"Although crash statistics and night driving features suggest that stronger consideration should be dedicated to night driving conditions, the current geometric design criteria all over the world do not take into account the driving at night. It is widely confirmed by all the studies proposing predictive models of operating speed and speed differentials that are focused on daytime driving conditions, neglecting nighttime driving conditionings.. This study was aimed at analyzing the driver speed behavior during day and night driving, comparing and modeling the operating speeds and speed differentials, identifying the significant factors that influence speed behavior under different lighting conditions. The research was carried out using a driving simulator, where a section of an existing two-lane rural road, composed by 39 tangent-curve configurations, was implemented. The speed profiles of 40 drivers were recorded both in simulated day and night driving conditions. The results of multiple linear regression analysis allowed to propose new predictive speed models, differentiated for daytime and nighttime driving, highlighting the effects of different geometric predictors under different visibility conditions. Specifically, the predictive models for operating speed on curves identified the inverse of the radius and the deflection angle of the curve as predictors in both scenarios. Also for the speed differential the same independent variables, the inverse of the approaching tangent length and the inverse of the curve radius, were found significant in both day and night driving conditions. On the contrary the tangent length was found significant for operating speed on independent tangents only for daytime model, while the inverse of the previous radius was confirmed as a predictor for both the visibility conditions.. "
Bella, F., Calvi, A., D'Amico, F. (2013). Modeling Driver Speed Behavior using Day and Night Tests in a Driving Simulator. In Proceedings of 4th International Conference on Road Safety and Simulation RSS2013.