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Freight Demand Modeling and Data Improvement (2013)

Chapter: Chapter 4 - Conclusions and Suggested Research

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Suggested Citation:"Chapter 4 - Conclusions and Suggested Research." National Academies of Sciences, Engineering, and Medicine. 2013. Freight Demand Modeling and Data Improvement. Washington, DC: The National Academies Press. doi: 10.17226/22734.
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Suggested Citation:"Chapter 4 - Conclusions and Suggested Research." National Academies of Sciences, Engineering, and Medicine. 2013. Freight Demand Modeling and Data Improvement. Washington, DC: The National Academies Press. doi: 10.17226/22734.
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Suggested Citation:"Chapter 4 - Conclusions and Suggested Research." National Academies of Sciences, Engineering, and Medicine. 2013. Freight Demand Modeling and Data Improvement. Washington, DC: The National Academies Press. doi: 10.17226/22734.
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Suggested Citation:"Chapter 4 - Conclusions and Suggested Research." National Academies of Sciences, Engineering, and Medicine. 2013. Freight Demand Modeling and Data Improvement. Washington, DC: The National Academies Press. doi: 10.17226/22734.
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Suggested Citation:"Chapter 4 - Conclusions and Suggested Research." National Academies of Sciences, Engineering, and Medicine. 2013. Freight Demand Modeling and Data Improvement. Washington, DC: The National Academies Press. doi: 10.17226/22734.
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Suggested Citation:"Chapter 4 - Conclusions and Suggested Research." National Academies of Sciences, Engineering, and Medicine. 2013. Freight Demand Modeling and Data Improvement. Washington, DC: The National Academies Press. doi: 10.17226/22734.
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Suggested Citation:"Chapter 4 - Conclusions and Suggested Research." National Academies of Sciences, Engineering, and Medicine. 2013. Freight Demand Modeling and Data Improvement. Washington, DC: The National Academies Press. doi: 10.17226/22734.
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Suggested Citation:"Chapter 4 - Conclusions and Suggested Research." National Academies of Sciences, Engineering, and Medicine. 2013. Freight Demand Modeling and Data Improvement. Washington, DC: The National Academies Press. doi: 10.17226/22734.
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Suggested Citation:"Chapter 4 - Conclusions and Suggested Research." National Academies of Sciences, Engineering, and Medicine. 2013. Freight Demand Modeling and Data Improvement. Washington, DC: The National Academies Press. doi: 10.17226/22734.
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Suggested Citation:"Chapter 4 - Conclusions and Suggested Research." National Academies of Sciences, Engineering, and Medicine. 2013. Freight Demand Modeling and Data Improvement. Washington, DC: The National Academies Press. doi: 10.17226/22734.
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Suggested Citation:"Chapter 4 - Conclusions and Suggested Research." National Academies of Sciences, Engineering, and Medicine. 2013. Freight Demand Modeling and Data Improvement. Washington, DC: The National Academies Press. doi: 10.17226/22734.
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Suggested Citation:"Chapter 4 - Conclusions and Suggested Research." National Academies of Sciences, Engineering, and Medicine. 2013. Freight Demand Modeling and Data Improvement. Washington, DC: The National Academies Press. doi: 10.17226/22734.
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Suggested Citation:"Chapter 4 - Conclusions and Suggested Research." National Academies of Sciences, Engineering, and Medicine. 2013. Freight Demand Modeling and Data Improvement. Washington, DC: The National Academies Press. doi: 10.17226/22734.
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Suggested Citation:"Chapter 4 - Conclusions and Suggested Research." National Academies of Sciences, Engineering, and Medicine. 2013. Freight Demand Modeling and Data Improvement. Washington, DC: The National Academies Press. doi: 10.17226/22734.
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Suggested Citation:"Chapter 4 - Conclusions and Suggested Research." National Academies of Sciences, Engineering, and Medicine. 2013. Freight Demand Modeling and Data Improvement. Washington, DC: The National Academies Press. doi: 10.17226/22734.
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Suggested Citation:"Chapter 4 - Conclusions and Suggested Research." National Academies of Sciences, Engineering, and Medicine. 2013. Freight Demand Modeling and Data Improvement. Washington, DC: The National Academies Press. doi: 10.17226/22734.
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Suggested Citation:"Chapter 4 - Conclusions and Suggested Research." National Academies of Sciences, Engineering, and Medicine. 2013. Freight Demand Modeling and Data Improvement. Washington, DC: The National Academies Press. doi: 10.17226/22734.
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Suggested Citation:"Chapter 4 - Conclusions and Suggested Research." National Academies of Sciences, Engineering, and Medicine. 2013. Freight Demand Modeling and Data Improvement. Washington, DC: The National Academies Press. doi: 10.17226/22734.
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Suggested Citation:"Chapter 4 - Conclusions and Suggested Research." National Academies of Sciences, Engineering, and Medicine. 2013. Freight Demand Modeling and Data Improvement. Washington, DC: The National Academies Press. doi: 10.17226/22734.
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Suggested Citation:"Chapter 4 - Conclusions and Suggested Research." National Academies of Sciences, Engineering, and Medicine. 2013. Freight Demand Modeling and Data Improvement. Washington, DC: The National Academies Press. doi: 10.17226/22734.
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Suggested Citation:"Chapter 4 - Conclusions and Suggested Research." National Academies of Sciences, Engineering, and Medicine. 2013. Freight Demand Modeling and Data Improvement. Washington, DC: The National Academies Press. doi: 10.17226/22734.
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Suggested Citation:"Chapter 4 - Conclusions and Suggested Research." National Academies of Sciences, Engineering, and Medicine. 2013. Freight Demand Modeling and Data Improvement. Washington, DC: The National Academies Press. doi: 10.17226/22734.
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Suggested Citation:"Chapter 4 - Conclusions and Suggested Research." National Academies of Sciences, Engineering, and Medicine. 2013. Freight Demand Modeling and Data Improvement. Washington, DC: The National Academies Press. doi: 10.17226/22734.
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Suggested Citation:"Chapter 4 - Conclusions and Suggested Research." National Academies of Sciences, Engineering, and Medicine. 2013. Freight Demand Modeling and Data Improvement. Washington, DC: The National Academies Press. doi: 10.17226/22734.
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Suggested Citation:"Chapter 4 - Conclusions and Suggested Research." National Academies of Sciences, Engineering, and Medicine. 2013. Freight Demand Modeling and Data Improvement. Washington, DC: The National Academies Press. doi: 10.17226/22734.
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52 freight transportation and the many elements that are neces- sary to achieve this long-term goal. In addition, goals will be achieved through the development of the applicable knowledge base needed, the development and dissemination of necessary support data, and the devel- opment of enhanced methods for disseminating informa- tion from these analytic tools for public stakeholders and decision makers. Although development of a full multimodal network-based freight forecasting model is the ultimate long-term goal, it is important to note that freight transportation has not tradi- tionally lent itself to innovative planning and forecasting prac- tices. This is because freight transport has historically been a relatively uncomplicated, low-tech process. In addition, past experience in freight transportation does not necessarily correlate well with future freight activity due to short-term changes in the forces of supply and demand. As a result, accurately planning for freight transportation will require a radical paradigm shift in the way the practice is currently conducted. Sample Research Initiatives The SHRP 2 C20 research effort culminated in the develop- ment of 13 sample research initiatives. Collectively, these research initiatives constitute a programmatic approach for systematically improving freight modeling and data availabil- ity and forecasting tools. Each of the research initiatives relates to at least one of the three main research dimensions: knowledge, models, and data. The sample research initiatives described in detail below are based on the SHRP C20 research, but they should be viewed in their proper context as steps in support of the seven strategic objectives. The specific research projects are initial recommendations for potential research to spur progress; many of these recommendations are likely to change based on funding sources, industry needs, and developments Policy makers, state DOTs, MPOs, and varied stakeholders acknowledge that freight transportation is an issue of grow- ing importance. Freight modeling practice, which informs freight planning and programming to support goods move- ment, is evolving. However, communication among practi- tioners, coordination and integration of models and data, the overall direction of the practice, and methods for address- ing decision-making needs all require improvement. A struc- tured approach to addressing the current shortcomings of freight modeling and data is critical to the evolution of the practice toward better freight planning. The SHRP 2 C20 research initiative has been developed to provide the strategic framework for making further inroads in freight forecasting, planning, and data, and to accelerate innovative breakthroughs with the aim of integrating freight considerations into the planning process with confidence. The Strategic Plan establishes a framework for joining the strategic objectives, the sample research initiatives, and the strategic directions for future research. Strategic Plan Introduction The SHRP 2 C20 Freight Demand Modeling and Data Improvement Strategic Plan (in this chapter and also provided as a stand-alone web document) advances a broad new direc- tion for improving freight planning, promoting continuous innovation for breakthrough solutions to freight analytic and data needs, and fostering a collaborative approach for private, public, and academic stakeholders. The long-term goal for the research documented is to build on the strategic objectives to ultimately develop 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 and the underlying demand for these services. This will be a dra- matic change in current freight planning and forecasting. It is a highly ambitious endeavor because of the complexity of C h a P t e R 4 Conclusions and Suggested Research

53 such as the GFRC and the future data and modeling sym- posia recommended in the Future Directions section of this chapter. Given that the required research related to planning and decision-making support will evolve over time, an impor- tant function for those involved in implementing the Stra- tegic Plan is to periodically assess changing research needs based on decision-making requirements relative to the chang- ing dynamics of goods movement. Sample Research Initiative A Determine the freight and logistics knowledge and skill require- ments needed for transportation decision makers and profes- sional and technical personnel. Develop the associated learning systems to address knowledge and skill deficits. Description The complex factors that drive freight transportation demand require an understanding of economics, land use, public policy, demographics, finance, and information technology. The education and development of professionals involved in freight planning and forecasting will be an effective strat- egy for improving freight planning, analysis, and decision making. Successful planning and forecasting in freight trans- portation can be enhanced through the dissemination of knowledge among professionals whose current training is likely to be oriented toward passenger travel or general trans- portation issues. The intermodal revolution of freight transportation in the past several decades, for example, was primarily aimed at enhancing efficiency for private sector interests. The dra- matic transformation of freight transportation during the same period, however, was not reflected in advances in plan- ning practices by state DOTs or MPOs. These advances in freight transportation practices were largely driven by advances in information technology and information man- agement. Consequently, this research is aimed at sparking a revolution in freight planning and forecasting through a broad-based initiative to enhance knowledge of public and private sector interests. The lack of uniformity in knowledge levels about freight issues in the transportation planning community is com- pounded by a significant disconnect between the goals and objectives of shippers and carriers in the private sector and planners in the public sector. This research identifies skill sets and techniques to help bridge these gaps and facilitate more effective planning and problem solving. An ideal skill set includes an integrated curriculum with subject areas such as computer science, planning, economics, political science, and organizational skills. Communication skills are also critical for professional development in this area. This initiative will be implemented in three major research phases: 1. Conduct an extensive knowledge and skills requirements analysis for all levels of transportation professionals and decision makers. 2. Over time, develop, pilot, and evaluate comprehensive knowledge transfer subject matter and media. This phase will include a wide range of approaches, even including brief employment swaps. 3. Develop the supporting organizational and structural approaches, such as national and regional freight innova- tion academies, to effectively deliver an ongoing knowledge and skills delivery system. Benefits and Expected Outcomes • Enhanced performance of individuals and organizations through a greater knowledge of freight and logistics; • Greater understanding of the need for public planning and analysis to incorporate freight and logistics; and • Greater collaboration between public and private sectors, to provide a much-needed understanding of the discrete segments of the freight transport community, including shippers, carriers, customers, and other elements of the supply chain. Implementation Sample Research Initiative A implementation ideas and con- siderations are summarized in Table 4.1. Other Considerations Sample Research Initiative A does not directly relate to any of the other initiatives and does not inform them directly as a research effort, but the knowledge base developed and culti- vated during the course of this effort will ultimately support all other ongoing research efforts related to freight planning, forecasting, and model and data development. Thus, this ini- tiative should be considered for early action. Although recent and ongoing research efforts, such as those presented at the 2010 Innovations in Freight Demand Model- ing and Data Symposium, tend to be highly technical and oriented toward specific transport modes or logistics pro- cesses, the underlying concepts of transportation economics and other factors that influence freight transportation demand should be incorporated in this initiative. The Freight Academy program organized by the I-95 Cor- ridor Coalition could inform this initiative, along with an

54 interesting global supply chain game developed by researchers at Delft University and the Robert H. Smith School of Busi- ness at the University of Maryland, and tested through course offerings at the business school since 2005 (Corsi et al. 2006). Possible champions of this effort could include universities, AASHTO, and perhaps some collaborative initiatives involving academia and private industry. Decision makers in govern- ment would benefit from an executive-level training program that provides a general overview of issues related to freight planning and forecasting. Sample Research Initiative B Establish techniques and standard practices to review and eval- uate freight forecasts. Description Freight modeling, like passenger travel demand modeling, has forecasting capabilities that are used to estimate the movement of freight on highways, railroads, and other ele- ments of the freight transportation system. The effectiveness of these modeling tools is rarely analyzed, mainly because review and evaluation processes completed years after fore- casts are done are perceived to be of dubious value in light of how rapidly circumstances may change during the planning period. This research aims at developing practices to review pre- vious freight forecasts over short- and intermediate-term horizons, with a review of factors used in forecasts and a backcasting comparison of actual freight values, mode shares, and other characteristics of freight transportation. In light of the ongoing developments in freight forecasting and the fact that the most robust forecasting tools used by public agencies have been developed within the past two decades, the review and evaluation of long-term projections is not considered a near-term research priority. This research effort will be oriented toward the freight fore- casting methods documented in NCHRP Report 388: A Guide- book for Forecasting Freight Transportation Demand (Cambridge Systematics 1997) and in National Highway Institute Course 139002: Uses of Multimodal Freight Forecasting in Transpor- tation Planning (Federal Highway Administration 2010). This research involves a historical survey of 15 to 20 public agencies that (1) have used one or more of the various tools described in these publications and (2) have documented or published results from their freight forecasting processes that can be assessed at the present time. The review and evaluation research is based on a mix of short-term (up to 3 years from original forecast) and intermediate-term (3 to 10 years from original forecast) results. The survey will document pro- jected versus actual conditions for these 15 to 20 models as measured by facility (highway, rail, and terminal) operating characteristics (volumes), mode choice, routing, and com- modity flows. The use of these measures will depend on their applicability to individual models. Benefits and Expected Outcomes • Improve freight forecasting through a structured learning process related to actual versus projected conditions; • Develop model calibration tools to improve models over time; • Provide guidance on additional data and other factors to be incorporated into the planning and forecasting pro- cess; and • Provide insight into how various factors used in previous freight forecasts can change over time and influence each other in ways not previously considered. Implementation Sample Research Initiative B implementation ideas and con- siderations are summarized in Table 4.2. Table 4.1. Sample Research Initiative A Implementation Products or Projects Phases and Time Lines Estimated Costs and Resource Opportunities Other Implementation Perspectives Analysis of knowledge and skills requirement. 9 months $150,000 na Develop, pilot, and evaluate a comprehensive knowledge transfer subject matter and media. Continuous, with implementation following knowledge and skills analysis as basis for moving forward. $1,000,000 annually na Develop the supporting organizational and structural approaches for a major knowledge transfer initiative. 12 months $200,000 To begin after analysis of knowledge and skills requirement is completed. Note: na = not applicable.

55 Other Considerations Because this research effort does not relate to the future high- way capacity considerations of the SHRP 2 program as directly as others, this initiative should not be considered a top priority for early implementation. It may provide more value some years down the road when recently developed models with freight forecasts can be tested. Sample Research Initiative B relates to the following initiatives: • Modeling approaches and tools included in Sample Research Initiatives C and E may offer some interesting possibilities for backcasting with previous models using different types of data; • The results of this research effort can support and inform the what-if scenarios described in Sample Research Ini- tiative D; and • The review of economic, demographic, and other factors described in Sample Research Initiative H can provide insight into additional factors to consider in documenting devia- tions between forecasts and actual results. Ongoing innovations in freight planning and forecasting practice that can inform and support this initiative include the following: • The modeling effort for the Mississippi Valley Region con- ducted by the Center for Freight Infrastructure Research and Education at the University of Wisconsin–Madison incorporates some narrowly defined commodity groups that might be somewhat easier to use for validation efforts than the aggregated commodities typically used in freight forecasts; • The evaluation metrics of the second Oregon Statewide Integrated Model (SWIM2) may offer some insight for this initiative; • The interclass correlation process described in Analysis and Multi-Level Modeling of Truck Freight Demand (Wang et al. 2010) may be informative for this research effort; • The touring element of freight transportation is typically the most difficult to validate. The research documented in Modeling Commercial Vehicle Daily Tour Chaining (Ruan et al. 2010) should be reviewed in detail for applicability to this initiative; and • Vilain and Muhammad (2010) have recently presented research on validating econometric models in Freight Demand Modeling Using Econometric Models. Sample Research Initiative C Establish modeling approaches for behavior-based freight movement. Description Analytic tools are needed to model or forecast freight flows and modal volumes in ways that generally reflect the decision making of shippers, carriers, and receivers of goods. These tools will assist state DOTs and MPOs in better planning and prioritizing system investments and assessing and measuring system performance. Private sector freight stakeholders must work with public sector stakeholders to establish model parameters, processing methods, and product elements. The end goal of this initiative is to establish tools that better model the freight movement patterns of various segments of the industry. Behaviors are not monolithic; that is, long-haul operations behave differently from hub-and-spoke opera- tions, which behave differently from local dray operations. Each has different behaviors and operating characteristics, such as time of day, preferred routes, parking needs, and congestion contribution levels. The research will cover the equipment choices, motivations, and economic choices germane to individual segments and stakeholders of the freight transport community. Specifically, each research task will provide an in-depth and complete look at a single segment of the industry. Examples include detailed explorations of deliveries to various-sized grocery stores, res- taurant delivery of food and beverages, fuel delivery, and par- cel package delivery. Table 4.2. Sample Research Initiative B Implementation Products or Projects Time Required Estimated Costs and Resource Opportunities Other Implementation Perspectives Historic survey of 15 to 20 freight forecasting tools and processes used by multistate regions, states, MPOs, and local and county agencies, including comparison of forecasts for short- and intermediate-term planning horizons with actual conditions. 12 months $150,000–$200,000 Should include two or more international models.

56 The tasks in this project will serve as building blocks in the development of a more comprehensive overall freight trans- port model. Much like subroutines embedded in a highly complex program, this research will provide a modeling approach that includes decision tree creation methodology. Benefits and Expected Outcomes • Provide a much-needed understanding of the discrete seg- ments of the freight transport community, including ship- pers, carriers, customers, and other elements of the supply chain; • Help public sector agencies gain a better understanding of the impacts policy decisions have on individual freight transport segments; • Develop a well-rounded and representative understanding of freight movement that does not generalize nor assume that freight movement activity is similar across different industry sectors; • Provide insight on service availability, pricing, and reli- ability as performance measures for different industry sec- tors; and • Develop an improved understanding of the intermodal freight movement. Implementation Sample Research Initiative C implementation ideas and con- siderations are summarized in Table 4.3. Other Considerations Sample Research Initiative C relates to the following initiatives: • This research can support the research efforts described in Sample Research Initiative G for different geographic scales, since both initiatives involve research into the char- acteristics of individual businesses and industries; • Some of this research can be used to document industry- specific limitations in mode choice related to reliability, ser- vice availability, and other factors to inform the what-if scenarios described in Sample Research Initiative D; and • This initiative can serve as the basis for the logistics prac- tices described in Sample Research Initiative L. Ongoing innovations in freight planning and forecasting practice that can inform and support this initiative include the following: • The microsimulation work on freight transport in the Mississippi Valley Region can provide insight into logis- tics practices for certain commodities that are critical to that region. • The research described in Modeling Commercial Vehicle Daily Tour Chaining (Ruan et al. 2010) is a good example of behavior-based modeling for local truck deliveries. Recent research from Japan, Modeling Truck Route Choice Behavior by Traffic Electronic Application Data (Hyodo 2010), is specific to local container truck movements. Table 4.3. Sample Research Initiative C Implementation Products or Projects Time Required Estimated Costs and Resource Opportunities Other Implementation Perspectives In-depth research of package delivery character- istics, including door-to-door delivery from long-haul to end receiver (e.g., multistory urban office complex). 12 months $90,000 na In-depth research of food and beverage delivery for restaurant industry (including door-to-door delivery for different population densities). 18 months $180,000 na In-depth research of grocery store distribution characteristics (including key products or product groups). 9–18 months $60,000–$180,000 Time line and cost are dependent on number of product lines or product types included in research. In-depth research of sensitive medical-related deliveries (including medication, equipment, organs, and other time-sensitive items). 12 months $90,000 na Other significant freight segments as proposed by researchers. 9–18 months $60,000–$250,000 Time line and cost are dependent on industries and commodity types. Regional shipper and carrier surveys to develop regional profiles described in Sample Research Initiative E. Up to 24 months $400,000 to $1.5 million, depending on number of business establishments surveyed. Should be done based on geographic levels (regional, state, major MSA, minor MSA) described in Sample Research Initiative E. Note: MSA = metropolitan statistical area; na = not applicable.

57 • The MOBILEC model documented in Innovative Freight Transportation Framework for Flanders (Maes and Ramaekers 2010) strongly relates to this initiative. • The combined PINGO model and logistics model docu- mented in A Model System for Forecasting National and International Freight Transport in Norway (Hovi and Hansen 2010) is an innovative approach to logistics-based freight forecasting. • For truck deliveries, this research can be informed by Fusing Public and Private Truck Data to Support Regional Freight Planning and Modeling (Liao 2010) and the analysis tool to process GPS data developed by Sharman and Roorda (2010) of the University of Toronto. • The tour-based entropy model documented in A Tour-Based Urban Freight Demand Model Using Entropy Maximi- zation (Wang and Holguín-Veras 2010) and the business establishment–based modeling process documented in A Firm-Based Freight Demand Modeling Framework Cap- turing Intra-Firm Interaction and Joint Logistic Decision Making (Guo and Gong 2010) are innovative research efforts that strongly tie to this initiative. • This research initiative can also be informed by the hybrid microsimulation model of urban freight transportation developed by Donnelly et al. (2010) in A Hybrid Micro- simulation Model of Urban Freight Travel Demand. Sample Research Initiative D Develop methods that predict mode shift and highway capacity implications of various what-if scenarios. Description Freight is substantially more varied than passenger transport in the complexity of its transportation processes and its global multimodal and intermodal nature. Some trucking compa- nies are now transportation brokers, making customer ser- vice, cost, and the freight’s delivery schedule the focus—not the mode. State DOTs and MPOs need to better understand how freight shifts across modes and how highway capacity is affected by such shifts. What-if scenarios are valuable for planning and testing alternative investment scenarios. Con- siderations in such scenarios include fuel costs, congestion pricing, toll increases, new or closed rail spurs, and improved waterway infrastructure. This area of research identifies the key decision points and factors that dictate mode shifts, route selections, equip- ment selection (e.g., size and type of truck, container or non container), trip frequency, and so forth. These decision points and factors vary greatly in any given situation. In addi- tion to the example considerations identified above, vari- ables include policy changes, customer demand, weather, infrastructure capacity, transportation company mergers, and strategic partnership development. Completed research will provide a decision tree model to illustrate what-if scenarios. The research effort described in this initiative will build on the underlying economic and demographic foundations of traditional econometric models used in freight forecast- ing, with enhancements related to considering intermodal transfers, the growing role of third-party and fourth-party logistics providers (3PLs and 4PLs, respectively) in the freight transportation industry, and ongoing refinements in supply chain management. Benefits and Expected Outcomes • Provide public agencies and private entities with a tool that can help determine unforeseen effects caused by a variety of factors facing the freight community on a regular basis; • Allow public agencies an opportunity to consider the impacts associated with infrastructure investments (or lack of investments) and also to create realistic contingency plans; • Help public sector agencies gain a better understanding of the impacts that policy and infrastructure investment deci- sions may have on individual elements of the freight trans- portation system or geographic regions; • As with Sample Research Initiative C, help provide insight on service availability, pricing, and reliability as perfor- mance measures for different industry sectors; and • An improved understanding of freight movement and the role of intermodal transfers and service providers (includ- ing less-than-truckload carriers and 3PL and 4PL firms) in freight transportation. Implementation Sample Research Initiative D implementation ideas and con- siderations are summarized in Table 4.4. Other Considerations Sample Research Initiative D relates to the following initiatives: • The research proposed in Sample Research Initiative C would be useful in understanding the needs of different industry sectors and the impact of service availability, reli- ability, pricing, and other factors on their mode decisions; • The extensive data collection effort in this initiative would help support the logistics and policy linkage effort in Sample Research Initiative L and potentially inform the localized decision-making needs documented in Sample Research Initiative E;

58 • Data collected in this effort could help in illustrating freight–passenger transportation relationships as described in Sample Research Initiative G; • Ongoing research in standardizing freight data, as described in Sample Research Initiative J and in NCFRP Report 9: Guid- ance for Developing a Freight Transportation Data Architecture (Quiroga et al. 2011), should be considered when assembling and analyzing data for this effort; and • The shipper and carrier research described in this initiative could inform the research proposed in Sample Research Initiative H. Ongoing innovations in freight planning and forecasting practice that can inform and support this initiative include the following: • This initiative can be informed and supported by ongoing research related to econometric models for freight analysis, including Freight Demand Modeling Using Econometric Models (Vilain and Muhammad 2010); • Some of the logistics and industry-related research used to support the MOBILEC model as described in Innovative Freight Transportation Framework for Flanders (Maes and Ramaekers 2010) may relate to this initiative; • Truck data obtained and documented in Fusing Public and Private Truck Data to Support Regional Freight Planning and Modeling (Liao 2010) may be useful for this effort; and • Urban delivery characteristics as developed in A Hybrid Microsimulation Model of Urban Freight Travel Demand (Donnelly et al. 2010) may provide some insight to support the what-if scenarios in this initiative. Sample Research Initiative E Develop a range of freight forecasting methods and tools that address decision-making needs and that can be applied at national, regional, state, MPO, and municipal levels. Description Many of the techniques currently used in forecasting freight movement are oriented toward specific geographic scales reflecting varied planning needs. Some of these methods require tools and data that are specific to one geographic scale and may not translate well from one geographic scale to another. Data sources that are most applicable to more coarse geographies (e.g., FAF, Transearch) do not translate to local levels, and local freight planning techniques are usu- ally vehicle-based, are inextricably linked to land uses, and do not take into account the broad economic factors that drive freight movement. In addition, wide differences exist between the warehousing and distribution practices for dif- ferent commodity types (e.g., the delivery process for a local food distributor versus a multistate distribution process for major retailers). This research will bridge the gap created by the varied geo- graphic scales used in freight planning by establishing a set of tools that can be applied to different geographies depend- ing on need. These tools can be developed and defined by research into • Freight and truck generation rates for different types of land uses and commodity types by trip type (local, long haul, drayage) and direction (inbound–outbound); • Different practices for warehousing and distribution for various commodity types, geographic areas, and popula- tion densities; and • Approaches to combine this information with data that are readily available on a broader geographic scale through existing industry sources in order to create planning and forecasting processes for up to four geographic scales. Sample Research Initiative E is similar to Sample Research Initiative C and will build on that research effort. Sample Research Initiative C relates specifically to freight data, while Sample Research Initiative E involves enhancing analytic Table 4.4. Sample Research Initiative D Implementation Products or Projects Time Required Estimated Costs and Resource Opportunities Other Implementation Perspectives Conduct research identifying the key factors involved in mode choice decisions by shippers, carriers, and 3PLs by commodity type. 9 months $150,000 Should be based on current practices in supply chain management and freight forecasting. National survey of shippers, carriers, and 3PLs. 18 months $250,000 to $1.5 million, depending on sample size na Develop tools to identify infrastructure impacts based on prior tasks within this initiative (as described above). 18 months $250,000 Must wait for completion of previous items in this initiative. Note: na = not applicable.

59 processes that build on previous and ongoing research in this area. Previous work related to this research effort includes NCFRP Report 8: Freight-Demand Modeling to Support Public- Sector Decision Making (Cambridge Systematics and GeoStats 2010); NCHRP Synthesis of Highway Practice 384: Forecasting Metropolitan Commercial and Freight Travel (Kuzmyak 2008); NCHRP Report 606: Forecasting Statewide Freight Toolkit (Cambridge Systematics et al. 2008); NCHRP Report 388: A Guidebook for Forecasting Freight Transportation Demand (Cambridge Systematics 1997); and others. The multitiered planning and forecasting processes documented in National Highway Institute Course 139002: Uses of Multimodal Freight Forecasting in Transportation Planning (Federal Highway Administration 2010) can also provide direction for this effort. The approach for this initiative involves research at four geographic levels: 1. Regional (e.g., mid-Atlantic region); 2. State (e.g., Pennsylvania); 3. Major metropolitan area (e.g., Philadelphia); and 4. Minor metropolitan area (e.g., Harrisburg). In general, the research involves documentation of current freight planning practices for the types of areas listed above and research on freight activity (by all modes) within each geographic level, including special generators such as rail terminals and port facilities. The nesting of data for different geographic scales and the relationships between different data types for different scales would ideally be studied through a region–state–MSA hierarchy in a single state in a single region, as indicated with the Pennsylvania examples cited above. Inter- nal links between generators and intermediate destinations (e.g., intermodal terminals and regional distribution centers) will be documented, along with links between these inter- mediate destinations and final freight delivery locations (e.g., retail establishments). Freight activity will be documented by mode, commodity, origin and destination (internal–internal, internal–external, external–internal, and through movements), and other pertinent characteristics for each geographic level. This effort will be carried out in close collaboration with the Sample Research Initiative C effort, as the final recommended research element of Sample Research Initiative C specifically ties to the four geographic levels described here. An innovative element of this effort is the use of data resources that may not be used in traditional freight fore- casting practices, such as state labor departments, economic development authorities, and similar public agencies. The development of data fusion tools, collective industry knowl- edge, and advanced technology (e.g., GPS data, RFID tech- nology for inventory control) to support these efforts is a potential outcome of this research. Further efforts to link these data to existing data resources used in traditional planning practices (e.g., FAF Version 3, Transearch) are also envisioned as an outcome of this initiative. Benefits and Expected Outcomes • Document current freight planning and forecasting practices for different geographic levels, along with ongoing research related to potential enhancements of these practices; • Provide a detailed view of freight planning and forecasting considerations for different geographic levels; • Link enhanced data resources from Sample Research Initia- tive B to new or enhanced forecasting methods for different geographic levels; • Develop new tools for linking disparate data resources not traditionally used for freight planning and forecasting; • Establish a correlation between or supplement to local data (including nontraditional data) and commodity flow data available for broad geographic scales, including national data sources such as FAF and Transearch; and • Develop methods for nesting local freight planning data and tools into those that are used for larger scales (i.e., development of local tools that function as subsets of national tools and data resources). Implementation Sample Research Initiative E implementation ideas and con- siderations are summarized in Table 4.5. Other Considerations Sample Research Initiative E relates to the following initiatives: • The data collection and research effort for Sample Research Initiative C should be done in conjunction with the research for this initiative; • This initiative, in conjunction with Sample Research Ini- tiative C, can inform and support the research in Sample Research Initiative I; • The data collection for this initiative will strongly inform the policy, planning, and programming efforts of Sample Research Initiative L; • To the extent that this initiative results in improved and enhanced methods of freight forecasting, it can help inform the review and evaluation methods described in Sample Research Initiative B; • In support of Sample Research Initiative H, this research can provide insight into other factors that influence freight transportation demand; and • The pooling and standardization process described for Sample Research Initiative J should be considered for all data collected for this initiative.

60 Ongoing innovations in freight planning and forecasting practice that can inform and support this initiative include the following: • The online freight data repository developed in California can provide some guidance on disparate data sources and fusion of data for this initiative. • The Oregon SWIM2 model can be an ideal resource for a state-level data tool in this research effort. • The modeling effort for the Mississippi Valley Region per- formed by the Center for Freight Infrastructure Research and Education at the University of Wisconsin–Madison offers insight on modeling freight in large geographic regions. Similarly, the PINGO and logistics models used in Norway’s national and international freight transport forecasting process will be a useful resource for this initiative. • The research effort Generation of a U.S. Commodity Flows Matrix Using Log-Linear Modeling and Iterative Propor- tional Fitting (Peterson and Southworth 2010) can inform the process for filling data gaps and nesting freight fore- casting tools for different regional scales. • Transportation Research Board Special Report 304: How We Travel: A Sustainable National Program for Travel Data (Committee on Strategies for Improved Passenger and Freight Travel Data 2011) recommends the organization of a national travel data program built on a core of essen- tial passenger and freight travel data sponsored at the fed- eral level and well integrated with travel data collected by states, MPOs, transit and other local agencies, and the private sector. • Various research efforts presented at the Data Collection and Visualization Techniques session of the 2010 Innovations in Freight Demand Modeling and Data Symposium can pro- vide insight into tour chaining and other freight activity on smaller geographic scales. Sample Research Initiative F Develop robust tools for freight cost–benefit analysis that go beyond financial considerations to the full range of benefits, costs, and externalities. Description Freight movement and logistics are significant components of the nation’s economy and gross domestic product. Trans- portation agencies are looking for ways to better link trans- portation planning decisions with economic development and other factors, both costs and benefits. This research is aimed at helping to better understand and estimate the full range of monetary and nonmonetary freight costs and ben- efits in support of more informed decision making and analyses of policies, programs, projects, and investments. The research objectives include the development of mea- sures that can standardize the disparate costs and benefits for use in an overall cost–benefit analysis. This effort includes metrics such as congestion, air quality, employment, social equity, property value impacts, community livability, diversi- fication of economic activity, system redundancy, and safety and security. Several research efforts provide guidance on nonmonetary metrics related to freight transport. These include Shipper Willingness to Pay to Increase Environmental Performance in Freight Transportation (Fries et al. 2010) and Building Resil- ience into Freight Transportation Systems (Ta et al. 2010). Table 4.5. Sample Research Initiative E Implementation Products or Projects Time Required Estimated Costs and Resource Opportunities Other Implementation Perspectives Research on current practices, tools, and data: Regional level (three regions). 9 months $60,000 These projects can run concurrently; should be done in conjunction with Sample Research Initiative C to the extent possible. Research on current practices, tools, and data: State level (10 states). 12 months $125,000 Research on current practices, tools, and data: Major MSA (10 MSAs). 12 months $125,000 Research on current practices, tools, and data: Minor MSA (15 MSAs). 12 months $150,000 Development of new tools for all four geographic areas, including the use of nontraditional data sources; should include links to current and newly developed national data sources. 24 months $400,000 The previous four items must be completed before this part of the project begins.

61 Benefits and Expected Outcomes • Identify nontraditional components necessary for a com- prehensive, holistic cost–benefit analysis; • Document and include external benefits and costs in infra- structure investment decisions; and • Garner support from a range of stakeholders, including those not directly involved in transportation, in the planning and decision-making processes for major transportation investments. Implementation Sample Research Initiative F implementation ideas and con- siderations are summarized in Table 4.6. Other Considerations Because this research effort does not relate to the highway capacity considerations of the SHRP 2 program as directly as others, this initiative should not be considered a top priority for early implementation. However, it can be pursued as a stand-alone research project because it does not directly affect other recommended initiatives. Sample Research Initiative F relates to the following initiatives: • Some of the data collected in Sample Research Initiatives C and I related to logistical practices may provide insight into additional factors outside the traditional cost–benefit measures that affect decisions in the freight transporta- tion field. • The interaction of freight and passenger transportation as documented in Sample Research Initiative G will likely yield some useful information about costs and benefits that go beyond traditional monetary factors and relate instead to issues of regional mobility for all types of users. • When applicable, the portfolio of core freight data sources described in Initiative J should incorporate additional metrics developed in this initiative. • The research documented in Sample Research Initiative H will likely include potential new performance measures and data resources beyond what has been traditionally used in freight planning and forecasting. When applicable, these metrics should inform this initiative. • Sample Research Initiative K involves the use of freight data and tools for operational and design considerations related to bridge and pavement design. To the extent that this initiative includes nonmonetary factors such as safety and redundancy, those items should be incorporated in this research effort if applicable. Ongoing innovations in freight planning and forecasting practice that can inform and support this initiative include the following: • The performance measures used in the MOBILEC model documented in Innovative Freight Transportation Frame- work for Flanders (Maes and Ramaekers 2010) may offer some insight into this research effort; • The research effort Transportation Research Board Special Report 304: How We Travel: A Sustainable National Program for Travel Data (Committee on Strategies for Improved Passenger and Freight Travel Data 2011) recommends the organization of a national travel data program built on a core of essential passenger and freight travel data spon- sored at the federal level and well integrated with travel data collected by states, MPOs, transit and other local agencies, and the private sector; and • Data used in various commodity-based and industry- based econometric models can serve as a basis for some nonmonetary metrics related to freight transportation (e.g., employment and production by industry type). Table 4.6. Sample Research Initiative F Implementation Products or Projects Time Required Estimated Costs and Resource Opportunities Other Implementation Perspectives Identify nonmonetary performance measures used in transportation planning efforts, particularly with regard to freight activity. 9 months $75,000 Should include a global perspective for added benefit. Develop nonmonetary performance measures and identify opportunities for inclusion in planning and project prioritization processes in five MPOs or state DOTs. Research will include documentation of standardized measures across all processes for these nonmonetary costs and benefits, if applicable. 12 months $125,000 Will require converting multiple types of data into measurable quantities.

62 Sample Research Initiative 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. Description One of the challenges in freight transportation planning is identifying how local and regional freight operations are affected by passenger travel and land uses that potentially conflict with freight activity, and vice versa. Issues to be exam- ined include the effects of trucking activity on congestion on the highway system during commuter peak periods, passenger and freight rail conflicts on shared rail alignments, and the effect of local ordinances limiting freight activity near certain types of land uses (e.g., residential, institutional). The purpose of this research is to develop, pilot, and vali- date new analytic techniques to effectively integrate freight movement behavior with passenger movements. The intent of this research is to demonstrate the interaction and impacts of freight on the overall transportation system. The research needs to be sensitive to variations in long-haul shipping and local deliveries, as each relates to land use patterns, population density, and underlying (non-freight-related) congestion on the transportation system. This sample research initiative attempts to identify the relationship between freight activity and infrastructure and land use constraints related to com- peting transportation needs and land uses that do not com- plement intensive freight activity. These efforts will be accomplished through research into • Decisions related to transport mode and route choice due to congestion on a freight transportation network; • Relationships between population density and freight activity on local and regional levels; • Changes in delivery schedules by time of day based on con- straints during periods of peak passenger travel; and • Variations in freight activity by time of day under local regulatory constraints (e.g., zoning ordinances restricting freight activity during overnight periods). The research as it relates to the second and fourth items above will build on some of the temporal freight data docu- mented extensively in Chapter 3 of NCFRP Report 8: Freight- Demand Modeling to Support Public-Sector Decision Making (Cambridge Systematics and GeoStats 2010). The approach for this initiative involves research in three metropolitan regions of North America, with existing travel demand modeling tools in place that have been subject to peer review and have been used extensively for general transpor- tation planning. The three regions include (1) a metropolitan area with freight movement activity associated with freight gen- erators such as manufacturing and warehousing centers and port and rail terminals (e.g., Columbus, Ohio); (2) a metro- politan area where most of the freight activity is associated with local consumer demand (e.g., New York City); and (3) a metropolitan area that serves as a major freight hub even as it supports a sizeable local consumer market (e.g., Chicago or Los Angeles). The cities used as the basis for the research documented in NCFRP Report 8 will be examined to deter- mine their applicability to this effort; using these metro- politan areas will provide this research effort with an extensive array of GPS data. Although the research documented in Chapter 3 of NCFRP Report 8 was done using FHWA’s Highway Performance Mon- itoring System and Vehicle Traveler Information System data, the present research is aimed at taking this type of base data and documenting the relationship between local trucking activity and local roadway congestion. A second element of this research involves a survey of business establishments in each of the three metropolitan areas to document how local congestion on the freight transportation system (primarily highway, but rail congestion will also be addressed) affects the business and operating decisions of shippers and carriers with respect to mode choice, operating hours, delivery processes, and routing. This research is qualitative by nature, but these business practices need to be quantified to the extent possible (e.g., “we start our driver shifts at 5:00 a.m. to avoid highway congestion” or “we use 30% more drivers today than we did 10 years ago due to increased congestion and fewer turns at the port terminals”). Work related to congestion pricing as it pertains to truck- ing may be applicable for this research, as well, such as Toll- ing Heavy Goods Vehicles: Overview of European Practice and Lessons from German Experience (Broaddus and Gertz 2008). Benefits and Expected Outcomes • Improved understanding of how freight movement affects the overall transportation system at a corridor, regional, or possibly larger geographic scale; • Improved analytic tools enhance the ability to develop long-range transportation plans that meaningfully con- sider goods movement, especially in the evaluation of long-term needs and investment alternatives; • Establish better coordination between transportation and land use planning; • Provide improved means to evaluate alternative system capacity investment scenarios; • Provide improved means to evaluate transportation opera- tions, including ITS applications;

63 • Provide a means to enhance public–private mutual under- standing and collaboration for freight planning and analysis; • Support the development of meaningful transportation system performance measures. • Develop common metrics for freight planning and model- ing for similar geographic scales (when applicable); • Develop an improved understanding of the intermodal freight movement; and • Enhance understanding of reliability as a performance mea- sure for freight movement. Implementation Sample Research Initiative G implementation ideas and con- siderations are summarized in Table 4.7. Other Considerations Sample Research Initiative G relates to the following initiatives: • The data collection and research effort for Sample Research Initiative C should be done in conjunction with the research for this initiative, since they both involve research into the characteristics of individual businesses and industries; • Some of this research can be used to inform the what-if scenarios described in Initiative D; • Many of the tools and data resources included in Sample Research Initiative E would be applicable here; in fact, this initiative involves a refinement of freight data and tools to a geographic level that is needed for this effort; and • This initiative is somewhat similar to Sample Research Ini- tiative H, which is a macro-level analysis of factors affect- ing freight movement. This initiative involves a close look at how freight operations are affected by constraints on a local scale. Ongoing innovations in freight planning and forecasting practice that can inform and support this initiative include the following: • The data, processes, and outputs of the Oregon SWIM2 model can serve as a basis for developing similar metrics on different geographic scales. • The synthetic generator development process used in the microsimulation work on baseline freight transport con- ditions in the Mississippi Valley Region offers interesting insight into filling data gaps. In addition, this project can be refined or expanded to different geographic scales and enhanced by the research proposed in this initiative. • Some of the data used in Analysis and Multi-Level Modeling of Truck Freight Demand (Wang et al. 2010) can provide insight into the correlation between truck volumes and eco- nomic and demographic factors for some geographic scales. • The direct and peddling research described in Modeling Commercial Vehicle Daily Tour Chaining (Ruan et al. 2010) may help fill data gaps for local truck trips at smaller geo- graphic scales. • The research paper Freight Demand Modeling Using Econometric Models (Vilain and Muhammad 2010) indi- cates that factors such as inventory levels, industrial pro- duction, and local employment have a strong correlation to freight movement as it relates to truck shipments. Sample Research Initiative H Determine how economic, demographic, and other factors and conditions drive freight patterns and characteristics. Document economic and demographic changes related to freight choices. Description Freight movement is part of a complex supply chain involving the movement of raw materials and products from a source to Table 4.7. Sample Research Initiative G Implementation Products or Projects Time Required Estimated Costs and Resource Opportunities Other Implementation Perspectives Documentation of freight activity, land uses, and highway data for three types of metropolitan area. 18 months $300,000 na Qualitative research of business establishments in these same three metropolitan areas (assume 50 business establishments per area). 18 months $150,000 na Statistical analysis of correlation factors between freight activity, congestion, coping strategies, and other measures implemented by various industries. 9 months $100,000 na Note: na = not applicable.

64 a point of consumption or to an intermediate point for man- ufacturing or distribution. The characteristics of this supply chain are heavily influenced by economic factors such as access to labor, markets, transportation infrastructure (vari- ous modes), and capital. This research topic involves the development of correlat- ing factors between market conditions for consumption and production and their impact on freight movement for different commodities. In addition, the economic benefits of freight activity and the relationship between freight move- ment and land use needs and decisions will be explored. This research is built on current principles and practices in econo- metric modeling, with additional research into factors beyond population and labor (i.e., age and income-based modeling and other demographic factors). This research also includes a variation of econometric modeling to assess economic and demographic changes that may result from decisions by ship- pers and carriers related to site selection, operations, and other considerations. This effort can be informed by recent and ongoing research related to local trip generation, land use, and zoning, such as NCHRP Synthesis 298: Truck Trip Generation Data (Cambridge Systematics and Jack Faucett Associates 2001) and NCFRP Project 25: Freight Trip Generation and Land Use (Holguín- Veras 2011). However, the aim of this research is to go beyond traditional factors related to freight movement in terms of land use, industry types, and other issues. One key outcome of this effort will be the documentation of the economic benefits of freight activity related to various industries rather than the impacts of freight activity on infrastructure. The industrial real estate development and brokerage com- munities will likely serve as good sources of information for this research. Benefits and Expected Outcomes • Establish correlating coefficients and analytic tools that can be applied to regional and state transportation plan development; • Enhance existing econometric models to reflect additional factors that drive freight transportation demand; • Provide supporting data to enhance cost–benefit analysis tools for infrastructure investment decision making; and • Support more robust analyses of alternative investment scenarios by including economic development and land use considerations. Implementation Sample Research Initiative H implementation ideas and con- siderations are summarized in Table 4.8. Other Considerations Sample Research Initiative H relates to the following initiatives: • The Sample Research Initiative L research effort involving industrial real estate should be done in conjunction with this initiative; • Some of the data refinement in the Sample Research Ini- tiative I research effort may relate to this initiative for subregional levels; • The results of this effort can inform Sample Research Ini- tiative E; and • The passenger travel characteristics of Sample Research Initiative G may provide some demographic data to sup- port this initiative. Table 4.8. Sample Research Initiative H Implementation Products or Projects Time Required Estimated Costs and Resource Opportunities Other Implementation Perspectives In-depth research of industrial real estate sector and factors related to site selection and facility devel- opment, including access to labor, transportation infrastructure, and tax incentives. This research should include manufacturing, distribution, and transportation subsectors for 12 to 15 different industries. 12 months $125,000 Should be done in conjunction with similar industry research described in Sample Research Initiative L. Research of 25 to 30 industrial sites that have been developed within the past 5 years, documenting economic benefits (e.g., wages and primary and secondary economic activity, tax revenues) for local and regional areas. Demographic changes (e.g., age cohorts and migration patterns) related to these industrial developments should also be documented. 18 months $150,000 The research described in the previous item should be completed before this research begins.

65 In addition to NCFRP Project 25, ongoing innovations in the freight planning and forecasting practice that can inform and support this initiative include the following: • The Oregon SWIM2 model contains a wealth of informa- tion related to labor, land development characteristics, industry sectors, household income and size categories, labor and service occupations, and land use types that all relate strongly to this initiative; • The factors detailed in the Analysis and Multi-Level Model- ing of Truck Freight Demand (Wang et al. 2010) are income, population, and numbers of business establishments, all of which can provide insight into this initiative; • The MOBILEC model described in Innovative Freight Trans- portation Framework for Flanders (Maes and Ramaekers 2010) offers an abundance of information for factors that drive freight demand; and • The PINGO and logistics models in A Model System for Forecasting National and International Freight Transport in Norway (Hovi and Hansen 2010) should be reviewed for this initiative. Sample Research Initiative I Develop freight data resources for application at subregional levels. Description A major breakthrough for freight data analysis and modeling will be the ability to conduct meaningful analyses at small geo- graphic levels that are not currently supported by national- level or large metropolitan area data sets. This research includes the refinement of these current data sources or development of new data sources on smaller geographic scales (e.g., by county, municipality, or zip code). In addition, this research incorporates existing and emerging freight-related data to include permitting data, WIM data, license plate reader data, toll agency data, Highway Performance Monitoring System traffic count data, and others. All of these resources are to be measurable at the local and corridor-specific levels. This research identifies the data currently collected by state and local jurisdictions and recommends standard formats for consolidating these currently unlinked data sources so that collectively they can provide reliable assessments of freight movement at a more refined granular geographic level. This research will build on applicable efforts recently doc- umented in NCFRP Report 8: Freight-Demand Modeling to Support Public-Sector Decision Making (Cambridge System- atics and GeoStats 2010), specifically as they pertain to local decision-making needs. In addition, the tour-based com- mercial vehicle model used in Calgary, Alberta, documented by Kuzmyak (2008) in NCHRP Synthesis of Highway Practice 384: Forecasting Metropolitan Commercial and Freight Travel, is a useful reference for the types of local data that support freight forecasting efforts on a small geographic scale. Benefits and Expected Outcomes • Identify disparate sources of data that exist but are not used for modeling freight movements; • Develop recommendations for refinement and augmenta- tion of existing public data resources (e.g., FAF, CFS, U.S. Census Bureau) and private data sets (e.g., Transearch) to address gaps in data and enhance or supplement these data sources to support analytic tools on smaller geographic scales; • In conjunction with the efforts described for Sample Research Initiative J, provide guidance to all states and local jurisdictions on acceptable data formats that will facilitate data incorporation into local freight models and allow for transferability of data across institutional and jurisdictional boundaries; • Establish methods for local agencies to fill gaps in data and create placeholders for freight data that they may not col- lect currently but plan to incorporate in future freight fore- casting methods; and • Improve understanding of local freight activity that is not captured accurately in national and regional data sets (including local distribution, touring, and intermodal transfers). Implementation Sample Research Initiative I implementation ideas and con- siderations are summarized in Table 4.9. Other Considerations Sample Research Initiative I relates to the following initiatives: • The proposed research in Sample Research Initiative C will be critical in identifying real-world logistics practices that are not accurately reflected in data sets, as well as planning tools that are geared toward large geographic scales; • The extensive data collection effort in Sample Research Initiative D would be an ideal source of information to document the influence of local deliveries and logistics practices on freight flows; • The data collected in this effort can inform the integration of logistics practices and policy and programming decision making as described in Sample Research Initiative L; • This initiative can help inform the review and evaluation research documented in Sample Research Initiative B;

66 • This research area relates strongly to Sample Research Initiative E. This effort involves the collection, aggrega- tion, and disaggregation of data, while Sample Research Initiative E is a similar effort related to methods and tools; and • The results of this research should support and inform the research effort in Sample Research Initiative J. Ongoing innovations in freight planning and forecasting practice that can inform and support this initiative include the following: • The Oregon SWIM2 model can be an instructive example of an application of local data resources to planning and forecasting efforts. • The microsimulation work on freight transport in the Mississippi Valley Region has some valuable insight into the process of refining data to local levels and filling data gaps. • The data gap closure process documented in a recent research effort by the Oak Ridge National Laboratory, Generation of a U.S. Commodity Flows Matrix Using Log-Linear Modeling and Iterative Proportional Fitting (Peterson and Southworth 2010), should inform this initiative. • Transportation Research Board Special Report 304: How We Travel: A Sustainable National Program for Travel Data (Committee on Strategies for Improved Passenger and Freight Travel Data 2011) recommends the organization of a national travel data program built on a core of essential passenger and freight travel data sponsored at the federal level and well integrated with travel data collected by states, MPOs, transit and other local agencies, and the private sector. • The small geographic scale of the research effort described in Modeling Commercial Vehicle Daily Tour Chaining (Ruan et al. 2010) would be applicable to this research. • Several recent research efforts that could provide insight into metrics, indicators, and methods for developing local data include A Bayesian Hierarchical Network for Truck Demand Modeling (Boile et al. 2010) and Freight Demand Modeling Using Econometric Models (Vilain and Muhammad 2010). • Various research efforts presented at the Data Collection and Visualization Techniques session of the 2010 Innova- tions in Freight Demand Modeling and Data Symposium can inform this research. Sample Research Initiative J Establish, pool, and standardize a portfolio of core freight data sources and data sets that supports planning, programming, and project prioritization. Description This research initiative recognizes that varied sources of freight data are used by planners and state DOTs. It also rec- ognizes that the use of freight data for analytic, planning, and Table 4.9. Sample Research Initiative I Implementation Products or Projects Time Required Estimated Costs and Resource Opportunities Other Implementation Perspectives Review of current practice, including national survey of state DOTs, local and regional governments, and other agencies to identify data sets currently used in freight planning and forecasting; a key element of this effort is to document methods currently used to dis aggregate national and regional data and methods used to fill gaps through local data collection efforts. 12 months $250,000 Assumes 15 states, 45 MPOs and local and county governments, and 10 other agencies, including toll authorities and port authorities. Develop recommendations for data collection at county, municipality, and zip code levels to support enhanced forecasting and modeling capabilities. Identify potential improvements in disaggregation methods (if applicable) for current national and regional data resources. 9 months $150,000 Must wait for completion of previous item in this initiative. Should be done in conjunction with other initiatives as described below. Develop improved methods in combining commodity flow data on large geographic scales with local data related to distribution, touring, and intermodal transfers. 18 months $300,000 Related closely to Sample Research Initiative C.

67 decision-making purposes is far more of a hodgepodge than a uniform approach. The research for this initiative is built on the following assumptions: • Flexibility in data sources and analytic methods remains important to individual jurisdictional needs and require ments; • The general myth that private sector freight data are un attainable can be substantially debunked through the development of some common or core data sets; • The benefits of this research will be substantially greater than its cost because fewer MPOs and state DOTs will duplicate efforts; and • The development of web-based data resources will be an ideal mechanism for sharing and disseminating data. This research is timely because government agencies are operating under unprecedented fiscal austerity that will likely become even greater. Data collection is typically viewed as expensive and discretionary. This research in effect becomes an intelligent way of pooling resources nationally rather than continuing a fragmented approach to data collection. The organization of this research must include a wide cross-section of private and public sector users and data suppliers in the process. A solid foundation for this effort has already been estab- lished through the recently published NCFRP Report 9: Guid- ance for Developing a Freight Transportation Data Architecture (Quiroga et al. 2011). The recommendations and specifica- tions of that report will serve as the basis of this research effort, specifically with regard to its three-tiered (single-application, intermediate, and holistic) approach for developing a national freight data architecture. This effort will also include research into existing electronic data interchange protocols and processes used in freight transportation (e.g., ANSI 856 Ship Notice/Manifest). Benefits and Expected Outcomes • Improved and more reliable analytic results; • Greater efficiency and cost-effectiveness of planning and analysis; • Opportunity to overcome some of the perceived barriers related to data availability; • Improved understanding of processes in individual indus- tries and their impact on freight demand at different geo- graphic levels; • Improved understanding of the relationships between businesses and industries; • More comprehensive understanding of issues related to full supply chains (as opposed to discrete freight move- ments); and • Elimination of costly freight data redundancies. Implementation Sample Research Initiative J implementation ideas and con- siderations are summarized in Table 4.10. Other Considerations This research effort should be considered for immediate implementation for several reasons: the extent of the need, the long timeline for some of its elements, and the extensive availability of data from a myriad of sources that can be used to inform the process. The recommendations documented in NCFRP Report 9 (Quiroga et al. 2011) should be a spring- board for early action items. Table 4.10. Sample Research Initiative J Implementation Products or Projects Time Required Estimated Costs and Resource Opportunities Other Implementation Perspectives Development of data architecture standards for five applications following the single-application approach documented in NCFRP Report 9. 18 months $150,000–$400,000 na Development of data architecture standards for 10 to 15 applications following the intermediate approaches (documented in NCFRP Report 9) that can be implemented to address existing needs. 24–60 months $1.5 million to $3 million, depending on complexity of process Should incorporate research in single- application approach. Ongoing development of data architecture standards for a holistic approach (documented in NCFRP Report 9) aimed at oversight of application development for the purpose of ensuring compatibility even while single-application and intermediate approaches may require flexibility. Ongoing $1.5 million to $2 million annually na Note: na = not applicable.

68 This initiative does not directly relate to other initiatives in terms of its detailed recommendations, but all of the other recommended research areas should be executed with the awareness that this data standardization process is also under way and will likely have an evolving influence over all research areas involving freight data and tools. Ongoing innovations in freight planning and forecasting practice that can inform and support Sample Research Initia- tive J include the following: • The Online Freight Data Repository for Freight Modeling and Analysis developed by the University of California at Irvine can inform this research; and • Various research efforts presented at the 2010 Innovations in Freight Demand Modeling and Data Symposium that involve data fusion and the use of private sector data to support planning and vehicle routing analyses will advance this initiative. Sample Research Initiative K Develop procedures for applying freight forecasting to the design of transportation infrastructure, particularly pavement and bridges. Description Freight movements have unique characteristics and infra- structure needs. Often these needs are not fully considered in infrastructure design, particularly with regard to pavement and bridge design. Procedures and processes to integrate true freight forecasting into this design will ensure that current and near-term projects will not become future freight con- straints. Research includes collecting best practices for con- sidering freight needs in the design, construction, operations, and maintenance of roadway infrastructure. The research efforts in this initiative involve documenting the role of truck weight and volumes in pavement and bridge design among public agencies in the United States and abroad, along with the role that forecasting tools play in the design process. Design vehicles, current and projected volumes, and oversized load considerations will be examined. The focus of this effort is to bring freight planning tools and data into the design process for pavement and bridges. The recommended approach for this research includes an initial survey of highway departments and toll authorities, with detailed documentation of how these agencies incorpo- rate truck activity into the design and maintenance of their infrastructure. The primary design parameters to be included in this effort include truck volumes and vehicle weight, length, height, and axle configuration. This research will build on ongoing developments in GPS tracking and asset management for truck fleets, along with existing WIM, permit, and routing data. Collaboration with private industry for vehicle configuration and weight data (when possible) is crucial to the success of this effort. Benefits and Expected Outcomes • Document the role of truck size and weight characteristics in the planning, design, and maintenance of highway infra- structure across an array of different agencies; • Identify design parameters for which changes in future truck activity, measured in terms of increased truck vol- umes, or changes in truck sizes, or changes in load charac- teristics (i.e., full trucks versus empty trucks), can influence life cycles and design standards; and • Identify ways to incorporate freight planning tools and data into the planning, design, and maintenance of highway infrastructure. Implementation Sample Research Initiative K implementation ideas and con- siderations are summarized in Table 4.11. Other Considerations Because this research effort does not directly relate to future highway capacity considerations of the SHRP 2 program and is oriented toward design considerations more than planning and forecasting, this initiative should not be considered a top priority for early implementation. Sample Research Initiative K relates to the following initiatives: • Some of the research related to Sample Research Initia- tives E and I, particularly the refinement of data and tools to local and corridor levels, should support and inform this research; • The standardized pool of freight data developed in Sample Research Initiative J should serve as the basis of the pro- grammatic elements of this research track; • To the extent that this research effort involves nonmonetary design considerations such as safety and redundancy, the results should inform Sample Research Initiative F; and • Life-cycle costs related to functional obsolescence, as described in Sample Research Initiative L, should inform this research if applicable. Because this research area requires some degree of coor- dination with other initiatives described in this SHRP 2 C20 research program, the final three research projects described

69 here should be done after the other initiatives mentioned above are well under way. Ongoing innovations in freight planning and forecasting practice that can inform and support this initiative include the following: • Research efforts related to truck demand and route choice modeling; and • The use of electronic data collection to calibrate truck models; and • Other efforts using truck data for planning and forecasting truck activity by region and roadway segment. Sample Research Initiative L Advance research to effectively integrate logistics practices (private sector) with transportation policy, planning, and programming (public sector). Description There is a substantial disconnect between private and public sector decision making related to the movement of goods and the infrastructure that supports those activities. This research builds on the behavior-based freight research documented in Sample Research Initiative C and attempts to integrate the real- world supply chain management practices of the private sector with the policy and planning decision making of the public sector. Although it is unrealistic to expect that the timelines and planning horizons of the public and private sectors will be fully harmonized in an effective manner (public sector plan- ning horizons are typically years or decades in length, but the decision-making needs of private industry can change almost on an hourly basis), a thorough understanding of the decision- making needs of both private and public sectors enhances the interests of both. Once these needs are identified, regional, state, and MPO planning capabilities (and resources) to meet those needs must then be assessed. This research is an initiative to determine areas of mutual benefit for improving data and planning tools across public and private sectors and to develop ideas for planning processes to incorporate actual supply chain management processes and logistics decisions to the extent possible. The research includes answers to why, how, when, where, what, who, and how much in order to bridge the gap between how shippers and carriers operate on a short-term basis and what the public sector needs to make decisions, taking into account that decision making often requires years of planning. Recommendations for streamlining public sector decision-making processes are beyond the scope of this effort, but the research will yield interesting ideas about approaches to public sector planning and programming efforts that include phased implementation, interim short-term improvements in place of costly long-term investments, and conditional approvals. NCHRP Report 594: Guidebook for Integrating Freight into Transportation Planning and Project Selection Processes (Cambridge Systematics et al. 2007) will serve as a found- ation and an instructional guide for this sample research initiative. The areas included in this research and some of the perti- nent questions related to these areas are as follows: • Real estate—What are the standard time lines for private sector investments in real estate that are related to freight Table 4.11. Sample Research Initiative K Implementation Products or Projects Time Required Estimated Costs and Resource Opportunities Other Implementation Perspectives Survey of highway and toll authorities across the United States to document current practices in bridge and pavement design and maintenance standards (assume a maximum of 60 agencies surveyed in all). 9 months $125,000 These projects can run concurrently. Survey of best practices among transportation departments internationally (assume data collected from 15 to 25 international agencies). 9 months $100,000 Documentation of freight planning tools and data that can be used in the design and maintenance process for bridges and pavement and develop- ment of programmatic tools for incorporating other tools and data into the design and main- tenance processes (including those described for future development in this SHRP 2 C20 research program). 12 months $200,000 Must be done after previous two projects in this initiative are complete.

70 activity, including port and rail terminals, distribution centers, and truck terminals and hubs? What are some of the variations among different types of ownership and operating arrangements, including site selection, modifi- cations of existing facilities versus construction of new facilities, and build-to-suit versus “spec” buildings? • Facility operations—What are some of the ongoing changes in facility operations that extend the useful life of existing facilities or result in dramatic changes in off-site impacts? What are some of the factors that drive functional obsoles- cence of facilities far in advance of physical depreciation of these assets, and what are the life-cycle implications of these factors? Examples of these factors include port and rail ter- minal hours of operation, the gradual transformation of warehouses (focused on product storage) to distribution centers (focused on load consolidation and distribution, with accompanying reductions in on-site product inven- tory), and other improvements in the efficiency of facility operations. • Vehicles and vessels—This research area includes freight rolling stock, such as trucks and rail cars, in addition to freight vessels (including barges). How have the dimensions of these various elements of the freight system changed over time, and what are the implications of these changes with regard to roadway design, bridge height and weight limits, channel depth, and other infrastructure considerations? How frequently do these elements of the system change relative to the life cycles of the accompanying infrastruc- ture? What are the life cycles of these vehicles and vessels, and how quickly do different types of carriers change their fleet management decisions to reflect changing business conditions? • Infrastructure—What are the time lines for various types of infrastructure improvements, including roadway and rail rehabilitation, new construction of these types of facil- ities, channel deepening, and other major infrastructure development? What potential changes should be made in the permitting and approval processes for these elements to provide short-term or conditional capacity enhance- ments to reflect the needs of private industry as docu- mented in the above items? Benefits and Expected Outcomes • An understanding of the investment cycles for different elements of the freight transportation industry, includ- ing shippers, carriers, and public agencies, encompassing decision-making cycles for land use, facilities, and infrastructure; • A detailed view of operational, rolling stock, and supply chain decisions that may change on a short-term basis to reflect private sector needs; • Documentation of potential inefficiencies in the freight transportation system related to the disparate decision- making cycles of the public and private sectors; and • Development of potential approaches through which public agencies can implement short-term or interim measures to reflect the changing, dynamic needs of the private sector. Implementation Sample Research Initiative L implementation ideas and con- siderations are summarized in Table 4.12. Table 4.12. Sample Research Initiative L Implementation Products or Projects Time Required Estimated Costs and Resource Opportunities Other Implementation Perspectives In-depth research of the industrial real estate sector and the key decision cycles for site selection and facility development. 12 months $90,000 Include manufacturing and warehousing subsectors. In-depth research of industrial facility management issues and key issues related to facility operations and functional obsolescence. 12 months $90,000 Include manufacturing and warehousing subsectors. In-depth research of the freight transportation sector, including history of vehicle and vessel size charac- teristics, asset replacement cycles, and impacts of these issues on freight transportation infrastructure. 9 months $60,000 Include truck, rail, and marine transpor- tation (ship and barge) subsectors. In-depth research of transportation infrastructure planning and implementation horizons, along with development of potential short-term measures to address dynamic changes in private sector needs. 12 months $125,000 Include manufacturing and warehousing subsectors.

71 Other Considerations Sample Research Initiative L relates to the following initiatives: • The research efforts in this initiative can be conducted in conjunction with the behavior-based freight movement research described in Sample Research Initiative C; • The mode shift analyses described in Sample Research Initiative D may inform this research effort to a small degree; and • The relationship between public sector and private sector decision making in this initiative can support and inform the analytic approaches of Sample Research Initiative G. Ongoing innovations in freight planning and forecasting practice that can inform and support this research include the following: • Some of the recent and ongoing research in tour chaining and local logistics practices can be of some use in document- ing decision-making cycles for different industry groups; an example of this research is Modeling Commercial Vehicle Daily Tour Chaining (Ruan et al. 2010); • The Innovative Freight Transportation Framework for Flan- ders (Maes and Ramaekers 2010) may serve as a good start- ing point for some of the logistics practices in this initiative; • Additional insight into logistics practices to inform this research may be found in A Model System for Forecasting National and International Freight Transport in Norway (Hovi and Hansen 2010); • The modeling process documented in A Firm-Based Freight Demand Modeling Framework Capturing Intra-Firm Inter- action and Joint Logistic Decision Making (Guo and Gong 2010), which is based on business establishment activity, may provide good information about decision-making cycles across different industries in the private sector; and • The interaction of importers, exporters, and other trans- shipment activity, as described in A Hybrid Microsimula- tion Model of Urban Freight Travel Demand (Donnelly et al. 2010), may also be informative for this research initiative. Sample Research Initiative M Develop visualization tools for freight planning and modeling through a two-pronged approach of discovery and addressing known decision-making needs. Description Visualization tools provide a powerful means of communi- cating complex concepts and data. This research is aimed at providing analysts with tools for managing data (discovery) in an organized and intuitive way to make freight information more accessible, understandable, and usable. In addition, this effort seeks to apply visualization techniques to provide a sensible platform for developing more robust forecasting models, while communicating concepts and analyses to deci- sion makers. A general need for the freight planning and fore- casting industry is the enhancement of computer science skills related to graphic presentation. The nature of the freight transportation process across different geographic scales lends itself well to a geographic information system platform for visualization. An innova- tive element of this research involves developing standard visualization techniques and tying them to improved web access to data for different geographic levels. Private compa- nies such as Esri that already develop products for a multi- tude of clients will be good partners and research champions, along with other private sector firms that develop tools to meet their own internal needs. This initiative includes the use of gaming technologies and methods as part of learning systems to address knowledge and skill deficits in freight planning and modeling. Existing tools and methods currently being applied in all levels of edu- cation (e.g., Epistemic Games at the University of Wisconsin– Madison and the Virtual Construction Simulator at Penn State University) are examples of ongoing developments that may be applicable to this field. Benefits and Expected Outcomes • Fostering of a strong interest among data visualization and user interface experts currently applying their skills in other industries to bring their talents to bear on freight demand modeling and data to assist this industry with seeing, under- standing, and communicating; • Application of visualization tools and techniques systemati- cally across other appropriate research initiatives in this effort to enhance their effectiveness and promote unconventional thinking; • Use of visualization as a common language to promote a greater understanding and more productive dialogue among modelers, planners, researchers, the private sector, and other stakeholders; and • Incorporation of visualization techniques to evaluate inno- vative freight demand models. Implementation Sample Research Initiative M implementation ideas and con- siderations are summarized in Table 4.13. Other Considerations Sample Research Initiative M directly influences and sup- ports all of the other research documented in this plan to

72 some degree, as enhanced visualization techniques would be valuable assets for all of them. This initiative should be con- sidered for early action and conducted in parallel with the others to the extent possible, with a process in place to regu- larly inform the other research teams of progress, develop- ments, and innovations in visualization. The relationship between Sample Research Initiative M and Sample Research Initiative A is particularly important, since training in the use of visualization tools (as well as the development of such tools) should be considered among the training and educa- tion needs in Sample Research Initiative A. Some ongoing innovations in the freight planning and forecasting practice that can inform and support this initia- tive include the following: • A number of visualization elements of the Oregon SWIM2 model could inform this research and • The various commercial vehicle touring research efforts presented at the 2010 Innovations in Freight Demand Modeling and Data Symposium could inform this research and could be enhanced through visualization tools that help describe route choices, congestion impacts, bottle- necks, time-of-day variations, and other factors. Future Directions This section highlights future directions for building momen- tum beyond the completion of this SHRP 2 C20 report. This road map provides a broad direction and an organizing pro- cess for sustaining innovation in freight planning and mod- eling. The approach is designed to address the wide range of opportunities and needs that have been identified to date and expressed broadly by the seven strategic objectives. The future directions build on a strong foundation of the SHRP 2 C20 project accomplishments, including • Fostering interest among the freight community based on extensive outreach and engagement of public and private freight stakeholders; • Documenting freight decision-making needs, particularly those of state DOTs and MPOs; • Piloting a successful Innovations in Freight Demand Modeling and Data Symposium with national and inter- national participation to spur breakthrough thinking and innovative ideas; and • Developing an initial set of sample research initiatives vali- dated by freight stakeholders. Organizing Concept: Global Freight Research Consortium SHRP 2 C20’s project leadership stressed that the future directions should not include an inflexible bureaucratic organization or cumbersome administration. Rather than establishing a program as part of a government organiza- tion, the organizing concept lays out a flexible mechanism— an agile, collaborative framework—for achieving the strategic objectives. To meet this expectation, a Global Freight Research Con- sortium (GFRC) is recommended. This consortium would promote research through funding agencies and others 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. This peer-based consortium would enable, fund, and pro- mote research, supported through national and international public organizations, together with private organizations whose efforts serve the freight transportation sector. The member organizations will include public domestic agencies, modal and other associations, universities, and the transportation research entities of other countries. It is also envisioned that the private sector will participate in the GFRC. Firms such as Con-way, Wal-Mart, EXCEL Logistics, FedEx, and UPS also have a stake in the research innovation that the consortium will promote. Table 4.14 summarizes the organizational mix that will potentially represent the core of the consortium. Table 4.13. Sample Research Initiative M Implementation Products or Projects Time Required Estimated Costs and Resource Opportunities Other Implementation Perspectives Survey of visualization techniques in other industry sectors and elsewhere in the transportation planning and operations field. 12 months $125,000 Gaming technologies and methods may be of great interest here. Develop a guide of applicable visualization techniques for freight forecasting and modeling that go beyond traditional pie charts and two-dimensional graphs. 18 months $400,000 na Provide visualization support to other ongoing research efforts, including the initiatives documented in this plan. Continuous $200,000 annually na Note: na = not applicable.

73 This partnership will support independent research and reward innovative and compelling investigations and experi- ments by sponsoring an annual research competition span- ning 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., foun- dation, institute, charity) for the GFRC and completing 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, which could include developing a draft GFRC work program, organizing additional research idea competitions, holding annual competitions for grants, and facilitating the first few GFRC meetings; and • Regularly restructure and renew the governance model to ensure an entrepreneurial approach and genuine innovation. The SHRP 2 C20 Technical Expert Task Group partici- pated in a facilitated discussion to help frame the future directions. That consensus-building exercise helped estab- lish basic definitions and parameters for the GFRC, includ- ing what the consortium should and should not be, as seen in Table 4.15. These important attributes are documented for reference as this initiative goes forward. Global Freight Research Consortium: A Win–Win Proposition The GFRC provides an effective means for public–private– academic collaboration on freight modeling and planning with abundant benefits for all participants. Further, these benefits can be accomplished without creating another formal organization bureaucracy. The consortium’s power is one of influence: it brings together those with a shared stake in greater innovation and successful implementation of new forecasting and analytic tools. The wins may differ by organization or sector, but they include the following: • Improved infrastructure investment from a freight trans- portation perspective; • Achieving a global perspective that reflects freight’s global dimensions; • Improved performance of the transportation system over time as a result of better investment decisions; • An opportunity to validate research from the standpoint of its utility to the freight industry; • An opportunity to gain a better mutual understanding of the analytic needs of the public and private sectors and how they intersect; • An opportunity to validate any research or tools from a practitioner standpoint; • Greater understanding of freight movement requirements and performance criteria and how any new analytic tools must reflect such key factors; and • An opportunity to shape the knowledge and skill require- ments for employees in public and private organizations and to influence the instructional focus for universities. Table 4.14. 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 development of training and outreach materials suggested by the future directions U.S. DOT modal administrations (e.g., Federal Highway Administration [FHWA], Federal Railroad Administration) Supporting pilots of advanced freight demand models U.S. DOT intermodal organizations (e.g., FHWA, Research and Innovative Technology Adminis- tration, Bureau of Transportation Statistics) Improving and expanding freight data resources Academic institutions and university transporta- tion 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 technology and business practices for future research

74 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 symposium’s success rested on several factors: • The symposium provided a low-cost approach to generat- ing a variety of research concepts; • The competitive nature of the symposium generated numer- ous 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. Future symposia may have a different focus or emphasis area, but the principles of collaboration, competition, and communication represent significant building blocks for suc- cessful symposia. The Innovations in Freight Demand Modeling and Data Symposium was held in Herndon, Virginia, with about 50 in attendance. The symposium featured 18 presentations selected to address the challenge of developing the next gen- eration of freight demand models (symposium materials are available at www.trb.org/Main/Blurbs/167629.aspx). A monetary award was presented to Tetsuro Hyodo, Tokyo University of Marine Science and Technology, for the top presentation. The symposium model was characterized by a combination of modeling data and ideas presented by U.S. and international practitioners and academics, followed by open and direct dialogue and debate. Major needs identified during the sym- posium include the following: • A priority need to include international research addressing the macro view of global freight and its impact on multi- modal freight traffic; • A need to share unfettered domestic and international research; and • A need to weave data, modeling, and knowledge (and ter- minology) within the public infrastructure modeling and policy view, as well as private sector logistics and distribu- tion forecasting efforts. In short, the successful Innovations in Freight Demand Modeling and Data Symposium provided a strong founda- tion for moving forward in the following ways: • 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 researchers. Global Freight Research Consortium Initiatives and Focus Areas for Achieving Strategic Objectives This section briefly describes six major activities or initiatives that the GFRC would address as part of its overall approach to achieving the strategic objectives. The list is by no means exhaustive, recognizing that the ultimate activities of the Table 4.15. Defining the Global Freight Research Consortium What the GFRC is What the GFRC is not • More innovation • Nonlinear progress • Mechanism similar to National Academies • All relevant research funders at the table • Mechanism to broadly diffuse the research agenda • Way to seek resources • Vehicle to attract participants and one or more champions • Methods to stimulate innovative ideas and research and assessment of who is capable of pursuing less conventional methods • An approach for infusing freight modeling efforts with knowledge, innovation, and capacity building • Bridge to greater public and private understanding of system devel- opment needs • Framework to broadcast information and ideas • Living, dynamic, experimental, and evolutionary • Window on other fields that might be able to provide input • Explicit—not implied—international outlook and focus • Momentum builder, viral • Bridge between private sector and public sector freight planning • A research program • A single university transportation center–run research program • Centralized • A formal organization with a governing body • A governance program • Concentration on one or a few projects that narrow the scope • A predefined end product • A Procrustean bed (an arbitrary standard to which exact conformity is forced) for ideas or processes • Hosted by an academic organization (although academic institutions will participate in the GFRC)

75 consortium will be determined based on the combined inter- ests and priorities of the participants. Each of the six major initiatives is briefly defined below and is followed by a bullet list of actions to advance that initiative. Define Priority Research Issues The GFRC will periodically issue a list of research priority areas based on submissions to GFRC-sponsored calls for ideas, similar to the process followed for the 2010 Innova- tions in Freight Demand Modeling and Data Symposium. Defining research focus areas that reflect the decision-making needs of state DOTs and MPOs in relation to freight plan- ning, policy making, and project development will be of par- ticular importance. Ideally, these research focus areas will reflect a dynamic communication and consensus building between the private and public sectors, both on the GFRC and between state DOTs and MPOs with the freight industry, and with international practitioners, as well. Actions • Establish the initial set of problems or research issues demanding attention; • Publish and widely distribute a call for ideas; and • Communicate the submission format approach standards and the incentives or awards being made available. Provide Recognition and Incentives to Spur Breakthroughs The 2010 Innovations in Freight Demand Modeling and Data Symposium confirmed that recognition and a nominal finan- cial award are powerful inducements for generating ideas. The GFRC is encouraged to recognize the value in continuing to offer awards and recognition, particularly for meritorious research ideas with potentially breakthrough solutions. Non- financial recognition is also important. Efforts will be made to promote this process to the greatest extent possible as a way of doing business for the GFRC. Actions • Establish initial sources for the first call for innovative ideas; • Consider establishing GFRC following a foundation model to provide a basis for contributions for funding awards, prizes, and related activities; and • Over time, as funding for awards increases, establish multi- ple categories and multiple award winners. Conduct Regular Innovation Forums An annual forum, similar to the 2010 Innovations in Freight Demand Modeling and Data Symposium, will be conducted for presenting innovative research and selecting the most prom- ising ideas in freight modeling and data for further develop- ment. Each forum will publish a report that will frame the near- and long-term freight modeling and data research agenda. Actions • Determine the content, themes, or focus areas for periodic innovation forums; • Review and incorporate the results of the forums in rela- tion 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 The SHRP 2 C20 Technical Expert Task Group has expressed the need to consider solutions to modeling needs from other fields that can be transferable or adaptable to freight transpor- tation. Transferable solutions will be promoted regularly and serve as a focus for a broader outreach to various utilities and other sectors, and will also be a consideration in screening ideas. Actions • Organize a forum that would bring together presenters from other sectors to consider how their modeling and planning techniques might be adaptable to freight fore- casting; and • Organize a competition devoted to adopting and adapting analytic techniques from other sectors. 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. Actions • 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 ap proaches employed in other nations. Recognize the Application of Completed Research Another important component of recognition and informa- tion dissemination for the consortium will be to periodically draw attention to the impacts and benefits of applied freight

76 modeling and data research. This activity will be particularly important from the standpoint of promoting broader imple- mentation of successful freight analytic approaches. Actions • Advance a general tracking activity to capture the benefits and experiences of freight professionals using new research approaches; and • Publish this information periodically to reflect the long- term benefit of GFRC efforts. Achieving Tangible Progress The formation of a GFRC represents a significant institu- tional breakthrough with a strong potential for success. It is important to move to a start-up or implementation phase sometime within the first 6 to 12 months of the publication of this report to build on the momentum achieved to date through the Innovations in Freight Demand Modeling and Data Symposium and other stakeholder forums. Early activities should include bringing together the pro- spective members of the GFRC for a facilitated organizational meeting or strategy workshop. The initial focus would include presenting the business case for the GFRC and seeking partici- pant buy-in and input on how to strengthen the consortium approach and implementation. A draft work program for the first year or two of activities should also be presented for review of those initially involved. Of particular importance is that all of the current research funding agencies be at the table with the other prospective partners, as consideration should be given to how freight modeling and data research will be prioritized, which promising areas of research from SHRP 2 C20 should be advanced, and what other areas of research should be identi- fied. This early work plan development and GFRC formation should be consultant-supported as there is no one agency or organization positioned to carry out the process on its own. Conclusion This second decade of the twenty-first century will place even greater emphasis on global trade, technology, innovation, and competitiveness. These megaissues will strongly influence transportation strategy and decisions about system invest- ments. These strategies and decisions, in turn, will require capacity building for state DOTs and MPOs and greater col- laboration 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 performance of public and private infrastructure, which is a 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 trans- portation. The result is that public decision makers lack the information they need to effectively support freight-related transportation decision making. This research has estab- lished a road map to move freight tools and data innovation forward through • Implementing sample research initiatives that support the seven key strategic objectives; and • Expanding the dialogue on freight analysis and data inno- vation through the GFRC, an ongoing international forum of key stakeholders comprising a public–private–academic collaboration to encourage innovative research to support decision-making needs. By the end of this decade, a vision for improved freight modeling and data will be characterized 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 develop- ment of better tools and data; and • Decision makers recognize that transportation invest- ments are to a greater degree being informed by an under- standing of the implications, benefits, and trade-offs relative to freight.

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TRB’s second Strategic Highway Research Program (SHRP 2) Report S2-C20-RR-1: Freight Demand Modeling and Data Improvement documents the state of the practice for freight demand modeling. The report also explores the fundamental changes in freight modeling, and data and data collection that could help public and private sector decision-makers make better and more informed decisions.

SHRP 2 Capacity Project C20, which produced Report S2-C20-RR-1, also produced the following items:

• A Freight Demand Modeling and Data Improvement Strategic Plan, which outlines seven strategic objectives that are designed to serve as the basis for future innovation in freight travel demand forecasting and data, and to guide both near- and long-term implementation:

• A speaker's kit, which is intended to be a "starter" set of materials for use in presenting the freight modeling and data improvement strategic plan to a group of interested professionals; and

• A 2010 Innovations in Freight Demand Modeling and Data Symposium.

An e-book version of this report is available for purchase at Amazon, Google, and iTunes.

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