BACKGROUND
Transportation planning and design require different levels of detail with respect to forecasting travel behavior, demand, and use. Whereas many planning decisions can be supported by typical outputs of a four-step travel demand model, these same outputs are insufficiently precise to support many of the decisions being made during detailed project development and design.
Truck traffic forecasting is often conducted as a post process of the data results from a travel demand model or is conducted using commodity flow or other economic and statistical models. Yet, a variety of specific decisions regarding the placement, quantity, length, and geometry of facilities to support a specific volume and type of truck traffic would benefit from more specific data, methods, and techniques for using truck traffic forecasts in project design.
Among the limitations of typical travel forecasting models are the ability to predict the specific volumes, weights, and movements of trucks on highways. Truck traffic imposes specific design requirements to accommodate their unique weights and configurations. Transportation agencies and freight distributors need to assess truck travel in different contexts, including but not limited to long-haul goods transportation, local, and last-mile freight deliveries.
State departments of transportation (DOTs) and modeling professionals are responding to these challenges by creating more accurate and responsive models and model applications, particularly as the research and development of modeling methods and techniques continue to advance. Nevertheless, the transportation industry is not uniform with respect to its technical knowledge, capabilities, budgets, or other resources needed to develop and apply sophisticated models and decision tools to support project design decision-making. While some state DOTs have the resources to supplement in-house staff or hire outside experts to conduct model runs and analyses, many others simply do not have that capacity.
OBJECTIVE
The objective of this research is to develop a guide to assist state DOTs and other agencies in the selection and use of forecasting models, applications, procedures, tools, and techniques needed to support project design.
At a minimum, the research team shall:
1. Identify and evaluate the range of existing and emerging technical approaches, data sources, models, model applications, and tools available to generate and apply truck traffic forecasts to support project design;
2. Identify gaps and needs for improvement in those applications, tools, and techniques within the context of design decision-making;
3. Compare (quantitatively) the accuracy of new methods to traditional methods;
4. Provide user-friendly instructions on the appropriate selection and use of specific model applications, tools, or techniques during project design; and
5. Refer to the truck classifications 5-13 provided in the Federal Highway Administration Traffic Monitoring Guide, updated October, 2016.
Deliverables shall at a minimum include:
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A guide to the design and application of truck traffic forecasting methods to support design.
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An implementation plan for the dissemination and use of the final product or products of this research project by agency staff.
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A contractor’s final report detailing the full research process and results.
STATUS: Rensselaer Polytechnic Institute has won the contract to conduct the study, active as of January 16, 2024.
