Evaluation of the Interaction of Weather and Roadway Characteristics on Freeway Operating Speeds

Abstract: This research explores the macroscopic relationships between freeway operating speed and a wide range of environmental conditions (such as rain intensity, wind speed, road surface condition (e.g., dry, icy, wet), and visibility), traffic volume, and infrastructure characteristics (such as roadway lighting and access/egress density) across the state of Virginia. A key aspect of this study is that it examines the interaction of weather, traffic, and roadway characteristics to understand their compound effect on traffic speed. While previous studies have largely confined their analyses to only several weather-related factors, this study explores various weather variables obtained from Road Weather Information Systems (RWIS) and expands the scope to include the impact of infrastructure characteristics, providing a more comprehensive view of the variables influencing freeway operating speed.
By analyzing data across diverse geographic regions, including both rural and urban areas, this research examines the complex interactions between these factors and their collective influence on operating speed. This insight is critical for infrastructure owner-operators (IOOs), such as state Departments of Transportation, to optimize traffic management, improve road safety, and inform future transportation planning and infrastructure development.
Statistical approaches, including statistical analyses of variable distributions, correlation studies, and decision tree modeling, were applied to quantify the influence of environmental conditions and infrastructure characteristics on operating speed. Weather data, including variables such as precipitation intensity, visibility, and roadway surface conditions, were collected from six Road Weather Information Systems (RWIS) stations located along major Interstates across a variety of geographic locations. Traffic volume data was gathered from Continuous Count Stations (CCS), and speed data was collected from a third-party probe speed dataset. Results indicate that certain environmental conditions, such as heavy rain and adverse surface state, lead to significant speed drops, particularly when traffic demand is higher. Moreover, infrastructure features like presence of entrance/exit ramps and absence of roadway lighting can interact with weather variables to further aggravate these effects.
This research offers practical insights for transportation agencies, enabling them to better develop customized adaptive speed control systems and improve roadway designs in diverse conditions. For instance, when the model predicts an operating speed that exceeds safe limits under certain weather scenarios, control mechanisms such as variable speed limits (VSLs) can be deployed to mitigate risks and enhance safety. Furthermore, the study identifies the most critical RWIS weather elements that predict operating speeds, guiding IOOs in prioritizing data collection and maintenance efforts from RWIS to focus on the elements with the greatest predictive power.