Read the research paper here: http://www.cl.cam.ac.uk/~nkl25/publications/papers/lathia_transport_research_c.pdf

1. London Shared Bicycles:
Measuring Intervention Impact
@neal_lathia
Computer Laboratory, University of Cambridge
Friday, 7 December 12

2. Measure Analyse
Research
Cycle
Intervene Model
Friday, 7 December 12

3. Measure Analyse
Research
Cycle
Intervene Model
Friday, 7 December 12

4. How Smart is Your Smart card?
Measuring Travel Behaviours, Perceptions, & Incentives
ACM Ubicomp 2011, Beijing, China
Friday, 7 December 12

5. Uncovers the (typical) mismatch between
perceived behaviour and actions transcribed in
smart card data.
Highlights cases where travel ‘incentives’ (e.g.,
peak-time fares) do not produce the expected
behaviours.
Friday, 7 December 12

6. Individuals Among Commuters:
Building Personalised Transport Information Services from Fare
Collection Systems
IEEE Pervasive and Mobile Computing, in press.
Friday, 7 December 12

7. There is a widespread variability in travellers’
experiences, as captured by transport data.
This data can be used to design future travel
information systems.
Friday, 7 December 12

8. Measure Analyse
Research
Cycle
Intervene Model
Friday, 7 December 12

9. Mining Mobility Data to Minimise Travellers’ Spending on Public
Transport
ACM KDD 2011, San Diego, California.
Friday, 7 December 12

10. Passengers’ trust in public transport relates to
their perception of its cost.
Their mobility patterns can be used to provide
them with tailored fare recommendations
(and help them save money).
Friday, 7 December 12

11. Measure Analyse
Research
Cycle
Intervene Model
This would be the ‘holy grail’
Friday, 7 December 12

12. Friday, 7 December 12

13. System
Opened: July 30, 2010
5,000 bikes; 315 stations; 44 km2
Payment
Membership: £1 (24hrs) to £45 (annual)
Usage: Free (30 mins) to £50 (24hrs)
Access
July 30 - Dec 3: register for access key
Dec 3 - today: key or credit card at station
Friday, 7 December 12

14. How are policy changes reflected in the city’s
mobility data?
Can this kind of analysis produce a granular
picture of the effect of interventions?
Friday, 7 December 12

15. Friday, 7 December 12

16. Conducting a “quasi-experiment:”
We observe a city’s data over the timespan
where policy changes are implemented.
The main challenge, though, remains:
can we attribute or infer any causality from
our observations?
Friday, 7 December 12

17. Cluster 0 Cluster 1
0.8
0.78
0.6
0.4
0.74
0.2
0 2 4 6 8 10 13 16 19 22 0 2 4 6 8 10 13 16 19 22
Cluster 2 Cluster 3
0.6
0.65
0.4
0.55
0.2
0 2 4 6 8 10 13 16 19 22 0 2 4 6 8 10 13 16 19 22
Cluster 4 Cluster 5
0.55
0.35
0.45
0.25
0.35
0 2 4 6 8 10 13 16 19 22 0 2 4 6 8 10 13 16 19 22
Friday, 7 December 12

18. Cluster 0 Cluster 1
0.8
0.6
0.6
0.4
0.2
0.2
0 2 4 6 8 10 13 16 19 22 0 2 4 6 8 10 13 16 19 22
Cluster 2 Cluster 3
0.6
0.65
0.4
0.55
0.2
0 2 4 6 8 10 13 16 19 22 0 2 4 6 8 10 13 16 19 22
Cluster 4 Cluster 5
0.30 0.34 0.38 0.42
0.40 0.50 0.60 0.70
0 2 4 6 8 10 13 16 19 22 0 2 4 6 8 10 13 16 19 22
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19. Friday, 7 December 12

20. Hyde Park Corner Hyde Park Corner
1.0
1.0
0.8
0.8
Normalised Available Bicycles
Normalised Available Bicycles
0.6
0.6
0.4
0.4
0.2
0.2
Week Day Week Day
0.0
Week End 0.0 Week End
0 2 4 6 8 10 12 14 16 18 20 22 24 0 2 4 6 8 10 12 14 16 18 20 22 24
Hour of Day Hour of Day
Friday, 7 December 12

21. Note: Station was moved!
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22. Friday, 7 December 12

23. Who cares?
Data insights provide targets for qualitative
work; they provide localised success metrics;
they inform the design of future interventions.
Friday, 7 December 12

24. Who cares?
Data insights provide targets for qualitative
work; they provide localised success metrics;
they inform the design of future interventions.
Friday, 7 December 12

25. Finally, what is still missing?
Friday, 7 December 12

26. Finally, what is still missing?
Friday, 7 December 12

27. 1: What about other cities?
2: What are peoples’ habits in this context?
There is no available data to answer this!
3: How does this use case relate to other data
from the city?
Friday, 7 December 12

28. Contact: neal.lathia@cl.cam.ac.uk
N. Lathia, L. Capra. How Smart is Your Smart card? Measuring Travel
Behaviours, Perceptions, Incentives. In ACM Ubicomp 2011, Beijing, China.
N. Lathia, C. Smith, J. Froehlich, L. Capra. Individuals Among Commuters:
Building Personalised Transport Information Services from Fare Collection
Systems. In IEEE Pervasive and Mobile Computing, in press.
N. Lathia, L. Capra. Mining Mobility Data to Minimise Travellers’ Spending on
Public Transport. ACM KDD 2011, San Diego, California.
N. Lathia, S. Ahmed, L. Capra. Measuring the Impact of Opening the London Shared
Bicycle Scheme to Casual Users. In Transportation Research Part C, December 2011.
Bike Sharing Research and Practice Google Group
http://groups.google.com/group/bikesharingsystems
Friday, 7 December 12