Mobility Mining for Fare Recommendation

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Mobility Mining for Fare Recommendation

  1. 1. recommendations forurban & transport contexts neal lathia (@neal_lathia) ucl media futures seminar may 25, 2011
  2. 2. research: personalisation to aide mobility in cities i.e., getting from a to b (habit) finding z (discovery)
  3. 3. sensing mobility: 5%-sample, 2 x 83-daystime-stamped location (entry, exit), modality payments (top-ups, travel cards) card-types (e.g., student)
  4. 4. what tools can we design to help travellers?previously:(getting from a to b) N. Lathia, J. Froehlich, L. Capra. Mining Public Transport Usage forPersonalised Intelligent Transport Systems. In IEEE ICDM 2010, Sydney, Australia.(discovering z) D. Quercia, N. Lathia, F. Calabrese, G. Di Lorenzo, J. Crowcroft. RecommendingSocial Events from Mobile Phone Location Data. In IEEE ICDM 2010, Sydney, Australia.
  5. 5. there is more to urban mobility than just moving.context: purpose/intent, events, disruptions, cost, social connections.for example,
  6. 6. mobility vs. cost
  7. 7. who are you? how? when?where cash or travel card?to? how long for?whatroute?
  8. 8. N. Lathia, L. Capra. Mining Mobility Data to Minimise TravellersSpending on Public Transport. In ACM KDD 2011, San Diego, USA.questions(1) what is the relation between how we travel & how we spend?(2) do travellers make the correct decisions? (no)(3) can we help them with recommendations? (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. the data shows that:(a) there is a high regularity in travel & purchase behaviour(b) travellers buy in small increments and short-terms(c) most purchases happen upon refused entry
  12. 12. (2) do travellers make the correct decisions?compare actual purchases to the optimal (per traveller)how:(a) clean data(b) build & search on a tree ~ sequence of choices
  13. 13. data cleaning overview 83-days 83-daysthe “arrow” of time
  14. 14. data cleaning overview origin = destination 83-days 83-daysthe “arrow” of time no purchase observed no purchase observed -20% of users
  15. 15. how: build a tree with each users mobility datawhere a node is a purchase (expire, cost)that is expanded when it has expired(reduced) example: PAYG, 7-day 30-day £aa.aa £bb.bb £cc.cc
  16. 16. how: build a tree with each users mobility datawhere a node is a purchase (expire, cost)that is expanded when it has expired(reduced) example: PAYG, 7-day 30-day £aa.aa £bb.bb £cc.cc PAYG, 30-day £aa.aa 7-day £cc.cc £bb.bb we reduce the space-complexity of searching on this tree by implementing expansion rules, pruning heuristics
  17. 17. the cheapest sequence of fares can then becompared to what the user actually spent PAYG, £aa.aa PAYG, £aa.aa 7-day £bb.bb 30-day £cc.cc
  18. 18. the cheapest sequence of fares can then becompared to what the user actually spent PAYG, £aa.aa in each 83-day dataset, the 5% sample of users where overspending by ~ £2.5 million PAYG, £aa.aa An estimate of how much everybody (100%) is overspending during an entire year (365 days) is thus £200 million 7-day £bb.bb 30-day £cc.cc
  19. 19. overspending comes from(a) failing to predict ones own mobility needs ...but we have observed that mobility is predictable(b) failing to match mobility with fares (in a complex fare system) ...which is an easy problem for a computercan we help travellers?
  20. 20. recommender systemsaim to match users to items that will be of interest to them
  21. 21. recommender systemsaim to match users mobility profiles to items fares that will be ofinterest the cheapest for them
  22. 22. two prediction problems(in the paper) first, predict how a person is going to travel(focus) then, predict what the cheapest fare will be
  23. 23. key fact:the factors that influence cost on public transport are notdetermined by your actual movements (a to b) but by genericfeatures that are city-dependent (e.g., Zone 1 - 2)
  24. 24. three steps 1. for a given set of travel histories, compute the cheapestfare (by tree expansion) 2. reduce each travel history into a set of generic features,describing the mobility (next slide) 3. train classifiers to predict the cheapest fare given the setof features
  25. 25. we have a set of {d, f, b, r, pt, ot, N} = F where d = number of trips f = average trips per day b / r = proportion of trips on the bus / railpt / ot = proportion of peak & off-peak trips N = zone O-D matrix F = cheapest fare (label)
  26. 26. two baselines, three algorithms:0. baseline – everyone on pay as you go1. naïve bayes – estimating probabilities2. k-nearest neighbours – looking at similar profiles3. decision trees (C4.5) – recursively partitions data to infer rules4. oracle – perfect knowledge
  27. 27. 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
  28. 28. N. Lathia, L. Capra. Mining Mobility Data to Minimise TravellersSpending on Public Transport. In ACM KDD 2011, San Diego, USA.questions(1) what is the relation between how we travel & how we spend?(2) do travellers make the correct decisions? (no)(3) can we help them with recommendations? (yes)
  29. 29. recommendations forurban & transport contexts neal lathia (@neal_lathia) ucl media futures seminar may 25, 2011

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