In recent years, transportation agencies have increasingly turned to machine learning (ML) to enhance the effectiveness of infrastructure asset management. However, limited local inventory data often hinders building accurate and reliable ML models. Additionally, data privacy and ownership concerns discourage...

This report presents several advancements in the empirical modeling of liquefaction-induced lateral spread. It starts with a newly collected dataset of 5,560 historical lateral spread displacement vectors, a sample size over 10 times larger than the existing databases and subsurface data comprising over...

The study provides information to aid transportation decision makers in planning and programming for future commodity transportation needs. The study describes recent trends in potato production and processing within the state; examines the logistics of potato transportation, storage, and processing...

This research examines a range of incident detection technologies to determine a recommended combination of approaches for use in the Utah Department of Transportation (UDOT) Advanced Traffic Management System (ATMS). The technologies that were examined are computer-based Automatic Incident Detection...