Aurora

About the research

Variable speed limits (VSL) are useful in promoting highway safety. According to the Federal Highway Administration (FHWA), “the use of VSLs during inclement weather or other less than ideal conditions can improve safety by decreasing the risks associated with traveling at speeds that are higher than appropriate for the conditions.”

The project’s research objective was to investigate beneficial ways to recommend VSLs under a variety of adverse weather and road surface conditions. The report presents findings from three key research areas: (1) VSL data analysis and machine learning (ML) model development, (2) VSL physical model development, and (3) a review of VSL requirements and state department of transportation (DOT) rules of practice.

The research team gathered road weather information system (RWIS) and VSL event data covering the winter and early spring seasons from 2021 through 2023 across two study areas in Wyoming and Utah. Due to the timeframes and locations covered by the collected data, the study datasets included significant adverse and impactful weather events. The team used the collected data to develop an analysis methodology and algorithms for issuing VSLs that consider different weather conditions, terrain types, roadway geometries, and road surface conditions using ML and physical models. To understand how state DOTs currently issue VSLs and to guide research efforts, an extensive survey of VSL requirements and DOT rules of practice across multiple state DOTs was conducted, and the findings were summarized. Summary results from each of the three research areas are discussed in the report, followed by conclusions and recommendations for methods and techniques that state DOTs should consider when implementing VSLs or performing future VSL research. Detailed research findings can be found in the appendices.