• Training and technology transfer programs: Federally funded programs, often matched by state funds, provide technology transfer and training to local governments and Native American tribes so that innovations developed at the federal and state levels can be disseminated to these local entities, which typically have little or no money to conduct their own R&D.

LESSONS LEARNED FROM THE ORIGINAL SHRP

Two of the better known and more widely implemented results of the first SHRP are the Superpave® system for designing asphalt pavements and a collection of methods and technologies that significantly improved snow and ice control on roadways. By 2005, a little more than a decade after the research phase of SHRP 1 had ended, nearly all U.S. state DOTs, as well as several Canadian DOTs and other U.S. agencies, had implemented Superpave to some degree (see Figure 6-1), a remarkable penetration of innovation for this industry. This degree of implementation was projected in 1997 to provide $22.5 billion in savings for public agencies and highway users combined (FHWA 1997a). Half this level of market penetration for SHRP 1 snow and ice control products was projected to save $55 billion per year for agencies, not including improved safety and mobility for highway users (FHWA 1997b).

Activities that supported the implementation of these and other SHRP 1 research products were administered by FHWA with funds authorized in the Intermodal Surface Transportation Efficiency Act of 1991. During the time that specific funds were authorized for SHRP 1 implementation, FHWA played the lead role in overseeing, coordinating, encouraging, and facilitating widespread deployment of SHRP 1 results. The SHRP 1 implementation experience provides many examples and lessons that could be useful in planning the implementation phase for SHRP 2. These are categorized below under research products, implementation agents, implementation mechanisms, and resources.

Lessons Learned from SHRP 1 Concerning Research Products

Recognize That Research “Findings” Are Not “Products”

Research results must be translated into products that a user wants to implement. This translation requires a constant revisualization of the product; researchers are often not good at this step, so it is critical that users be



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