[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, 
[email protected] 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

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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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