In the 11th International Conference on Mobile and Ubiquitous Systems. London, UK. December 2014.

1. Tube Star: Crowd-Sourced Experiences on
Public Transport
@neal_lathia: University of Cambridge
Licia Capra: University College London
#MBiq14

2. Situation / Problem

5. Preferences
Mining Public Transport Usage for Personalised Intelligent
Transport Systems. IEEE ICDM 2010.
Individuals Among Commuters: Building Personalised Transport
Information Services from Fare Collection Systems. Elsevier PMC
2012.
Recommendations
Mining Mobility Data to Minimise Travellers' Spending on Public
Transport. ACM KDD 2011.
Policies vs. Behaviours
How Smart is Your Smartcard? Measuring Travel Behaviours,
Perceptions, and Incentives. ACM Ubicomp 2011.
Communities
The Hidden Image of the City: Sensing
Community Well-Being from Urban Mobility.
Pervasive 2012.

6. Existing Solutions?

10. The London Case Study

12. http://www.steveprentice.net/tube/TfLSillyMaps/

16. Application Iterative Design

20. Ready, Set, Deploy!

23. Rating & Thematic Analysis
215 ratings / 44 users

28. Limitations & Future

29. Limitations
– Design v. Deploy
– Contact with Participants v. Realistic Usage
– Evaluation

30. Unanswered Questions
– How much of this is city-specifi c?
– What approaches would be more successful?
– How else to encourage participation?

31. Unanswered Questions
– How much of this is city-specifi c?
– What approaches would be more successful?
– How else to encourage participation?

32. Tube Star: Crowd-Sourced Experiences on
Public Transport
@neal_lathia, licia capra