Shih-Fen Cheng is Associate Professor of Information Systems and Deputy Director of the Fujitsu-SMU Urban Computing and Engineering Corp Lab at the Singapore Management University. He received his Ph.D. degree in industrial and operations engineering from the University of Michigan, Ann Arbor, and B.S.E. degree in mechanical engineering from the National Taiwan University. His research focuses on the modeling and optimization of complex systems in engineering and business domains. He is particularly interested in the application areas of transportation, computational markets, and human decision-making. He is a member of INFORMS, AAAI, and IEEE, and serves as Area Editor for Electronic Commerce Research and Applications.

1. 未來城市的任意⾨門
Mobility
on
Demand
for
Future
Cities
Shih-­‐Fen
Cheng
鄭世昐
Associate
Professor
of
Information
Systems
Deputy
Director,
UNiCEN Corp
Lab
Singapore
Management
University
2016臺灣資料科學愛好者年會, July 17, 2016

2. Dream
of
Urban
Planner
Photo
Credit:
http://doraemon.wikia.com/wiki/File:Dokodemodoa.jpg

3. A
50-­‐Lane
Traffic
Jam
Near
Beijing*
京港澳⾼高速公路 (G4)，2015年⼗十⼀一連假的收假⽇日。
* Number
5
on
the
Mega-­‐City
list.

4. A
Traffic
Jam
near
Jakarta* that
Kills
12
* Number
3
on
the
Mega-­‐City
list.
At
the
end
of
2016
Ramadan.
Traffic
jam
reached
20-­‐km
long
near
Brebes Timur.
12
dies
of
fatigue
and
fume
poisoning.

5. Cities
are
Growing
Larger
• Cities
are
growing
larger
at
unprecedented
rate
(54%
urban
today
➞ 66%
urban
in
2050)1.
• Megacities (>
10m
population):
– 1950:
Only
New
York
City.
– 2015: 35
globally;
with
27
in
developing
nations2.
• Nightmare
for
urban
planners
everywhere.
1 UN
World
Urbanization
Prospects
2014
2 See
https://en.wikipedia.org/wiki/Megacity
Come
up
with
attractive
“alternatives”
to
private
transport.

6. Why
is
Private
Transport
Bad?
• Inefficiency
in
road
space
usage
• Pollution
• Parking
space
– Across
the
world
cars
seem
to
be
parked
at
least
92%
of
the
time
and
typically
~96%
of
the
time1.
– For
every
car
in
the
United
States,
there
are
approximately
3
non-­‐residential
spots2.
• Every
collective
car
removes
more
than
10
privately
owned
cars
from
the
street3.
1 http://www.reinventingparking.org/2013/02/cars-­‐are-­‐parked-­‐95-­‐of-­‐time-­‐lets-­‐check.html
2 https://mitpress.mit.edu/books/rethinking-­‐lot
3 http://trrjournalonline.trb.org/doi/abs/10.3141/2143-­‐19

7. A
Tale
of
Two
Cities
Taipei Metro AreaSingapore
Population Land Area (km2
)
6,669,133 2,324
Population Land Area (km2
)
5,469,700 718.3

8. A
Tale
of
Two
Cities
Population
Land Area
(sq km) Automobiles Motorcycles
Taipei 2,702,315 272 787,676 980,577
New Taipei 3,966,818 2,053 987,361 2,191,138
Taipei Metro Area 6,669,133 2,324 1,775,037 3,171,715
Singapore 5,469,700 718.3 827,011 145,026
MRT Bus Taxi
Operating
KMs Train KMs
Daily Passenger
Trips Bus KMs
Daily Passenger
Trips Population
Average daily
trips
Taipei
129.2 21,330,255 1,861,661 195,620,000 1,421,868
30,130
11.9
New Taipei 22,765
Taipei Metro Area 52,895
Singapore 154.2 28,178,000 2,762,000 329,120,500 3,751,000 28,736 20
Data
Source:
Taipei:
台北市交通局交通統計年報 / 中華民國統計資訊網
Singapore:
LTA
Annual
Report
/
Singapore
Department
of
Statistics
Road Traffic Condition (Singapore)
Express Way: 64.1 km/h
Arterial Roads: 28.9 km/h

9. The
Role
of
Taxi
Industry
• A
particular
form
of
car-­‐sharing.
– Dynamic:
move
on
the
road
instead
of
parked
at
designated
spots.
– Providing
driving
as
a
service.
• We
call
it
“Mobility-­‐on-­‐Demand”
service,
and
it
covers
more
than
just
taxis.
– E.g.,
All
Uber-­‐like
services
fall
under
similar
category
as
well.
• We
focus
on
taxis
as
it
is
usually
the
most
inefficient
in
the
MOD
sector.

10. Burning
Issues
in
Taxi
Operations
• Supply/Demand
mismatch:
– Demands
might
appear
anywhere,
and
stay
undetected
(for
street
hail
and
taxi
queues).
– Drivers
might
not
be
able
to
position
themselves
at
the
right
place
at
the
right
time.
• Insufficient
capacity
during
peak
hours.
• Uber-­‐like
services
can
be
much
more
efficient
as
they
only
cater
to
the
“Booking”
service
mode,
and
can
use
price
surge
to
incentivize
(direct)
drivers.
Street
Hail Taxi
Queue Taxi
Booking

11. Objectives
Project
signed
with
Land
Transport
Authority
(LTA)
in
April
2016,
for
the
following
objectives:
• Balance
taxi
demand
and
supply
dynamically,
i.e.,
reduce
empty
taxi
cruising
time.
– Anticipate
where
demands
would
most
likely
be.
– Provide
guidance
to
drivers
on
where
to
go.
• Enable
taxi
ride-­‐sharing
for
last-­‐mile
services
and
crowd
dispersion.
Based
on
real-­‐world
data;
aim
to
develop
working
technology.

12. Taxi
Industry
in
SG
• Almost
all
taxis
(~28K)
are
owned
by
5
operators;
largest
operator
has
~60%
of
market
share.
– Companies
are
free
to
set
their
own
fare
structures.
• How
to
drive
a
taxi:
– Singapore
citizen,
at
least
30
years
old.
– Hold
a
taxi
vocational
license.
– Cost:
• Daily
rent
(from
any
operator)
is
around
S$75
~
130.
• Fuel
cost:
around
S$30-­‐40
(diesel).
• Primary
drivers
(who
hold
contracts
with
the
operator)
are
allowed
to
identify
a
secondary
driver
to
share
the
daily
rent.
– How
to
divide
driving
time
is
up
to
them;
but
drivers
usually
split
shift
to
be
6am
– 4pm
and
5pm
– 4am.
– Drivers
can
also
negotiate
on
how
to
share
the
taxi
rental.

13. Taxi
Industry
in
SG
LTA
regulates
the
taxi
industry
tightly:
• Monitors
various
indices
on
service
quality:
– Percentage
of
taxis
on
the
roads
during
peak
periods.
(7-­‐11am,
5-­‐11pm:
85%;
6-­‐7am,
11-­‐12pm:
60%)
– Percentage
of
taxis
with
minimum
daily
mileage
of
250km
(85%
on
weekdays,
75%
on
weekends
&
public
holidays)
• Sets
fleet
size
for
each
operator
depending
on
its
performance
on
the
above
indices.
• Asks
operators
to
provide
all
sorts
of
data
to
help
with
the
above
evaluation.
• Strong
desire
to
make
taxi
service
even
more
efficient.

14. The
Taxi
Dataset
• For
each
active
taxi
(fleet
size
28,000),
following
information
is
sent
every
30
seconds:
– Taxi
ID:
unique
ID
for
each
taxi
– Timestamp:
date
&
time
– GPS
coordinate:
latitude,
longitude
– Taxi
state:
free,
occupied,
on-­‐call,
busy,
etc.
• Size:
– ~1.6B
records
per
month
– ~57M
records
per
day
– ~2.5M
records
per
hour
– ~42K
records
per
minute
• Not
particularly
large,
yet
very
challenging
to
process
– Contains
both
“spatial”
and
“temporal”
components
– Lots
of
noises
and
errors

15. Derived
Information
• Based
on
state
transitions,
different
types
of
taxi
trips
can
be
inferred,
e.g.,:
– Free
➞ Occupied:
Street
hail
– On-­‐call
➞ Occupied:
Booking
thru
operator
– Busy ➞ Occupied:
Booking
thru
3rd-­‐party
App
• Trip
information:
– Time
and
coordinate
of
“origin”
– Time
and
coordinate
of
“destination”
– Estimated
distance
/
fare

16. Trip
Counts
Over
the
Hours
0
5,000
10,000
15,000
20,000
25,000
30,000
35,000
40,000
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Workday
Holiday

17. Trip
Counts
Over
the
Weekdays
480,000
500,000
520,000
540,000
560,000
580,000
600,000
620,000
640,000
Monday Tuesday Wednesday Thursday Friday Saturday Sunday

18. Trip
Origins
Over
the
Hours

19. Distribution
of
Trip
Distances
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 5 10 15 20 25 30 35 40
%
of
Total
Running
Sum
of
Count
of
Distance

20. Taxi
Availability
vs
Taxi
Bookings
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
availability booking-­‐ratio

21. Daily
Income
Distribution
Average
net
daily
earning
by
drivers.

22. The
Tale
of
Two
Taxis:
1251
vs
13335
6am
– 5:30pm
April
30,
2015
1251:
made
65
trips
13335:
made
15
trips
Average:
~19
trips

23. The
Art
of
Taxi
Driving
The
“spatial”
and
“temporal”
patterns
of
taxi
demands
are
pretty
predictable.
• An
experienced
driver
should
know
where
and
when
to
look
for
passengers.
• So
why
is
driver’s
income
varying
so
greatly?!

24. The
Art
of
Taxi
Driving
• Drivers
have
to
constantly
decide
where
to
go
and
what
to
do;
with
mostly
local
information.
– Cannot
see
out-­‐of-­‐sight
demand
– Cannot
see
out-­‐of-­‐sight
competition
???

25. The
Need
for
Guidance
To
better
understand
supply/demand
mismatches,
we
divide
Singapore
into
87
zones,
and
monitors:
1)
incoming
taxis
(supply),
and
2)
outgoing
trips
(demand)

26. • Taxis
are
available
even
during
peak
hours
• Demand
and
supply
mismatches
are
highly
dynamic
The
Need
for
Guidance

27. Challenges
in
Making
Guidance
System
• Only
booking
demands
can
be
observed,
while
street-­‐hail
demands
and
demands
at
most
queues
need
to
be
inferred.
– Most
existing
approaches
use
only
historical
information,
and
not
responsive
to
real-­‐time
information.
• Even
with
known
demands,
generating
decisions
for
“ALL”
drivers
is
not
easy.

28. Making
a
Case
for
Guidance
System
Why
we
believe
guidance
would
work:
• By
providing
taxi
queue
information
to
drivers
at
the
Changi
airport
(from
Dec
2009),
we
notice
significant
increase
in
productivity.
• Key:
To
provide
“relevant”
and
“easy-­‐to-­‐process”
information.
play Boards

29. Why
is
it
Hard
to
Guide
ALL
Drivers?
• Say
we
are
recommending
either
A
or
B
to
a
driver
John.
• By
going
to
A,
John
has
50%
of
chance
getting
a
passenger.
• By
going
to
B,
John
has
100%
of
chance
getting
a
passenger.
üRecommendation:
B
A
B
John

30. Why
is
it
Hard
to
Guide
ALL
Drivers?
• Yet
this
recommendation
will
fail
if
we
have
5
or
more
drivers.
• E.g.,
if
we
have
5
drivers,
1
should
go
A,
and
4
should
go
B.
A
B
John

31. A
Multi-­‐Taxi
Recommender
Recommendations
are
generated…
• every
30
minutes
(using
both
historical
information
and
most
recent
supply/demand
information).
• for
all
zones,
all
time
periods
(i.e.,
where
should
a
taxi
go
if
it
is
in
a
particular
zone
in
a
particular
time
period).
• considering
both
revenue
potential
and
fuel
cost.
• as
a
probability
distribution
(30%
drivers
are
sent
to
A,
50%
are
sent
to
B,
20%
are
send
to
C).

32. A
Multi-­‐Taxi
Recommender
Some
more
details:
• When
a
taxi
is
hired,
the
rider
decides
where
to
go!
(driver
cannot
make
decision
when
occupied)
• Traveling
between
different
zones
takes
time.
The
recommendation
should
work
even
with
thousands
of
taxis.
• And
following
the
recommendation
should
always
be
better!

33. A
Multi-­‐Taxi
Recommender
• How
do
we
know
if
the
recommender
is
good?
– By
testing
the
generated
recommendation
against
historical
data.
– What
should
be
the
“comparison
baseline”
that
is
representative
of
a
typical
human
decision
maker?

34. A
Multi-­‐Taxi
Recommender
• From
historical
data,
we
can
quantify
each
driver’s
strategic
reasoning
capacity.
• Driver’s
strategic
reasoning
capacity
can
be
measured
using
Cognitive
Hierarchy
(CH)
Model:
– Level
0:
random
– Level
1:
best
response
to
level
0
– Level
2:
best
response
to
levels
0
&
1
– …
– Level
n:
best
response
to
lower
levels

35. Limitations
of
Human
Decision
Maker
1.68 1.77 1.85
• From
the
data:
the
more
you
think,
the
better
you
perform.
• With
sufficient
computation
efforts,
our
algorithm
can
reason
with
infinite
depth.

36. Ride-­‐Sharing:
Connecting
Last
Mile
• Optimize
usage
of
taxis
as
a
dynamic
bridging
service
for
public
transport.
– Through
ride-­‐sharing
– Develop
and
experiment
with
service
process
that
could
be
dynamic
and
sustainable
• To
ease
congestion
at
high-­‐demand
locations
or
events.

37. LM-­‐MOD:
Connecting
Last
Mile
20% (30%) of all taxi trips
are within 2 (3) km!
319.5
Short
trips
outside
of
central
region
mostly
originate
from
MRT
stations.
*
Yishun
station
is
the
station
that
has
the
highest
LM
demands.

38. LM-­‐MOD:
The
Case
of
Yishun
Khoo Teck
Puat
Hospital
Condos
By
analyzing
short-­‐distance
taxi
trips,
we
can
detect
neighborhoods
that
can
benefit
from
better
FM/LM
connection
services.

39. LM-­‐MOD:
The
Case
of
Yishun
• Demands
are
recurrent.
• Yet
demands
are
not
high
enough
to
warrant
regular
connection
services.
• Taxi
sharing
can
lower
demand
pressure
in
these
areas.
• We
focus
on
LM
demands
as
all
demands
depart
from
the
same
location,
making
it
easier
to
arrange
service.
0
20
40
60
80
100
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
April,
2015LM
FM

40. Ride-­‐Sharing
Last-­‐Mile
Service
• Step
1:
Travelers
to
submit
their
LM
requests
(destinations)
via
mobile
phones
or
at
a
service
counter
(kiosk).
• Step
2: The
real-­‐time
planner
determines
the
“LM
demand
clusters”
to
be
served
by
individual
vehicles.
• Step
3: The
service
sequence
and
associated
payment
for
each
LM
request
in
a
cluster
is
determined,
i.e.,
route
guidance
to
drivers
to
serve
multiple
destinations
Hub
S1: Submit demands
S2: Demand clustering
S3: Determine service order and
individual payments.
p1
p2
p3
p4
p7
p6
p8
p5

41. Will
People
Share
Taxi
Rides?
• A
pilot
study
was
performed
20-­‐27
Dec
2015
at
the
Suntec Convention
Centre
in
Singapore
• Major
findings:
− Young
people
are
more
open
to
sharing
taxis
with
strangers.
− Female
passengers
are
more
open
to
ride
sharing.
− For
shorter
travels,
major
concern
is
total
journey
time
(waiting
+
travel).
For
longer
travels,
major
concern
is
cost.
− The
importance
of
waiting
time
increases
with
rider’s
age.
− Bus
riders
would
consider
shared
taxis
if
price
is
right
(rider
source:
64%
taxis,
31%
buses,
5%
MRT).

42. Conclusions
• Guidance
can
improve
driver’s
performance.
• Preparing
for
the
realization
of
a
“car-­‐lite”
city.
– Mass
transit
– Mobility-­‐on-­‐demand
• Shared
vehicles
• Autonomous
vehicles

43. We
are
Hiring!
Fujitsu-­‐SMU Urban
Computing
&
Engineering
Corporate
Lab
• A
5-­‐year,
S$27m
center
supported
by
both
Fujitsu
&
NRF
• Research
and
solutions
to
address
urban
and
social
issues,
with
focus
on
crowd and
congestion
• Goal:
To
develop
industry-­‐relevant
applications
• Openings:
– Research
Engineer
(BS/MS)
– Research
Fellow
(PhD)
• General
Enquiry:
Shih-­‐Fen
Cheng
(sfcheng@smu.edu.sg)