Data-driven safety analysis models help State and local agencies quantify safety data, identify high-risk roadway features, and predict the effects of proposed safety measures. However, even when a model performs well overall, it may not accurately represent the interactions between variables for a specific location or crash because the underlying relationships in the real world are unknown. One proposed solution is to generate realistic artificial datasets (RADs) with predetermined safety relationships built into them. Because these relationships are known, the RAD can serve as a testbed, revealing how well a model reflects those underlying cause-and-effect relationships.

This study describes the development of RAD for ramp terminals and speed change lanes at diamond interchanges. A web-based software was developed under the Federal Highway Administration’s Exploratory Advanced Research Program. The software provides the ability to generate RAD for multiple years and locations as well as access to pregenerated datasets. This report will be of interest to academics and researchers developing crash modification functions and statistical models to determine how the models best represent real-world relationships.

Recommended citation: Federal Highway Administration,MIMIC—Multidisciplinary Initiative on Methods to Integrate and Create Realistic Artificial Data
(Washington, DC: 2023) https://doi.org/10.21949/1521958.