A Dynamic Hybrid Choice Model to Quantify Stress in a Simulated Driving Environment

Abstract

In this article, a dynamic hybrid choice model of the driver stress arising from cognitive workload is developed and applied to a driving simulator experiment conducted with students at the American University of Beirut. A latent or unobserved variable quantifying the state stress over time is integrated with a discrete choice model of red-light violations. The driver state stress is induced by additional cognitive workload and situational factors in an urban driving context and is dependent on a time-invariant agent effect or individual trait. Driving performance (e.g., speed and acceleration) and physiological measures (e.g., heart rate) are used as indicators of the underlying state stress. The driver state stress is found to be significantly affected by road events or situations (e.g., encountering pedestrians, trucks, and traffic light), the level of cognitive workload, the individual propensity for stress, and the mere driving task (e.g., maintaining control of the car). Moreover, results show that there is a pattern of regulatory driving behavior in response to the increasing stress. The developed model is a mathematical conceptualization of the transactional model of the driver stress and can be integrated within in-vehicle systems to detect/predict the driver state and enhance safety and well-being.