Influencing commuter behavior in transportation systems, presented by Balaji Prabhakar, Stanford University at Transforming Transportation 2014.

1. Designing Large-scale Nudge Engines
Influencing Commuter Behavior in Transportation Systems!
! Presented at Transforming Transportation 2014!
! Balaji Prabhakar!
! Departments of Computer Science and Electrical Engineering!
! Stanford University!
Transforming Transportation 2014"

2. Designing
Large-­‐scale
Nudge
Engines:
Influencing
Commuter
Behavior
in
Transporta>on
Systems
Balaji
Prabhakar
Departments
of
Computer
Science
and
Electrical
Engineering
Stanford
University

3. Urban
overload
• Real
world
systems
are
constrained
China
India
USA
Japan
Korea

4. Two
Kinds
of
Problem
in
Urban
Systems
1. Broken
hydrants:
visible,
large
faults
Fukushima,
BP
oil
spill
2. Leaky
faucets:
a
billion
trickles
of
waste
Road
conges>on
─ US-­‐wide
$110B
p.a.
in
wasted
>me
and
fuel
─ Per
trip:
less
than
$1
à
commuter
not
incen>vized
• Difference
─ With
leaky
faucets,
need
to
shiW
human
behavior

5. Transporta>on
architecture
now
Commuters
Network
Operators

6. Transporta>on
in
the
Future
Commuters
Network
User
behavior
data
Real-­‐>me
usage
data
Operators
Incen>ve
money
for
users
Micro-­‐targeted
real-­‐
>me
incen>ves
Real-­‐>me
analy>cs
Incen>ve
engine
Social
s>muli
Ever-­‐smarter
big
data
analy>cs
Behavior
s>mulus/response
pa[erns

7. Main
elements
of
approach
• Random
rewards
and
redemp>on
games
‒ Small
determinis>c
rewards
don’t
incen>vize
change,
we
use
a
raffle-­‐like
system
‒ Games
of
chance
are
essen>ally
“self-­‐administered”
raffles:
intui>ve,
fun
and
engaging
• Social
nudging
‒ Is
powerful:
friends
significantly
affect
behavior
shiW
• Personalized
recommenda>ons
‒ Incen>vize
customer
segments
depending
on
impact
on
conges>on
and
propensity
for
shiWing
• Detailed
analy>cs
‒ Commuter
behavior
is
analyzed
along
temporal,
spa>al
and
behavioral
axes
• Smartphone
apps
‒ Act
as
sensor;
give
real-­‐>me
informa>on,
recommenda>ons
to
users

8. Insinc
Jurong
East
Outram
Park
Commu>ng
History
Electronic
Ticket
kms
to
credits
3x
for
off-­‐peak
Commuter
The
Insinc
portal
Credit
History
Date
15th June
16th June
16th June
18th June
Time
Credits
2010 09:00:19
20
2010 08:10:45
10
2010 16:20:17
22
2010 06:15:20
20
Rewards

9. Insinc:
Incen>ves
Singapore’s
Commuters
• Goals:
− Incen>vize
offpeak
travel
− Mode
shiWing:
Move
people
from
private
to
public
transporta>on
• Launch
and
current
status
− Stanford
+
NUS,
Jan
10,
2012
to
July
10,
2012
− Extended
to
more
par>cipants
from
July
11,
2012
by
LTA/MoT,
Singapore
− Currently:
o
o
o
o
160,000+
registered
par>cipants
Over
70%
sign-­‐ups
due
to
friend
recommenda>ons
>10%
shiW
in
peak
load,
depending
on
commuter
segment
Engagement:
~35%
weekly
users,
~50%
monthly
users

10. 0.70
FRACTION OF COMMUTERS IN 5-MINUTE SLOTS
FRACTION OF COMMUTERS IN 5-MINUTE SLOTS
0.45
Before
After
0.40
0.35
0.30
0.25
0.20
0.15
0.10
0.05
-7.49%
0.50
0.40
0.30
0.20
0.10
-10.1%
0.00
0.00
5
5
6
7
8
9
TRIP START TIME
10
11
6
7
12
0.80
8
9
TRIP START TIME
10
11
12
1.00
FRACTION OF COMMUTERS IN 5-MINUTE SLOTS
FRACTION OF COMMUTERS IN 5-MINUTE SLOTS
Before
After
0.60
Before
After
0.70
0.60
0.50
0.40
0.30
0.20
0.10
-10.65%
0.00
0.90
Before
After
0.80
0.70
0.60
0.50
0.40
0.30
0.20
-11.27%
0.10
0.00
5
6
7
8
9
TRIP START TIME
10
11
12
5
6
7
8
9
TRIP START TIME
10
11
12

11. Type
of
par*cipants
All
in
the
group
Mild
peakers
Medium
peakers
Heavy
peakers
All
par>cipants
-­‐
7.49
-­‐
10.10
-­‐
10.65
-­‐
11.27
Those
with
Insinc
friends
-­‐
9.70
-­‐
10.61
-­‐
11.14
-­‐
11.41
Those
without
Insinc
friends
-­‐
3.70
-­‐
9.00
-­‐
9.69
-­‐
10.75
Game
players
-­‐
8.40
-­‐
10.79
-­‐
10.92
-­‐
11.32
Fixed
exchange
-­‐
5.07
-­‐
10.24
-­‐
10.96
-­‐
12.19
Short
distance
commuters
-­‐
4.96
-­‐
10.49
-­‐
10.83
-­‐
11.88
Long
distance
commuters
-­‐
9.13
-­‐
9.77
-­‐
10.51
-­‐
10.81