Vulnerable road users (VRUs) involved crashes are a major road safety concern due to the
high likelihood of fatal and severe injury. The use of data-driven methods and heterogeneity …

This research leverages a novel deep learning model, Inception-v3, to predict pedestrian
crash severity using data collected over five years (2016–2021) from Louisiana. The final …

Freight truck-related crashes in urban contexts have caused significant economic losses and
casualties, making it increasingly essential to understand the spatial patterns of such …

Pedestrian crashes at high-speed locations are a persistent road safety concern. Driving at
high speeds indicates that the driver would get considerably less time to react and make …

This paper presents a generic analytical framework tailored for surrogate safety measures
(SSMs) that is versatile across various highway geometries, capable of encompassing …

Understanding the causes of traffic road accidents is crucial; however, as data collection is
conducted by traffic police, accident-related environmental information is not available. To fill …

Human drivers are requested by the Automated Vehicle (AV) to perform takeover actions if
needed. Existing research mainly focuses on predicting the takeover quality due to …

Equity is of significant concern in roadway safety research, but existing research often
overlooks confounding factors from roadway environment and traffic exposure. In this …

Abstract Augmented Reality Head-Up Display (AR-HUD) is a promising solution to the
current warning system distraction problem. However, how to effectively convey warnings …

Objective Modern transportation amenities and lifestyles have changed people's behavioral
patterns while using the road, specifically at nighttime. Pedestrian and driver maneuver …