Detecting crashes and near-crashes in real-time can greatly benefit traffic safety management, development of safety countermeasures, and naturalistic driving data analysis.
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
Safe-D researchers at TTI are now offering an at-home lesson for students grade 4 to 6 on the science behind reflective road signs, a property called retroreflectivity.
Please join us for the next webinar in the Safe-D Webinar Series, to be held February 25th from 4-5pm EST.
Please join us for the next webinar in the Safe-D Webinar Series, to be held February 18th from 4-5pm EST.
Please join us for the next webinar in the Safe-D Webinar Series, to be held February 12th from 4-5pm EST.