UCL Bite-Sized Lunch Lecture

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October 7th, 2011

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UCL Bite-Sized Lunch Lecture

  1. 1. bite-sized lecture @neal_lathia october 7, 2011
  2. 2. my research:urban data mining
  3. 3. over half of us livein cities, by 2050 –70% will
  4. 4. the oyster card
  5. 5. what tools can we design to helptravellers?
  6. 6. one example:there is more to urban mobilitythan just moving.
  7. 7. who are you?where do you want to go?how often?how?when?how are you paying?what route?
  8. 8. + how do we travel? //how do we spend?+ do travellers make thecorrect decisions? (no)+ can we help them withrecommendations? (yes)
  9. 9. (%) pay as you go purchases 49.8 < 5 GBP 24.2 5 – 10 GBP 15.5 10 – 20 GBP (%) travel card purchases 70.8 7-day travel card 15.8 1-month travel card 11.6 7-day bus/tram pass Purchase Behaviour 30 Travel 25 Cards PAYG 20% Purchases 15 10 5 0 Mon Tue Wed Thu Fri Sat Sun
  10. 10. Purchase Geography Mobility Flow45 Zone 140 PAYG Zone 2 Travel Cards Zone 335 Zone 430 Zone 5 Zone 625 arrive201510 5 depart 0 1 2 3 4 5 6 7 8 9
  11. 11. high regularity – in movement,purchasessmall increments, short termsis this ideal?
  12. 12. luckily,computers are good atcounting. let them do it.idea:compare what you bought towhat you could have bought(was it cheaper?).repeat 300,000 times.
  13. 13. results for this data:£2.5 million overspend
  14. 14. using this sample to estimate the entirecity means we overspend by:£200 million per yearby making the wrong decisions.
  15. 15. £200 million per yearby making the wrong decision?not understanding how we willneed public transport (but..)failing to match fares with ourneeds (but...)
  16. 16. pop quiz:who has bought something onamazon?
  17. 17. so you know what arecommender system is?
  18. 18. recommender system:data + machine learning forpersonalised results
  19. 19. we testedrecommender systems for oysterpurchases, which were 74-98% accurate. Accuracy (%) Savings (GBP) Dataset 1 Dataset 2 Dataset 1 Dataset 2Baseline 74.99 76.91 326,447.95 306,145.85Naïve Bayes 77.46 80.71 393,585.81 369,232.24k-NN (5) 96.74 97.09 465,822.17 426,375.85C4.5 98.01 98.29 473,918.38 434,082.81Oracle 100 100 479,583.91 438,923.30
  20. 20. bite-sized lecture @neal_lathia october 7, 2011
  21. 21. further reading:N. Lathia, L. Capra. Mining Mobility Data to Minimise TravellersSpending on Public Transport. In ACM KDD 2011, San Diego, USA.N. Lathia, J. Froehlich, L. Capra. Mining Public Transport Data forPersonalised Intelligent Transport Systems. In IEEE ICDM 2010,Sydney, Australia.N. Lathia and L. Capra. How Smart is Your Smart card? MeasuringTravel Behaviours, Perceptions, and Incentives. In ACM UbiComp2011, Beijing, China.

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