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Innovations in Freight Demand Modeling and Data Improvement (2014)

Chapter:CHAPTER 3: Relationship to C20

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Suggested Citation:"CHAPTER 3: Relationship to C20." National Academies of Sciences, Engineering, and Medicine. 2014. Innovations in Freight Demand Modeling and Data Improvement. Washington, DC: The National Academies Press. doi: 10.17226/22336.
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Suggested Citation:"CHAPTER 3: Relationship to C20." National Academies of Sciences, Engineering, and Medicine. 2014. Innovations in Freight Demand Modeling and Data Improvement. Washington, DC: The National Academies Press. doi: 10.17226/22336.
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Suggested Citation:"CHAPTER 3: Relationship to C20." National Academies of Sciences, Engineering, and Medicine. 2014. Innovations in Freight Demand Modeling and Data Improvement. Washington, DC: The National Academies Press. doi: 10.17226/22336.
×
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Suggested Citation:"CHAPTER 3: Relationship to C20." National Academies of Sciences, Engineering, and Medicine. 2014. Innovations in Freight Demand Modeling and Data Improvement. Washington, DC: The National Academies Press. doi: 10.17226/22336.
×
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Suggested Citation:"CHAPTER 3: Relationship to C20." National Academies of Sciences, Engineering, and Medicine. 2014. Innovations in Freight Demand Modeling and Data Improvement. Washington, DC: The National Academies Press. doi: 10.17226/22336.
×
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Suggested Citation:"CHAPTER 3: Relationship to C20." National Academies of Sciences, Engineering, and Medicine. 2014. Innovations in Freight Demand Modeling and Data Improvement. Washington, DC: The National Academies Press. doi: 10.17226/22336.
×
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Suggested Citation:"CHAPTER 3: Relationship to C20." National Academies of Sciences, Engineering, and Medicine. 2014. Innovations in Freight Demand Modeling and Data Improvement. Washington, DC: The National Academies Press. doi: 10.17226/22336.
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CHAPTER 3 Relationship to C20 The SHRP 2 Second Symposium on Innovations in Freight Demand Modeling and Data (C43) follows in the footsteps of the C20 project, which resulted in the Freight Demand Modeling and Data Improvement Strategic Plan (2013). This plan advanced seven strategic objectives to stimulate innovative research for enhanced freight planning, forecasting, and data analysis. These objectives are 1. Improve and expand the knowledge base for planners and decision makers; 2. Develop and refine forecasting and modeling practices that accurately reflect supply- chain management; 3. Develop and refine forecasting and modeling practices based on sound economic and demographic principles; 4. Develop standard freight data (e.g., Commodity Flow Survey [CFS], Freight Analysis Framework [FAF], and possible future variations of these tools) for smaller geographic scales; 5. Establish methods for maximizing the beneficial use of new freight analytic tools by state departments of transportation (DOTs) and metropolitan planning organizations (MPOs) in their planning and programming activities; 6. Improve the availability and visibility of data among agencies and between the public and private sectors; and 7. Develop new and enhanced visualization tools and techniques for freight planning and forecasting. C43 accomplished a number of these objectives by bringing together practitioners and provided a platform for their research and a forum for knowledge transfer and education. Additionally, the symposium gave insight into the state of the practice three years after the first symposium and gave insight into how the field has advanced since then. Furthermore, presentations were selected with their applicability to the C20 research initiatives. The list of presentations made at C43 is shown in Table 3.1. Many of the selected topics met multiple research initiatives, as illustrated in Figure 3.1. In many cases, the presentation may have fulfilled a research initiative indirectly or as a secondary consideration to its primary research focus. The best presentations graded by the panel of experts were not necessarily the ones that met the most research initiatives, but those that did the most to advance freight data modeling innovation in their selected topics. In particular, research presented at C43 made strides in some but not all research areas. 6

The three research initiatives that were most evident in the presentations were • Research Initiative C: Establish modeling approaches for behavior-based freight movement. Most presentations, with the exception of a few, showed developed models that went beyond the traditional four-step transportation model by utilizing other data to project how freight transportation providers utilized the transportation network. The presentations included the application of agent-based, tour-based models using GPS data, or other methods that factored in discrete data to account for other variables, such as agricultural cycles. • Research Initiative E: Develop a range of freight forecasting methods and tools that address decision-making needs and that can be applied at all levels (i.e., national, regional, state, MPO, and municipal). The presentations encompassed a variety of models that were tailored to the diverse geographic scales of their topic. While many of the methods or tools could be customized to meet the needs of a higher or lower geographic scale, other models were best applied to smaller geographic areas like cities, where their detail or applicability would be lost if data was aggregated to a larger level. Likewise, other models were tuned to address data collected at a state level, but applying the same method while disaggregating the data would be problematic. Instead of applying one type of method or tool to meet all levels, practitioners are developing models that are most appropriate to the scale of their research. • Research Initiative I: Develop freight data resources for application at sub-regional levels. As previously discussed, the need to refine models to a more suitable geographic scale has also led to innovation in the types of data resources that are being utilized to similarly provide models with robust information that can be used for the scale of analysis. Parcel data and raster data are two such examples of freight data resources utilized to inform models at a sub-regional level. On the other hand, several other research initiatives were not as well advanced in C43, including • Research Initiative D: Develop methods that predict mode shift and highway capacity implications of various “what-if” scenarios. 7

Very few models attempted to develop “what-if” scenarios, including factoring risk, into their research. As supply chains become more complex, and, as a result, potentially more susceptible to shocks to the system, this initiative will be increasingly important to manage how freight transportation is utilized when the network is not operating under optimized conditions. • Research Initiative F: Develop robust tools for freight cost-benefit analysis that go beyond financial to the full range of benefits, costs, and externalities. Similar to the development of scenario-based freight modeling, the application of cost- benefit analysis was not well represented throughout the presentations. Some models did attempt to factor it into their research, though not necessarily directly. For public policymakers, a robust model that can further inform the true costs of decisions will be necessary to advance the needs of transportation planning and the freight modeling practice. • Research Initiative K: Develop procedures for applying freight forecasting to the design of transportation infrastructure such as pavement and bridges. None of the presentations at C43 addressed this research initiative. In practice, utilizing freight modeling research into asset management may have not yet made its way into the conversations because of the different planning communities that make these types of decisions. This can also be a function of the typical utility of these datasets where their development is based on the need to address a specific issue, though there are opportunities to adapt this information to other models with different objectives. As costs for maintenance of infrastructure continue to grow and available funding competes with other modes, the ability to determine how freight movements affect the transportation network and the relationship between this and the infrastructure it utilizes are becoming increasingly important. C43 illustrated that several advances in freight modeling and data have been made in a relatively short period of time. While these presentations are encouraging steps in the right direction, emphasis on all levels of C20 research initiatives should be maintained. 8

Table 3.1. SHRP 2 Second Symposium on Innovations in Freight Demand Modeling and Data Improvement: Speaker List SESSION 1: State DOT Freight Modeling A Creating a Supply-Chain Methodology for Freight Forecasting in Wisconsin Jennifer Murray, Wisconsin Department of Transportation B Rail Freight Commodity Models: A First Generation Effort in Iowa Phillip Mescher, Iowa State Department of Transportation SESSION 2: Regional/Urban Tour Modeling A Innovative Tour-Based Truck Travel Model Using Truck GPS Data Arun Kuppam, Cambridge Systematics Inc. B Design of an Agent-Based Computational Economies Approach to Forecasting Future Freight Flows for the Chicago Region John Gliebe, Resource Systems Group Inc. C Florida Multimodal Statewide Freight Model: A Model Incorporating Supply-Chain Methods and Providing Linkages to Regional Tour-Based Truck Models Colin Smith, Resource Systems Group Inc. SESSION 3: International Models A Logistics in Freight Modeling—A Report from the Delft Group Lori Tavasszy, Delft University of Technology and TNO (Award Winner) B Discrete Model of Freight Mode and Shipment Size Choice Magersa Abate, The Swedish National Road and Transport Research Institute (VTI) C Freight Models, Constrained Economic Models and Natural Resource Data Ming Chen, TNO SESSION 4: Private-Sector Supply-Chain Decision Making A NMHG Shipment Modeling Andy Street, NACCO Materials Handling, Inc. (Award Winner) B The Caterpillar Building Construction Products (BCP) Transportation Strategy: Thinking Outside AND Inside the Box William Lucas, Caterpillar, Inc. and Matthew Drown, Caterpillar, Inc. 9

SESSION 5: Freight Data Innovations A Exploratory Use of Raster Images for Freight Modeling Pedro Camargo, University of California, Irvine B Disaggregate State-Level Freight Data to County Level Ho-Ling Hwang, Oak Ridge National Laboratory C Statewide Freight Demand Modeling to Support Long-Range Transportation Planning in North Dakota EunSu Lee, North Dakota State University SESSION 6: Business Implications A On the Evaluation of Incentive Structures Fostering Off-Hour Deliveries Felipe Aros-Vera, Rensselaer Polytechnic Institute B Analyzing Future Freight Challenges in Maryland Using Freight Data Sources and the Maryland Statewide Transportation Model (MSTM) Subrat Mahapatra, Maryland Department of Transportation C Examining Carrier Transportation Characteristics along the Supply Chain Anne Goodchild, University of Washington 10

C20 Research Initiatives ● – Primary Research Focus ○ – Secondary Research Focus C43 Symposium Research Category State DOT Freight Modeling Region/Urban Tour Modeling International Models Private Sector Supply Chain Freight Data Innovations Business Implications 1A 1B 2A 2B 2C 3A 3B 3C 4A 4B 5A 5B 5C 6A 6B 6C A: Determine the freight and logistics knowledge and skill requirements for transportation decision makers and professional and technical personnel. Develop the associated learning systems to address knowledge and skill deficits. B: Establish techniques and standard practices to validate freight forecasts. ○ ○ C: Establish modeling approaches for behavior-based freight movement. ● ● ● ● ● ● ● ● ● ● ○ ● ● D: Develop methods that predict mode shift and highway capacity implications of various “what-if” scenarios. ● ○ ● ● E: Develop a range of freight forecasting methods and tools that address decision making needs and that can be applied at all levels (i.e., national, regional, state, MPO, municipal). ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ● ○ F: Develop robust tools for freight cost-benefit analysis that go beyond financial to the full range of benefits, costs, and externalities. ● ● ○ G: Establish analytical approaches that describe how elements of the freight transportation system operate, perform, and impact the larger overall transportation system. ● ○ ○ ○ ● ○ ● H: Determine how economic, demographic, and other factors and conditions drive freight patterns and characteristics. Document economic and demographic changes related to freight choices. ○ ○ ● ○ ○ ○ I: Develop freight data resources for application at sub-regional levels. ● ● ● ● ○ ○ ○ ● ● ○ ● 11

Figure 3.1. C43 Symposium research focus relationship to C20 research initiatives. J: Establish, pool, and standardize a portfolio of core freight data sources/sets that support planning, programming, and project prioritization. ○ ○ ● ● ○ ○ K: Develop procedures for applying freight forecasting to the design of transportation infrastructure such as pavement and bridges. L: Advance research to integrate logistics practices (private sector) with transportation policy, planning, and programming (public sector). ● ○ ○ ● ● ● ○ ● ● ● ● ● M: Develop visualization tools for freight planning and modeling through a two- pronged approach of discovery and addressing known decision-making needs. ○ ● ○ BOLD indicates priority research topics. 12

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TRB’s second Strategic Highway Research Program (SHRP 2) Report: Innovations in Freight Demand Modeling and Data Improvement provides detail to the events of "The TRB Second Symposium on Innovations in Freight Demand Modeling and Data," which took place October 21-22, 2013. The symposium explored the progress of innovative freight modeling approaches as recommended by the Freight Demand Modeling and Data Improvement Strategic Plan.

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