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Predicting YouBike

Predicting YouBike

Modeling demand for Taipei area bike share stations


brandon liu

May 28, 2015


  1. Predicting YouBike YouBike 預測 劉知岳 (Brandon)

  2. Why Forecast?

  3. UNDERSTAND Observe past phenomena confirm or deny intuitions based on

    data quantify service quality
  4. MAKE DECISIONS See what is likely to happen in the

    immediate future
  5. What applications?

  6. None
  7. Public YouBike API gwjs_cityhall.json


  9. TOOLS LevelDB - data store Go, nginx, python, EC2 -

    server Leaflet, d3 - web page
  10. The shape of the data tells a story

  11. How might we classify YouBike stations based on usage patterns?

  12. Pattern: Morning/Evening Commute (信義/杭州路⼝口)

  13. Pattern: Night Owls (國⽗父紀念堂)

  14. Pattern: School (公館(台⼤大前⾯面)

  15. Choosing a metric What can we measure that affects users?

    How many minutes per day you can’t rent a bike (or what % of the day)
  16. Pricing change March 2015(師⼤大)8% Unavailable

  17. Pricing change April 2015(師⼤大)3% Unavailable

  18. Improve the model? Weekend/Weekday (信義/杭州)

  19. Improve the model? Weather (師⼤大) 下⼤大⾬雨

  20. Adjustments