Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter.
Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.
OCR for page 45
3
New Approaches
for Meeting Travel
Data Needs
T
his chapter explores new approaches for meeting travel data needs.
It begins with a summary of key barriers to survey data collection.
Then, opportunities for addressing these challenges are discussed.
These opportunities range from greater use of technology for more accurate
and timely data capture, to alternative methods of data collection that
have the potential to yield improved understanding of travel behavior and
more stable cost and staffing requirements than are obtained through
traditional large-scale periodic surveys. The discussion includes the pros
and cons of these approaches, drawing on examples of their use. The
chapter ends with a series of findings regarding implications for travel
data programs.
Barriers to Survey Data Collection
Travel data are collected using a wide range of means, from surveys, to
administrative records (e.g., the rail Carload Waybill Sample), to automated
data collection (e.g., use of Global Positioning System [GPS] tracking).
This section focuses on survey data because the flagship passenger and
freight travel surveys—the National Household Transportation Survey
(NHTS) and Commodity Flow Survey (CFS), respectively—are the primary
sources of multimodal travel data. In examining barriers to the collection
of travel data with surveys, it is important to distinguish between the
different types of respondents. Households and individuals are the
45
OCR for page 46
46 How We Travel: A Sustainable National Program for Travel Data
units surveyed to obtain data on personal travel, whereas businesses
(e.g., establishments, shippers, carriers) are surveyed to obtain data on
freight movement. Each target group poses different challenges.
Personal Travel Data
The past several decades have seen a general decline in the willingness of
the public to respond to surveys; at best, response rates have remained
constant (Zmud 2010a). In the telephone survey area, particularly relevant
to the NHTS, response rates have fallen steadily over time to very low
levels (see Curtin et al. 2005). Response rates for other survey modes
have also either declined or remained relatively constant, but at much
greater cost. A recent and visible example of steady response rates at
greatly increased cost is the 2010 U.S. census. The mail portion of the
census achieved a 74 percent response rate, matching the response rate of
the 2000 census. Both the 2000 and 2010 censuses had substantially
higher response rates for the mail portion than the 1990 low of 65 percent
(Billitteri 2010; Zmud 2010a). However, achieving this response rate
came at a significant cost. Overall, the mail and subsequent face-to-face
follow-up cost was $13 billion, representing the most expensive census
ever conducted (GAO 2010).1 A large share of this cost was allocated to
efforts to boost response rates, including an extensive media campaign
emphasizing the importance of the census to local communities, use of the
Internet to publicize the importance of the census for the entire country,
and a significant simplification of the census instrument itself to a brief
10-question form to reduce respondent burden (Billitteri 2010).2
Travel surveys have much less visibility and far fewer resources than
the census. The typical cost of a local travel survey for a large metropolitan
area, for example, is about $2–4 million, or about $150 per surveyed
household, and typical response rates are generally in the range of
30–40 percent (Zmud 2010a).3 The response to the initial recruitment
1. The cost was twice the $6.5 billion cost of the 2000 census, or 1.57 times the 2000 cost in inflation-adjusted
dollars (GAO 2001; GAO 2010).
2. The simplification was possible because the “long form” Census questionnaire, administered to
approximately one of every six households in the previous censuses, was replaced with a separate con-
tinuous survey—the American Community Survey (ACS).
3. The total cost figures were reported by Ronald Kirby, Transportation Director of the Washington
Area Council of Governments, in a briefing to the committee at its second meeting (February 18, 2010)
for recent household travel surveys conducted for the Washington and Baltimore metropolitan areas.
The estimated cost of the 2010 Travel Behavior Inventory, a local travel survey conducted by the
Minneapolis-St. Paul metropolitan area every 10 years in conjunction with the decennial census, is
$4 million (information provided by committee member Timothy Henkel, Aug. 2010).
OCR for page 47
New Approaches for Meeting Travel Data Needs 47
for the 2009 NHTS was only 23 percent, nearly 60 percent less than the
56 percent response rate for the 2001 survey.4,5 Of those households
that did agree to participate, however, 80 percent completed the survey,
some 10 percentage points higher than the 70 percent completion
rate for the 2001 survey, reflecting in part the increased training and
effort involved in ensuring that initial recruits would actually complete
the survey.6
What accounts for the decline in willingness to participate in surveys?
The decline has been attributed to a wide range of societal factors and
technological changes. Less discretionary time has reduced the moti-
vation of respondents to cooperate and limited opportunities for contact,
particularly at home (Lepkowski 2010a; Zmud 2010a). Norms of civic
duty and cooperation for the common good are less powerful motivators
than in the past, affecting participation in publicly sponsored surveys in
particular. Declining participation has also been the overall result of
declining levels of trust in government (Pew Research Center 2010), greater
concerns about privacy, the rise of telemarketing and the corresponding
introduction of no-call registers, and the ability to screen out calls
(Stopher 2009). A random telephone survey of U.S. residents, for example,
conducted since 1982 by the Council for Marketing and Opinion Research,
a nonprofit organization working on behalf of the survey research
industry to improve respondent cooperation, found that the percentage
of those who had “refused to participate in a survey in the past year” had
risen from 15 percent in 1982, to 31 percent in 1992, to 45 percent in 2001
(Zmud 2010a). Finally, the population’s increased mobility and location in
large metropolitan areas has made it more difficult both to find and to
contact respondents. The most difficult populations to reach are males;
young people; the less well educated; nonwhites; and the nonemployed,
including students (Princeton Survey Research Associates 2008). Tech-
nological changes have played a role as well, particularly the use of cellular
phones and the Internet, which have increased the difficulty of reaching
younger, minority, and lower-income groups through traditional survey
methods. A growing number of households, for example, no longer use
landline telephones, still the primary method for conducting the NHTS
household interviews.
4. This is the response rate reported to the Office of Management and Budget.
5. T. Tang, Federal Highway Administration (FHWA), personal communication, June 11, 2010.
6. T. Tang, FHWA, personal communication, June 11, 2010.
OCR for page 48
48 How We Travel: A Sustainable National Program for Travel Data
Freight Travel Data
Collecting freight travel data typically involves the private sector and a
different set of challenges for data collection managers. It is difficult to
generalize about response rates because some surveys, such as the CFS,
are mandatory.7 The collection and reporting of other administrative data,
such as data on rail carload waybills and on waterborne commerce, are
required by federal regulation or statute for railroads and domestic vessel
operators, respectively. Nevertheless, as shown by the experience with the
most recent 2007 CFS—with a response rate of 83.1 percent8—nonresponse
can be an issue. Respondent burden in filling out the traditional mail-out,
mail-back survey is part of the explanation. The accuracy of survey
responses is also a problem; for example, only 58.7 percent of the total
number of establishments sampled in the 2007 CFS provided complete
and usable responses.9
More generally, data providers in the private sector are most concerned
about protection of proprietary data.10 In the context of growing interest
in detailed travel data by transportation planners and modelers, companies
are worried about the risk of revealing such data to competitors. Many
businesses also are skeptical of data collection by the federal government,
particularly for open-ended purposes. The fear is that the data will be used
to regulate the industry or in legal action against it. This is a key concern,
for example, with the use of electronic data recorders, which many trucking
companies have adopted to track the locations of drivers and shipments
(Murray 2010). In the event of a crash, the recorder data could be sub-
poenaed to determine culpability. Many companies also are in the business
of selling data, not giving them away for free. Thus, they are looking for
some exchange of value or incentive to share data with the public sector,
with the exception, of course, of data that must be provided by law or
regulation. Some federal agencies are already purchasing private data
(e.g., the U.S. Army Corps of Engineers purchases data on foreign water-
borne commerce from the Port Import Export Reporting Service [PIERS]),
and, as discussed subsequently, new data ownership and licensing arrange-
ments are emerging. Finally, the burden of lengthy surveys or those
7. However, enforcement measures, such as civil penalties, to coerce firms to participate have not been used.
8. This is the official rate reported to the Office of Management and Budget.
9. R. Duych, BTS, personal communication, April 14, 2010.
10. This discussion draws heavily on briefings to the committee by committee members Joseph Bryan and
Daniel Murray at the committee’s third meeting (May 6, 2010) and Thom Pronk, CR England, who
participated in a roundtable at the committee’s second meeting (February 18, 2010).
OCR for page 49
New Approaches for Meeting Travel Data Needs 49
conducted over an extended period is an issue for busy company staff and
may encourage ignoring the request or handing the survey off to less
knowledgeable staff unless it is perceived to be of value to the company.
Implications of These Barriers
The increasing difficulty of collecting travel data, particularly through
surveys, has important implications for data providers and users. First,
the cost of data collection is increasing, often just to keep response rates
constant. Second, declining response rates may introduce bias, calling into
question the representativeness of survey results.11 For household surveys,
the difficult-to-reach nonrespondents are a key problem. A pilot test of a
sample of cellular telephone–only users conducted for the 2009 NHTS,
for example, found different travel patterns for this group (Contrino 2010).
To what extent do other nonrespondent groups have different travel
patterns? The link between response rates and bias is not well understood,
and existing research on the topic may offer guidance to the transportation
community.
For freight surveys, particularly the mandated CFS, the issue is less
nonresponse to the survey than the completeness and accuracy of the data.
Third-party logistics companies, for example, which handle shipments
for many large firms and carriers, are not surveyed in the CFS. As a result,
those who do fill out the establishment-based survey may not have the
detailed knowledge about freight shipments that they once did when
transport and logistics typically were handled in house. Another explana-
tion may lie in the fact that respondents do not see the value of the data or
understand the purpose for which they will be used.12 Both factors under-
score the importance of establishing close ties with data providers and
users, involving them in helping to structure data collection instruments.
Overcoming the Barriers
Strategies for overcoming the barriers discussed above fall into two broad
categories: capitalizing on technology and other techniques to improve data
collection, and employing alternative methods of data collection for surveys.
11. The issue here is nonresponse bias that is introduced when some members of the population are more
likely to be included than others, and their responses differ from those of nonrespondents.
12. This discussion draws heavily on briefings to the committee by committee members Joseph Bryan and
Daniel Murray at the committee’s third meeting (May 6, 2010).
OCR for page 50
50 How We Travel: A Sustainable National Program for Travel Data
Capitalizing on Technology and Other Techniques
to Improve Data Collection
A range of techniques are being used to help overcome many of the barriers
described in the previous section, especially to improve survey response
rates. In particular, greater use of technology has the potential to improve
the timeliness, efficiency, and accuracy of current travel data collection
efforts by substituting automated methods for manual processes. New data
collection methods reduce some barriers but do not solve all problems. On
the contrary, new issues arise, such as extensive post-processing of data,
technical difficulties resulting in missing information, and difficulties
collecting socio-demographic information about mode of transport, trip-
purpose, and vehicle occupancy (Stopher et al. 2010). Moreover, none of these
techniques is likely to reduce the cost of data collection in the short term.
Improving Response Rates of Existing Travel Surveys
For household surveys, data collectors are using a variety of approaches to
improve response rates, ranging from media campaigns to use of incentives
(e.g., compensating survey respondents) (see Box 3-1). The use of incentives
Box 3-1
Approaches to Overcoming Barriers to
the Collection of Passenger Travel Data
Most approaches to overcoming barriers to the collection of
passenger travel data are focused on boosting response rates to
household travel surveys. These approaches include
• Media campaigns,
• “Rest and recycle” (staged telephone callbacks) for telephone
interviews,
• Data gathering at a convenient time for the respondent and not
necessarily by telephone (e.g., scheduled personal interview),
• Special targeting of difficult-to-access socioeconomic groups, and
• Use of incentives.
OCR for page 51
New Approaches for Meeting Travel Data Needs 51
has become a routine part of many survey research efforts, and survey
researchers are generally convinced that incentives should be used to
obtain respondent cooperation and ensure proper sample representation
(Berry et al. 2008). Nevertheless, their use raises many complex issues.
Incentives improve cooperation but do they reduce bias in the estimates
produced? Is the use of incentives a reflection of changes in societal
norms away from a more altruistic view of survey participation and toward
an economic information exchange model? While the use of differential
incentives to different groups may prove cost-effective, is the practice
“fair”? These questions are beyond the scope of this study, and they
represent important questions to be addressed within the recommended
travel data program going forward.
Many advances in household travel surveys, including greater use of
technology, especially GPS tracking, have become commonplace within
the United States over the past decade (Zmud 2010b). Until 2006,
vehicle-based studies were dominant due to technology limitations of
wearable GPS devices. With the relatively recent “explosion” of small,
battery-powered, commercially available GPS data loggers, these GPS
augments have switched almost entirely to a person-based approach,
given the desire to capture detailed data on all modes of travel. A split tech-
nology design (in-vehicle or wearable) allows for the collection of many
days of highly accurate vehicle-based GPS data with minimal respondent
burden. Passive data collection of travel with GPS equipment has many
proven benefits, including trip-making rate correction due to under-
reporting, improved accuracy of travel times and trip destinations, and
detailed travel paths. In addition, multiday data collection enables the
evaluation of day-to-day variability of travel along with weekend travel
patterns, which can be useful in designing policies to affect choice of time
or route of travel (Wolf 2009).
A concern for the environment (specifically air quality and emissions
regulations), coupled with the modeling community’s desire for more
robust data, has led to an increase in the use of on-board diagnostic
(OBD) sensors in air quality studies. These sensors monitor vehicle engine
performance and store engine operating parameters useful for evaluating
the environmental impacts of personal travel and activity patterns. By
coupling GPS-based location details with OBD-provided vehicle operations
data, engine and vehicle activity can be mapped to the transportation
network. In the California Statewide Travel Survey, the California Energy
Commission and the California Air Resources Board are planning to fund
OCR for page 52
52 How We Travel: A Sustainable National Program for Travel Data
an additional in-vehicle GPS/OBD sample focused on alternative fuel,
flex fuel, and hybrid vehicle owners.13
With the rapid introduction and use of smart phones, their use to track
travel is the next horizon beyond GPS (Schuman 2010). For example,
mobile text surveys, completed in real time on hand-held devices, are
being increasingly used to collect travel data and beam location or GPS
information. Data can be collected on origin–destination flows, travel times,
and speeds (The Economist 2007). This technology application may be a
mechanism for reducing nonresponse, particularly among hard-to-survey
population groups, such as young adults. This is a relatively new use for
the transportation field, however, and there has not been a great deal of
study on how taking surveys on a mobile device may change the survey
process or results. A number of problems must be addressed. For example,
the signals are recorded in the cellular phone network, and thus the
data belong to the service provider and require provider cooperation for
release. Moreover, subscriber cooperation and identification are needed
so that the traveler can be contacted and the reasons for the travel added
to the flow data—all of which are currently major limitations to gathering
survey data with smart phones. And the distribution of smart phones is
not universal. Economic disparities related to smart-phone penetration
may lead to biased estimation when persons with lower socioeconomic
status are under covered. Nevertheless, California is exploring the use of
smart phones for data collection for a portion of its next statewide house-
hold travel survey, a $12 million project (Zmud 2010b).
Greater use of the Internet to gather survey data has the potential to
increase the efficiency and timeliness of data collection and may also
reduce respondent burden. Travel surveys using paper travel diaries can
take a long time to complete and process.14 Web-based diaries not only
can “remember” and automatically populate repetitive information, but
also are typically linked to interactive maps (such as Google Maps) that
allow easy identification of exact locations. Automatic error checking can
be built into these web-based diaries as well, making the information
provided by respondents more accurate than that recorded in paper
diaries. Electronic processing and cleaning of the travel diary data is also
more efficient and less prone to errors. At present, however, travel diary
13. Personal communication with J. Wolf, GeoStats, Feb. 11, 2011.
14. The diaries capture information on the total number of trips as well as their characteristics, including
purpose, time of travel, transportation mode, and location (i.e., origins and destinations), among other
information.
OCR for page 53
New Approaches for Meeting Travel Data Needs 53
surveys generally are not being conducted online. Rather, the Internet is
being used to advertise the survey, recruit respondents, and display
survey results (Zmud 2010b). Greater use of the Internet is limited by
household access to high-speed connections, although such access has
been growing.15 Another difficulty is obtaining a representative sample;
no list of households with Internet access and e-mail addresses currently
exists from which a sample can be drawn. Opt-in respondents are the
hallmark of many web surveys but are not a suitable sample for travel
surveys because of self-selection bias, among other issues.
To date, use of technologies that are becoming state-of-the-practice for
data collection in local travel surveys is limited for the flagship NHTS.
FHWA has recognized the problem and is undertaking a $1.6 million
project to explore a wide range of methods (e.g., different sampling frames,
different response options) for conducting the next NHTS to boost
response rates.16,17
A broader-based research initiative is needed, however, focused on the
CFS as well. Some technology innovations were introduced for the most
recent CFS but did not directly affect how the survey was conducted.18
Staff acknowledged the need to do much more electronically to move
away from the traditional mail-out, mail-back survey approach and help
reduce respondent burden (Fowler, 2009). More generally, numerous
approaches for overcoming barriers to the collection of freight travel
data are being explored and implemented (see Box 3-2). Most apply to
data collected from the private sector that are not required by statute or
regulation. The focus is less on technology than on arrangements for data
sharing and protection of proprietary data. Nevertheless, technology is
playing a role. As more source documents become electronic (e.g., rail
carload waybills, automated customs data on imports and exports used by
PIERS), respondent burden is reduced or eliminated entirely, the speed of
data collection is enhanced, and the cost may be reduced. As the PIERS
15. In the 2007 Internet and Computer Use Supplement to the Current Population Survey, the Census Bureau
found that 62 percent of households reported having Internet access in the home in 2007, an increase
from 18 percent in 1997, the first year the bureau collected such data (U.S. Census Bureau 2009).
16. T. Tang, FHWA, personal communication, June 11, 2010.
17. The project is funded by FHWA ($1 million) and the Office of the Secretary ($600,000). To date, no
funds have been provided by the Research and Innovative Technology Administration, but its Bureau
of Transportation Statistics is part of the study team.
18. For example, a geographic information system (GIS) postprocessing routing tool was developed to
compute mileage for origin–destination data reported on freight shipments to improve accuracy
(Duych 2009).
OCR for page 54
54 How We Travel: A Sustainable National Program for Travel Data
Box 3-2
Approaches for Overcoming Barriers to
the Collection of Freight Travel Data
A broad range of approaches, focused mainly on arrangements for
data sharing with the private sector and protection of proprietary
data, are being considered and implemented to overcome barriers
to the collection of freight travel data. These approaches include
• New data ownership arrangements, with the data being pur-
chased or leased from the private sector for public use;
• More cooperative public–private arrangements and data sharing
to increase value to private data providers;
• Greater clarity about the use of the data, increasingly specified
in licensing agreements;
• Sanitizing of the data to substantially alleviate disclosure con-
cerns, either by the Census Bureau (for the CFS) or through
cooperative agreements with third-party providers;
• Fusion of disparate data sources for the purpose of obscuring
competitive information;
• Greater use of modeling in cases where the data are particularly
sensitive; and
• Use of incentives.
example discussed in Appendix E illustrates, however, considerable funds
still must be spent on data quality control.
In summary, a wide range of methods are being explored, including
greater use of technology, to reduce respondent burden and improve survey
response rates and increase the accuracy and efficiency of both passenger
and freight travel data collection. However, use of these methods, particu-
larly technology, requires the resolution of numerous issues, which often
involves further research and testing before the effectiveness of the
methods can be confirmed and they can be widely adopted. Nor will their
use necessarily reduce the cost of travel data collection.
OCR for page 55
New Approaches for Meeting Travel Data Needs 55
Gathering New Kinds of Travel Data
Some of the most innovative uses of technology for gathering travel data are
occurring in the private sector, where the focus has been less on conducting
surveys than on capturing raw data, often in real time, an approach made
possible only recently with the widespread introduction and adoption
of new smart technologies and applications. To date, the usefulness of
both passenger and freight travel data has been hampered by the lack of
timeliness and inadequate detail of the data, particularly for metropolitan
and smaller geographic areas.
Using technology, the private sector is offering solutions to both of
these problems. Two examples are provided here to illustrate the type of
automated travel data being collected by the private sector, its public
applications, and the implications for data ownership and use. To date,
the major focus has been on new ways of tracking vehicle movements.
INRIX, a leading provider of traffic and navigation services in North
America, aggregates traffic data from more than 2 million GPS-enabled
vehicles and cellular probes in its Smart Driver Network, along with other
traffic-related data sources, to provide real-time traffic information to both
private- and public-sector clients (INRIX 2010a) (see Box 3-3).19 Coverage
includes about 100,000 miles of arterials, city streets, and secondary
roads, as well as nearly all limited-access highways in the United States
(INRIX 2010b).
INRIX provides its data to the public sector through licensing agreements
with public agencies.20 The data can be used at various levels of aggregation
and road coverage for operational purposes, such as dynamic message
signs, weather safety alerts, and statewide 511 services (INRIX 2008).21
The data also can be used for congestion analysis on major corridors in
19. The 2 million drivers of the vehicles currently in the INRIX Smart Driver Network report their loca-
tion, heading, and speed from vehicles with embedded GPS systems, portable navigation devices, and
smartphones. The data are combined with traditional road sensor information, and real-time and
predictive traffic speeds are sent to INRIX commercial customers and drivers in the INRIX Smart
Driver Network (Schuman 2010). INRIX pays some drivers to provide the needed data where the
location information is critical to support its traffic data services. For others, INRIX provides the
data free or at a reduced price in exchange for drivers passively reporting their location and speed
(personal communication with R. Schuman, INRIX, June 10, 2010).
20. INRIX provides clients with ready access to data through a simple application programming inter-
face, a web-based monitoring site, and traffic tile map overlays (INRIX 2008).
21. The telephone number 511 is designated by the Federal Trade Commission for traveler information.
Established in 1999, 511 information services vary widely both by provider (ranging from state
departments of transportation [DOTs] to local transportation and transit agencies) and by information
provided (from traffic delays and weather, to transit and tourism information) (description provided
by the 511 Deployment Coalition at http://www.deploy511.org/whatis511.html).
OCR for page 64
64 How We Travel: A Sustainable National Program for Travel Data
synthetic data and modeling.33 Data managers viewing the ACS experience
are concerned that a shift to continuous survey data collection for the
NHTS will pose similar challenges and trade-offs with respect to small-
area estimates.
Continuous surveys have long been successful in other fields, such as
health, where they have generally proved less expensive than periodic
surveys and provided better value, largely through smaller, better-trained,
and more experienced staff (Lepkowski 2010a). Continuous surveys also
are used in other countries.34 For example, Great Britain has successfully
used continuous surveying since 1988 for its National Travel Survey
(see Box 3-5). That survey provides regular, up-to-date data on personal
travel, including long-distance travel (i.e., greater than 50 miles) within
Great Britain, which enables monitoring of changes in travel behavior
and helps inform the development of policy (Anderson et al. 2009). The
smallest geographic units for which the data are generally published are
the nine Government Office Regions.35
Panel Surveys
Panel surveys are another way of collecting data that can be particularly
useful in understanding the dynamics of travel behavior, although experi-
ence with these surveys in transportation research, particularly in the
United States, is limited. In comparison with periodic and continuous
surveys, which rely on cross-sectional designs, longitudinal panels enable
analysts not only to study changes in travel behavior over time, but also to
understand the reasons for shifts in behavior or attitudes because the same
group (panel) of respondents is queried in each survey wave (Zmud 2009).36
33. Synthetic data replace underlying microdata with values derived from a model-dependent imputation
approach (e.g., using regression models), data swapping, or an additive noise technique. A random
component is used in the generation of synthetic data, and thus “noise” is added to the data as a means
of disclosure control. For example, in a particular locality where revealing household identity
could be an issue, the characteristics of one household could be swapped with those of another to
protect the identity of persons in the households. The goal of the approach is to retain household
characteristics and travel patterns at an aggregate level, capture the error component due to the masking
procedure, and retain multivariate associations between household characteristics (T. Krenzke, Westat,
personal communication, Aug. 17, 2010).
34. Committee member Johanna Zmud briefed the committee on international practices, particularly the
use of panel surveys, at the third committee meeting in Session 3: Alternative Data Collection
Methods to Support Future Data Programs. She also directed the committee to a book, summarizing
the results of the 8th International Conference on Survey Methods in Transport at Annecy, France, in
2008 (Bonnel et al. 2009a), which provided many examples of international practice.
35. Analyses at finer geographic levels (e.g., urban, rural) are possible if sample sizes are large enough.
36. In a panel survey, a wave is the interviewing period during which the entire panel is surveyed and
asked the same questions. A panel survey consists of multiple waves.
OCR for page 65
New Approaches for Meeting Travel Data Needs 65
Box 3-5
The National Travel Survey of Great Britain
An Example of a Continuous Survey
The National Travel Survey (NTS) of Great Britain, sponsored by
the Department for Transport (DfT), provides continuous data on
personal travel within Great Britain. The sample frame is postal
addresses in Great Britain, and data are collected continuously
during every month of the year on the basis of a stratified sample
of 40 regions (relating roughly to counties or groups of counties
in England and groups of unitary authorities or council areas in
Scotland and Wales), with oversampling in London. The results
are weighted to help reduce the effect of nonresponse bias.
The process of recruiting and interviewing households includes
an advance recruitment letter, followed by a face-to-face interview
with all household members (or proxies). During the interview,
point data on household characteristics and vehicle ownership are
collected, and a £5 gift voucher is offered if all household members
complete every section of the survey. Households are informed of
their travel week and left with a 7-day travel diary in which they
record each trip, including origin–destination details, purpose,
mode used, distance traveled, trip time, and number traveling.
Within 6 days of the end of the travel week, a pick-up interview is
conducted, and the travel diaries are collected. The data are coded
and entered into a data system, and quality checks are performed.
Response rates are high—around 60 percent overall, but lower in
inner and outer London (46 percent and 49 percent, respectively,
in 2008) (Anderson et al. 2009).
The data are analyzed at various levels (e.g., by household,
individual, vehicle, day, trip), but the smallest geographic unit
typically published is at the Government Office Region level;
nine such regions exist in Great Britain. Long-distance trips
(more than 50 miles) within Great Britain are also recorded, with
respondents being asked to note any such journeys during their
travel week and during an additional week. Finally, questions may
be added periodically to gather information on a particular policy
(continued on next page)
OCR for page 66
66 How We Travel: A Sustainable National Program for Travel Data
Box 3-5 (continued)
The National Travel Survey of Great Britain
An Example of a Continuous Survey
or question. Key results are published annually in a statistical
bulletin available on the DfT website. Technical reports and
additional analyses, including a set of factsheets, are also available
on the web. Finally a nondisclosure version of the NTS data set is
deposited at the UK Data Archive at the University of Essex.
DfT funds the NTS, which is currently carried out under contract
by the National Centre for Social Research, an independent social
research institute. The contractor is responsible for questionnaire
development, sample selection, data collection and editing, and
data file production (Anderson et al. 2009). DfT, supported by
a staff of five full-time equivalents (FTEs), is responsible for the
building of the database, data analysis, publication, archiving,
and research on future survey methods. The total cost of the sur-
vey (contractor and DfT staff costs) is currently about £2.8 million
(about $4.18 million) annually, about two-thirds of which is for basic
fieldwork and incentives (L. Avery, Department for Transport,
UK, personal communication, June 24, 2010).
Thus, panel surveys provide a more sophisticated understanding of travel
behavior than can be derived from cross-sectional analyses, and the data
can be used in travel demand models to better predict travel behavior
(Zmud 2009). Questions can readily be added to the survey to explore
traveler responses to a particular policy or transportation investment
(e.g., expanded transit services). Panel surveys also provide timely infor-
mation and require smaller sample sizes than periodic or continuous
surveys and thus have lower recruitment and staff costs, at least in the
early years of a panel (Zmud 2009).
Panel surveys pose challenges, not the least of which are initial recruit-
ment in the face of the continuing nature of the survey, imposing a heavier
respondent burden; natural attrition of the panel and declining response
rates over time; and panel fatigue and poorer quality of responses in later
OCR for page 67
New Approaches for Meeting Travel Data Needs 67
survey waves (Zmud 2009).37 These problems can be addressed through
such measures as refreshing the panel by replacing members who have
left and providing incentives to panel members, but these measures
complicate longitudinal analyses. Finally, taking advantage of the data
provided by panel surveys requires knowledgeable staff and sophisticated
models. In fact, one of the reasons given for the lack of more panel surveys
in the United States is the absence of dynamic models, such as activity-
based models, which can make use of the results (Stopher 2009).38
The primary example of a panel survey in the United Sates is the Puget
Sound Transportation Panel, which ran nearly annually from 1989 to 2002.
Data on one day of travel activity were collected from about 1,700 respon-
dents in 10 annual survey waves (Zmud 2009). In 2002, the panel was
discontinued and replaced with a typical cross-sectional local travel survey.
The main reasons for its termination were the time-consuming nature of
maintaining the panel, the resulting cost, and the lack of sophisticated
dynamic models for using the data captured from the panel.39 The cost,
for example, increased by more than 2.5 times, from $75,000 in 1989
to $200,000 in 2002, or about 1.8 times in inflation-adjusted dollars
(Howard 2010). The cost of the cross-sectional household travel survey,
which was conducted in 2006 and replaced the panel, was $1 million; it is
planned to be repeated no later than 2015.40
One way to reduce the initial costs of establishing a panel and anticipate
the challenges of response bias, panel maintenance, and panel attrition is
to use an existing panel source. There are private firms that specialize in
running or establishing customized longitudinal panels for both public
and private clients.41 Special care must be taken to ensure that the selected
panel meets rigorous standards of accuracy and reliability through
probability-based, statistically valid (not opt-in) sampling, and that panel
37. Panel attrition is not a trivial problem. The Puget Sound Transportation Panel experienced about a
20 percent attrition rate between the first two survey waves, the German Mobility Panel a 43 percent
attrition rate, and the Dutch National Mobility Panel a 44 percent attrition rate (Zmud 2009, 3).
38. Activity-based models capture the dynamic interaction between the activities of households and
individuals and their travel decisions. They are based on a more comprehensive understanding of the
trade-offs that affect decisions about whether to make a trip, what time to make it, what destination to
visit, what mode to use, and what path to take (TRB 2007).
39. At the committee’s third meeting, on May 6, 2010, Elaine Murakami (FHWA) noted that one of the
reasons for the decision not to continue the Puget Sound Transportation Panel was the lack of modeling
capacity to take advantage of the survey-generated data.
40. N. Kilgren, Puget Sound Regional Council, personal communication, July 1 and July 6, 2010.
41. For example, D. K. Shifflet & Associates, which collects tourism-oriented travel data (described in
Appendix E), uses a panel company to recruit nationally representative panels of households, which
have agreed in advance to participate in periodic surveys.
OCR for page 68
68 How We Travel: A Sustainable National Program for Travel Data
recruits have similar access to technology (e.g., a computer and free
Internet service), particularly when online participation is desired.
The German Mobility Panels are an example of long-standing use of
panel surveys in transportation research. This panel has been conducted
nationally each year since 1994, with a sample of about 1,000 households
reporting on travel activity in a 7-day diary (Zmud 2009).42 A rotating
panel approach is used, whereby respondents participate for three con-
secutive years, replaced by new panel respondents, so as to ensure reliable
and motivated participants (Zumkeller et al. 2008).43 Provision is also
made for stratified recruitment of new cohorts to balance any dropout bias
(Zumkeller 2007). The national panel survey is complemented by several
similarly designed regional panels to obtain more detailed data on travel
in major regions of the country and to increase the opportunities for pooling
data (Zumkeller et al. 2008).44,45 The panel surveys are part of a family of
personal travel surveys, described in the following subsection.
A Hybrid Approach
In view of the pros and cons of the different survey methods, the most
efficacious strategy may be to combine several different types of sur-
veys to meet a range of needs that motivate the surveys (Bonnel et al.
2009b). Germany provides an excellent example of this approach for
household surveys. It conducts periodic national cross-sectional surveys
with large samples every 5 to 10 years. These surveys are supplemented
by two longitudinal panel surveys at the national level—the annual German
Mobility Panel focused on everyday travel (previously discussed) and
the INVERMO panel survey of long-distance travel (i.e., distances
greater than 100 kilometers)—as well as selected regional panel surveys
(also previously discussed) (Zumkeller 2007).46
42. The diary survey of travel activity is conducted during September through November of each year.
A 3-month odometer survey with a focus on fuel consumption is administered during April through
June (Zumkeller 2007).
43. Response rates are relatively low—about 20 percent of the original sample recruited by telephone.
44. Panel participants at the national level are not required to geocode their trips, easing respondent
burden. However, these data are collected in the regional panels because they are needed for planning
and modeling purposes (Zumkeller et al. 2008).
45. In the early years of a regional panel, household data from the national survey for a specific region are
pooled with the regional data, so that the regional authorities have immediate results. Over time,
the national sample data are phased out.
46. The INVERMO survey was last conducted between 1999 and 2002. Using a combination of a screen-
ing telephone interview and a postal survey, panel members reported their long-distance travel for a
2-month period over four reporting time frames (Zumkeller 2007).
OCR for page 69
New Approaches for Meeting Travel Data Needs 69
The primary sponsor of the German Mobility Panel is the German
Ministry of Transport.47 The cross-sectional surveys are cosponsored by
regional and state authorities, whose funding enlarges sample sizes for
their geographic areas. The INVERMO panel is funded by the German
Federal Ministry for Education and Research and includes several private
partners.48 Together these surveys provide a broad picture of personal
travel behavior in Germany and have enabled in-depth analyses of such
topics as the stability and variability of weekly travel behavior, fuel price
elasticities, coordination of travel among different household members,
and car dependency and multimodal travel behavior (Zumkeller 2007).
As discussed in the following subsection, a similar approach could be
adopted in the United States.
Implications and Assessment
The different approaches to data collection just reviewed suggest that there
is no one best method. Each approach has its pros and cons, and each
serves a particular purpose. The United States should consider adopting
an approach similar to the German model—using a portfolio of surveys at
the core of comprehensive data programs to meet future travel data needs,
both passenger and freight. This approach should include
• Consideration of continuous surveys to replace or supplement the
federal flagship surveys to provide more timely travel data or, at a
minimum, a regular cycle of periodic surveys with updates in interim
years using a smaller sample;
• Establishment of a national panel survey to improve understanding of
the dynamics of household travel behavior and to track national travel
trends over time, which could be supplemented by periodic surveys
targeting traveler response to particular policies and investments;
• Partnerships with state and local governments to expand national surveys
to collect more state- and regional-level data and to work toward more
common formats for state and local travel surveys so as to encourage
pooling of data, or substitution of modeled data, particularly for
use across small metropolitan areas with common characteristics; and
47. Technical support is provided by the University of Karlsruhe, and the fieldwork is conducted by several
market research companies.
48. Among these are the private German Rail system (Deutsche Bahn AG), Lufthansa German Airlines,
and the German arm of the global market research company TNS Infratest (Zumkeller 2007).
OCR for page 70
70 How We Travel: A Sustainable National Program for Travel Data
• Partnerships with the private sector to acquire more fine-grained data
on the travel patterns of individuals and private firms, using digital
methods of data capture and methods to protect sensitive competitive
data, and integrating and aggregating the automated data for analysis
and decision making.
Unlike the German top-down model, however, the portfolio approach
envisioned for the United States would be a more decentralized data
collection system. It would be built on a strong, federally supported core
of surveys and data collection activities to enable the gathering and dis-
semination of essential travel data, but well integrated with travel data
collected by the states, MPOs, transit agencies and other local authorities,
as well as the private sector. This concept is described in greater detail in
the next chapter.
Findings
The transportation community needs to change the way it collects travel
data to address many significant barriers to data collection. Traditional
methods of collecting essential national travel data through large-scale,
periodic surveys should be adapted to address issues of public willingness
to provide data and should take advantage of evolving technologies and
data collection approaches. Fortunately, alternative methods of data
collection are available, but each involves trade-offs compared with large-
scale, periodic surveys. Use of continuous cross-sectional surveys and panel
surveys can help spread out the costs of data collection, maintain a well-
trained core staff, and provide more timely results. Experience with such
approaches is limited in the transportation sector, however, and the learning
curve for properly collecting, analyzing, and using the data is likely to be
steep. In addition, more evidence is needed on whether these methods will
improve or stabilize response rates compared with periodic surveys.
Greater use of automated data sources (e.g., passive probes) and
technology (e.g., web surveys, GPS) may reduce respondent burden and
improve response accuracy, but most of these methods are unlikely to
reduce the costs of data collection. Furthermore, much of the data collected
with these methods is focused on vehicle movements and speeds and trip
origins and destinations, without being linked to information about who
is traveling and for what purpose—behavioral information critical for
modeling and analysis to support policy making.
OCR for page 71
New Approaches for Meeting Travel Data Needs 71
A program of methods research is needed to examine a wide range of
approaches to data collection. Such research would help determine the
optimal frequency for surveys and updates, involve pilot testing of new
techniques before they are adopted more widely, and identify opportuni-
ties for purchasing commercial data or contracting with private vendors
for data collection.
References
Abbreviations
AASHTO American Association of State Transportation and Highway
Officials
FHWA Federal Highway Administration
GAO U.S. Government Accountability Office
NCHRP National Cooperative Highway Research Program
TRB Transportation Research Board
AASHTO Journal. 2010. FHWA Launches New Technology Tool to Pinpoint Freight
Congestion, May 28.
Anderson, T., O. Christophersen, K. Pickering, H. Southwood, and S. Tipping.
2009. National Travel Survey 2008 Technical Report, No. P2820. Prepared for
the Department for Transport, National Centre for Social Research, London,
England, July.
Berry, S. H., J. S. Pevar, and M. Zander-Cotugno. 2008. Use of Incentives in Surveys
Supported by Federal Grants. Rand Corporation, Santa Monica, Calif. Paper
presented at the seminar of the Council of Professional Associations on Federal
Statistics on Survey Respondent Incentives: Research and Practice, Washington,
D.C., March 10.
Billitteri, T. J. 2010. Census Controversy. CQ Researchers, May 14, pp. 433–455.
Bonnel, P., M. Lee-Gosselin, J. Zmud, and J. L. Madre (eds). 2009a. Transport Survey
Methods: Keeping Up with a Changing World, Emerald Group Publishing Limited,
Bingley, United Kingdom.
Bonnel, P., M. Lee-Gosselin, J. L. Madre, and J. Zmud. 2009b. Keeping up with a
Changing World: Challenges in the Design of Transport Survey Methods. In
Transport Survey Methods: Keeping Up with a Changing World (Bonnel et al. eds.),
Emerald Group Publishing Limited, Bingley, United Kingdom, pp. 4–14.
Christopher, E. 2009. Census Data for Transportation Planning. Federal Highway
Administration, Matteson, Ill. Briefing presented to the Committee on Strategies
for Improved Passenger and Freight Travel Data, Washington, D.C., Dec. 10.
Contrino, H. 2010. The National Household Travel Survey. Federal Highway
Administration, Washington, D.C. Presentation to a stakeholder meeting at the
American Automobile Association, Washington, D.C., Feb. 25.
OCR for page 72
72 How We Travel: A Sustainable National Program for Travel Data
Curtin, R., S. Presser, and E. Singer. 2005. Changes in Telephone Survey Nonresponse
over the Past Quarter Century. Public Opinion Quarterly, Vol. 69, pp. 87–98.
Duych, R. 2009. Bureau of Transportation Statistics, Washington, D.C. Briefing
on the Commodity Flow Survey presented to the Committee on Strategies for
Improved Passenger and Freight Travel Data, Washington, D.C., Dec. 10.
Fowler, J. 2009. The U.S. Bureau of the Census, Washington, D.C. Briefing on the
Commodity Flow Survey presented to the Committee on Strategies for Improved
Passenger and Freight Travel Data, Washington, D.C., Dec. 10.
FPMweb. Undated. Accessing Freight Performance Measures through the Inter-
net. Federal Highway Administration, U.S. Department of Transportation,
Washington, D.C.
GAO. 2010. Data Collection Operations Were Generally Completed as Planned, but
Long-standing Challenges Suggest Need for Fundamental Reforms. GAO-11-193.
Washington, D.C., December.
GAO. 2001. Significant Increase in Cost per Housing Unit Compared to 1990 Census.
GAO-02-31. Washington, D.C., December.
Groves, R. M., and S. Heeringa. 2006. Responsive Design for Household Surveys:
Tools for Actively Controlling Survey Errors and Costs. Journal of the Royal
Statistical Society, Series A, Vol. 169, No. 3, pp. 439–457, July.
Howard, C. 2010. Alternative Data Collection Methods. Session 3. Puget Sound
Regional Council, Seattle, WA. Briefing presented to the Committee on Strategies
for Improved Passenger and Freight Travel Data, Washington, D.C., May 6.
INRIX. 2010a. INRIX Launches Free Traffic Operations Service for Transportation
Agencies Nationwide. INRIX News Alert, June 3.
INRIX. 2010b. INRIX Technology Breakthrough Significantly Improves Accuracy
of Real-Time Traffic Information for Navigation on Arterials, City Streets and
Secondary Roads. Press Release. http://www.inrix.com/pressrelease.asp?ID=88.
Accessed March 3, 2010.
INRIX. 2010c. INRIX National Traffic Scorecard. 2009 Annual Report, Synopsis.
http://scorecard.inrix.com. Accessed April 30, 2010.
INRIX. 2008. INRIX, the Public Sector’s Leading Source for Private Traffic Data.
Kirkland, Wash.
Jones, C. 2010. Border Data Initiatives. Federal Highway Administration, Wash-
ington, D.C. Briefing presented to the Committee on Strategies for Improved
Passenger and Freight Travel Data, Washington, D.C., Feb. 19.
Jones, C., and D. Murray. 2010. Freight Performance Measurement—FPM. Briefing
presented to the Committee on Strategies for Improved Passenger and Freight
Travel Data, Washington, D.C., Feb. 19.
Lepkowski, J. M. 2010a. Handout accompanying briefing on Barriers to Data
Collection, and How We Might Overcome Them. Committee on Strategies for
Improved Passenger and Freight Travel Data, Washington, D.C., May 6.
Lepkowski, J. M. 2010b. Alternative Data Collection Methods. Session 3. Uni-
versity of Michigan, Ann Arbor. Briefing presented to the Committee on
Strategies for Improved Passenger and Freight Travel Data, Washington, D.C.,
May 6.
OCR for page 73
New Approaches for Meeting Travel Data Needs 73
Lepkowski, J. M., W. A. Mosher, K. A. Davis, R. M Groves, and J. van Hoewyk.
2010. Continuous National Survey of Family Growth: New Concepts for Sample
Design and Analysis. National Center for Health Statistics, Hyattsville, Md.
Mallet, W., C. Jones, J. Sedoc, and J. Short. 2006. Freight Performance Measurement:
Travel Time in Freight-Significant Corridors. FHWA-HOP-07-071. FHWA, U.S.
Department of Transportation, Washington, D.C., December.
Murakami, E. 2009. Census Transportation Planning Products. FHWA, Seattle, WA.
Briefing presented to the Committee on Strategies for Improved Passenger and
Freight Travel Data, Washington, D.C., Dec. 10.
Murray, D. 2010. Barriers to Data Collection. Session 1. American Transportation
Research Institute, St. Paul, Minn. Briefing presented to the Committee on
Strategies for Improved Passenger and Freight Travel Data, Washington, D.C.,
May 6.
Pew Research Center. 2010. Distrust, Discontent, Anger and Partisan Rancor. Wash-
ington, D.C., April 18. http://pewresearch.org/pubs/1569/trust-in-government-
distrust-discontent-anger-partisan-rancor. Accessed April 30, 2010.
Plewes, T. 2010. Using the American Community Survey: Benefits and Challenges.
Committee on National Statistics, Washington, D.C. Briefing presented to
the Committee on Strategies for Improved Passenger and Freight Travel Data,
Washington, D.C., May 6.
Princeton Survey Research Associates. 2008. How Different are People Who
Don’t Respond to Pollsters? Pew Research Center Publications, April 21.
Raimond, T. 2009. Moving Towards Continuous Collection of Large-Scale Mobility
Surveys: Are There Compelling Reasons? A Discussant Response. In Transport
Survey Methods: Keeping Up with a Changing World (Bonnel et al. eds.), Emerald
Group Publishing Limited, Bingley, United Kingdom, pp. 541–548.
Schuman, R. 2010. Capitalizing on New Technologies: INRIX’s Perspective. Session 2.
INRIX, Kirkland, WA. Briefing presented to the Committee on Strategies for
Improved Passenger and Freight Travel Data, Washington, D.C., May 6.
Short, J., R. Pickett, and J. Christianson. 2009. Freight Performance Measures
Analysis of 30 Freight Bottlenecks. The American Transportation Research Insti-
tute, Arlington, Va., March.
Stopher, P. R., C. Prasad, and J. Zhang, 2010. Can GPS Replace Conventional Travel
Surveys? Some Findings. Proceedings of the 33rd Australasian Transport Research
Forum, Canberra, Australia, Sept. 29–Oct. 1.
Stopher, P. R. 2009. The Travel Survey Toolkit: Where to From Here? In Transport
Survey Methods: Keeping Up with a Changing World (Bonnel et al. eds.), Emerald
Group Publishing Limited, Bingley, United Kingdom, pp. 15–46.
The Economist. 2007. Visualisation: Go with the Flow. Economist Intelligence Unit,
March 8. http://www.ebusinessforum.com/index.asp?layout=rich_story&doc_
id=10276&title=Visualisation%3A+Go+with+the+flow&categoryid=1&channel
id=3. Accessed June 11, 2010.
TRB. Forthcoming. Producing Data Products from the American Community Survey
that Comply with Disclosure Rules. NCHRP Project No. 08-79. Transportation
Research Board, Washington, D.C.
OCR for page 74
74 How We Travel: A Sustainable National Program for Travel Data
TRB. 2007. Special Report 288: Metropolitan Travel Forecasting: Current Practice and
Future Direction. Transportation Research Board of the National Academies,
Washington, D.C.
U.S. Census Bureau. 2009. Internet Use Triples in a Decade. Press release,
June 3. http://www.census.gov/newsroom/releases/archives/communication_
industries/cb09-84.html. Accessed June 11, 2010.
Wolf, J. 2009. Mobile Technologies: Synthesis of a Workshop. In Transport Survey
Methods: Keeping Up with a Changing World (Bonnel et al. eds)., Emerald
Group Publishing Limited, Bingley, United Kingdom, pp. 393–402.
Zmud, J. 2010a. Barriers to Data Collection. Session 1. NuStats, LLC, Austin, TX.
Briefing presented to the Committee on Strategies for Improved Passenger and
Freight Travel Data, Washington, D.C., May 6, 2010.
Zmud, J. 2010b. Capitalizing on New Technologies. Session 2. NuStats, LLC,
Austin, TX. Briefing presented to the Committee on Strategies for Improved
Passenger and Freight Travel Data, Washington, D.C., May 6, 2010.
Zmud, J. 2009. Draft Technical Memorandum on Panel Design Options. Task 2.1.1.
NYMTC Regional Household Travel Survey, prepared for the New York
Metropolitan Transportation Council, Dec.
Zumkeller, C. 2007. Mobility Panel Surveys: The German Experience. Universität
Karlsruhe Research University, Germany, PowerPoint presentation in Paris,
Oct. 8.
Zumkeller, D., B. Chlond, and M. Kagerbauer. 2008. Regional Panels against the
Background of the German Mobility Panel. Institute for Transport Studies, Uni-
versity of Karlsruhe, Germany, prepared for the 8th International Conference
on Survey Methods in Transport, Annecy, France, May 25–31.
Zumkeller, D., and P. Ottmann. 2009. Moving from Cross-Sectional to Continuous
Surveying: Synthesis of a Workshop. In Transport Survey Methods: Keeping Up
with a Changing World (Bonnel et al., eds.), Emerald Group Publishing Limited,
Bingley, United Kingdom, pp. 533–539.
