TRB's National Cooperative Highway Research Program (NCHRP) Synthesis 406: Advanced Practices in Travel Forecasting explores the use of travel modeling and forecasting tools that could represent a significant advance over the current state of practice. The report examines five types of models: activity-based demand, dynamic network, land use, freight, and statewide.

TRB Special Report 288, Metropolitan Travel Forecasting: Current Practice and Future Direction, examines metropolitan travel forecasting models that provide public officials with information to inform decisions on major transportation system investments and policies. The report explores what improvements may be needed to the models and how federal, state, and local agencies can achieve them. According to the committee that produced the report, travel forecasting models in current use are not adequate for many of today's necessary planning and regulatory uses.

"This course attempts to communicate to travel modeling professionals some of the [travel demand forecasting] procedures developed by their colleagues around the U.S. and abroad, most of which have been implemented as part of an existing travel demand modeling system."--p.1-5

The most common method for producing regional or metropolitan area travel forecasts in the United States is to apply the following four modeling steps sequentially: trip generation; trip distribution; mode choice; and, route assignment. This traditional 4-step process passes output from one step to the next as input. While the process has produced forecast results sufficiently accurate for many types of long range transportation planning, it is commonly found that some of the outputs of the process are not consistent with inputs to earlier steps. The research undertaken in this project focused on methods to ensure that link speeds used in each step of the travel forecasting process are consistent with the final speeds estimated in the final step of the process. As a product of this research, a final report was prepared to provide guidance in the application of feedback.

Travel demand modeling is a core technology of transportation planning and has been so for half a century. This technology refers to the structured use of mathematical formulae and spatial data to forecast the likely travel impacts of possible transportation, land use, and demographic scenarios. Although this planning practice is pervasive, critics have long argued that is has been resistant to innovation. As the policy scenarios explored through modeling become increasingly complex, particularly in the face of climate change, the question arises of whether regional planning agencies will be able to change their practices through implementing innovation. This research addresses this question by examining the history of travel demand modeling as practiced at regional planning agencies, interviewing travel demand modeling experts, conducting detailed case studies of model practice evolution at two metropolitan planning organizations, the San Francisco Bay Area's Metropolitan Transportation Commission (MTC) and the capital region's Sacramento Area Council of Governments (SACOG), and analyzing the early impacts of California's groundbreaking climate change legislation on the modeling practiced in the Golden State. The findings suggest that far from being a static practice, travel demand modeling at regional agencies has advanced, particularly with public interest in exploring the impacts of major policy interventions. The nature of travel demand models does not naturally foster changes in practice; however, government action can structure the innovation process by establishing clear expectations of agency modeling capabilities to meet legislative mandates, providing resources for investments in new approaches, and creating forums for interagency interaction and information dissemination.

TRB’s National Cooperative Highway Research Program (NCHRP) Report 716: Travel Demand Forecasting: Parameters and Techniques provides guidelines on travel demand forecasting procedures and their application for helping to solve common transportation problems.