FHWA STAC Reduced Datasets Webinar Series
This webinar series was established to introduce the development, data, and uses of reduced data sets to help researchers in utilizing the Roadway Information Database as part of the SHRP 2 Naturalistic Driving Study to support safety analysis.
September 27, 2018 | 1:00 PM – 2:30 PM EST
Details | Webinar
This webinar will cover the process to develop the AADT reduced dataset, the database structure, metadata, potential uses, and limitations. The AADT reduced dataset is described below.
AADT is a frequently-requested attribute used in multiple research projects, including traffic safety. AADT is present in multiple features within the RID, but not necessarily consistently or systematically. The AADT reduced dataset creates a unified AADT feature from multiple sources.
State-provided AADT data, HPMS, any other data with AADT.
AADT is not consistent across all States, which is one reason why users inquire about where the traffic data are located. Some States provided multiple years of AADT, while other States did not provide any AADT data. For States that did not provide AADT data, users must rely on HPMS data to obtain AADT. This reduced dataset would create unified traffic data across all States, making it easier for all researchers to identify and use AADT.
Potential Research Topics:
Traffic data are a primary factor in both the safety and operations of the roadway. Providing easy access to AADT data will support multiple research efforts, including safety. AADT conveys the level of exposure needed in many safety analyses. AADT could support the use or estimation of crash rates and safety performance functions. It can also help to identify sites with potential for improvement or account for changes in traffic over time.
Skylar Knickerbocker, Center for Transportation Research and Education, Iowa State University
Zachary Hans, Center for Transportation Research and Education, Iowa State University
Omar Smadi, Center for Transportation Research and Education, Iowa State University