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OCR for page 138
APPENDIX
F
Integrating Data and Filling Gaps
The Case of Household Travel
.
Budget constraints make it difficult for statistical agencies to garner suffi-
cient resources to launch new data collection programs that are responsive to
changing policy concerns and at the same time maintain and improve needed data
series from the past. One way to free up resources for new or modified data
collection is to integrate two or more existing data systems into a more cost-
effective combined system. Even when data integration does not result in net
cost savings, it can still be useful to undertake if the combined data are relevant
for a wider range of analyses. Sometimes full integration is not possible or sen-
sible, but partial links among data systems, achieved through such means as the
use of consistent definitions for key variables, can significantly enhance their
analytical power. Finally, efforts to relate multiple data systems will often iden-
tify important gaps that none of them currently fills.
To develop examples of possibilities for linking and integrating transporta-
tion data sources that BTS might usefully explore with other relevant agencies,
we reviewed surveys that provide data on household transportation. Information
on household travel, taking account of all transportation modes, is critical for
many important transportation policy concerns, including access, safety, direct
costs to the household sector, and indirect costs in terms of energy use, environ-
mental effects, and economic productivity.
The two most important national surveys of household transportation are the
American Travel Survey (ATS) sponsored by BTS and the Nationwide Personal
Transportation Survey (NPTS) sponsored by the Office of Highway Information
Management (OHIM) in the Federal Highway Administration (FHWA). The
decennial census long-form sample, the Consumer Expenditure Survey (CEX) of
the Bureau of Labor Statistics (BLS), and the Residential Transportation Energy
138
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INTEGRATING DATA AND FILLING GAPS
139
Consumption Survey (RTECS) of the Energy Information Administration (EIA)
also provide relevant data. (EIA recently discontinued RTECS because of budget
reductions.)
We drew two main conclusions from this review. First, there appears to
be an opportunity to develop a more cost-effective data collection system for
household travel by integrating the ATS and the NPTS. Second, looking
across all of the existing surveys, there appear to be important data gaps that
should be filled.
INTEGRATING THE ATS AND THE NPTS
The NPTS, which is currently conducted on the same 5-year cycle as the
ATS, is designed to provide data on daily household travel patterns. The sample
includes about 22,000 households, who are asked about trips during a specified
travel day. They are also asked about longer trips (75 or more miles) over the
previous two weeks, but these data are not adequate for purposes of analysis
given the short reference period and small sample size. The NPTS sample size
also limits the geographic areas for which estimates can be published to the United
States, urban areas as a whole, rural areas as a whole, and groups of cities catego-
rized by population size. A few states and metropolitan planning organizations
(MPOs) pay for additional samples for their areas. (Many states, MPOs, and
localities also conduct their own travel surveys independently.)
The ATS, under its current design, includes a sample of 80,000 households,
who are asked 4 times over the course of a year about trips of 75 or more miles
during each 3-month reference period. The data provide a complete picture of
long trips for the year, but no questions are asked about shorter trips. The large
sample size of the ATS permits analysis of flows of people between states and
large metropolitan areas.
An integrated design for the ATS and the NPTS could provide useful data for
federal, state, and MPO analysis and planning purposes, including consistent es-
timates of daily and long-distance household travel patterns, in a more cost-effec-
tive manner than two separate surveys, neither of which provides a complete
picture of household transportation. A possible design (discussed in Chapter 3 in
the context of the ATS alone) would be to conduct an annual survey of a rela-
tively small sample of households to provide national estimates, with the sample
augmented periodically to provide estimates for states and large MPOs. Each
year's combined survey would ask questions both about daily travel patterns and
about longer trips. To make the integration of the ATS and NPTS questionnaires
feasible and not unduly burdensome to respondents, the sample could be divided
into three groups, with one group of households asked only about daily travel,
another group asked only about longer trips, and a third group asked about all
trips. (This type of design was in fact used for the Nationwide Personal Trans-
portation Survey in 1972 and 1977.)
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140
APPENDIX F
ADDRESSING GAPS IN HOUSEHOLD TRAVEL DATA
Considering all of the existing household surveys (ATS, NPTS, decennial
census, CEX, and RTECS), there are gaps in the data they provide. One such gap
is data on commuting. Each of the existing surveys offers data that are relevant to
commuting patterns. The decennial census long-form sample makes it possible
once every 10 years to map commuting flows among small geographic areas and
(since 1980) to determine travel time to work. The NPTS provides updates at 5-
year intervals of modes of commuting and distance and time to work, but the
sample size permits only limited geographic analysis. The ATS has a larger
sample but only covers commuting trips of 75 or more miles (one-way) and does
not ask about commuting time. None of these sources provides direct estimates
of commuting costs (or about the costs of non-work-related transportation).
The omission of cost information seems quite important, given the produc-
tivity implications of commuting time and the expenses incurred by workers. As
discussed in Chapter 3, BTS made a deliberate decision to exclude cost data from
the ATS on the grounds that households underreport transportation costs. It ex-
pects that the U.S. Travel Data Center will develop model-based estimates of
long-distance trip costs on the basis of trip characteristics. However, direct survey
reports of costs could be useful input to model-based estimates and for validation.
The RTECS asked about modes of commuting and obtained data that permit
a rough calculation of the costs of commuting for people who drive. The CEX
obtains detailed cost data on transportation for vehicles and trips and usual
monthly expenses for public transportation used for work and other purposes.
However, the CEX has no data on vehicle miles traveled to work or total vehicle
miles, and hence there is no ready way to calculate commuting costs for workers
who drive. In addition, there is no way to relate public transportation costs to
distance or time traveled. Both the RTECS and CEX sample sizes are quite
small, limiting geographic analysis.
In summary, it is not possible to obtain from these data sources a complete
picture of commuting flows, times, distances, and costs. The lack of complete
data on commuting is an example of a data gap that is likely important to fill for
transportation policy planning and analysis. Periodic reviews by BTS of existing
transportation data systems, assessed against BTS's vision of user requirements,
can identify data gaps and opportunities for data linkages that are important to
address in order to serve priority data needs.
Representative terms from entire chapter:
household transportation
