Multidisciplinary Initiative to Create and Integrate Realistic Artificial Datasets
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2023-06-01
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Edition:Research Summary Report
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Abstract:Safety models traditionally focus on crash frequencies or rates and do not always reflect the underlying causes of the crashes. Even models that accurately estimate crash measures and consist of multiple causal factors may not explain all causal relationships. Yet understanding what causes crashes is crucial to developing countermeasures and ensuring safety. To expand traditional modeling practices and results, the Federal Highway Administration’s Exploratory Advanced Research Program sponsored a project to develop a framework that would generate realistic artificial datasets (RADs) that mimic the known causal relationships between contributing factors and crashes. The researchers applied the framework to generate RADs for ramp terminals and speed change lane facilities at diamond interchanges. They also developed web-based software to provide easy access to the RADs, so other researchers could use them to test their models. The software contains 196 pregenerated datasets and the option to submit custom data requests. The framework is generic, so it can be used to generate RADs for other types of facilities. The researchers evaluated and compared the performance of different models using a model evaluation rubric. They also used RADs to evaluate new behavioral and roadway countermeasures by generating virtual reality simulation testbeds for crashes and near-crashes occurring at interchanges. They used safety-critical events recorded in the second Strategic Highway Research Program Naturalistic Driving Study to build the testbeds. In addition, the research team developed a graphical user interface to facilitate the testbeds for left-turn and speed change lane crashes. Virtual reality provides an engaging platform for evaluating countermeasures and educating the public about interchange crashes, which can help achieve the U.S. Department of Transportation’s goal of zero roadway fatalities.
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