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.
53 CURRENT STATE OF THE PRACTICE BACKGROUND Major changes in domestic and global freight transportation have occurred over the past several decades. These changes have been driven by population growth, changes in consumer behavior, dynamic market and economic forces, and advances in trans- portation and information technology. Public and private decision makers responsible for understanding the implications of these trends in relation to transportation investment and system operation must con- tend with the infl uence of increasingly complex supply chains and logistics processes. However, public sector transportation decision making remains relatively uninformed with respect to freight transportation due to the limits of the current models. These models are unable to accurately forecast the impacts of freight on transportation sys- tems, thus limiting the possibilities for policies and improvements to solve expected problems. The practice of freight demand forecasting has received greater attention with the recognition that effi cient freight and commercial truck travel is essential to national, state, and local transportation infrastructure planning and economic well-being. Incorporating freight movement considerations in the transportation planning process is diffi cult, but these considerations are increasingly critical to the ability to forecast long-term transportation trends and plan for future needs.
6FREIGHT DEMAND MODELING AND DATA IMPROVEMENT STRATEGIC PLAN SUMMARY ASSESSMENT OF CURRENT FREIGHT MODELS, DATA, AND TECHNIQUES Freight planning and forecasting employ a variety of tools and techniques, including economic flow models, land use and economic inputâoutput analyses, commodity- based models, vehicle- or trip-based models, and other analytic tools. The common underlying objective is to document baseline conditions related to freight movement and estimate future activity based on metrics involving economic activity, demographic changes, employment by economic sector, supply and demand of raw materials and finished products by consumers and industries, commodity flows, and other factors. Different tools are used by planners for different geographic scales, depending on the issues and scale of needs. The data used in these freight planning and forecasting processes are drawn from predominantly public resources. Freight models developed and maintained by public agencies typically use the following data sources: ⢠Local data sources (including traffic counts, traffic forecasts, demographic data and forecasts, and land use information); ⢠National Transportation Atlas Database; ⢠CFS; ⢠FAF; ⢠Transearch data; ⢠Other federal resources (e.g., U.S. Census Bureau, Bureau of Transportation Statis- tics, Surface Transportation Board Carload Waybill Sample); and ⢠Private sector data sets (e.g., private shipper data, purchase orders, bills of lading, ship manifests). The underlying methodology for most tools used in freight planning and fore- casting includes using these data resources to (1) document existing demographic and employment conditions and characteristics of freight transportation (including tonnage, geographic origins and destinations, and mode of transport) and (2) estimate future measures of freight transportation for these same parameters (tonnage, origins, destinations, mode of transport) based on changes in population and employment, productivity improvements by industry, and other economic forecasts. Depending on the geographic scale of the forecasting effort in question, the ultimate objective of freight planning and forecasting is to forecast freight activity and its effects on local or regional conditions related to economic activity, traffic congestion, air quality, and other impacts. However, current freight planning and forecasting tools, data, and techniques have various limitations. Key shortcomings in the current state of the practice in freight forecasting include the following: ⢠Existing data resources are best suited to large geographic scales and do not trans- late well to local planning efforts;
7FREIGHT DEMAND MODELING AND DATA IMPROVEMENT STRATEGIC PLAN ⢠Current planning tools and data do not accurately reflect the nature of supply chains and increasingly complex logistics practices in freight-dependent industries; ⢠Documenting the various factors that influence freight transportation needs is challenging because establishing links between disparate data resources (e.g., land use, demographics, employment by industry) and the freight activity that relates to these measures (e.g., truck counts, vessel activity, rail activity) is extremely difficult; ⢠Transportation forecasting and modeling practices tend to focus on average trip generation rates, but freight activity is heterogeneous and does not lend itself to average rates of production and consumption; and ⢠The growing role of third-party transportation providers makes freight less visible to many shippers and receivers; thus it is more difficult for the public sector to document detailed freight activity through information gained from traditional shipper surveys.
