Safe-D: Safety through Disruption

Category Archives: Webinar


Driving Risk Evaluation Web Application


Feng Guo

Detecting crashes and near-crashes in real-time can greatly benefit traffic safety management, development of safety countermeasures, and naturalistic driving data analysis. Continue reading

Webinar: Use of Disruptive Technologies to Support Safety Analysis and Meet New Federal Requirements


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 AADT estimates supplied by StreetLight Data Inc. Further, the research team developed safety performance functions (SPFs) for Texas and Virginia and performed a sensitivity analysis to determine the effects of AADT on the results obtained from the empirical Bayes (EB) method that uses SPFs. The results showed that the errors stemming from the probe AADT estimates were lower than those reported in a similar study that used 2015 AADT estimates. The sensitivity analysis revealed that the impact of AADT on safety analysis mainly depends on the size of the network, the AADT coefficients, and the overdispersion parameter of the SPFs.

Safe-D and TTI offering online science activity for 4th-6th grade students


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.

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