VMT Validity and Alternative Measures of Motorization
VMT Validity and Alternative Measures of Motorization
Prepared by: Amy Tzu-Yu Chen
University of California, Los Angeles
Instructor: Martin Wachs
There has been growing interest among economists, policy makers,
environmentalists, and transportation researchers in investigating the trend and effects
of the total volume of vehicle travel in the United States. The issue of vehicle travel
pattern intrigues economists because it has long been observed that the aggregate
vehicle travel in America correlates with economic growth, though the causality between
the two is yet to be confirmed. Politicians want to reduce the total mileage of vehicles in
order to bring down greenhouse gas emissions, lessen traffic congestion, and promote
the use of public transportation. Environmentalists are certainly the greatest supporter
for this new direction in transportation policy and they expect to see a gradual decrease
in the use of vehicles.
Among the many discussions of aggregate vehicle travel for either economic or
political purposes, VMT, or vehicle miles of travel, is the most widely used way of
measuring the gross mileage of vehicle travel. The Federal Highway Administration
(FHWA) under the U.S. Department of Transportation regularly compiles and publishes
monthly and annual VMT reports on the web. These reports play a crucial role in
numerous studies of U.S. motorization because they are almost the only source that
researchers referred to in order to understand current and historical vehicle travel
patterns. While economists propose potential causality between VMT and GDP and
transportation policy makers rely on VMT data to formulate future policies, it is important
to validate the statistical processes of VMT data collection and compilation.
Anyone can access current and historical VMT data through FHWA’s official
website and the data come in friendly formats for statistical analysis. The procedure of
VMT data formulation, however, is not widely discussed. Few mention the fact that VMT
data are collected separately by local departments of transportation and FHWA is
responsible for final compilation and supervision of data reports. FHWA published
general guidelines called Traffic Monitoring Guide, which can also be freely accessed
online, to local transportation researchers. The practical data collection and compilation
processes, however, can hardly be as consistent as FHWA intended them to be.
There are alternative ways to comprehend the national and local travel trends
other than VMT. Some researchers have integrated other sources of data in order to
have an objective view of travel patterns. These alternative sources could often reflect
more detailed or more accurate travel patterns in the U.S. than VMT. For example,
National Household Travel Survey collects means and purposes of daily travel of people
from different age groups. The intuitive measure of the number of registered vehicles
can also imply changes in travel behaviors as well as economic and societal
environments. By substituting VMT data from FHWA with alternative measures of travel
trends, researchers could sometimes prompt disparate conclusions.
Why Is VMT Important?
The relationship between VMT and economic growth is often studied. GDP, or
gross domestic products, is a simple indicator of economic growth. When researchers
paired VMT and GDP together, there exists observable similarities in which VMT and
GDP grew. The aggregate measures of GDP and VMT appear to be closely correlated
since 1936 and it was not until 2003 that people started to see the measures diverging.
It is tempting to simply say there existed a linear relationship between VMT and GDP so
that people could use current VMT or GDP to predict one another. Nevertheless, the
causality between the two measures is yet to be determined. One could argue that
decline in mobility discouraged economic activities, but one could also say that the
decline in mobility is a consequence of economic recession. The causality is difficult to
prove so whether the similar patterns in VMT and GDP have far-reaching meaning to
our society is still questionable. Nonetheless, further studies of the relationship between
VMT and economic growth requires dependable VMT data before researchers continue
exploration of the relationship between VMT and GDP.
Figure 1. Total Auto and Truck VMT (trillions) and GDP (trillions of $2005),
Note: VMT axis on left; GDP on right
Graph Source: Liisa Ecola, Martin Wachs, and The RAND Corporation, “Exploring the Relationship between Travel Demand and
Economic Growth”, December 2012. Figure 1.
Data sources: VMT: FHWA,1995 (Table VM-201); FHWA, 2012; BTS, 2012 (Table 1-35); GDP: BEA, 2012 (Current-dollar and “real”
GDP file as of February 29, 2012)
In response to the environmental concerns about global warming and air
pollution, the direction in transportation policy has been shifting towards reducing VMT
in order to bring down greenhouse gas emissions from vehicles. These policies
generally include charging drivers fees based on VMT data, promoting public
transportation ridership, and also control of fuel consumption. As transportation
departments usually rely on VMT reports as the primary source for understanding
current travel patterns, the validity of VMT data could largely influence the decisions on
the country’s transportation system.
VMT Measurement Process
FHWA is responsible for the publication of monthly and annual VMT measures.
These reports often provide grounds for researchers and policy makers to generate
conclusions about changes in travel behaviors. However, few queried the actual
processes in which these data are assembled before using them for academic and
political purposes. FHWA is widely referred to as the data source provider, however, the
agency does not assemble VMT data all by itself. Instead, the agency serves as the
supervisor and distributor of the VMT data and states report the latest local VMT
measures to FHWA, thus, the actual data collection is done at state level. FHWA
dispenses an official documentation on VMT estimation, which is called Travel
Monitoring Guide on Highway Statistics website, and states are liable for preparing the
VMT estimates based on the manual.
This hierarchical structure of VMT collection process has the potential weakness
that the data can be inconsistent or deficient. Although FHWA gives states instructions
on the statistical method for gathering, calculating, and reporting VMT, the actual
processes of data collection can still vary between states. For example, states use
counting stations in various locations to collect traffic counts; however, it is difficult to
have a consistent method of setting up the machines considering the differences in
geographical constraints and residents’ travel behaviors between states. Also, the
maintenance of counting machines can influence the quality of data. If a machine
suddenly breaks down and does not return the VMT data in time, the quality of state’s
VMT data would decrease yet the aggregate VMT report from FHWA does not include
incidents of malfunctioning counting stations. While FHWA claims that the method of
VMT data collection in each state is consistent and reliable, it evades the effects of the
erratic nature of having not-uniformly-trained data collectors to collect data across
geographically and socially diverse regions. It is crucial to sustain sampling variability
when local departments of transportation collect data. This requires states to
strategically and routinely change locations of counting machines so that the data can
be reflexive of the actual travel patterns of the entire state. However, it is unclear how
local transportation researchers decide when and how the rotations among counting
machines occur. Thus, the validity of VMT data reported by FHWA is under suspicion,
which requires in-depth comparisons of how each state obtains the data in order to
substantiate itself to be a dependable source for travel behavioral studies.
After investigations of states’ VMT reports and interviewing some local
transportation researchers, concerns regarding the validity of VMT data collection were
confirmed. From California’s monthly VMT reports, one can observe that many of the
counting stations used in VMT calculations were often unavailable hence were not able
to supply data. In most months, there were at least three counting stations, out of the
total of twenty counting stations used in VMT calculations, were not available for use
and the data were left in blank. FHWA often replaced these missing counts by the
average count of the entire state or nearby state, however, this may potentially ascribe
an average value to a location with abnormally high or low VMT or vice versa. Also, an
administrator at U.S. Department of Transportation admitted that, “States sometimes
report estimates that are out of the range of reasonableness, and do not necessarily
provide corrections when unreasonable numbers are reported” (Schmitt). FHWA cannot
be certain whether these abnormal data are statistically significant and it is likely that
these data are included in the historical VMT reports regardless. In addition, the VMT
measure is usually split into urban and rural sections thus an up-to-date division of
urban and rural areas could have a significant impact on the accuracy of VMT data.
Nonetheless, FHWA has not updated urban boundaries while the urban areas could
have extended into previously rural area a long time ago. The comparisons of VMT
between urban and rural could not be accurate without correctly defining the borderline.
As travel behavioral analyses commonly compare and contrast travel patterns in and
outside of the city, FHWA should provide VMT data that can reflect ongoing
There are data sources other than VMT that can reflect travel behavior patterns
in the United States. National Household Travel Survey emphasizes other aspects of
travel behaviors, such as means and purposes of a trip. VMT, in comparison, does not
offer such diverse and detailed insights about travel patterns. Another example of
reflective measure of travel behavior is simply the number of registered cars. The data
bypasses the miscellaneous possibilities of creating statistical error in data collection
since every driver is required to register their car and truck near their residence. Many
researchers have already incorporated them into their studies or cross validated them
with VMT measures.
National Household Travel Survey (NHTS), also prepared by FHWA, aims to
provide information that can comprehensively assist transportation researchers and
policy makers on travel patterns in the United States. The survey does not estimate
travel patterns by mileage counts, instead, it extensively inspects other components in
each trip of a household as well. Specifically, states request a sample of households to
record their daily travels, including purposes of the trip, means of transportation, time
and duration, and ridership statistics if it is a private vehicle trip. These questions are
designed to understand travel behaviors from a more humane perspective. Instead of
piling up aggregate mileage of travels, the survey is interested in how and why people
take the trip. Another advantage about NHTS is that it also contains demographic
information about the travelers. Researchers can thus interpret travel behaviors in
relation to the background of the travelers, which the rigid mileage data of VMT cannot
offer. The NHTS data can supply transportation planners with information about travel
modes. As the paradigm in urban planning shifts towards developments in public transit
and zero emission travels, records of trips that are as comprehensive as NHTS are
more practical than VMT reports for assessing topics such as effectiveness of public
transportation. However, unlike VMT measures which are published monthly, NHTS is
conducted almost every six or eight years. Since NHTS requires collaboration with
sample households and classification of answers from a multilevel survey, it is time-
consuming and demands more social resources than VMT. Moreover, similar to VMT
data collection, states could have different ways of administrating NHTS survey even
with a manual. For example, the sizes of samples among states and the choice of
households may differ, which will result in inconsistent data quality and inability to have
a credible conclusion about national travel patterns.
Figure 2. Registered light-duty vehicles, 1984-2011.
Graph Source: Michael Sivak, “Has Motorization in the U.S. Peaked?”, June 2013, Figure 1.
Data source: Registered Light-duty Vehicles: FHWA,2013.
Michael Sivak from Transportation Research Institute at University of Michigan
proposed a rather intuitive approach to the analysis of travel behavior in the United
States. In “Has Motorization in the U.S. Peaked?”, Sivak discussed the possibility of
examining recent trends in motorization based on the absolute numbers of registered
light-duty vehicles, which include cars, pick-up trucks, SUVs, and vans. According to
Figure 2, the change in the number of light-duty vehicles has a steadily increasing trend,
which is almost analogous to the VMT change in Figure 1. The number of light-duty
vehicles in 1984 was 156.8 million and the number climbed up to the maximum of 236.4
million in 2008. After 2008, an observable decrease in registered vehicles as a result of
economic recession, and the number went down to 233.8 million in 2011. Sivak did not
try to tie the economic status and motorization. He observed that the decrease in car
usage occurred prior to 2008, thus he suspected that other societal changes drove the
demand of cars to go down before economic downturn. By using the social and
economic event to try to make sense of the fluctuations in the number of registered
cars, researchers could also discuss the change in travel behaviors. Compared to VMT,
the data of registered cars are less likely to be erratic because all drivers are required to
register their cars by their residence. The downside of this measurement is that it does
not provide detailed information as NHTS and offer only an aggregate counts that can
often be too conclusive to show changes in travel behaviors.
The accessibility and transparence of the VMT makes validation of data quality
seem unnecessary. However, as researchers and policy makers frequently rely on VMT
as the primary source for understand current trends of travel behavior, whether VMT is a
credible data source that was compiled strictly based on statistical principles becomes
an important issue. There are possibilities of substituting or cross validating VMT with
alternative measures of travel patterns, such as NHTS and the total of registered cars.
Ecola, Liisa., Wachs, Martin., & The RAND Corporation. (2012).
Exploring the Relationship between Travel Demand and
Sivak, Michael. (2013). Has Motorization in the U.S. Peaked?
University of Michigan, Ann Arbor, Michigan: Transportation
Research Institute (UMTRI).