Spitsgids presentation at the ITS Belgium congress

Transcript

2. Who came by train today?
3. Whose train was really busy?
4. +28% Source: NMBS (2016)
5. Source: Leefmilieu Brussel (2013)
6. Source: Rail4Brussels (2016) NUMBER OF PASSENGERS OVER TIME OF DAY Weekday Saturday Sunday Morning peak (6-9h) Evening peak (15-19h)
7. How can we try to avoid busy trains? Incentivize people to take another train
8. Source: iRail data
9. Spitsgids We predict how busy your train will be, so you can leave earlier or later
10. Meet our 3 indicators
11. Passenger experience User feedback structurally busy trains
14. And now it’s our turn to publish Open Data Both real-time feedback as our predictions
15. User feedback Next step: challenging researchers to creating better predictions
16. iRail query logs User feedback Next step: challenging researchers to creating better predictions
17. User feedback weather Next step: challenging researchers to creating better predictions iRail query logs
18. User feedback weather Next step: challenging researchers to creating better predictions iRail query logs events
19. User feedback weather Next step: challenging researchers to creating better predictions iRail query logs events open datasets
20. The budget so far was crowd-funded And financially supported by
21. It was developed by our open Summer of code students Arne, Stan and Serkan
22. Thank you to our app builders Jan Cornelissen (Railer App - iPhone) Christophe Versieux (BeTrains - Android) and the iRail contributors
23. Let’s build a world where knowledge creates power for the many, not the few. @pietercolpaert https://hello.irail.be http://pieter.pm