Freight Demand Modeling and Data Improvement (2013) / Chapter Skim
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Pages 51-75
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52 freight transportation and the many elements that are necessary 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 development of enhanced methods for disseminating information from these analytic tools for public stakeholders and decision makers.
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53 such as the GFRC and the future data and modeling symposia 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 important function for those involved in implementing the Strategic Plan is to periodically assess changing research needs based on decision-making requirements relative to the changing dynamics of goods movement.
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54 interesting global supply chain game developed by researchers at Delft University and the Robert H Smith School of Business at the University of Maryland, and tested through course offerings at the business school since 2005 (Corsi et al.
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55 Other Considerations Because this research effort does not relate to the future highway 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.
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56 The tasks in this project will serve as building blocks in the development of a more comprehensive overall freight transport model. Much like subroutines embedded in a highly complex program, this research will provide a modeling approach that includes decision tree creation methodology.
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57 • The MOBILEC model documented in Innovative Freight Transportation Framework for Flanders (Maes and Ramaekers 2010) strongly relates to this initiative.
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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: Guidance for Developing a Freight Transportation Data Architecture (Quiroga et al.
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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 PublicSector Decision Making (Cambridge Systematics and GeoStats 2010)
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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.
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61 Benefits and Expected Outcomes • Identify nontraditional components necessary for a comprehensive, holistic cost–benefit analysis; • Document and include external benefits and costs in infrastructure 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 considerations are summarized in Table 4.6.
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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.
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63 • Provide a means to enhance public–private mutual understanding and collaboration for freight planning and analysis; • Support the development of meaningful transportation system performance measures. • Develop common metrics for freight planning and modeling for similar geographic scales (when applicable)
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64 a point of consumption or to an intermediate point for manufacturing or distribution. The characteristics of this supply chain are heavily influenced by economic factors such as access to labor, markets, transportation infrastructure (various modes)
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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 information 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 Modeling of Truck Freight Demand (Wang et al.
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66 • This research area relates strongly to Sample Research Initiative E This effort involves the collection, aggregation, 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
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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.
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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 Initiative 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.
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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.
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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, modifications of existing facilities versus construction of new facilities, and build-to-suit versus "spec" buildings?
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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 documenting decision-making cycles for different industry groups; an example of this research is Modeling Commercial Vehicle Daily Tour Chaining (Ruan et al.
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72 some degree, as enhanced visualization techniques would be valuable assets for all of them. This initiative should be considered for early action and conducted in parallel with the others to the extent possible, with a process in place to regularly inform the other research teams of progress, developments, and innovations in visualization.
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73 This partnership will support independent research and reward innovative and compelling investigations and experiments by sponsoring an annual research competition spanning various research tracks and providing a seed-grant award. Establishing and maintaining the GFRC will require careful planning.
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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 generating a variety of research concepts; • The competitive nature of the symposium generated numerous excellent ideas and promising research concepts; • The symposium brought together academic, private sector, and public sector perspectives; and • The symposium fostered a greater shared understanding of the issues and requirements for improved freight modeling and planning.
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75 consortium will be determined based on the combined interests 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.
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76 modeling and data research. This activity will be particularly important from the standpoint of promoting broader implementation of successful freight analytic approaches.
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