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Automating Variable Speed Limits Using Weather, Traffic, and Friction Data

Project Details
STATUS

In-Progress

PROJECT NUMBER

2022-10

START DATE

02/01/23

END DATE

01/31/25

SPONSORS

Federal Highway Administration Aurora Program Transportation Pooled Fund (TPF-5(435))

PARTNERS

Rutgers University, Western Transportation Institute - Montana State University

Researchers
Principal Investigator
Gerry Wiener

National Center for Atmospheric Research

About the research

Variable speed limits (VSLs) are useful in promoting highway safety. Along these lines, the Federal Highway Administration (FHWA) mentions, “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 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.

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