National Academies Press: OpenBook

Innovations in Freight Demand Modeling and Data Improvement (2014)

Chapter:CHAPTER 5: Presentation Abstracts

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Suggested Citation:"CHAPTER 5: Presentation Abstracts." 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 5: Presentation Abstracts." 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.
×
Page19
Page 20
Suggested Citation:"CHAPTER 5: Presentation Abstracts." 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.
×
Page20
Page 21
Suggested Citation:"CHAPTER 5: Presentation Abstracts." 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.
×
Page21
Page 22
Suggested Citation:"CHAPTER 5: Presentation Abstracts." 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.
×
Page22

Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

CHAPTER 5 Presentation Abstracts The abstracts in this report are presented as they were submitted to the planning team during the call for papers, with the exception of any graphics and references to the graphics that may have been included. Some notes on the presentations from the symposium have been included ahead of their abstract content, and, where necessary, note when the presenter is different from the paper’s author. 18

Day One Introductory Remarks Rob Handfield, Ph.D. Supply Chain Research Cooperative North Carolina State University North Carolina State University has established a supply chain cooperative, which puts students together with companies to deal with real-world problems. It is a multinational scholarly effort asking the question, “What are the big trends and issues shaping the global supply chain today?” We interviewed 60 supply-chain officers and sent surveys to 1,700 global professionals. The main takeaway is this: The world is becoming a much more complex place as it moves to a network economy. We are connected to the entire world. For companies, the number one issue is an increase in customer expectations, but at the same time the lack of reliable infrastructure and increased regulation are growing concerns. They are using technology as an enabler, but the overall problem of infrastructure is pervasive. Customers want customized solutions—packages a certain way, delivered in a certain way, at a certain time—and on-time delivery is a prerequisite for survival in this modern economy. These factors are dependent on infrastructure. Everyone believes that the cost of logistics will increase. It may be through congestion, environmental regulations, or moving to same-day shipments. E-commerce has exploded the number of sales channels, and this has further led to smaller shipments. More than half of manufacturing has moved off shore, but have the changes in the supply chain been addressed? This will require public-private sector joint products. The private sector is adjusting by improved cost estimation and network planning capacity. They are developing standardized processes than can still be flexible and investing in technology to track and trace shipments. Industries are developing a global logistics governance playbook. This requires increased collaboration with the public sector and having a single voice. This means working with local governments. Top-level visits by a CEO can help address issues. There is a growth in new technology and the sharing of data that comes along with it. From radio-frequency identification to inventory optimization software, this information is being integrated to be accessible for analytics on smartphones. The number one type of data that people want to share is transportation. 19

Creating a Supply-Chain Methodology for Freight Forecasting in Wisconsin Jennifer Murray Wisconsin Department of Transportation Presentation Notes: Wisconsin Department of Transportation managed nine travel demand modelers, but there were few sketch-planning regional level successes. There was a need for a robust model that could meet day-to-day needs, so the fusion model was developed as a forecasting model but also to create a framework for performance measures and investment strategies. The model works to address three things: provide corridor commodity flows, visualize the data in one place, and align investment with needs across multiple modes. As first steps to developing the model, it was necessary to define the goals and agree on the level of detail and the last mile connections. The fusion model is based on the four-step model but factors in the economics of moving freight with business locations and product types. Further data improvement is needed, such as vehicle classification data, more commodity-specific information, modal shipment type, and commodity freights. There is a web-based mapping application being developed that includes locations, highway conditions, and annual average daily traffic (AADT). Abstract This presentation lays out a proposal for a new tool set that will forecast freight transportation in Wisconsin, called the Multimodal Freight Fusion Forecasting Model. Envisioned as a hybrid tool set, the Fusion Model will not function like a strict, traditional travel demand forecasting model based in time series, behavior, input-outputs, supply chain, or simulation. The Fusion Model will fully integrate the Wisconsin Department of Transportation (WisDOT) freight data with forecasting procedures to clarify current conditions, evaluate performance measures, and determine future alternatives. The Multimodal Freight Fusion Forecasting Model will link industry concepts with WisDOT long-range transportation planning goals, providing a context for discussion on freight movement in Wisconsin. Currently, WisDOT and Wisconsin metropolitan planning organizations (MPOs) are examining freight data, creating tools, clarifying freight policies, and better collaborating with freight businesses. WisDOT manages specific freight functions, robust datasets, and reports. Current condition data summaries, combined with information collected during transportation projects and traffic forecast reports (future year truck AADT volume data) help long-range planners or transportation engineers determine future transportation needs. In the last year, WisDOT has used the data to create a decision-support tool called the Multimodal Freight Network. The network data provides a scoring mechanism for WisDOT plans, programs and projects. As a data warehouse, proprietary data can be added to clarify 20

things like commodity flows and business point locations. The network has been translated into an interactive web mapping application. Freight forecasting information has not yet been updated for these efforts. As part of the freight efforts, WisDOT has assessed its 2007 statewide travel demand model (with a truck freight component). The statewide model was not utilized for freight forecasting and had relatively few sketch-planning, regional freight flow level successes. Because it aggregates data, splits out modes by percentages, and tosses out long-haul, shorter- haul, and drayage freight data sensitivity, the model has not worked very well. To correct this, Wisconsin’s freight forecasting tools should link these objectives with one another to solve mobility and preservation goals for long-haul and localized capacity, complete adequate roadway designs for heavy-weight vehicles, link areas (last mile connections), and increase the economic viability of private businesses. Good data would help WisDOT. Data that could assist WisDOT in freight decision making is not limited to and includes • Commodity information, including shipping costs, shipping value, commodity amounts, commodity weights, and the impacts to construction costs, depending on transportation mode; • Freight or intermodal terminal and supply-chain data to better estimate travel times and trips made with empty containers; • New business data affecting localized areas. To promote data integrity, better understand data sources, and quickly respond to change, WisDOT is bringing multiple data components together. As WisDOT continues to develop the Multimodal Freight Network, freight forecasting tools will also need development. Tools will need flexibility and will need to be tailored to customer needs. The proposed Multimodal Freight Fusion Forecasting Model will serve as a set of plug-ins to the Multimodal Freight Network. As such, the Fusion Model should consider data quality improvement, data collection, data processing, and data analysis in a cyclical way to be responsive to ongoing freight activities and to increase WisDOT’s ability to respond quickly to issues. Possibilities for the Multimodal Freight Fusion Forecasting Model are endless. For example, indicators or indices outlining economic development potential could be created. WisDOT could partner with government and industry to better understand business location and site variability factors. To better understand supply chain activities, WisDOT could collect higher quality local commodity flows that include commodities hauled, shipment sizes, and trip end activity. Multimodal freight data could be gathered and warehoused together. As the Fusion Model comes together, WisDOT could also develop new, standardized forecasting approaches. For example, freight business time frame forecasts (3–5 years) could be outlined on forecast reports relative to typical 20-year growth rates. WisDOT could develop forecast confidence scores based on the statistical significance of freight factors. Corridors could 21

be evaluated based on mode, condition, and costs. Overall, the Multimodal Freight Fusion Forecasting Model should quantify future year transportation mode shifts and future year tonnage capacities on different corridors and link these with Wisconsin’s economy. This information could be output as part of recognized business practices. To develop the Fusion Model tool set, a schematic business plan approach is needed outlining long-range expectations, guidelines, stakeholder commitments, and technologies. Along with performance measures, WisDOT should create a solid data architecture framework. Data collection, data processing, data analysis, and data refinement must fit WisDOT, as a customer. The Multimodal Freight Fusion Forecasting Model will be unique and tailored to Wisconsin. It will be grounded in the principles of travel demand or statistical modeling and will act as a series of plug-in components to the already underway Multimodal Freight Network. The tool will be responsive to current freight issues between WisDOT and business and will be useful from an asset approach for long-range transportation planning, analysis, and forecasting. 22

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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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