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1Introduction Research Objective The SHRP 2 C20 research initiative provides the strategic framework for continuous improve- ment and innovative breakthroughs in freight transportation forecasting, planning, and data. The stated research objective was to âfoster fresh ideas and new approaches to designing and implementing freight demand modeling.â This objective recognizes that fundamental change is necessary to better integrate freight considerations into the transportation planning process. Various short-term measures have resulted in marginal improvement to the current state of the practice for freight planning, but they contain many inherent weaknesses. Fundamental change in freight modeling and data is needed and opportune. Freight is grow- ing in volume, economic importance, and complexity, particularly in relation to sophisticated modal and information technology advances. The effective and efficient movement of goods affects nearly every aspect of life. However, the analytic tools and methods used to forecast freight demand are inadequate to deal with the scale and importance of freight transportation on our multimodal system and our economy. Historically, travel demand forecasting has been oriented toward the long-standing methods used for passenger transportation. Passenger-oriented forecasting models draw on economic and demographic variables that are insufficient and largely irrelevant for estimating freight demand, which is shaped by a wider range of factors that reflect a complex logistics chain. By developing better freight demand models and data sources, public and private sector deci- sion makers will be able to make better and more informed decisions related to transportation infrastructure, land use, economic development, and other policies fundamental to prosperity and quality of life. Ultimately, these decisions should consider relevant information such as the current movement of goods, modal mix and variations, shipping costs, time in transit, consump- tion rates, logistics chains, and other factors critical to the freight industry. Research Scope and Approach The Freight Demand Modeling and Data Improvement Strategic Plan was developed through an inclusive process of public and private stakeholders from U.S. and international freight planning communities that culminated in the Innovations in Freight Demand Modeling and Data Sym- posium conducted in September 2010. The planâs development focused on collecting information and ideas to ⢠Determine freight demand modeling and data needs, in part by defining an optimal scenario or desired future state of what the freight planning process should be with all of the model parameters clearly identified and the necessary data available. Executive Summary
2⢠Identify and promote innovative research efforts to help develop new modeling and data col- lection and processing tools in the near and long-term future. ⢠Establish and strengthen links between freight transportation planning tools and supporting data, and also consider the relationships between freight transportation and other areas of public interest, such as development and land use, in which freight movement has major implications. ⢠Leverage and link existing practices, innovations, and technologies into a feasible approach for improved freight transportation planning and modeling. ⢠Establish a recognized and regular venue to promote and support innovative ideas, modeling methods, data collection, and analysis tools as the basis for informing and sustaining further research. The Freight Demand Modeling and Data Improvement Strategic Plan identifies a compelling direction for the freight planning community centered on meeting the immediate needs of deci- sion makers. The pragmatic focus on application and results also recognizes the parallel need to foster continued research innovation and breakthroughs. This confluence of steady improve- ments in practice and continued research focus will be the basis for long-term improvements to freight modeling and data. The SHRP 2 C20 research team focused, therefore, on defining the critical gaps in models, data, and decision making as the means to formulate strategic and cohe- sive future directions to guide the long-term initiatives identified throughout the research pro- cess. For the purposes of this research, innovations in the freight modeling and data community are defined as significant (or potentially significant) movements toward the betterment of freight models, tools, data, or knowledge in freight planning practices. A robust approach was followed to define needs and innovations and to shape the long-term goals. The hallmark of this effort was maximizing input from practitioners and decision makers and considering the current state of the practice. This process entailed a review of research con- ducted on freight modeling and data improvement, as well as an analysis of current practices within the industry (both domestic and international). The research approach elements, their purpose, and outcomes are shown in Table ES.1. Findings Decision-Making Needs The research and associated stakeholder outreach efforts identified a variety of freight planning and analytic needs. Common threads and recurring themes among the wide array of private, public, and academic participants included the following: ⢠Freight forecasting and analysis should be enhanced through the development and amalgamation of a recognized and valid inventory of standardized data sources with common definitions. ⢠There is strong interest in developing a statistical sampling of truck shipment data, similar to the Carload Waybill Sample available for railroads. ⢠There is a real need for a range of standardized analytic tools and applications to address diverse decision-making needs. ⢠Behavior-based facets of freight decision making (i.e., the dynamics of shipper and carrier decisions) must be incorporated into modeling, or at least better understood as important context. ⢠Better information is needed to understand intermodal transfers, particularly the types, vol- umes, and significant trends. This is particularly important as public policy is promoting systems thinking and intermodalism. ⢠Industry-level freight data are needed at a subregional level to enable reliable freight analyses at a smaller geographic scale. Similarly, there is a need to better understand the patterns and dynamics of local deliveries in urban areas.
3⢠Freight models should begin to incorporate local land use policies and controls to increase the accuracy and value of freight forecasting at the local level. ⢠There is a need to better understand the correlation between freight activity and various eco- nomic influences such as fuel price, currency valuation, and macroeconomic trends. ⢠Enhanced tools and processes would be beneficial to measure the accuracy of freight analyses and data forecasts. ⢠There is an overarching process need to implement a process to routinely generate new data sources and problem-solving methods. ⢠Attention should be given to using intelligent transportation systems (ITS) resources and related technologies such as global positioning systems (GPS) and IntelliDrive to generate data to support freight planning and modeling. ⢠There is a recognized long-term need to develop a full multimodal, network-based freight demand model that incorporates all modes of transport to a similar level of detail (vehicle, railcar, vessel, and so forth) for various geographic scales. ⢠Freight stakeholders emphasize the practical need for benefitâcost analysis tools and methods that go beyond traditional financial measures by including other direct and indirect impacts, benefits, and costs (both public and private). Table ES.1. Research Approach Elements Approach Element Purpose Outcome Technical Expert Task Group ⢠Articulate the project and industry vision ⢠Advise project team ⢠Review interim and final findings ⢠Overall project oversight and direction Background Research ⢠Identify domestic and international best practices ⢠Identify historic freight modeling and data challenges ⢠Identify opportunities, innovations, and unique data sources ⢠Catalog of current and best freight modeling and data collection practices ⢠Determination of potential areas for improvement and innovation ⢠Background in defining strategic needs Innovations in Freight Demand Modeling and Data Symposium ⢠Identify domestic and international innovative practices ⢠Discuss applicability and improve- ments to innovations ⢠Launch a forum for sharing of freight demand modeling and data innovations ⢠Current innovative initiatives ⢠Brought the data and modeling community together to foster the best thinking on the subject ⢠Venue for future sharing of innova- tive ideas ⢠Formal structure for rewarding freight modeling and data innovations Stakeholders Outreach and Workshops ⢠Validate the strategic directions ⢠Discuss a series of key issues ⢠Review, critique, and validate strate- gic research initiatives that will affect freight transportation for years to come ⢠Validation and supporting ideas and discussion on the Strategic Plan ⢠Validation and discussion on research priorities ⢠Ideas for continuing innovations to meet decision-making needs Strategic Plan ⢠Frame the long-term direction for freight modeling and data improvement ⢠Foster innovative practices in mod- eling and data ⢠Set an agenda for short- and long- term research initiatives ⢠Documented strategic needs and innovative research efforts ⢠Developed a feasible approach to freight transportation modeling and data improvement ⢠Identified short- and long-term stra- tegic research initiatives ⢠Developed a strategic plan and road map
4⢠More effective methodologies are needed to apply freight forecasts to funding and financial analyses, such as revenue projections. ⢠There is a strong interest among highway agencies to develop tools that use freight forecasts to support the agenciesâ infrastructure design processes. ⢠Stakeholders consistently emphasized the importance of a concentrated effort to develop the requisite knowledge and skills to support freight analysis and foster greater public and private collaboration and mutual understanding of respective processes and requirements. Decision-Making Gaps Table ES.2 highlights freight decision-making needs, the gaps between those needs and the cur- rent modeling and data practices, and the data and modeling requirements to meet those needs. This information represents the foundation for the actions that have been incorporated into the Strategic Plan. Conclusions The second decade of the twenty-first century will see an even greater emphasis on global trade, technology, innovation, and competitiveness. These megaissues should strongly influence trans- portation strategy and decisions about system investments; these will, in turn, require capacity building for state departments of transportation (DOTs) and metropolitan planning organiza- tions (MPOs). Responding effectively to these megaissues will also require greater collaboration with the freight industry at every level, including collaboration on the types of freight planning research described in this report. The long-term ability to effectively and efficiently move goods will depend on the perfor- mance of public and private infrastructure, which is key strategic asset to enterprises that ship and receive freight of all types in a fiercely competitive business environment. Ironically, in this information age when the linkage between goods movement and information technology continues to expand, state DOTs and MPOs lack the kind of data and analytic tools needed to effectively plan for freight transportation. The result is that public decision makers lack the information they need to effectively support freight-related transportation decision making. By the end of this decade, a vision for improved freight modeling and data will be character- ized as follows: ⢠A robust freight forecasting toolkit has been developed and is the standard for public sector freight transportation planning. ⢠Forecasting tools and data link dynamically with other key variables, such as development and land use, and their application to local scale, corridors, or regions is also dynamic. ⢠The challenges associated with the data necessary to support new planning tools have been addressed through a broad-based effort bringing together the varied resources of the public and private sectors. ⢠The knowledge and skills of state DOT and MPO staff have been methodically enhanced to complement the development of better tools and data. ⢠Decision makers recognize that transportation investments are to a greater degree being informed by an understanding of the implications, benefits, and trade-offs relative to freight. Recommendations A framework or future direction for building momentum beyond the completion of the SHRP 2 C20 report was developed to provide a broad direction and an organizing process for sustaining innovation in freight planning and modeling. The approach is designed to address the range of opportunities and needs that have been identified to date.
5Table ES.2. Freight Decision-Making Needs and Gaps Decision-Making Needs Gaps Between Needs and Current Practices Data or Modeling Requirements to Close Gaps Standardized data sources with common definitions ⢠Various data sources collected through different programs result in extensive inconsistencies. ⢠Homogeneous data for ease of incorporation into freight models and for consistency of freight models in different regions. ⢠Reduction in data manipulation to improve accuracy. Statistical sampling of truck shipments ⢠Detailed knowledge of truck movements in local areas. ⢠Understanding of current truck activity by different industry seg- ments (long-haul, local, drayage). ⢠An ongoing standard data-collection program to gather local truck movements. ⢠Compilation of truck data to a level comparable to rail industry data (i.e., Carload Waybill Sample). Standardized analytic tools and applications ⢠Range of various tools that require unique data sets. ⢠Consistency in modeling approaches and data needs for similar geo- graphic scales. Inclusion of behavior- based elements in freight models ⢠Current practices use truck move- ments and commodity flows, but should be based on the behaviors, economic principles, and business practices that dictate the move- ment of freight. ⢠Current modeling tools do not accurately reflect real-world sup- ply chains and logistics practices. ⢠Determination of the influencing behavioral factors that affect freight movement and ongoing data collec- tion to inform models. ⢠Behavior-based freight modeling tools to take advantage of newly col- lected data sets for various geo- graphic analyses. ⢠Incorporation of intermodal transfers, consolidation and distribution prac- tices, and other shipper and carrier practices in modeling tools. Data development to understand the nature, volume, and trends of intermodal transfers ⢠Public sector access to intermodal transfer data of containers, bulk material, and roll-onâroll-off cargo is lacking for most transfer facili- ties other than those of large ports and rail yards. ⢠Data sets developed through collab- oration with the private sector to inform the planning practice knowl- edge base and models on intermodal transfers. ⢠Protocols to collect data on a regular basis. Industry-level freight data development at a subregional level and within urban areas ⢠Freight data are generally not industry-specific, which translates into forecasts that are not sensi- tive to the unique industry trends that are critical to regions that rely heavily on specific industries. ⢠Industry-level forecasts that are sen- sitive to the unique factors of differ- ent industries. ⢠Tools and data at a disaggregated level (local) that can be aggregated for larger geographic analyses. ⢠Tools and models to take advantage of the new data sets. Incorporation of local land use policies and controls for better local forecasting accuracy ⢠Current freight data and models lack local detail related to the gen- eration of freight activity, which hampers local efforts to effectively plan for the last mile. ⢠Enhanced understanding of land use decisions and their implications on freight activity. ⢠Resources for local organizations to incorporate land use considerations into freight planning data and models. Development of a corre- lation between freight activity and various economic influences and macroeconomic trends ⢠Freight models are typically based on population-, employment-, and industry-level productivity fore- casts, with no consideration for the impacts of other economic factors. ⢠Enhanced models that incorporate a wide array of economic factors in forecasting freight demand. Better accuracy of freight forecasts ⢠Freight models rarely (if ever) are reviewed to check the accuracy of their forecasts, calling into ques- tion their reliability and validity. ⢠A systematic approach for freight model and data owners to review and evaluate forecasts (every 3 to 5 years) and adjust models and data methods accordingly. Development of a pro- cess to routinely gen- erate new data sources and problem- solving methods ⢠The improvement of freight plan- ning nationally depends on con- tinuing innovation and steady progress in the development of models, analytic tools, and knowl- edge acquisition. ⢠A value-adding and sustainable pro- cess to generate new and innovative ideas. ⢠Acknowledgment of failed practices that can contribute to the knowledge base of practitioners. Use of ITS resources to generate data for freight modeling ⢠Technologies that can be used to collect freight data have not been used to their potential. ⢠Data can provide a wealth of infor- mation related to current condi- tions and diversions as a result of traffic incidents. ⢠An understanding of the information needed by the modeling community and the standard to which it can be used. ⢠An accessible data bank for freight modeling developed with the coop- eration of GPS device providers, ITS infrastructure owners, and other data providers. Development of a uni- versal multimodal, network-based model for various geo- graphic scales ⢠The fragmentation of modeling techniques and data means that practitioners typically must develop or improvise data and models for their own applications. ⢠Agencies with fewer resources are not able to adequately analyze freight movements. ⢠Some freight transport modes are analyzed more than others because they have more data available for analysis. ⢠An open-source data bank and uni- versal freight modeling tool is the ultimate goal. ⢠A level playing field among different modes of freight transportation in terms of quantity and accuracy of data and complexity of modeling tools. Development of benefitâ cost analysis tools that go beyond tradi- tional financial measures ⢠Analysis of the benefits of project- based scenarios lacks the preci- sion required for those decisions, including direct and indirect impacts, costs, and benefits. ⢠Tools that incorporate a comprehen- sive analysis of the factors associ- ated with infrastructure development, expansion, and enhancement specif- ically related to freight. Development of funding assessments result- ing from freight forecasts ⢠Transportation funding scenarios and what-if analyses are limited in their ability to forecast revenues associated with freight movement. ⢠Estimated costs and potential fund- ing sources that can be justified based on credible freight forecasts. Creation of tools to sup- port the infrastructure design process ⢠Infrastructure design, unless spe- cific to freight, rarely focuses efforts on how best to accommo- date freight movements. ⢠Incorporation of freight forecasts into infrastructure design related to vehi- cle size and weight and future freight activity (i.e., tonnage) by mode. Development of knowl- edge and skills among the freight planning community as a foundation for improved analysis ⢠The freight planning community is relatively small and knowledge transfer is challenging. ⢠Talented innovators who can lead new approaches to freight trans- portation planning are pursuing careers in other industries. ⢠A comprehensive knowledge base for planning professionals that includes the wide range of subject areas related to freight transportation. ⢠Greater recognition or formal stand- ing of freight planning as a profes- sion with an associated body of knowledge.
7Strategic Objectives and Sample Research Initiatives The SHRP 2 C20 Freight Demand Modeling and Data Improvement Strategic Plan is built on a foundation of seven strategic objectives for future innovation in freight travel demand forecast- ing and data. The desired direction for enhanced freight planning, forecasting, and data analysis as expressed by the many stakeholders who participated in this project is reflected in these objectives, which are aimed at stimulating innovation through the avenues laid out in the stra- tegic plan. The seven strategic objectives are 1. Improve and expand the knowledge base for planners and decision makers. 2. Develop and refine forecasting and modeling practices that accurately reflect supply chain management. 3. Develop and refine forecasting and modeling practices based on sound economic and demo- graphic principles. 4. Develop standard freight data (e.g., Commodity Flow Survey, Freight Analysis Framework, and possible future variations of these tools) to smaller geographic scales. 5. Establish methods for maximizing the beneficial use of new freight analytic tools by state DOTs and MPOs in their planning and programming activities. 6. Improve the availability and visibility of data among agencies and between the public and private sectors. 7. Develop new and enhanced visualization tools and techniques for freight planning and forecasting. Building on the foundation of the seven objectives listed above, the SHRP 2 C20 research effort culminated in the development of 13 research areas. These research areas, called sample research initiatives, are shown in Table ES.3. Collectively, these sample research initiatives consti- tute a programmatic approach for systematically improving freight modeling and data avail- ability and forecasting tools. Each of these initiatives is tied to one or more of the seven strategic objectives, with the ultimate goal of promoting and cultivating innovation through Strategic Objectives 2 and 3, supported by the innovations in data development in Strategic Objective 4 and visualization in Strategic Objective 7. Each of the 13 research initiatives also relates to one or more of the three main research dimen- sions identified at the 2010 Innovations in Freight Demand Modeling and Data Symposium: ⢠Knowledge relates to a general understanding of freight transportation issues and the extensive array of elements involved in planning and forecasting freight demand; ⢠Models are the tools used to plan and forecast freight transportârelated activities at various geographic levels; and ⢠Data are the underlying information resources for modeling and planning efforts; these data often represent an important limitation of modeling. The ultimate long-term goal is to build on Strategic Objectives 2 and 3 to promote the development of a full network-based freight forecasting model that incorporates all modes of freight transport and accurately reflects the various factors related to the supply of freight infrastructure and services (Strategic Objective 2) and the underlying demand for these services (Strategic Objective 3). This model will effect a dramatic change in current freight planning and forecasting. It is a highly ambitious endeavor because of the complexity of freight transportation and the numerous elements that are necessary to achieve this long- term goal. The other five strategic objectives are tied to this goal through the development of the appli- cable knowledge base needed to further the goal (Strategic Objective 1), the development and
8 Table ES.3. Sample Research Initiatives Sample Research Initiativesa Research Dimensions Strategic Objectives Knowledge Models Data 1. Improve and expand knowledge base. 2. Develop modeling methods to reflect actual supply chain management practices. 3. Develop modeling methods based on sound economic and demographic principles. 4. Develop standard freight data to smaller geographic scales. 5. Maximize use of freight tools by public sector for planning and programming. 6. Improve availability and visibility of data between public and private sectors. 7. Develop new and enhanced visualization tools and techniques. A: Determine the freight and logistics knowledge and skill requirements for transportation decision makers and pro- fessional and technical personnel. Develop the associated learning systems to address knowledge and skill deficits. l n B: Establish techniques and standard practices to review and evaluate freight forecasts. l n M M C: Establish modeling approaches for behavior-based freight movement. l l n D: Develop methods that predict mode shift and highway capacity implications of various what-if scenarios. l l n n E: Develop a range of freight forecasting methods and tools that address decision-making needs and that can be applied at all levels (national, regional, state, metropolitan planning organization, municipal). l l n n M F: Develop robust tools for freight costâbenefit analysis that go beyond financial considerations to the full range of ben- efits, costs, and externalities. l l M n G: Establish analytic approaches that describe how elements of the freight transportation system operate and perform and how they affect the larger overall transportation system. l l n M H: Determine how economic, demographic, and other factors and conditions drive freight patterns and characteristics. Document economic and demographic changes related to freight choices. l n I: Develop freight data resources for application at subregional levels. l M M n J: Establish, pool, and standardize a portfolio of core freight data sources and data sets that supports planning, programming, and project prioritization. l n n M K: Develop procedures for applying freight forecasting to the design of transportation infrastructure, particularly pavement and bridges. l n L: Advance research to effectively integrate logistics practices (private sector) with transportation policy, planning, and programming (public sector). l M n n M: Develop visualization tools for freight planning and model- ing through a two-pronged approach of discovery and addressing known decision-making needs. l l l n Note: Directly Addresses Objective n; Indirectly Addresses Objective M. a The sample research initiatives outlined as part of the SHRP 2 C20 research project demonstrate how the strategic objectives could be advanced. Each initiative also applies to one or more of the three research dimensions (indicated by l).
9dissemination of data necessary to support it (Strategic Objectives 4 and 6), and the develop- ment of enhanced methods for disseminating information from these analytic tools for public stakeholders (Strategic Objective 5) and decision makers (Strategic Objective 7). The specific research initiatives are initial recommendations for potential research to help move this process forward, but they will likely change as a result of funding availability and industry needs; future developments that spring from some of the other elements of the Strate- gic Plan, such as the Global Freight Research Consortium (GFRC) and future data and modeling symposia; other data and modeling innovations featured in TRB conferences; and research from the National Cooperative Highway Research Program (NCHRP) and the National Cooperative Freight Research Program (NCFRP). Innovations in Freight Demand Modeling and Data Symposium: A Foundation for Moving Forward The successful Innovations in Freight Demand Modeling and Data Symposium held in September 2010 provided a solid foundation for future efforts. The symposiumâs success rested on several factors: ⢠The symposium provided a low-cost approach to generating a variety of research concepts; ⢠The competitive nature of the symposium generated numerous excellent ideas and promising research concepts; ⢠The symposium brought together academic, private sector, and public sector perspectives; and ⢠The symposium fostered a greater shared understanding of the issues and requirements for improved freight modeling and planning. The focus and emphasis areas of future symposia will vary, but the principles of collabora- tion, competition, and communication represent significant building blocks for successful symposia. The symposium featured 18 presentations selected to address the challenge of developing the next generation of freight demand models. It was characterized by a combination of model- ing data and ideas presented by U.S. and international practitioners and academics, followed by open and direct dialogue and debate. It provided a strong foundation for moving forward in that it: ⢠Generated ideas; ⢠Attracted international attention and participation; ⢠Resulted in the identification of several promising areas of research; and ⢠Provided a forum for public and private sector stakeholders, as well as university expertise. Organizing Concept: Global Freight Research Consortium SHRP 2 C20âs project leadership stressed that the future directions should not be burdened with an inflexible bureaucratic organization or cumbersome administration. Rather than establishing a program as part of a government organization, the organizing concept lays out a flexible mechanismâan agile, collaborative frameworkâfor achieving the strategic objectives. To meet this expectation, a GFRC is recommended. This consortium would promote a body of research through funding agencies and other organizations having a stake in improved freight system performance and decision making, supported by enhanced analytic approaches. Partici- pation would be voluntary, attracting those sectors that have a stake in the achievement of the strategic objectives.
10 This peer-based consortium would enable, fund, and promote research, supported through national and international public organizations and private organizations whose efforts serve the freight transportation sector. The member organizations will include public domestic agencies, modal and other associa- tions, universities, and the transportation research entities of other countries. It is also envisioned that the private sector will participate in the GFRC. Table ES.4 summarizes the organizational mix that will potentially represent the core of the consortium. This partnership will support independent research and reward innovative and compelling investigations and experiments by sponsoring an annual research competition spanning various research tracks and providing a seed-grant award. Establishing and maintaining the GFRC will require careful planning. ⢠Investigate the appropriate governance model (e.g., foundation, institute, charity) for the GFRC and complete its charter; ⢠Perform outreach to possible member organizations to promote participation; ⢠Obtain public and private start-up funding as appropriate; ⢠Secure the services of a qualified consultant to assist in the early organizing and start-up activities of the GFRC. This could include developing a draft GFRC work program, organizing additional research idea competitions, holding annual competitions for grants, and facilitat- ing the first few GFRC meetings; and ⢠Regularly restructure and renew the governance model to ensure an entrepreneurial approach and genuine innovation. Recommended Global Freight Research Consortium Initiatives The research team recommends that the GFRC address six major initiatives as part of its overall approach to achieving the strategic objectives. The list is by no means exhaustive, recognizing Table ES.4. Illustrative Organizations for GFRC Participation Agency Role and Focus Area TRB cooperative research programs (e.g., NCFRP, NCHRP) Funding applied research on freight modeling and data; integrating existing separate research tracks with freight TRB, Second Strategic Highway Research Program (until March 2015) Sponsoring innovation symposia; funding develop- ment of training and outreach materials sug- gested by the future directions U.S. DOT modal administrations (e.g., Federal High- way Administration [FHWA], Federal Railroad Administration) Supporting pilots of advanced freight demand models U.S. DOT intermodal organizations (e.g., FHWA, Research and Innovative Technology Administra- tion, Bureau of Transportation Statistics) Improving and expanding freight data resources Academic institutions and university transportation centers Funding and conducting basic research on freight models and data collection and fusion; pooled fund consortia Associations such as the American Trucking Association Networking work and priorities of GFRC to industry and modal operators and carriers State DOTs and MPOs Piloting and application of research Private sector Improving and expanding freight data resources; identifying advances in freight transportation tech- nology and business practices for future research
11 that the ultimate activities of the consortium will be determined by the combined interests and priorities of the participants. Define Priority Research Issues The GFRC will periodically issue a list of research priority areas based on submissions to GFRC- sponsored calls for ideas. The actions needed to define priority research issues include ⢠Establish the initial set of problems or research issues demanding attention; ⢠Publish and widely distribute a call for ideas; and ⢠Communicate the submission format standards and the available incentives or awards. Provide Recognition and Incentives to Spur Breakthroughs The GFRC should recognize the value in offering awards and recognition, particularly for meri- torious research ideas with potentially breakthrough solutions. Nonfinancial recognition is also important. Efforts to promote this process to the greatest extent possible as a way of doing busi- ness for the GFRC will include the following actions. ⢠Establish initial sources for the first call for innovative ideas; ⢠Consider establishing GFRC following a foundation model, to provide a basis for contribu- tions for funding awards, prizes, and related activities; and ⢠Over time, as funding for awards increases, establish multiple categories and multiple award winners. Conduct Regular Innovation Forums An annual forum should be conducted for presenting innovative research and selecting the most promising ideas in freight modeling and data for further development. Each forum should publish a report that would frame the freight modeling and data research agenda for the follow- ing year. Actions needed for ongoing forums include ⢠Determine the content, themes, or focus areas for periodic innovation forums; ⢠Review and incorporate the results of the forums in relation to other GFRC activities; and ⢠Provide guidance for maximizing the dissemination of forum results and promoting forum participation among colleagues and peers. Promote Technology Transfer from Other Disciplines Solutions from other fields that can be transferable or adaptable to freight transportation model- ing needs will be promoted regularly and will serve as a focus for a broader outreach to various utilities and other sectors. Transferable solutions will also be a consideration in screening ideas. Effective appropriation and modification of analytic techniques from other disciplines will be encouraged by the following actions. ⢠Organize a forum that would include presenters from other sectors to consider how their modeling and planning techniques might be adaptable to freight forecasting; and ⢠Organize a competition devoted to adopting and adapting analytic techniques from other sectors.
12 Promote an International Focus Research innovation for freight demand and analysis must necessarily reflect the global nature of freight movement. Implementation must draw on global research and promote participation from all relevant freight sectors and academic institutions worldwide. To encourage an inter- national focus, GFRC organizers could ⢠Secure public, private, and academic participants from other nations through the contacts and networks of those who have already been involved in SHRP 2 C20; ⢠Conduct an early GFRC meeting in a strategically selected country; and ⢠Regularly showcase freight planning and modeling approaches employed in other nations. Recognize the Application of Completed Research Another important component of recognition and information dissemination for the consor- tium will be to periodically draw attention to the impacts and benefits of applied freight model- ing and data research. This activity, which will be particularly important for promoting broader implementation of successful freight analytic approaches, could include ⢠Advance a general tracking activity to capture the benefits and experiences of freight profes- sionals using new research approaches; and ⢠Periodically publish this information to reflect the long-term benefit of GFRC efforts.
