Assessment of Fuel Economy Technologies for Medium and Heavy ...

Background: Improvements to intermodal transport, such as rail capacity improvements and
bottleneck relief, intermodal (truck-rail) terminals, and financial/pricing incentives, could
potentially encourage shippers to make greater use of rail in place of truck, increasing the
efficiency of freight movement on a ton-mile basis.
Work in progress for U.S. DOT on transportation GHG reduction strategies includes a literature
review of the potential GHG benefits of shifting freight from truck to rail through intermodal
improvements. 38 Reductions in fuel consumption on the order of 60 percent per ton-mile are
typical for shifts from trucking (trailers or containers) to long-haul intermodal rail, with
reductions decreasing with shorter distances. Savings can vary significantly, however,
depending upon the distance of the movement and type of cargo.
Estimates of total potential freight mode-shifting have been aspirational in nature, rather than
based on empirical data, due in large part to the complex nature of competition between trucks
and rail. The potential for mode-shifting is limited to certain types of commodities—those that
are heavy, low-value, and do not have an acute need for reliable and timely delivery—e.g.,
building stone and waste, as well as certain movements—in particular, long-haul movements
where the efficiency benefits of rail outweigh the additional handling/logistics costs and time at
either end, generally shipments longer than 1,000 miles. Furthermore, market demand both
affects and is dependent upon the quality of service. Rail service improves significantly as
demand between market pairs increases – increased traffic (trains per day) increases the level of
service that railroads provide to customers, and means that improved access is possible since
(shippers need access to rail facilities to ship via rail). In short, shippers choose a mode that
minimizes their total logistics cost.
There are numerous ways to estimate diversion, but each has its flaws. In general, simple
techniques (e.g., the ‗Delphi Method,‘ comparative market analysis, and elasticity methods) rely
on simplifying assumptions and sketch planning techniques while complicated techniques (such
as FHWA and the Federal Railroad Administration‘s Intermodal Transportation and Inventory
Cost Model 39 and econometric models) require significant data resources, time resources, and
computation power. Furthermore, complicated techniques are very sensitive to inputs and the
inputs are often modeled. For example, public truck flow data, by commodity, do not exist while
rail data is sampled, proprietary, and requires traffic modeling for model estimation, all of which
decrease the reliability of results.
Despite the difficulties of estimating the size of diversion impacts, it is generally accepted that
this phenomenon exists and that there are a consistent set of variables that impact the outcome.
Actions that can affect a truck-rail mode shift include investment in rail and intermodal terminal
infrastructure, direct operating subsidies for railroads, land use regulations (for example, to
preserve rail sidings for rail-oriented businesses), and taxes to increase the cost of truck travel, as
previously discussed.
38
Cambridge Systematics, Inc., Transportation’s Role in Reducing Greenhouse Gas Emissions, Forthcoming,
prepared for Federal Highway Administration.
39
Federal Highway Administration and Federal Railroad Administration, Intermodal Transportation and
Inventory Cost Model, Highway-to-Rail Intermodal User’s Manual, March 2005.
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