Paradigm Inertia in the U.S. National
Household Travel Survey (NHTS)
Hart Nadav Feuer
Arava Institute for Environmental Studies
February 1, 2006

“You see, transport assessments are largely built around a view of travel as stable and repetitive—the commuter
who makes the same journey every day, the shopper doing the same journey every week. Without question,such patterns exist, and they dominate our perceptions of our own lives, and our interpretation of otherpeople’s.”– P.B. Goodwin, Inaugural Lecture for the Professorship of Transport Policy,
University College, London, 23 October 1997.

The transport field, like many academic fields, and yet more industrial fields, isfundamentally torn between the need and desire to innovate, even introduce radical change,
and the creeping dependence on its own methodologies and accommodating the data needs
and expectations of stakeholders. A basic change to the National Household Travel Survey(NHTS) would disrupt not only the routines, rhythms, and expertise-need within the U.S.
Department of Transportation (DOT) and the Federal Highway Administration (FHWA), butwould send shocks throughout dependent government agencies, not to mention the Bureau ofTransportation Statistics (BTS), and elicit an angry response from transport researchers
demanding comparability and consistency across survey years. As a result, improvements inthe quality and relevance of the survey are a fairly poor incentive for tampering with theinertia of the status quo. A more compelling reason given for switching the NHTS from across-sectional survey to a longitudinal survey, for instance, relates to the appealing potential
within the DOT and FHWA to spread resources and expertise over time (longitudinally)rather than suffer a spike in costs and labor during the intense implementation year of thecross-sectional survey. In spite of this, the NHTS team is to be commended for theirtransparency, academic rigor, and responsiveness within the context of a constrained
institution system characterized by high inertia. That being said, the author believes the goalof a transportation survey should be to assess opportunities for interdiction of mobility reform
rather than facilitating the extension of time-series data of questionable value. Major shifts intechnology, methodology, and expertise mark the history of the NHTS—yet researchers stillfind a way to measure across space and time. NHTS should, in the face of criticism, assumedata users will welcome new methodologies and will also find a way to combat changes inmethodology. To lower the anxiety of creating new methods, NHTS team members shouldcommit to ongoing field trials and tests of new methodologies that can phase into the current
NHTS. This paper will critically analyze the NHTS and its respective audience andstakeholders, while keeping in the background the alternative of panel surveys.

1

Before launching into the 32-year history of the Nationwide Personal Transportation
Survey (NPTS) leading up to the transition to the NHTS for 2001,1 it is important to sample
counterpart transportation surveys outside of the United States. Firstly, it should be pointed
out that the resources required to conduct such an expansive transportation survey (in theorder of tens of thousands of households) requires resources that many countries are simply
not willing to proffer.2 As a result, the major transportation surveys largely take place in themost economically fit countries outside of the U.S, namely the Netherlands, Britain (anewcomer), Germany, and France.3 None of these travel surveys are as old as the U.S.
survey, and, without exception, they are all panel, rather than cross-sectional, surveys. Thelong history of the NPTS relative to its counterparts is at once a benefit and also a drawback.
While it can, to some extent, be used to measure trends dating back to 1969, it has also
gained an equaled level of inertia and bureaucratic stubbornness.
Conducted in 1969, 1977, 1983, 1990, and 1995, the Nationwide Personal Travel
Survey has remained the predominant authoritative source for everyday personaltransportation statistics in the United States. The uneven implementation of the NPTS
throughout the years represents the fickle nature of transport priorities and intragovernmental
agency funding.4 However, with the emergence of the NHTS in 2001, and the continuing
embededness of the national transport survey within industry and in the public and academic
spheres, public financial guarantees and specific timing are becoming more consistent. The
new NHTS will take place in 2007/8. As opposed to previous iterations of the NHTS, afuture date survey time was set right away—a concrete planning action by the FHWA andDOT that indicates their desire for the current NHTS model to remain the predominantsurvey technique. This is borne out in the NHTS User Guide (2004), in which one of thefunctions of the NHTS is reported as the ability to “analyze changes in travel characteristics
over time.” This type of goal can serve to lock in the innovation potential of the NHTS,
1

Since 1969, and after a long tenure as the eminent American travel survey, the Nationwide PersonalTransportation Survey (NPTS) was augmented by the American Transportation Survey (ATS), and, to reflect
the change, was renamed the National Household Travel Survey.
2
The actual costs of some surveys, in dollars from the named year.
Survey Total Cost ($)
No. of Households
Cost per Household ($)
1995 NPTS
4,096,000
21,000195
1995 ATS
18,000,000
67,000269
2001 NHTS
10,275,000
25,000411
Source: June 18th Letter Report of the Committee to Review the Bureau of Transportation Statistics’ Survey
Programs, Joseph L. Schofer.
3
Specifically, these are the German Mobility Panel (MOP), the Dutch National Mobility Panel, the U.K.
National Travel Survey, and the French Parc Auto Survey (SOFRES-INRETS-ADEME).
4
Fickle-nature of funding and timing of the national surveys is mentioned in the June 18th Letter Report of theCommittee to Review the Bureau of Transportation Statistics’ Survey Programs, Joseph L. Schofer.

2

given that a major overhaul would likely harm survey comparability. Instead, the regime ofmodest aims adopted for each survey will likely continue. For the recent NHTS, these goals
included reviewing: telecommuting, Internet usage, and expanding the capture period fortransit, walk, and bike trips.
The intragovernmental team charged with administering, processing, and evaluating
the NHTS 2001 results have maintained a very high level of integrity, transparency, andopenness in the process. Researchers are clearly working hard to deliver the best available
survey to satisfy the most important stakeholders and for this general level of conduct theyshould receive no criticism. As a user of the NHTS 2001 data with my own peculiar
demands, I sympathize with the NHTS team’s role trying to balance the often-irreconcilable
needs of various constituents whilst working within a restricting government framework andbeing subject to the pressure of certain departments in the government. One unlikely
example of this phenomenon is the Department of Energy’s (DOE) dependency on the NHTS
as a stable survey model to calculate fuel consumption and energy demands of the transport
sector. The competing demands from governmental departments even closer to the field oftransport are likely more demanding. Any routine behaviors, or inertia characteristics
overall, of other governmental stakeholders pressures the NHTS to stay static. Add to thisthe scrutiny and varied claims coming from transport researchers, and one arrives at a design
environment that is hardly flexible or sympathetic.
Parallel to the discussion of stakeholders is the matter of goals and agendas for thetravel survey. Is the NHTS team strictly to provide data in as scientific manner as possible,
conduct basic exploratory analysis, and allow data-users to take care of the rest? Does orshould the NHTS team have a higher social agenda, such as creating statistics that truly help
with pressing environmental and social needs of pollution reduction, transit advocacy, andstructural congestion alleviation? A shift to this type of mentality might naturally bring aboutsignificant innovation in the national travel survey, but might overly politicize the role of asurvey and make it susceptible to partisan tampering. It might simply be that the
NHTS/NPTS style of survey exists because it has survived all of the bureaucratic hurdles andnot because it is particularly useful in an absolute sense.
At least two ways become clear for navigating this situation. The first involves
redefining the goal of the survey in a way that supports the capture and sampling of“mobility” rather than “transport.” The survey can remain consistent in many respects, butshould have the capacity and wiggle-room to encourage those looking to sample behavior ofmobility pioneers or fringe mobility populations (younger people, hippies, populations in3

areas with new transport policies, etc.). The objective of consistency could be maintained
while allowing resources to flow toward data that might help reform transportation. The
second option for working within the constraints involves a more dynamic inter-survey
period, filled with experimental surveys and field techniques, and other creative endeavors of
the survey team and perhaps of transport scholars. The current mode stresses preparing for
one or two large field tests to work out bugs in the main survey system and does not give
license to creative surveying. This type of process could, foremost, benefit the quality of thetransport survey through the discovery better measurement methodology and, second, provide
a platform for innovation and paradigm-shaking theories to surface. Some might say thenational transportation survey team already does this to a certain extent. My argument is that,
due to the incremental and diminutive “safe” changes between surveys, the NHTS has only
accomplished the task of become a veritable jack-of-all-trades—unable to provide adequate
data to sufficiently explore many burning issues while seeming to cover them all.
Given the conservative context in which the travel survey designers find themselves,
it is no wonder change comes hesitantly. Even the fairly moderate changes made to the travel
survey in this period have, in the best cases, come as acceptable, and in the worst cases, beencondemned for muddling the data. Transport academics at the fairly progressive National
Center for Transit Research (NCTR) in Florida, for instance, welcomed the addition of averbal probe for walking trips for its effect in capturing more walking trips, but seem reticent
about the influence this might have on their data comparability (Ausman 2004). In somecases, it seems that the changes have come as such a nuisance, that, even though precision issacrificed, the researchers cannot help but frame conclusions forcefully:

Recognizing that the mode share is slightly lower overall in 2001 as a result of several factors including
modal definition, the inclusion of children under 5 and the increase in walk trips, the difference
between the findings appear to generally be consistent with the overall differences with a few
exceptions (Ausman 2004 )By this, the authors essentially admit that they cannot give up their aggregating methods, andcouch their conclusions in safety words such as “appear to generally be consistent” despite
the fact that the NHTS team made it very clear that significant changes in the survey makecomparisons of these elements across time undesirable.
The NHTS team, however, is cognizant of the desire for data comparable over timeand has even taken steps to ensure consistency that might raise the eyebrow of a savvy

4

statistician or economist. The language of the NHTS User Guide is indicative of the type of
dilemma before the survey designers:

Although these improvements [in the 1995 NPTS survey] enhance the completeness and accuracy of
trip reporting, they prevent any direct comparisons between the 1990 and 1995 travel data. Any travelchanges observed between the 1990 and 1995 surveys now reflect not only actual changes in travel
during the period but also artifacts of differences in survey methodology. That is, any changes observed
between the 1990 and 1995 travel data are presumably attributable to: (1) actual changes in travel
behavior, (2) use of travel diaries, (3) use of household rostering, and (4) other improvements in the
1995 survey method such as a better coding scheme to decipher trip purposes. (Appendix 3:
Adjustment of 1990 Travel Data, emphasis added)

Some of these “artifacts” are of particularly significant proportions. The User Guide points
out that the number of trip purposes defined as “Other” in the 1990 declined by 64% to 1995.
In order to make the survey material comparable, the NHTS team documents (verytransparently, I might add) their methodology for a fairly significant “adjustment” of the 1990
data in the NHTS User Guide. The treatment they give the data can best be described using
the survey team’s own words:
To more accurately reflect travel trends, the 1990 travel data were adjusted to account for two majorchanges in survey methodology: (1) travel diary, and (2) household rostering. In essence, the 1990
travel data were adjusted in such a way as if a travel diary and household rostering were used in the1990 survey. The theory is that more trips would have been recorded in the 1990 survey if travel diaries
were used.

In order to apply it to the data, the NHTS team essentially normalized the 1990 data to fit
against the 1995 data using information from “non-accompany” (lone) trips as an overriding
ratio, under the larger assumption that differences between 1990 and 1995 were primarily due
to diary use and household rostering. This is akin to saying, “don’t worry, we know whichtrips you forgot to mention in 1990.” The idea that this can be overcome by a statisticalmaneuver at all is questionable...designing that maneuver is yet a more tenuous task. But
once again, the motivation for this adjustment must be considered. The NHTS team decided
that a good use of their survey interim would be to carry out an adjustment to assuage data
users’ fears that a trend comparison was precluded by improvements to the survey
methodology. But in any case, what are the functional results of this change? If the U.S.
Department of Energy uses adjusted 1990 data to assess5Other serious considerations to the survey are routinely ignored, spoken about candidly as
a commonly accepted problem with the methodology, or merely mentioned in passing, suchas the oft cited, but rarely acted-upon fact that September 11 and the Anthrax scares in the
United States took place within the survey sampling period. In this context, sampling issues
are a pervasive problem with the NHTS. Foremost is the significantly low response rate
(28.6% to 41%, depending on specification), even in the face of aggressive refusal
conversation efforts. Given that the German Mobility Panel has recently reached response
rates of 59%, even as a panel survey with a heavier burden and expectedly high attrition rate,
the fairly low response rate (28.6% - 41%) for the NHTS is concerning.5 Disqualified
surveys were a major source of this poor response rate: of over 106,598 households
interviewed with the 2004 release of the data, 36,781 were thrown out as not useable. In
addition, the NHTS suffers from some non-trivial sampling problems mentioned in the NHTS
User Guide and summarized by the author here:

1. Cell phone households. By one study, cell phone households compriseapproximately 6% of the total households, many of them single personhouseholds, central city residents, and renters, or precisely the populations one
expects to be more progressive in their mobility (Tuckle 2003)
2. Certain types of emancipated households. Individuals above the age of 18
living together must be 100% rostered and interviewed in order to consider thehousehold useable, which is unlikely given the nature of emancipatedhouseholds. Additionally, college dormitories, fraternities, and sororities are
only included if the residents number 10 or below. This effectively ignores
most such communities.
3. Minority undersampling. Hispanics undersampled due to language barriers,
distrust of the government, privacy, and high survey burden due to large
family sizes. Blacks are less likely to respond. (Sharp & Murakami 200?)

These particular sample biases (from a much larger list) are focused because it inadequately
samples minorities and (particularly meaningful for the author, as a young adult) those in
their early to mid 20’s; these are precisely the subgroups that are expected, and have shown,5

To the chagrin of the NHTS team, an internal critique reported that “the response rate [for the NHTS] wasconsiderably lower than what is commonly expected in a Federal statistical survey and the survey was only
reluctantly approved by the Office of Management and Budget (OMB)” (Sharp & Murakami 200?)6to be more creative and perhaps pioneering in meeting their transportation demands. No
amount of weighting can adjust for a deep systematic sampling bias of this degree. In any
case, the case of the young adults bears out visually in the statistics: those in their 20’s areunderrepresented in the NHTS (see the following page for density distributions of age in the
NHTS and in the 2000 Census).

7Age Distribution in NHTS 2001 Sample

01Percent2

3

4Personal data file: r_age

0

20

40

60

80

100

Age

8

INSTITUTIONAL RESPONSE TO THE NHTS DATA

Under the notion that “it takes two to tango,” it would be unfair to scrutinize the data
providers without surveying the data users. How various government departments, real estate
firms, transit organizations, transport scholars, and others use the data testifies to theusefulness of what is provided and gives an indication of why certain pressures for a static
survey might be mounting. Many reports, for example, do focus on the issue of transit, but
surprisingly seem satisfied with travel surveys they are given to use. The National Center forTransit Research report, for example, rarely calls for changes in the survey design, although itis there are many places where they find (or do not find) an issue that has additional demandson the survey. The overall reticence in changing the national travel survey is in contrast to
institutions in Europe, for example, that exuberantly criticize national surveys, compare themto others, and mount pressure for change. In the context of the United States, it can often be
useful to examine the little things that have defined the evolution of the national travel
survey.

Young Adult Sampling

Given that the subpopulation of 20-30 year-olds is undersampled for a variety ofreasons, one would expect the issue to be mentioned and addressed in some manner.
Intuition would lead us to believe that this age group is perhaps the most mobile orpotentially mobile of the entire population. These are the college students with high ideals,
the risk-taking high-school graduates, new entrants to the workplace, and those still dreamingup creative lifestyles; more specifically, these are individuals who, relative to their elders,have not completely been absorbed into the mainstream transportation paradigm. A forwardlooking institution might even consider oversampling this subpopulation separate from themain survey material to study how this age group has managed to deal with their transport
needs.
It appears that this sampling deficiency is not only ignored by the official data
exploration, but also glossed over by independent institutions and researchers. In some cases,
it seems that the issue is purposefully hidden or diluted. The Center for Urban Transportation
9

Research (CUTR), for example, simply lumped 21-40 year-olds in the same category in a setof graphs widely distributed in the field. Analysis completed by the U.S. DOT has narrowed
this a little by using a 21-35 age category. Researchers often come to the conclusion that, yes,
while transit ridership includes those with all socioeconomic and demographic
characteristics, ridership is still mostly represented by “captive travelers,” or those inhibitedby economic, physical, or other conditions that make auto travel less available (Ausman
2004). Young adults who use transit are carefully wiped away by a double whammy: first
their existence is diluted by older people in their age category, and second, researchers arehesitant to disrupt their gloomy assumption that transit ridership is due to some sort ofhandicap. In fact, a landmark report of the British, German, and German transportation
statistics (panel surveys) reveals that demotorization is most common among the youngest
age group (Dargay et al. 2003). This dual assertion manages to both rephrase the well-known
dominance of autos and diminishes the role of those progressively choosing to ride transit(perhaps just the subgroup being discussed here).
In fact, the Center for Transportation Research (Ausman 2004) paper points out
rightly that it is not possible to using the NHTS to capture households choosing to forgo auto
ownership without the typical financial, legal, physical, or mental problem. By including
only negative or neutral descriptors for personal data, it is as if the NHTS is seeking to find areason or “problem” to explain why someone does not own a car. This is made moreworrisome by Ausman’s (2004) result from the NHTS data that transit share percent is notcorrelated with employment type. Managers are just as likely to use transit as sales/service
people; this hints at the possibility that the progressive transit ridership share is notadequately being captured. Imagine this hypothetical scenario:

During the 6-yearly NPTS, a sampled 16-year old high school student has hitherto been driven to school by her
parents for a number of reasons, one of which is safety. She will probably continue riding with friends orotherwise find a way to reach school with a private automobile. In her twenties, she ideologically supports
transit and advocates for transit within her immediate community, but she is also characterized by some of thefinancial and lifestyle characteristics that dispose her to ride transit. She is “undersampled” by the NPTS
carried out when she is 23. Without her data, no policies are generated to build upon her willingness to support
progressive transport. By the time she reaches her late 20's, she has become married, been employed, and,seeing no alternative, shifted back to car dependency. On her 30th birthday, she is “sampled” correctly. The
NPTS determines that her life is characterized entirely by car dependency

10

The implications of this type of sampling error are not all speculation. Despite the large
sample size, transit data for various subsets of the population are very sparse. In fact,
researchers found that the NHTS mode share trend between 1995 and 2001 does notcorroborate with ridership calculated by the American Public Transport Association (APTA,
using fare box counts) in a report amusingly entitled “Counting Transit So That Transit
Counts” (2004).

Data Quirks

While testing an improved software suite for inputting addresses for travel surveys,
Michigan Department of Transportation engineers threw out 16% of their locations. In thiscase, 4% was thrown out because average speed fell above or below thresholds and 2.7% was
geocoded incorrectly and the rest was a mixture of different types of reporting problems.
This explained, for example, why certain commercial air flights reportedly only traveled anaverage speed of 4 MPH and certain bicycles traveled at 1,800 MPH. This also explained
why the Michigan DOT and MORPACE (one of the contractors for the NHTS interviews)
presented a report entitled “Improving Household Travel Survey Quality Through Time andDistance Data Checking” (2004).
While it is praiseworthy that the issue of distance and time checking is being
recognized and thoroughly addressed for the next survey, it seemed appropriate to examine
the NHTS 2001 for this. Using a rubric partially enumerated in the Michigan DOT and
MORPACE article, speed thresholds were generated for each mode of transport and used toflag potential mistakes.6 The end result is that 4.75% of speeds fall outside of reasonable
thresholds (some are clearly mistakes). The question is, how does this affect average speed?


Average bicycle speed drops 10%



Average walking speed drops 9%

All non-motorized modes drop 9%

Car speed and speed for all privately operated vehicles goes up 1%
o Car and POV speed only removing upper bounds: down 3%Transit speed increases 8%

6

Private car thresholds were explained in the Wargelin (2004) article, and using this overall guideline, theremaining thresholds were generated. These can be found in Appendix A.

11

o Transit speed removing only upper bounds: down 5%Other modes of travel not meaningfully affected

Survey Accessibility

While it is clear that the personal vehicle dominates every aspect of what is presently
called “travel,” and it might be realistic to wryly admit this in informal discussions, it isanother to define “travel” as “car ridership.” Furthermore, traffic often becomes the central
issue related to the study. And while traffic is perhaps the largest problem, is the NHTS
sample population simply being enlisted to conduct a large traffic survey for the government?
Clearly no, but is this how they feel while on their specified “Travel Day”? The invitation forparticipants to the Kentucky add-on for the NHTS arrived at potential participants doorsteps
with this initial message: “Dear Kentucky Traveler: Are you concerned about the increase intraffic on Kentucky's roads?” How might a non-driver or infrequent driver respond to such a
letter? The main NHTS invitation speaks similarly, asking, “Are you concerned about
traffic?” and going on to talk about traffic increases.7 While I understand that response rates
were dismal for the survey, it is not clear whether this was justification for specifying traffic
patterns as the key element to the study. The implication of always opening with the problemof traffic, is that the first mention of mode is cars, which is then followed by transit and nonmotorized.

Churn

The issue of churn is rarely discussed in the context of the U.S. national travel survey
because the lack of a longitudinal element to the survey negates a fair assessment of thiseffect. Critics of the churn philosophy might point out that looking at the “equilibrium
responses” of different age groups effectively captures the life cycle effect in the population.
Proponents would retort by mentioning that is the conditions at the time of decision that are
more important to analyze. Philip Goodwin (2005) remarks that, “Churn is the vital, ignored,
dimension in behavioural change. This happens anyway all the time, for reasons other thantransport policy, but is not captured in conventional travel surveys.” As an example using theprevious discussion on young adult undersampling: if a subpopulation of twenty-year-olds
7

Baltimore Metropolitan Council also issued an invitation letter opening “Are you concerned about congestion?
Traffic delays?”12

drives to work because the conditions for reaching their workplace preclude other modes, but
inwardly wish they could ride a bike, a simple cross-sectional sample analysis might
conclude that twenty-year-olds prefer cars. A churn assessment might catch these twentyyear-olds selling their cars after a move to a more transit accessible area, and thereby coming
to a much different conclusion.
Demotorization is one aspect of churn that has received a lot of attention in theEuropean transport community. A survey of churn results from travel surveys in France,
Britain, and Germany quickly show the benefits and possibilities of employing panel surveys.
Dargay (2003) reports, for instance, that moving houses doubles the chance fordemotorization, changing employment or place of education encourages demotorization, and,more obviously, demotorization occurs when access to public transit is improved and theparking situation deteriorates. Perceptions can also be measured and assessed over time—inFrance, the preference for public transit has been increasing since 1999, paralleling thepreference for cycling. Capturing preferences using a panel survey is superior to a crosssectional sampling because it keeps the individual constant and therefore measures changes in
condition in a scientifically controlled manner.
The type of question often asked, and left unanswered in a scientific way, is, in thecase of transit, the lack of quality or service or the preference for cars that is driving change?
Sometimes this can be answered indirectly by a cross-sectional analysis; Ausman (2004)
points out that vehicle owners have faster transit speeds—meaning they choose higher quality
services when they do use transit. The resulting answer from the NHTS: only service of acertain quality will attract people away from their cars, but we cannot know what thethreshold of that quality is. One way to answer this question would be to look speeds, or totake another example, rates of transferring on transit using a panel survey. Many transit
scholars agree that poor transferring conditions (too long or too exposed) is a big deterrent fortransit. A panel survey would detect precisely under which transferring conditions one
chooses start using or discontinues using transit.
The newly established American Community Survey (ACS) is a move in the directionof a panel survey, although it remains a cross-sectional snapshot of socioeconomic factors.
Instead of sampling every 10 years, it samples each year. Although individuals remain
uncontrolled, it gives the results a more specific idea of how changes in conditions in thecommunity affected perceptions and behavior. It does not give an idea of how changes inpersonal situation or lifestyle affect decisions. But the forces that coaxed the U.S. Bureau of
the Census to shift to the ACS is a sign that there is a growing demand for this type of13

information. Unfortunately, the transit data users community is surprisingly empty ofsuggestions for a move to panel survey. One reason for this might be the concern that the
response rate is so low, it would be a disaster to move to a system characterized by moresurvey burden and survey fatigue. However, one argument against this mentality is that the
response rate simply cannot get much lower!
In retrospect, the need for such a survey in the past decade was absolutely critical. As
the Ausman (2004) reports, average travel speed has begun to slow for the first time. The
report continues with a series of hypotheticals, many of which could have been borne out by
a panel survey:

This may suggest the end of the opportunities to continue to increase travel speeds via shifts in travel
time, mode, and route. The multi decade period of travel demand outpacing capacity expansion may
have created sufficient congestion on the roadway network that adaptations that previously enabledtravelers to increase travel speeds may no longer be available. Alternative travel paths, times andmodes may no longer be available to enable travelers to avoid congestion [...] As roadways havebecome more congested the travel time reliability has deteriorated and created a longer perceived travel
time (as travelers need to plan more time for time sensitive travel such as work in order to ensure ontime arrivals given the probability of incidents resulting in delays on a regular basis).

Being able to assess drivers’ personal responses (or adaptations) to the worsening of roadconditions would be a solid foundation from which to explore policy options.

CONCLUSION

A radical change in NHTS methodology might imply the beginnings of a feared
technological regime shift in the entire auto industry. A travel survey is an integral part of
the 4th aspect of a technological regime, as defined by Hoogma et al. (2002), influencing how
perceptions and expectations of the current technology shift or stay the same. The issues andproblems inherent to the NHTS are therefore those of the NHTS survey team, as well as
demand preferences of its major stakeholders (the USDOT and USFHWA, perhaps USDoE),
and its minor stakeholders (data users, scientists, organizations). But the overarching themeof this article is the valuation of the “mobility or transport” paradigm. Are we measuring
traffic or transport—transport or mobility? Unfortunately, there are fundamental conflicts

14with data consistency and survey innovation that are compounded by an unclear set of
demands from stakeholders, that is preventing the NHTS from innovating.
The desire to stay consistent is most easily shown by the austere and perhaps
unjustifiable measure taken to reconcile 1990 data with that of 1995. The reason for thisbehavior is clear—the demand for consistency is driven by most stakeholders all the way
from government down to the individual data users. Even stakeholders focusing on public
transportation lament losses in inconsistency, when it is clear that significant innovation in
the survey design could give a much clearer (if not fairer) picture of transit in the UnitedStates. The examination of panel surveys from Britain, Germany, and France showed thatmany of the most common complaints and data demands of the current NHTS would satisfied
by a panel survey. In addition, a panel survey (perhaps run concurrently) could help diagnose
problems in the cross-sectional data, and vice versa.
With the demand for consistency so clear, the most wiggle room available for the
NHTS survey team was to “improve” the survey, by fixing or expanding upon small issues
and adding new sections. But at the same time, we see the aspiration for survey quality
causing 20-year-olds and minorities, some of the most mobility-rich people, being squeezed
out of the sample. The demand for consistency, subsequently, can lull data users and the
NHTS team themselves into a harmful routine of “same-analysis-different-year,” andinevitable conclusions of the “car-car-car” variety. The invitation letters for the NHTS,
which seem to enlist participants into a national traffic survey, is a clear example of how thetransport community has become inextricably engorged on the personal vehicle.
But the advent of governmental support for new analysis systems, namely theAmerican Community Survey and the American Time Use Survey, show that there is amovement to capture some alternative behavioral patterns. During the last couple of decades,
travel behavior researchers have become increasingly focused on activity-based travel survey
approaches. This is due in part to the desire to understand peoples travel in the context of
their daily activities and allows analysts to bring this context into travel analysis andmodeling (Harvey 2003).
Short of shifting to a panel survey, the USDOT and USFHWA have the opportunity to use
the survey interim period to focus on experimental analysis types and examinations of fringe
mobility groups. This might, over time, give the NHTS team the ideas, theoretical backing,and experimental justification for introducing more creative innovations.

15Appendix A
Speed Thresholds for Modes of Transportation
Mode

Threshold limits

Less Than 30 Trip Miles
Private car

65 < Speed < 5

SUV

65 < Speed < 5Van

65 < Speed < 5

Pickup truck

65 < Speed < 5

Other truck

65 < Speed < 5

RV

65 < Speed < 5Motorcycle

65 < Speed < 5

Taxicab65 < Speed < 5Limousine

65 < Speed < 5Hotel/airport shuttle

65 < Speed < 5More Than 30 Trip Miles
Private car

80 < Speed < 5

SUV

80 < Speed < 5Van

80 < Speed < 5

Pickup truck

80 < Speed < 5

Other truck

80 < Speed < 5

RV

80 < Speed < 5Motorcycle

80 < Speed < 5

Taxicab80 < Speed < 5Limousine

80 < Speed < 5Hotel/airport shuttle

80 < Speed < 5

Other Modes
Airplanes (commercial, private)

600 < Speed < 100Transit buses

55 < Speed < 5Commuter buses

70 < Speed < 5Commuter / Distance Trains

70 < Speed < 5

Subway / Trolley

40 < Speed < 5Walking

10 < Speed

Bicycle10 < Speed

Nonsampled: Sailboat, motorboat, yacht; Other

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