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A Discrete Choice Model of Change in Departure Time in Response to the BART Perks Social-Media-Based Incentive Program
TRB Innovations in Travel Modeling
Atlanta, June 27, 2018
with
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Since 2010, BART Ridership Grew Rapidly
with Increasing SF EmploymentS
F B
ased J
ob
s
Source: Bureau of Labor Statistics
Overview of Perks Program
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Program ran approximately 6 months:
Sept. 2016 – Feb. 2017
BART Perks
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Recruitment near
downtown SF BART
stations during peak
hours and via media.
Participants registered
on Perks website.
Participants got points
for all BART trips.
Got extra points for
trips in the 2 hours
straddling the AM peak
(6:30-7:29, 8:31-9:29)
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Extra Points for Traveling in the Bonus Hours
STATUS Bronze Silver Gold Platinum
Bonus hour point multiplier
3 x usual points
4 x usual points
5 x usual points
6 x usual points
Maximum
reward (in game play)
$10 $20 $50 $100
Number of
bonus hour
trips required for this status
02/week for at least 2 weeks
3/week for at least 2 weeks
4/week for at least 2 weeks
STA
TU
S R
UL
ES
9
9
0%
1%
2%
3%
4%
5%
6%
7%
8%
9%
Faregate Entry Time
Transbay Inbound AM Participants –Before vs. During Perks Program
Before During
10% Shift
Disaggregate Model of In-Program
vs. Pre-Program Behavior
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Data for Model Estimation
• Urban Engines monitored participants’ travel via smart card
(Clipper Card) transactions
• Also had Clipper Card data for the same participants’ trips
for 6 months before and 3 months after the program
• Model based on participants with 10+ pre-program trip
records and 10+ in-program trip records
• Over 1 million in-program trip records from 13,849
participants
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Pre-program trips(March – Sept. 2016)
In-program trips (Sept. 2016 – March 2017)
Post-program trips(March – June 2017)
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Discrete Choice Model Specification
Explain the choice of time periods for in-program trips as
a function of:
• Pre-program trip frequency and time-of-day pattern
• Points offered during the program
• How earned points were redeemed
• Socio-demographics, job type
• Self-reported impediments to shifting
(1) Early AM (5:00-6:29)
(2) Early AM shoulder (6:30-7:29)
(3) AM Peak (7:30-8:29)
(4) Late AM shoulder (8:30-9:29)
(5) Late AM (9:30-11:59)
Data from Urban Engines
Data from a supplemental
participant survey
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Nested Logit: Time Period Nesting Structure
Nest 1
(3) AM Peak (7:30-8:29)
Nest 2
(2) Early AM shoulder (6:30-7:29)
(4) Late AM shoulder (8:30-9:29)
Nest 3
(1) Early AM (5:00-6:29)
(5) Late AM (9:30-11:59)
Nesting logsum parameter of 0.6
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Some Key Model Results
• Pre-program trip time-of-day pattern very significant… Higher pre-program percent in and near the shoulder periods
>>> Higher percent in the shoulder periods during program.
• Westbound TransBay trips (target market) less likely
to shift. (Some shifting previously occurred due to congestion?)
• Self-reported impediments to shifting later important: (Main reasons were employer policy and childcare scheduling.)
• Level of bonus points offered very significant >>> Points for special “bonus box” offers also very significant.
• Method of redeeming earned points was important: Active game-players were most likely to shift departure time.
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Active Game Playing Strongly Associated with
a Greater Shift
86%
13%1%
Autoplay Manual game play Cash buyout
Payment Mechanism
9% shift
28% shift
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Do the program benefits persist over time?
• Another model specification included post-program
trips as well as in-program trips.
• Compared to the pre-program departure time pattern,
the in-program departure time pattern is a stronger
predictor of post-program choices.
• Aggregate analyses also indicated that most of the
program time shift persisted, at least for a few months.
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Pre-program trips(March – Sept. 2016)
In-program trips (Sept. 2016 – March 2017)
Post-program trips(March – June 2017)
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The program was effective overall, but only
13% of users in the peak Transbay market.
BART is working on a Phase 2 program,
building on lessons learned in Phase 1.
Transbay
sometimes
peak: 1,160
Not regular
commuters:
8,900
Regular commuters,
not Transbay: 2,200
Transbay
commuters,
not peak
hour:
3,400
Transbay
peak: 1,230
13%
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Authors
Mark Bradley
Joe Castiglione
Ryan Greene-Roesel
Camille Guiriba
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Questions
