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Multidisciplinary Initiative on Methods to Integrate and Create Artificial Realistic Data

Project Information

Project Abstract: 

Traditional safety modeling efforts primarily focus on accurate estimation of crash frequencies or rates. The true relationship between crashes and potential causal factors are not always easily discernible from safety models. While a model consisting of multiple causal factors may produce accurate estimates of crash measures, it may not accurately explain all causal relationships. Knowing the true cause-and-effect relationships are important when choosing countermeasures to address safety problems. This Exploratory Advanced Research Program project will generate artificial realistic datasets (ARD) that will mimic the known causal relationships. Thus, the best-performing methods can be applied by practitioners with confidence in safety evaluation and countermeasure selection. Not all countermeasures can be deployed in the field due to safety concerns. Driving simulator studies offer another source of artificial realistic data for evaluating new countermeasures. This project will generate artificial realistic simulator testbeds using real-world crash data. The project team consisting of experts from safety, crash modeling, machine learning, statistics, simulation, and human factors, will develop ARD datasets and testbeds for urban interchange facilities.

Project Status: 
Project Funding Amount (Contract Award Amount): 
Start Date: 
Thursday, September 5, 2019
End Date: 
Saturday, September 4, 2021
Public Access Plan: 
FHWA AMRP Program: 
Exploratory Advanced Research

Contact Information

Office of Safety Research and Development
Office of Research, Development, and Technology