  Observed accidents have been the main resource for road safety analysis over
the past decades. Although such reliance seems quite straightforward, the rare
nature of these events has made safety difficult to assess, especially for new
and innovative traffic treatments. Surrogate measures of safety have allowed to
step away from traditional safety performance functions and analyze safety
performance without relying on accident records. In recent years, the use of
extreme value theory (EV) models in combination with surrogate safety measures
to estimate accident probabilities has gained popularity within the safety
community. In this paper we extend existing efforts on EV for accident
probability estimation for two dependent surrogate measures. Using detailed
trajectory data from a driving simulator, we model the joint probability of
head-on and rear-end collisions in passing maneuvers. In our estimation we
account for driver specific characteristics and road infrastructure variables.
We show that accounting for these factors improve the head-on collision
probability estimation. This work highlights the importance of considering
driver and road heterogeneity in evaluating related safety events, of relevance
to interventions both for in-vehicle and infrastructure-based solutions. Such
features are essential to keep up with the expectations from surrogate safety
measures for the integrated analysis of accident phenomena.
