Successful safety management practices require a thorough understanding of the factors contributing to motor vehicle crashes. The continuous advancements in the science of data-driven safety analysis, as well as the countermeasures and technologies available for addressing crashes, create challenges in maintaining a safety workforce proficient in the state of the practice. In many cases, agencies continue to use approaches such as descriptive statistics and anecdotal information to perform the diagnostic assessment without a thorough understanding of the expectations for a given context or road type. Additionally, choosing an effective countermeasure requires an examination of the human factors, behavioral factors, future development, prevailing or predicted crash types, and mix of road users to determine the most appropriate treatments to apply. Doing so allows the selected countermeasure to reduce crashes to the greatest extent possible. However, in many cases, practitioners have limited understanding of the potential for a treatment selection to affect other road users. For instance, installing a turn lane might also increase vehicle speeds or pedestrian crossing distance. A better understanding of these relationships and tradeoffs could inform design choices and ultimately result in safer roadways for all road users.
Research is needed to develop diagnostic tools that leverage crash, roadway, traffic volume, human factors, behavioral, socioeconomic, and demographic data, as well as non-traditional data sources in order to advance the state of the practice in crash diagnostics and countermeasure selection that considers both modal priorities and facility context. It is common to characterize traffic safety plans as the 4Es of highway safety – engineering, education, enforcement, and emergency medical services. The evaluation, analysis, and diagnosis (the 5th E of safety) of these aspects of crashes in modal and facility contexts should significantly improve the selection and design of countermeasures.
The objective of this research is to develop new methods and tools for diagnosing contributing factors leading to crashes that will aid practitioners in selecting appropriate countermeasures in modally diverse contexts. The methods and tools should address a wide variety of contributing factors leading to crashes (e.g., roadway, technological, behavioral, human factors, socioeconomic, demographic, weather, and land use) in order to further practitioner understanding of how to most effectively balance tradeoff decisions in a given modal priority and facility context.
Research for this project is ongoing.