Modelling the effects of stress on gap-acceptance decisions combining data from driving simulator and physiological sensors

Abstract Driving behaviour is an inherently complex process affected by various factors ranging from network topography, traffic conditions and vehicle features to driver characteristics like age, experience, aggressiveness and emotional state. Among these, the effects of emotional state and stress have received considerable attention in the context of crash analysis and safety research where driving behaviour has been found to be affected by drivers’ mental state/stress, cognitive workload and distraction. However, these studies are mostly based on questionnaire surveys and self-reports which can be prone to response bias and reporting/measurement errors. The analyses are also often descriptive in nature. In a parallel stream of research, advances in sensor technologies have made it possible to observe drivers’ stress through human physiological responses, e.g. heart rate, electro-dermal activity etc. However, these studies have primarily focused on detecting stress rather than quantifying or modelling its effects on driving decisions. The present paper combines these two approaches in a single framework and investigates the gap-acceptance behaviour of drivers during an intersection crossing, using data collected using a driving simulator. The participants are deliberately subjected to stress induced by time pressure, and their stress levels are measured using two physiological indicators, namely Electrodermal Activity (skin conductance) and heart rate. In addition to statistical analyses, discrete choice models are developed to link the accept-reject choices of a driver with the driver demographics, traffic conditions and stress levels. The results of the models indicate that increased stress levels significantly increase the probabilities of accepting a gap. The improvement in model fit and safety implications derived from model estimates are also discussed. The insights from the results can be used for designing appropriate intervention strategies to improve safety.

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