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 …

The large-scale deployment of automated vehicles on public roads has the potential to
vastly change the transportation modalities of today's society. Although this pursuit has been …

This study designs a framework of feature extraction and selection, to assess vehicle driving
and predict risk levels. The framework integrates learning-based feature selection …

This paper shows that the behavior of driver models, either individually or entangled in
stochastic traffic simulation, is affected by the accuracy of empirical vehicle trajectories. To …

The primary objective of this study was to identify if the VISSIM simulation model and the
Surrogate Safety Assessment Model (SSAM) approach provided reasonable estimates for …

The safety of signalized intersections has often been evaluated at an aggregate level
relating collisions to annual traffic volume and the geometric characteristics of the …

Several studies have advocated the use of extreme value theory (EVT) traffic conflict models
for real-time crash risk prediction using real-time safety indices such as the risk of crash (RC) …

Highlights•Bivariate extreme value models are developed for several conflict indicator
combinations.•Six combinations of TTC, MTTC, PET, and DRAC are considered.•Validation …

Risky lane-changing (LC) behavior of vehicles on the road has negative effects on traffic
safety. This study presents a research framework for key feature selection and risk prediction …

Automated driving is a promising tool for reducing traffic accidents. While some companies
claim that many cutting-edge automated driving functions have been developed, how to …