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dc.contributor.authorTsapakis, Ioannisen
dc.contributor.authorDas, Subasishen
dc.contributor.authorKhodadadi, Alien
dc.contributor.authorLord, Dominiqueen
dc.contributor.authorMorris, Jessicaen
dc.contributor.authorLi, Ericen
dc.description.abstractStates 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 and Virginia Departments of Transportation against probebased 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 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.en
dc.publisherSAFE-D: Safety Through Disruption National University Transportation Centeren
dc.rightsCC0 1.0 Universalen
dc.subjectannual average daily trafficen
dc.subjectsafety impacten
dc.subjectsafety analysisen
dc.subjectlow-volume roadsen
dc.subjectnon-federal aid-system roadsen
dc.subjectSafety Performance Functionsen
dc.titleUse of Disruptive Technologies to Support Safety Analysis and Meet New Federal Requirementsen

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CC0 1.0 Universal
License: CC0 1.0 Universal