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Initial Stage Investigations

After the initial set of think tank forums, the EAR Program began to conduct smaller, more focused investigations. This process includes conducting reference searches of published and unpublished materials, visiting leading researchers and laboratories (many in disciplines not traditionally associated with highway research), and convening workshops to bring together researchers from different fields and leaders in highway research to explore how fundamental scientific and engineering advances could respond to current or emerging highway needs.

How Initial-Stage Investigations Lead to EAR Program Investment in High-Return Research

Not all innovative ideas lead to EAR Program investments. Of the 15 to 20 ideas that the EAR Program considers annually, about 25 percent actually lead to funded research. The EAR Program seeks to leverage advances in science and engineering that could lead to breakthrough research for critical current and emerging issues in highway transportation. The focus of this effort is to investigate issues that can be clearly articulated but where solutions are not obvious and to engage a community of experts from different disciplines who likely have the talent and the interest to research solutions but would not otherwise be able to do so without EAR Program funding.

Examples of EAR Program Investments Based on Initial-Stage Investigations

Nanoscale Science

Since 2001, the Federal government has invested over $18 billion on nanoscale research. The 2013 Federal Budget provides $1.8 billion for nanotechnology research, but work that is directly related to highway research represents less than one-tenth of 1 percent of that figure. See the National Nanotechnology Initiative website for more information.

FHWA is working closely with government, industry, academic, and international partners to push forward a strategic investment in nanoscale research. The EAR Program, along with with the Office of Infrastructure Research and Development (R&D) and Office of Safety R&D, has been been investigating nanoscale research conducted in relation to highways for several years, and this process led to a March 2009 workshop involving almost two dozen experts from academia and other Federal programs. These experts met to share their understanding of nanoscale research and to learn about key highway research issues in infrastructure, safety, operations, and environment. For more information on nanoscale approaches for highway research, read the Fact Sheet.

Based on information from scanning and from the convening workshop, the EAR Program sought research proposals that took advantage of nanoscale principles. One such study examines the materials, structures, and sensors that form the building blocks of transportation infrastructure. "Nano Material and Simulation by New Multiple Length/Time Scale Theories and Algorithms" was an EAR Program–sponsored project, conducted in partnership with The George Washington University, aimed at developing a new approach to understanding the physical behavior of materials that cover multiple length and time scales. Researchers anticipate that FHWA will use the new techniques and tools from this project to investigate material failures in roadside safety equipment and critical structural elements. Helping transportation engineers and managers better understand material behavior is likely to enable improved design and a more efficient, cost-effective, and sustainable transportation infrastructure. For more information on this project, see the fact sheetNano Material Modeling and Simulation: Developing a New Approach to Understanding Material Behaviors by Multiple Length/Time Scale Theories (FHWA-HRT-12-029).

Another EAR Program–sponsored nanoscale science project examines corrosion of highway bridges, a problem that has been estimated to cost the Nation $8.29 billion annually.1 "Multifunctional Nanomaterials and Processes for Infrastructure Repair and Corrosion Inhibition," an EAR Program project awarded to Florida State University by FHWA, investigated a corrosion-inhibiting nanocomposite solution that can be used to repair, strengthen, and protect highway infrastructure. This project sought to develop technologies for new in situ nanomaterial-based repair methods that can tailor the materials to include multifunctional properties of carbon nanotubes. The technical innovations that the investigators researched could lead to a multifunctional composite coating for corrosion control as well as to the strengthening and repair of infrastructure. For more information on this project, read the Fact Sheet.

Cement Hydration Modeling

Proportioning and placing portland cement concrete is performed thousands of times each day, as it is the most used building material on Earth; however, there are many common problems that can arise in the field, primarily associated with hydration—the chemical reaction that transforms dry cement into a binder that provides strength and durability to concrete. A general lack of knowledge about hydration processes makes improving, predicting, and controlling the performance of portland cement concrete a difficult task, accomplished by trial-and-error experimentation combined with the experience of engineers, technologists, contractors, and producers. The quest to identify the underlying mechanisms that control cement hydration continues to be a challenge for modern materials science but has the potential to alter the fabric of constructed infrastructure for the global benefit of all.

In 2009, the International Summit on Cement Hydration Kinetics and Modeling, a workshop supported by the National Science Foundation, FHWA, and other participating partners, examined various aspects of cement hydration. Although a number of isolated studies had looked at various aspects of hydration, until the 2009 International Summit there had been no industry-wide focal point and support for large-scale discussion on the topic. Activities such as this aim to encourage researchers and users of concrete to consider developing and using models to examine the effects of changes in proportions and materials. The workshop was followed by an August 2010 Web conference to report on progress since the summit, and the drafting of an industry hydration roadmap. For more information, read the Fact Sheet.

Investigations in cement hydration modeling led to the EAR Program, with the Office of Infrastructure R&D, awarding research funding in 2011 to a team of investigators led by Princeton University.  In addition, funds were also allocated for research at FHWA’s Turner-Fairbank Highway Research Center (TFHRC) and at the National Institute for Standards and Technology Engineering Laboratory. 

Agent-Based Modeling

Agent-based modeling (ABM) is a relatively new approach to modeling systems that is comprised of autonomous and interacting agents, and there are a growing number of agent-based applications in a variety of fields. ABM can be used to study such phenomena as consumer choice, the spread of epidemics, and behavioral economics in stock market analyses. In addition, it can also be used to understand how people's behavior affects activities like pedestrian movement, transportation, and traffic. In 2010, the EAR Program— with the Office of Operations R&D, Office of Safety R&D, and Office of Planning— convened a 1-day workshop on Agent-Based Modeling and Simulation (ABMS). A panel of five speakers presented tools, methods, and concepts related to ABMS. Following the presentations, these speakers and representatives from academia, the transportation industry, and other professional areas specific to ABM discussed applications to transportation, knowledge gaps, and barriers to implementation. For more information, read the Workshop Summary

Based on the workshop, the EAR Program sought and awarded research projects to examine ABM techniques in relation to driver and traveler behavior. In addition, FHWA awarded a research project, "Driver Behavior in Traffic," in 2009 to Virginia Tech University, in partnership with PTV America and the Virginia Transportation Research Council; the project was refocused on modeling driver behavior in traffic using naturalistic driving data with ABM techniques. Existing traffic analysis and management tools cannot effectively model the ability of drivers to recognize their environment and respond to it with behaviors that vary according to the encountered driving situation. The research resulted in the development of a hybrid car-following model and involved the innovative use of agent-based artificial intelligence machine-learning to model driving behavior. The methods developed as part of this research will enable future research to develop new generations of traffic simulation models that accurately model driver behavior during incidents and other complex traffic situations. For more information, read the Fact Sheet.

Experimental Economics

In 2009, the EAR Program, with the Office of Transportation Policy Studies, investigated the capabilities of laboratories that brought together empirical testing of human behavior and economics and the interest of investigators to apply experimental economics methodology to highway transportation.  Based on the reference scan, FHWA sought to do research using experimental economics. As a result, an experimental economics study led by the University of Central Florida and Georgia State University, titled "Experiments on Driving Under Uncertain Congestion Conditions and the Effects on Traffic Networks from Congestion Pricing Initiatives," examined when and why drivers choose a priced or tolled facility over an untolled but congested parallel route. It also examined how drivers’ risk preferences influence their choice of route and travel departure time. Understanding drivers’ reactions to congestion pricing initiatives has previously been limited by the type of data commonly collected; however, this study moved away from the conventional use of stated preference data and simple responses to surveys, known to generate biased and volatile responses, and instead explored the possibility of using experimental economics to make observations on actual choices with precise monetary incentives. As part of the study, participants were provided with real travel options that came with actual financial consequences and choice situations that varied according to congestion and congestion pricing. The overall objective of the project was to understand why drivers change their route choices when tolls change, with a particular focus on how responses depend on varying preferences and perceptions of travel times and travel time reliability. For more information, read the Fact Sheet

National Transportation Demand Model

Population and economic growth, along with other driving forces, are expected to continue to cause increased medium- and long-distance passenger travel demand in the United States. Significant new investments for multimodal interregional travel are necessary for maintaining transportation efficiency and supporting economic development. As nationwide debates continue on cost-effective strategies for meeting future interregional travel demand, there is a need to systematically evaluate national transportation investment strategies, such as expanding the capacity of the Interstate Highway System, upgrading other facilities of the National Highway System, providing high-speed rail services along selected corridors, and building a next-generation air transportation system.

“Methodological and Data Options for Developing a U.S. National Multimodal Inter-Regional Passenger Travel Demand Model” was an EAR Program investigation in cooperation with the Office of Highway Policy Information and supported by the University of Maryland and Oak Ridge National Laboratory. The investigation included a synthesis of data sources and methodologies for national travel demand modeling; convened an expert workshop in 2010 to learn from national travel modeling practices around the world and help define future research needs; outlined a research map; and identified strategies required to estimate multimodal interregional travel in the United States. The initial-stage investigation led to U.S. Department of Transportation (USDOT) funding for incremental improvements to long-distance travel data collection and model processes, as well as EAR Program awards seeking longer term breakthroughs for data collection and modeling for multimodal, long-distance passenger travel.

Updated: Monday, December 2, 2019