鄭世昐/未來城市的任意門 (Mobility on Demand for Future Cities)

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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.

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鄭世昐/未來城市的任意門 (Mobility on Demand for Future Cities)

  1. 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. 2. Dream  of  Urban  Planner Photo  Credit:  http://doraemon.wikia.com/wiki/File:Dokodemodoa.jpg
  3. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 18. Trip  Origins  Over  the  Hours
  19. 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. 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. 21. Daily  Income  Distribution Average  net  daily  earning  by  drivers.
  22. 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. 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. 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. 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. 26. • Taxis  are  available  even  during   peak  hours • Demand  and  supply  mismatches   are  highly  dynamic The  Need  for  Guidance
  27. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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)

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