Agency Information Collection Activities: Request for Comments for a New Information Collection |
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Michael Howell
Federal Highway Administration
4 November 2020
[Federal Register Volume 85, Number 214 (Wednesday, November 4, 2020)]
[Notices]
[Pages 70223-70225]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 2020-24437]
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DEPARTMENT OF TRANSPORTATION
Federal Highway Administration
[Docket No. FHWA-2020-0023]
Agency Information Collection Activities: Request for Comments
for a New Information Collection
AGENCY: Federal Highway Administration (FHWA), DOT.
ACTION: Notice and request for comments.
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SUMMARY: The FHWA invites public comments about our intention to
request the Office of Management and Budget's (OMB) approval for a new
information collection, which is summarized below under SUPPLEMENTARY
INFORMATION. We are required to publish this notice in the
[[Page 70224]]
Federal Register by the Paperwork Reduction Act of 1995.
DATES: Please submit comments by January 4, 2021.
ADDRESSES: You may submit comments identified by DOT Docket ID Number
2020-0023 by any of the following methods:
Website: For access to the docket to read background documents or
comments received go to the Federal eRulemaking Portal: Go to http://www.regulations.gov. Follow the online instructions for submitting
comments.
Fax: 1-202-493-2251.
Mail: Docket Management Facility, U.S. Department of
Transportation, West Building Ground Floor, Room W12-140, 1200 New
Jersey Avenue SE, Washington, DC 20590-0001.
Hand Delivery or Courier: U.S. Department of Transportation, West
Building Ground Floor, Room W12-140, 1200 New Jersey Avenue SE,
Washington, DC 20590, between 9 a.m. and 5 p.m. ET, Monday through
Friday, except Federal holidays.
FOR FURTHER INFORMATION CONTACT: Allen Greenberg,
Allen.Greenberg@dot.gov or 202-366-2425, Office of Transportation
Management, Federal Highway Administration, U.S. Department of
Transportation, 1200 New Jersey Avenue SE, Washington, DC 20590. Office
hours are from 8 a.m. to 5 p.m., Monday through Friday, except Federal
holidays.
SUPPLEMENTARY INFORMATION:
Title: Data Collection for Smartphone Travel Incentives Study.
Background: This study seeks to gain a deeper understanding of the
factors influencing individual travel decisions at different times and
for a range of trip purposes. Of primary interest is learning about
participants weighing of travel options that have differing congestion
impacts and, if participants consider but do not ultimately choose an
option with low congestion impacts, to engage in a discovery process to
ascertain the degree to which certain types and levels of encouragement
and incentives could influence decision making. Such knowledge will
help FHWA and state and local transportation departments to offer
transportation services and engage the public in ways that minimize
congestion and better serve travelers.
Up to 7,500 volunteers, in total, would be recruited from up to 15
cities to participate in this study for a period of not more than two
years for the purpose of testing the impacts of a range of personal
interventions on travel behavior. Participants may be surveyed at the
beginning of the study. Such a general survey may include questions
related to demographics (to ensure population representation and to
learn about different views and impacts on different population
segments); travel preferences and habits; familiarity and comfort with
and views about different transportation modes; and perceptions of
travel related trade-offs.
Through a smartphone application, trips would be tracked with user
consent, and strong user privacy protocols would be followed. A small
control group would occasionally be surveyed about their travel
opinions and preferences, but otherwise would just have their travel
observed without intervention. A hierarchy of engagement techniques
would be deployed for other participants, starting first with
information, followed by prompts to take an action, and then with
incentives. Messages, action prompts, and incentives would be designed
to encourage users to make more system-efficient travel choices. By
continuously observing travel behaviors, changes in behavior may be
linked to specific engagement techniques.
The first stage of information engagement would entail providing
users ``information tiles'' where the general advantages to users of
shifting travel times and/or modes that would reduce their congestion
impacts on the system are highlighted to them. The second stage of
information engagement would entail providing users ``action tiles''
where very specific actions they could take, reflective of recent
travel choices they had made, would be shown on the smartphone
application along with the associated benefits to them (e.g.,
anticipated travel time-savings for shifting departure time to 30
minutes earlier than normal, or one or two specific bus departure times
and routes that may serve as a reasonable substitute for a drive-alone
trip and allow the participant to use his or her commute time more
efficiently). After either the first or second stage of information
engagement, participants may soon thereafter be given a very brief in-
app, follow-up survey asking about whether they would be willing to
consider trying the alternative or alternatives. The degree of
additional surveying a participant would face would be based on their
responses to information engagement, with those who are less responsive
being queried more frequently. If neither of these information-
providing techniques leads to an observed travel behavior change, an
``incentive treatment'' would then be tested.
The incentive treatment may entail a participant being presented
one or more additional travel choices that would reduce congestion as
compared to the participant repeating an earlier-observed travel
departure time or mode, or a user being asked to declare a second and
perhaps even a third choice travel option, and if either or both of
their second or third choice is more system efficient than the first
choice, ascertaining what level of incentive the user would require to
make the switch.
To understand the strength of participant preferences, and to
ascertain the level of incentive required to change the order of
preferences, a reverse auction mechanism with a randomly generated
award (RGA) amount (limited to, say, between 1 cent and $10) may be
deployed. In this instance, a user would be queried about their
willingness to accept (WTA) payment requirement amount to move from
their first choice to their second choice and/or to their third choice
travel mode(s) or departure time, if these choices would cause less
congestion than their first choice. If the user's WTA compensation
requirement is lower than the RGA payment amount, then they would be
given the RGA payment in exchange for shifting to their second or third
choice travel mode or departure time. If the RGA payment amount is
lower than their WTA compensation requirement, then the user would
continue with his or her first choice and receive no award.
The above approach is particularly advantageous from a data
gathering standpoint, as the users communicate their precise WTA
compensation to make a change for each trip, rather than the WTA having
to be estimated/modeled after the user responds to being given
different award offers over many different trips. With such an
unfamiliar approach, users would need to be taught how the awards work
and convinced (correctly) that bidding their actual WTA is always the
best strategy. To ensure that users understand how such bidding may
work, they may be asked ``quiz type'' questions after the strategy is
described and corrected if user responses indicate a lack of
understanding.
When users make a change in travel mode or departure time in
response to the study, an in-app micro survey around the specific trip
taken may be administered, such as to confirm travel mode(s), to
discern satisfaction, and to assess if users believe that in the future
they will repeat any travel choice change that they had made.
So that the choice set presented is personally relevant to
individuals, users may be enabled/encouraged to customize the output
from their app to exclude choices/services that they never want to use
(whether riding bikeshare if
[[Page 70225]]
they are not able to or comfortable bicycling, driving their own car if
they do not own one, using vehicles from a carsharing company if they
have not and do not plan to sign up for such a service, or taking the
bus if they simply refuse to do so under any circumstance). Further,
machine learning could enable the application to present options the
user is more likely to see as attractive under specific trip
circumstances (e.g., focusing on transit for commute trips while TNC
options for late-night trips).
The application might add a proactive feature to enable and
encourage users to indicate within the app their desired travel
destination(s), departure time, and mode. Such a feature may be
especially important to learn more about users whose trip patterns are
quite varied, thereby making it difficult for the study team to predict
what trips might be repeated and thus what specific messages should be
communicated and for what trips WTA incentives should be offered. Here,
participants planning to travel at a time or in a manner that would
mean they will be substantially contributing to congestion would be
randomly assigned to one of a few different groups within the study.
The ``no treatment'' group within the proactive feature might just
receive an in-app response note saying: ``Thanks for letting us know.
Have a good trip.'' The study interest in this group is to ascertain
whether the trip is taken as planned. The proactive feature would not
include an ``information tile'' group, as it would not be expected that
someone with a specific travel intention would make a change after a
somewhat generic positive statement is communicated about an
alternative without the needed practical details about using the
alternative for the specific trip also being presented. There would be
an ``action tile'' treatment group that would be presented with a range
of travel departure and mode choice alternatives that would have
reduced congestion impacts to what the user indicated was his or her
travel plan, along with costs and estimated travel times associated
with the different alternatives. Perhaps, too, users would be provided
within the app the ability to book such a trip, such as with a
transportation network company (TNC) or through the organization of a
real-time carpool. The action tiles presented to this group may be
tailored to individuals based upon their previous survey responses and/
or reported/observed travel behaviors. A third group would also be
presented the information about trip alternatives contained in the
action tiles, and then would be assigned to the WTA survey and
treatment, as described above.
Learnings about the effects of the various treatments on individual
travel decisions would expand the knowledge and tools available to
policy makers to further engage travelers by providing information and
offering incentives that are shown to yield more system-efficient
travel choices. This will enable an assessment of the expected impacts
of city or metropolitan level policy scenarios to encourage the use of
apps that offer real-time travel information about a range of
alternatives, and provide incentives such as through public-private
partnerships (PPPs) that encourage travel choices that reduce
congestion.
Respondents: As noted above, up to 7,500 total field-test
participants nationwide would be recruited from up to 15 cities.
Frequency: One time collecton.
Estimated Average Burden per Response: Approximately 20 minutes
prior to field testing, 1 hour and 30 minutes during field testing and
15 minutes as the participant exits field-testing. Approximately 2
hours and 5 minutes per participant in total is anticipated over the 2-
year study.
Estimated Total Annual Burden Hours: Approximately 15,625 hours in
total is estimated. Significantly, many travel options presented to
participants will save them time over alternatives (especially if trip
times are shifted to avoid congestion), and thus many participants are
expected to experience net time savings. All participation is
voluntary, and some participants will be offered compensation.
Public Comments Invited: You are asked to comment on any aspect of
this information collection, including: (1) Whether the proposed
collection is necessary for the FHWA's performance; (2) the accuracy of
the estimated burdens; (3) ways for the FHWA to enhance the quality,
usefulness, and clarity of the collected information; and (4) ways that
the burden could be minimized without reducing the quality of the
collected information. The agency will summarize and/or include your
comments in the request for OMB's clearance of this information
collection.
Authority: The Paperwork Reduction Act of 1995; 44 U.S.C.
Chapter 35, as amended; and 49 CFR 1.48.
Issued On: October 30, 2020.
Michael Howell,
Information Collection Officer.
[FR Doc. 2020-24437 Filed 11-3-20; 8:45 am]
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