7A_2_Preliminary results of a spatial analysis of dublin citys bike rental scheme


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Session 7A, Paper 2

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7A_2_Preliminary results of a spatial analysis of dublin citys bike rental scheme

  1. 1. Preliminary Results of a Spatial Analysis of Dublin City's Bike Rental Scheme Peter Mooney Padraig Corcoran Adam C. Winstanley
  2. 2. Contact Details [email_address] [email_address]
  3. 3. DublinBikes (DB) is a recently introduced (Sept, 2009) bike rental scheme for Dublin city, Ireland Commercial company (JCDecaux) and city council partnership Approximately 50,000 subscribers Commercial company (JCDecaux) and city council partnership 95% of rentals are free - under 30 minutes
  4. 4. Project Motivation <ul><li>Access to a novel dynamic spatio-temporal dataset for Dublin </li></ul><ul><li>Independent verification of usage statistics from Dublin Bikes </li></ul><ul><li>Investigate if it is possible to confirm/reject predicted patterns of usage </li></ul>
  5. 5. Ireland's First National Cycle Policy <ul><li>“ Create a strong cycle culture” </li></ul><ul><li>“ 10% of all trips by bicycle by 2020” </li></ul><ul><li>“ provide proper integration with public transportation” </li></ul><ul><li>“ monitor success of these measures” </li></ul>
  6. 6. Doherty (2009) reported on bike usage as a means of commuting Only 1.9% of workers commute by bike in Ireland Bike usage as a means of work transport down 33% since 1986 Poor continuity of cycle lanes and integration with other transportation systems
  7. 7. Other studies reveal more obvious reasons why cycling has become less popular as a mode of transport “ 16% of people said DISTANCE was the biggest factor preventing them cycling to work” Keegan and Galbrait (2005) “ The streets are too dangerous” “ Cars use the bike lane” Keegan and Galbrait (2005) Irish Times (2007)
  8. 8. DB Terminals are clustered around the city center - approx 400 meter separation between stations
  9. 9. DublinBikes (DB) website shows current status of every terminal
  10. 10. By examining the DB javascript we found the web services providing terminal information
  11. 11. Yahoo Weather RSS are downloaded, parsed, and stored Each 30 minutes
  12. 12. Data capture is handled by a set of scheduled PHP scripts CRON Task 4 minute intervals All 40 Stations PHP GET HTTP Scrip t April 13 th (2,078,366) rows
  13. 13. Data Constraints <ul><li>We only have access to (number of spaces, number of bikes) at any station </li></ul><ul><li>No flow data – ie we do not know where bike 00060 travels from when leaving station x to finish at station y </li></ul>
  14. 14. OpenStreetMap contains all 40 DublinBike locations
  15. 15. We built a web application to monitor data capturing
  16. 16. Froehlich et al (2009) studied Barcelona's BICING system 390 STATIONS 6,000 BIKES 150,000 Subscribers Data Capture: Web Page Scraping
  17. 17. Froechlich et al (2009) reveal some interesting patterns about bike usage in Barcelona <ul><li>“ Late lunch” Spike –2PM lunch time in Spain </li></ul><ul><li>Monday – busiest day </li></ul><ul><li>Friday – quietest day (reduced working hours) </li></ul>&quot;demonstrate the potential of using shared bikes as a data source to gain insights into the city dynamics and aggregated human behaviour&quot; &quot;explore the relationship between spatiotemporal patterns of bike usage and underlying city behaviour and geography&quot;
  18. 18. Some DB stations are integrated with LUAS TRAM stops
  19. 19. Almost 95% of DB terminals are more than 400 meters from suburban train stations Marteens et al (2004) - “best integration of bikes – 400 meters from train stations”
  20. 20. Dublin's extensive bus network integrates well with DB terminals Currie (2009) – high service level bus stops
  21. 21. Including all modes – there is reasonably good integration North-west inner city DB terminals are somewhat isolated.
  22. 22. Analysis of checkout statistics for Weekdays and Weekends
  23. 23. Availability of bikes at the three busiest stations on the network
  24. 24. Looking at the periods of inactivity at a station gives an indication of how “busy” the terminal is Mean number of minutes during weekdays where no checkout or return occurs at this station
  25. 25. Normalised checkouts - weekdays
  26. 26. Normalised Checkouts - Weekends
  27. 27. Weekends vrs Weekday – busy station patterns City center cluster is always busy
  28. 28. Total number of bikes available in the network – September - April Christmas period No Data Daily – maximum usage
  29. 29. Usage patterns during the Dublin Marathon 2009 Spectators using Dublin Bikes to “follow” the race
  30. 30. Last Sunday..... “First day of Spring” Heavy demand on bikes in mid-afternoon – steady flow of return
  31. 31. “ Typical” Weekday Rush hour pattern – bikes out – and bike returns to other stations
  32. 32. Wettest November (2009) days on record 60 bikes MAX Little or no usage – until a massive rush for bikes in the late evening
  33. 33. We have loosely confirmed some behavioural patterns with the spatio-temporal data collected <ul><li>“ Source and Sink” stations – usually close to public transportation hubs </li></ul><ul><li>Shopping and Leisure orientated usage at weekends – ie Jervis Street, Grafton Street </li></ul><ul><li>Business district – very low usage at weekends </li></ul>
  34. 34. There are a number of issues for future Work Gain access to the trips database – model flows of bike trips etc Prediction of Station Usage Integrate population models - see areas of the city where large numbers of people are working/etc Compare with other similiar sized free bike rental schemes
  35. 35. Overall there are lots of patterns which could be investigated <ul><li>Large and growing dataset </li></ul><ul><li>OpenStreetMap providing spatial data </li></ul><ul><li>More rigourous geo-statistical approach needs to be applied </li></ul>Questions and Comments