Limitations of crash data and crash-based methods have given rise to the study of alternate
measures of safety that are not predicated on the occurrence of a crash such as traffic …

Recent decades have seen considerable growth in computer capabilities, data collection
technology and communication mediums. This growth has had considerable impact on our …

In this study, we explore the problem of adaptive vehicle trajectory control for different risk
levels. Firstly, we introduce a sliding window-based car-following scenario extraction …

The autonomous vehicle is regarded as a promising technology with the potential to
reshape mobility and solve many traffic issues, such as accessibility, efficiency …

Naturalistic driving is an innovative method for investigating driver behavior and traffic
safety. However, as the number of crashes observed in naturalistic driving studies is typically …

BACKGROUND: Traditional crash prediction models, such as generalized linear regression
model, are incapable of taking into account multilevel data structure. Therefore they suffer …

A critical part of any risk assessment is identifying how to represent exposure to the risk
involved. Recent research shows that the relationship between crash count and traffic …

Recent studies have developed univariate and bivariate Bayesian hierarchical extreme
value models to improve traffic conflict-based crash estimation through addressing the …

Purpose–This chapter overviews surrogate measures of safety to help better understand the
related challenges and opportunities. The chapter is meant to serve as a primer for …

The limited ability of existing safety models to properly reflect crash causality has its source
in cross-sectional analysis applied to the estimation of the intrinsically complex safety factors …