This document summarizes research on analyzing urban transportation data to help travelers make more cost-effective decisions. The researcher analyzed anonymous payment data from London's Oyster cards, finding that travelers collectively overspend £200 million per year by not choosing the optimal fares. Machine learning recommender systems were tested on this data and achieved 74-98% accuracy in recommending lower-cost fare options. This research aims to design decision support tools that can save travelers money by better matching their transit needs and patterns to available fare options.

1. bite-sized lecture
@neal_lathia
october 7, 2011

2. my research:
urban data mining

3. over half of us live
in cities, by 2050 –
70% will

8. the oyster card

9. what tools can we design to help
travellers?

10. one example:
there is more to urban mobility
than just moving.

12. who are you?
where do you want to go?
how often?
how?
when?
how are you paying?
what route?

13. + how do we travel? //
how do we spend?
+ do travellers make the
correct decisions? (no)
+ can we help them with
recommendations? (yes)

14. (%) 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

15. Purchase Geography Mobility Flow
45
Zone 1
40
PAYG Zone 2
Travel Cards Zone 3
35
Zone 4
30 Zone 5
Zone 6
25
arrive
20
15
10
5 depart
0
1 2 3 4 5 6 7 8 9

16. high regularity – in movement,
purchases
small increments, short terms
is this ideal?

17. luckily,
computers are good at
counting. let them do it.
idea:
compare what you bought to
what you could have bought
(was it cheaper?).
repeat 300,000 times.

18. results for this data:
£2.5 million overspend

19. using this sample to estimate the entire
city means we overspend by:
£200 million per year
by making the wrong decisions.

20. £200 million per year
by making the wrong decision?
not understanding how we will
need public transport (but..)
failing to match fares with our
needs (but...)

21. pop quiz:
who has bought something on
amazon?

22. so you know what a
recommender system is?

23. recommender system:
data + machine learning for
personalised results

24. we tested
recommender systems for oyster
purchases, which were 74-98% accurate.
Accuracy (%) Savings (GBP)
Dataset 1 Dataset 2 Dataset 1 Dataset 2
Baseline 74.99 76.91 326,447.95 306,145.85
Naïve Bayes 77.46 80.71 393,585.81 369,232.24
k-NN (5) 96.74 97.09 465,822.17 426,375.85
C4.5 98.01 98.29 473,918.38 434,082.81
Oracle 100 100 479,583.91 438,923.30

27. bite-sized lecture
@neal_lathia
october 7, 2011

28. further reading:
N. Lathia, L. Capra. Mining Mobility Data to Minimise Travellers'
Spending on Public Transport. In ACM KDD 2011, San Diego, USA.
N. Lathia, J. Froehlich, L. Capra. Mining Public Transport Data for
Personalised Intelligent Transport Systems. In IEEE ICDM 2010,
Sydney, Australia.
N. Lathia and L. Capra. How Smart is Your Smart card? Measuring
Travel Behaviours, Perceptions, and Incentives. In ACM UbiComp
2011, Beijing, China.