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  LabSingapore  Management  University

2016臺灣資料科學愛好者年會, July 17, 2016

Dream  of  Urban  Planner

Photo  Credit:  http://doraemon.wikia.com/wiki/File:Dokodemodoa.jpg

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.

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.

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  20142 See  https://en.wikipedia.org/wiki/Megacity

Come  up  with  attractive  “alternatives”  to  private  transport.

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.html2 https://mitpress.mit.edu/books/rethinking-­‐lot3 http://trrjournalonline.trb.org/doi/abs/10.3141/2143-­‐19

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

A  Tale  of  Two  Cities

PopulationLand  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 3,171,715Singapore 5,469,700 718.3 827,011 145,026

MRT Bus TaxiOperating  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 52,895Singapore 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/hArterial  Roads:  28.9  km/h

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.

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

ObjectivesProject  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  servicesand  crowd  dispersion.

Based  on  real-­‐world  data;  aim  to  develop  working  technology.

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.

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.

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

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

Trip  Counts  Over  the  Hours

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5,000

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40,000

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Workday

Holiday

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

Trip  Origins  Over  the  Hours

Distribution  of  Trip  Distances

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%  of  Total  Running  Sum  of  Count  of  Distance

Taxi  Availability  vs  Taxi  Bookings

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60.0%

70.0%

80.0%

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availability booking-­‐ratio

Daily  Income  Distribution

Average  net  daily  earning  by  drivers.

The  Tale  of  Two  Taxis:  1251  vs  13335  

6am  – 5:30pmApril  30,  20151251:  made  65  trips13335:  made  15  tripsAverage:  ~19  trips

The  Art  of  Taxi  DrivingThe  “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?!

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

???

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

• Taxis  are  available  even  during  peak  hours

• Demand  and  supply  mismatches  are  highly  dynamic

The  Need  for  Guidance

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.

Making  a  Case  for  Guidance  SystemWhy  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.

The new Taxi Management System is part of Changi Airport Group’s on-going effort to

improve airport’s operations and passengers’ experience. Taxi companies and drivers

were consulted on the design of the new system to ensure its relevance to their needs.

###

New Taxi Management System Display Boards

Master Display Board at Airport Boulevard Road Display Board after Terminal 3 Departure kerbside

About Changi Airport Group

Changi Airport Group was formed on 1 July 2009 as a result of the corporatisation of Singapore

Changi Airport. As the airport company managing Changi Airport, one of the world’s best

airports, Changi Airport Group undertakes operational functions focusing on airport operations

and management, commercial activities and airport emergency services. Through its subsidiary

Changi Airports International, the Group invests in and manages foreign airports to spread the

success of Changi Airport far and wide.

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.

üRecommendation:  B

A

B

John

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

A  Multi-­‐Taxi  RecommenderRecommendations  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).

A  Multi-­‐Taxi  RecommenderSome  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!

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?

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

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.

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.

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.

LM-­‐MOD:  The  Case  of  Yishun

Khoo Teck  PuatHospital

Condos

By  analyzing  short-­‐distance  taxi  trips,  we  can  detect  neighborhoods   that  can  benefit   from  better  FM/LM  connection  services.

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.

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April,  2015LM

FM

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.

p1p2p3 p4

p7

p6

p8p5

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).

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

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)