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Seeding Ideas for Intelligent Transport
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Seeding Ideas for Intelligent Transport


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Presented at ICT KIC Meeting (Imperial)

Presented at ICT KIC Meeting (Imperial)

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  • 1. personalisation for smarter cities (intelligent transport systems research @ UCL CS) neal lathia software systems engineering group [email_address]
  • 2. EU FP7 i-TOUR
  • 3. Needless Motivation
    • 40,000+ people die on European roads each year
      • Over 1 million people since I was born
    • Congestion costs an estimated 1% of EU GDP
      • That's 100 billion Euros ~ 88 Billion GBP;
      • 4. Or more than two fully funded PhDs for each person in the whole world
    • Transport accounts for 30% of total energy consumption in the EU
    • 5. It is expected that 70% of the world population will live in cities by 2050
      • When I'll be 66
  • 6. Lots of Problems to be solved
    • Saving lives
      • Road safety, traffic monitoring
    • Saving the planet
      • Reducing congestion, designing for sustainability
    • Saving money
      • Improving public transport
    • Improving the city
      • Urban navigation
  • 7. London's unique position
  • 8. the following 4 slides: ideas, completed work & work in progress,
  • 9. (1) Personalising Travel Info
    • How long will it take me to get there?
      • We designed and tested algorithms that leverage Oyster data in order to provide personalised, contextual predictions of how long travel will take.
      • 10. Accurate to < 3 minutes
    • What disruptions are relevant to my travels ?
      • We designed and tested ranking algorithms that use Oyster data to determine which station disruptions are relevant to each individual traveller.
    N. Lathia, J. Froehlich, L. Capra. Mining Public Transport Usage for Personalised Intelligent Transport Systems . In IEEE International Conference on Data Mining, Sydney, Australia. December 2010
  • 11. (2) Personalising Ticket Purchases
    • What is the relationship between how you move and how you spend?
      • Combinatorial search algorithms mixed with actual Oyster purchase data tell us that Londoners waste up to £200 million per year by buying the incorrect fare.
    • How to alleviate this problem?
      • We designed and tested recommendation algorithms that predict the correct fare based on your mobility (98% accurate)
    N. Lathia, L. Capra. Mining Mobility Data to Minimise Travellers' Spending on Public Transport . In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Diego, California. August 2011.
  • 12. (3) Measuring Incentives
    • How do people respond to incentives to use public transport?
      • We look for changes in behaviour in people's Oyster histories; to inform travellers + transport operators
      • 13. Do peak fares actually discourage non-essential travel? (no), do students buy discounted tickets? (no), does fare-capping encourage people to use public transport more? (yes)
    N. Lathia, L. Capra. How Smart is Your Smart Card? Measuring Travel Behaviours, Perceptions, and Incentives . Under Submission, May 2011.
  • 14. (4) Building services
    • Shared bicycles: how can we build better mobile services to encourage bicycle usage?
      • Student project!
      • 15. Analyse the Barclays cycle hire usage; observe the relation between usage and policy
      • 16. Implement and test predictive algorithms for bikes/parking slots at each station
    S. Ahmed, N. Lathia, L. Capra. Data Analysis and Predictive Modelling of Bicycle Station Usage for the Barclays Cycle Hire . In the making, May 2011.
  • 17. Research Opportunities
    • Analyse, Measure, Test (Data)
    • 18. Build, Deploy, Test (Field)
    • 19. Engage, Disseminate, Discuss
  • 20. neal lathia twitter: @neal_lathia