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 means that the driver has less time to react and make evasive maneuvers to …

As electric bikes (e-bikes) rapidly develop in China, their traffic safety issues are becoming
increasingly prominent. Accurately detecting risky riding behaviors and conducting …

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 …

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

The existence of internal and external heterogeneity has been established by numerous
studies across various fields, including transportation and safety analysis. The findings from …

Near-miss traffic risk estimation using Extreme Value Theory (EVT) models within a real-time
framework offers a promising alternative to traditional historical crash-based methods …

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