling anger and aggressive driving behavior in a dynamic choice–latent variable model

• The state–trait anger theory was expressed mathematically in a dynamic hybrid choice–latent variable model.
• State anger was modeled as a latent variable that evolves over time and affects drivers’ decisions and behavior.
• State anger was considered to be dependent on the unobserved trait anger and observed contextual factors and frustrating events.
• The proposed model was applied to data from a driving simulator experiment.
• State anger was used to predict the probability of red light violations and surrogate safety measures following signalized intersections.

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.