1. The document discusses using anonymized smart card data from public transport systems to analyze travel behaviors and develop personalized recommendations and predictions.
2. Models are able to accurately predict station disruptions, travel times, and recommend cheaper fare purchase options using only anonymized user data without direct information on schedules or networks.
3. Analyzing such data could enable powerful personalized information systems while also providing insights into policy impacts and community well-being.
26. percentile ranking
0.0 (best)
…
0.05 (“those who touch in here also touch in at...”)
...
0.06 (factor in user's history)
...
0.25 (rank stations by popularity)
...
0.5 (random)
…
1.0 (inverse)
36. mean absolute error (minutes)
0.0 (best)
…
3.28 (“people who travel at this time...”)
3.30 (mean time)
...
9.82 (time tabled)
37. mean absolute error (minutes)
0.0 (best)
…
3.17 (“people who are as familiar as you...”)
3.28 (“people who travel at this time...”)
3.30 (mean time)
...
9.82 (time tabled)
38. mean absolute error (minutes)
0.0 (best)
…
3.13 (“your trips in the past...”)
3.17 (“people who are as familiar as you...”)
3.28 (“people who travel at this time...”)
3.30 (mean time)
...
9.82 (time tabled)
39. accurate predictions without
1 explicitly asking
2 network topology, rail schedule
3 ongoing disruptions, delays
40. using transport data for...
1 predicting disruption relevance
2 personalised travel time
3 fare purchase recommendation
41. 30
Purchase Behaviour
Travel Cards
25
PAYG
20
% Purchases
15
10
5
0
Mon Tue Wed Thu Fri Sat Sun
45
Purchase Geography
Mobility Flow
40
PAYG Zone 1
Travel Cards Zone 2
35 arrive Zone 3
30 Zone 4
Zone 5
25 Zone 6
20
15
10
5
0
1 2 3 4 5 6 7 8 9
42. (a) high regularity in purchases & movements
(b) small increments, short terms
(c) purchase on refused entry?
51. classification accuracy
0.0 (worst)
…
77% everyone on pay as you go
80% naïve bayes
…
97% (“people like you should have bought...”)
98% decision trees
100% (oracle)
52. money saved
£0.0 (worst)
…
£326,447.95 everyone on pay as you go
£393,585.81 naïve bayes
…
£465,822.17 (“people like you...”)
£473,918.38 decision trees
£479,583.91 (oracle)
53. “smart” cards
1 facilitate payment
2 collect user data
3 enable powerful,
personalised
information systems
54.
55. using transport data for...
1 behaviours ~ policy & incentives
2 community well-being
56. References
N. Lathia, J. Froehlich, L. Capra. Mining Public Transport Usage for Personalised Intelligent
Transport Systems. In IEEE International Conference on Data Mining. December 2010, Sydney,
Australia.
N. Lathia, C. Smith, J. Froehlich, L. Capra. Individuals Among Commuters: Building
Personalised Transport Information Systems from Fare Collection Systems. Under submission.
N. Lathia, L. Capra. Mining Mobility Data to Minimise Travellers' Spending on Public Transport.
In ACM International Conference on Knowledge Discovery and Data Mining. August 2011. San
Diego, USA.
N. Lathia, L. Capra. How Smart is Your Smart Card? Measuring Travel Behaviours,
Perceptions, and Incentives. In ACM International Conference on Ubiquitous Computing.
September 2011. Beijing, China.
N. Lathia, D. Quercia, J. Crowcroft. The Hidden Image of the City: Sensing Community Well-
Being from Urban Mobility. To Appear, 10th International Conference on Pervasive Computing.
June 2012. Newcastle, UK.