The National Academies

NCHRP 04-29 [Completed]

Selection of Materials to Optimize Sign Performance

  Project Data
Funds: $300,000
Research Agency: University of Iowa
Principal Investigator: Thomas Schnell
Effective Date: 5/2/2001
Completion Date: 6/1/2006

For many years, engineering-grade retroreflective sheeting was the only type of sheeting material available for traffic signs. In recent years, there have been many new developments, and traffic engineers can now choose among engineering-grade, super-engineering-grade, high-intensity, and super-high-intensity materials (ASTM D4956 designations Types I through VI, with Types VII through IX pending). New products are being developed and introduced on a continual basis. The industry manufacturing these materials has grown to include multiple suppliers in most categories, and this has given rise to variations in material characteristics, performance, and price.

Over the years, there also have been important changes in the vehicle fleet and driving population, as well as changes in traffic levels and mix. Vehicle developments have included new headlight designs and technologies and tinted windows. The nature of the vehicle fleet has changed as a result of the popularity of "sport utility vehicles" and a continued increase in the number of trucks on the road. These vehicles typically have greater height differential between headlights and the driver's eye, resulting in a greater observation angle. It is also well documented that the driving population is aging, and steps have been initiated to modify traffic control devices to meet their changing visual capabilities.

The objective of the research was to develop a simple, user-friendly decision-making tool that will aid transportation agencies in the selection of retroreflective materials for traffic signs, based on roadway conditions and other factors that most critically affect sign performance.

Status: The preliminary draft final report has been reviewed by the project panel and a decision was made not to publish the results.  It was found that the recommendations of the decision tool were not adquately supported by the underlying data and models. 

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