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Freight Demand Modeling and Data Improvement (C20)

Freight Demand Modeling and Data Improvement (C20)

Product Overview

Efficient freight and commercial truck travel are essential to national, state, and local transportation infrastructure planning and our economic well-being. Understanding and forecasting freight flows is critical to planning for future transportation capacity, operations, preservation, safety and security, energy and economy investment needs. Incorporating freight movement considerations in the transportation planning process, however, may be difficult. Many current freight demand forecasting models and data sources are more appropriate for passenger transportation than for forecasting freight movements and understanding freight travel behavior.

The Freight Demand Modeling and Data Improvement (C20) product offers a road map that will lead to improved freight data sets and freight modeling practices. The project includes the Freight Demand Modeling and Data Improvement Strategic Plan, which outlines an organizational approach that will help identify freight modeling and data priority needs, spur innovative ideas, and result in breakthrough solutions for wide application.

Freight planners and decision makers will benefit from having a consistent approach to evaluating transportation projects that affect freight movement. Creating better data and models will enable state, regional, and local planners to better predict freight movement trends, and make more informed project investment decisions.

Tools and Reports
Presentations and Webinars
Case Studies
  • Behavior-Based Freight Modeling
    • The Maricopa Association of Governments in Arizona developed a multi-modal freight model to better replicate the economic behaviors of establishments, shippers, and carriers by modeling travel and tour formations in Arizona’s Sun Corridor mega-region.
    • The Maryland Department of Transportation and Baltimore Regional Transportation Board developed a regional tour-based truck model covering intra-local distribution with sensitivity to the long-distance truck flows represented in the statewide freight model
    • Portland Metro in Oregon used a hybrid approach to understand the local portion of the region’s supply chains, as well as the tour-based behavior of individual trips to address economic policy questions and depict truck volumes and flow of goods.
    • The Wisconsin Department of Transportation developed a hybridized model for statewide freight forecasting and quantifying how different scenarios affect freight transportation.
  • Innovations in Local Freight Data
    • The Florida Department of Transportation improved the accuracy of freight forecasts by collecting data representing the supply and demand chain for petroleum commodities distributed throughout South Florida.
    • The Mid-America Regional Council in Missouri used a combination of existing data and new sources of commercial waybill data to demonstrate the impacts of congestion to the cost of freight movement.
    • The Capital District Transportation Committee in New York created a unified data set for the region at the zip code or transportation analysis zone (TAZ) level by integrating diverse data sources that will support analysis of freight.
    • The Winston-Salem Metropolitan Planning Organization, as part of the Piedmont Triad Regional Model Team in North Carolina collected data to support development of an advanced freight model.
    • The Delaware Valley Regional Planning Commission in Pennsylvania and New Jersey advanced a freight data planning program to better understand intermodal transportation for freight and created a platform for sharing freight data.
    • The South Dakota Department of Transportation studied the growth in agricultural production and the current and expected location, timing, and impact of commodity shipping on South Dakota’s highways.
    • The Washington State Department of Transportation collected detailed information from industry and local urban truck deliveries for the state's food distribution supply chain to accurately model behavioral responses to different state policy scenarios aimed at reducing emissions.
Implementation Assistance Program
  • 11 state DOTs and MPOs were funded through the Improvement Implementation Assistance Program (IAP): Maricopa Association of Governments (MAG), Florida DOT, Maryland DOT, Mid-America Regional Council, Capital District Transportation Committee, Winston-Salem MPO, Oregon DOT, Delaware Valley Regional Planning Commission, South Dakota DOT, Washington DOT, Wisconsin DOT.
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