Introduction

It is crucial to effectively incorporate freight considerations in transportation planning and policy making as the freight system is an essential part of a vibrant economy, a determinant of quality of life, and a key component of efforts to combat global warming and climate change. In essence, the freight transportation system is important because of both its positive and negative contributions to modern life. On the positive side, an efficient freight system is necessary for economic competitiveness and to realize the full potential of economic globalization. On the negative side, freight—as with the rest of the transportation sector—produces significant amounts of negative externalities that, in turn, generate community opposition. Transportation agencies and Metropolitan Planning Organizations (MPOs) are therefore under pressure to balance the conflicting objectives of those involved and impacted by freight activity. These considerations acquire greater significance given the major economic trends shaping the 21st Century which suggest that the freight system will have to cover a larger geographic area, be more responsive to user needs and expectations, cooperate in national security efforts, reduce the impacts of truck traffic, and do all of this in a context in which providing additional freight infrastructure capacity will become more difficult and expensive. Essentially, the freight transportation system will have to do more with less.

This challenge is compounded by the complexity of freight activity, and the lack of appropriate freight models currently affecting all facets of demand modeling: generation, distribution, mode choice, and traffic assignment. There is a great need to enhance the quantitative aspects of freight demand modeling, which is the rationale for NCFRP 44. The ultimate goal is to ensure that freight models: (a) have solid behavioral foundations; (b) are multimodal; and (c) are able to include feedback effects from changes in policy variables (Hedges 1971). In the area of freight mode choice policy, there is a particularly woeful lack of research and data on the impacts of policy-induced freight modal shifts. The last freight mode choice research efforts at the national level were conducted more than twenty five years ago (McFadden et al. 1986, Abdelwahab and Sargious 1991). A better understanding of the variables influencing freight mode choice would enable more accurate demand forecasts, better quantification of the impacts of freight activity, and more effective policies. Although small efforts have been conducted to collect mode choice data, their small sample size and reliance on Internet-based questionnaires leads to concerns about selectivity-bias and data quality.

The main goal of NCFRP 44 was “…to develop a handbook for public practitioners that describes the factors shippers and carriers consider when choosing freight modes and provides an analytical methodology for public practitioners to quantify the probability and outcomes of policy-induced modal shifts…”. The team accomplished this goal by means of an arduous multi-prong research effort that: involved in-depth-analyses of the historical patterns of freight mode shares (Chapter 2); a technical identification of the influencing factors at the market (macro) level, and the shipper (micro) level that shape freight mode choice decisions (Chapter 3); conducted an in-depth technical review of the potential modeling methodologies, both econometric and supply-chain based, that could be used to develop freight mode choice models (Chapter 4); conducted a critical evaluation of the advantages and disadvantage of the various methodologies and selected the most appropriate ones to be pursued in NCFRP 44 (Chapter 5); gathered the 4.5 million records in the confidential CFS microdata file, merged them with the even larger Longitudinal Business Database (LBD), prepared custom made data sets with the modal attributes (i.e., door-to-door travel time, referred to in this report as “transit time”; freight rates; and three different versions of generalized costs), and merged these data to prepare the master dataset for estimation of the freight mode choice models (Chapter 6); estimated four sets of market-share models (transit times and freight rates, and the three versions of generalized costs),  and twelve sets of shipment-level models (transit times and freight rates, and the three versions of generalized costs; and three different weighting schemes to ensure the models replicated the market shares in the CFS microdata, the domestic market shares embedded in the Freight Analysis Framework, and the total cargo handled in the country, both domestic and imports and exports) at the level of 2-digits North America Industrial Classification System (Chapter 7); analyzed the more than one thousand models estimated by the team to identify the ones that meet the conditions of being conceptually valid and statistically significant (Chapter 8); conducted six case studies of freight mode policy efforts in the US (Chapter 9); and conducted numerical experiments, using hypothetical examples inspired by the Crescent and Heartland Corridors, to gain insight on the ability of the models to produce sound estimates of the impacts of hypothetical policies.

This arduous effort—which required more than one hundred person-trips to the Census Bureau’s secured Regional Data Center, to use the confidential CFS microdata, and the LBD—represents the largest and most comprehensive freight mode choice research effort in the US and the world, and the first one that used the confidential CFS microdata. The team is very grateful to all the agencies that collaborated to make this effort possible: the National Cooperative Freight Research Program that funded part of the effort, the VREF Center of Excellence for Sustainable Urban Freight Systems that co-funded part of the research, the Census Bureau for providing access to the CFS and LBD confidential files, the staff from the Bureau of Transportation Statistics for helping the team understand the data constraints, the Federal Highway Administration’s Freight Office for sharing data about highway networks, the Surface Transportation Board for sharing the confidential waybill data, the Federal Railroad Administration for sharing expertise and access to the rail network data, and the Caliper Corporation for sharing the HERE data with the team.

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