Safe-D director Zac Doerzaph (VTTI) sat down with Greg Winfree, director of TTI, to talk about the nation’s mobility priorities and what university-based research can do to support them on the Thinking Transportation Podcast.
Safe-D researcher Jean Paul Talledo Vilela (VTTI) was featured in the Virginia Tech Doodle on September 20th. The doodle shows him updating GPS and Gyroscope software.
Detecting crashes and near-crashes in real-time can greatly benefit traffic safety management, development of safety countermeasures, and naturalistic driving data analysis.
VTTI researchers are using advanced machine learning and statistical methods to examine key characteristics of high G-force events and their connection with crashes and develop machine-learning models that can help predict crashes from high G-force events in real-time.
As part of a research project (Data Mining Twitter to Improve Automated Vehicle Safety), Safe-D researchers have created guidelines for automated vehicle crash responses to help public information officers structure their communications about crashes.
States are required to have access to annual average daily traffic (AADT) for all public paved roads, including non-Federal aid system (NFAS) roadways. The expectation is to use AADT estimates in data-driven safety analysis. Because collecting data on NFAS roads is financially difficult, agencies are interested in exploring affordable ways to estimate AADT. The goal of this project was to determine the accuracy of AADT estimates developed from alternative data sources and quantify the impact of AADT on safety analysis. The researchers compared 2017 AADT data provided by the Texas Department of Transportation (DOT) and the Virginia DOT against probe-based … Webinar: Use of Disruptive Technologies to Support Safety Analysis and Meet New Federal Requirements