There are approximately 4,000 fatalities in crashes involving trucks and buses in the United
States each year. Though estimates are wide-ranging, possibly 10 to 20 percent of these …

This study designs and evaluates a contextual and temporal algorithm for detecting
drowsiness-related lane. The algorithm uses steering angle, pedal input, vehicle speed and …

The application of naturalistic driving data (NDD) has the potential to answer critical
research questions in the area of driving behavior assessment, as well as the impact of …

In the last several years, the focus of traffic safety research—especially when performed in
association with the automotive industry—has shifted from preventing injury during a crash …

Various social events, such as holidays, important sporting events, and major celebrations,
may result in sudden large-scale passenger flows in certain sections and stations of urban …

Background Kinematic driving data studies are a novel methodology relevant to health care,
but prior studies have considerable variance in their methods, populations, and findings …

This paper introduces Probabilistic Topic Modeling (PTM) as a promising approach to
naturalistic driving data analyses. Naturalistic driving data present an unprecedented …

This chapter reviews current development for tracking “the brain in the Wild” and explains
how the examined older life gains from this technology in terms of healthy brain aging …

This systematic review documents current best practices, standards, and approaches for
transportation safety data analytics. While standards exist for defining measures, there are …

One challenge in using naturalistic driving data is producing a holistic analysis of these
highly variable datasets. Typical analyses focus on isolated events, such as large g-force …