# Road safety of passing maneuvers: a bivariate extreme value theory approach under non-stationary conditions

## TL;DR

This paper develops a non-stationary bivariate extreme value model to estimate the probability of head-on and rear-end collisions during passing maneuvers, incorporating driver and infrastructure heterogeneity for improved safety assessment.

## Contribution

It extends existing extreme value theory models to jointly analyze dependent surrogate safety measures under non-stationary conditions, considering driver and road factors.

## Key findings

- Accounting for driver and infrastructure variables improves collision probability estimates.
- Joint modeling of head-on and rear-end collisions provides better safety insights.
- Heterogeneity considerations are crucial for accurate surrogate safety measure analysis.

## Abstract

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.