Variable speed limits (VSLs) are useful in promoting highway safety. “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,” according to the Federal Highway Administration. The goal of this proposal is to automatically recommend speeds for various weather conditions (rainfall, snow, ice, fog, etc.) at roadway segments that are good candidates for VSL. This means that the roadway segments should frequently experience adverse weather conditions (such as snow, rain, fog, etc.), high traffic, or safety hazards. The crash rate at such road segments should generally be higher than average.

The research team expects to gather Road Weather Information System (RWIS), traffic, friction, incident, and potentially other data sets over one or more seasons that typically exhibit adverse weather. The team will then utilize the collected data and develop analysis methodology in establishing VSL algorithms that consider different terrain types, roadway geometries, and weather conditions (rainfall, snow, ice, fog, etc.). The team will explore the usage of machine learning (ML) algorithms and other approaches in establishing VSL. The speed limits will be set to satisfy the driver’s visibility and stopping sight distance requirements and also prevent lateral slippage at curved sections considering the loss of friction due to inclement weather conditions.