The document discusses how mobility and fare purchase data from London's transit system could be used to build recommender systems to help travelers choose fare options that save them money. It finds that simple algorithms like naive Bayes and k-nearest neighbors can predict the optimal fare over 75% of the time, while decision trees achieve close to 98% accuracy. Currently travelers may overspend by £200 million by not choosing the best fare for their travel patterns. The data could help match travelers to the fares that are most cost effective for their individual mobility needs.

1. @neal_lathia: cambridge computer lab

3. research:
how data can help in our everyday lives
mobility:
getting from a to b (habit)
finding z (discovery)

4. our everyday lives

6. time-stamped locations,
modality, payments,
user categories
anonymised with
persistent user ids

7. what tools can be build using this data?
i.e., what if TfL had a data science team?

9. ● what is the relation between mobility and fare purchase?
● are we buying the best fares? (no)
● can our data help us? (yes)

10. Purchase Behaviour
30
Travel Cards
25
PAYG
20
% Purchases
15
10
5
0
Mon Tue Wed Thu Fri Sat Sun
45
Purchase Geography
Mobility Flow
PAYG
40 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

11. (a) high regularity in purchases & movements
(b) small increments, short terms
(c) purchase on refused entry?

12. are people making the right choice?

13. £200 million
overspend

14. (a) failure to predict your movements
(b) failing to match mobility with fares

15. recommender systems
matching “users” with “items” of interest

16. recommender systems
matching “users” travellers with “items” fares of
interest that are cheap for them

17. as the machine sees it:
given {d, f, b, r, pt, ot, N} predict F

18. time to get out the algorithms

19. 0. baseline – everyone on pay as you go
1. naïve bayes
2. k-nearest neighbours
3. decision trees (c4.5)
5. oracle

20. 0. baseline ~ 75.95%
1. naïve bayes ~ 79.09%
2. k-nearest neighbours ~ 96.91%
3. decision trees (c4.5) ~ 98.15%
5. oracle - 100%

21. final words

22. not my work:

23. customer trust
data science

25. @neal_lathia
Is #recsys your thing?
http://www.meetup.com/london-recsys/