1. U SING O PEN D ATA TO D EVELOP M ULTIMODAL T RIP P LANNERS FOR L IVABLE C OMMUNITIES Sean J. Barbeau Edward L. Hillsman Center for Urban Transportation Research @ University of South Florida GIS in Transit Conference St. Petersburg, Florida September 14, 2011 Funded by the Florida Department of Transportation and the National Center for Transit Research

2. P URPOSE Advise on two emerging technologies – Multimodal trip planning – Crowd-sourced data/applications Explain state-of-the-art and relationship to GIS

3. W HY MULTIMODAL TRIP PLANNERS ? If you want to drive, the question is “How do I get there?” – Road networks are dense, connected, complete – Google, Mapquest, Yahoo can easily tell you For bike/walk/bus, the question is “Can I get there (by a safe route)?” – Networks are sparse, incomplete, or both – Route-specific info is more important than when driving

4. Multimodal – Options to mix modes for a trip – Examples Bike to bus, ride bus, bike or walk to final destination Drive/bike to park-and-ride, take bus Wheelchair-accessible routes Various access to/from bike- sharing, car-sharing T RIP PLANNING SOFTWARE TYPES Unimodal – Similar to what Google Maps/Transit/Bikes, Yahoo Maps, Mapquest offer – One mode per trip:

5. P ROPRIETARY T RIP - PLANNING SOFTWARE Custom-built software and data are expensive – Goroo® in Chicago cost more than $1 million and is still being improved Web-based software is proprietary and closed – Google, Yahoo, etc. are free to use, but Services depend on the needs and desires of the providers Providers limit use and presentation of their systems (frequency, branding)

6. O PEN T RIP P LANNER Free, open-source software - opentripplanner.org Development spearheaded by Tri-Met in Portland, with grant funding (2009-present) Active worldwide developers’ group Available for anyone to download, install, modify – (and, with approval, contribute back) Non-profit OpenPlans can provide installation, customization, maintenance support OpenPlans will be giving Keynote on OTP status and roadmap on Thurs. morning at 8:30am

7. O PEN T RIP P LANNER – T RUE M ULTIMODAL USF’s OTP Demo for Tampa, Fl - http://opentripplanner.usf.eduhttp://opentripplanner.usf.edu – Example: Bike->Bus->Bike

8. O PEN T RIP P LANNER – I NTERLINING BETWEEN TRANSIT SYSTEMS HART USF Bull Runner

9. W HY DON ’ T WE JUST USE G OOGLE M APS ? In USF community, Google Maps can’t find USF building names or abbreviations Google Maps gives walking directions on Alumni Dr. (where there were no sidewalks) and using a cross-street (instead of the nearby crosswalk) Google MapsOpenTripPlanner © 2011 Google – Map data © 2011 Google Data CC-By-SA OpenStreetMap

10. OTP W HEELCHAIR ACCESSIBLE ROUTING OPTIONS Regular route with stairs

11. Wheelchair-accessible route OTP W HEELCHAIR ACCESSIBLE ROUTING OPTIONS

12. GIS D ATA To provide this kind of service, you need data – Transit routes and schedules – Street network (plus addresses, points of interest for geocoding) – Bicycling facilities (lanes, routes, parking) – Sidewalks, crosswalks, and other pedestrian infrastructure – Future: Park-and-ride lots, car-sharing, and/or bike-sharing stations 12

13. O PEN D ATA S OURCES FOR O PEN T RIP P LANNER General Transit Feed Specification (GTFS) – Over 140 agencies in US have transit data in this format, more than 447 world-wide – Most agencies did this to get on Google Transit – But, GTFS is open-data format that anyone can use Used by many mobile apps OpenTripPlanner Becoming a de facto standard – See “GTFS Data Exchange” for list of agencies with GTFS data http://www.gtfs-data-exchange.com/ Or, ask your local agency – Major transit scheduling software packages can prepare GTFS

14. O PEN D ATA S OURCES FOR O PEN T RIP P LANNER OpenStreetMap.org – Think “Wikipedia for geographic data” – People contribute data under a Creative Commons Attribution- ShareAlike 2.0 license – Edit online, using custom GPS traces, or programmatically – Anyone can download and use the data (not just the maps)

15. O PEN D ATA S OURCES FOR O PEN T RIP P LANNER National Elevation Dataset (NED) – Provides elevation data for biking/walking in OTP – Currently used to produce elevation graph, and for some biking routing decisions

16. O PEN D ATA S OURCES FOR O PEN T RIP P LANNER Geographic Information Systems (GIS) files – OpenTripPlanner can also support loading GIS (e.g.,.shp) files – Local government sources: City County Special Districts (parks, etc.) Ask your local government what data might be available – Especially if there isn’t much OpenStreetMap activity in your area

17. OPEN ISSUES Multimodal trip planning is a new field, and there are still... 17

18. P EDESTRIAN S IGNALS & C ROSSINGS “Implicit” vs. “Explicit” data coding of pedestrian infrastructure in OpenStreetMap Implicit – less work when sidewalks are always present and follow roads (e.g., downtown): Explicit – less work when sidewalks are sparse, or don’t follow roads: 18

19. 19 Explicit example

20. 20 Explicit coding example

21. 21 "highway=footway” "footway=crossing” "highway=crossing” + "crossing=pedestrian signals“ "marking=zebra” "wheelchair=yes” Explicit coding example

22. 22 "highway=footway” (normal sidewalk tag) "footway=crossing" (new tag) "highway=crossing” + P EDESTRIAN S IGNALS & C ROSSINGS "crossing=pedestrian signals” "marking=zebra” "wheelchair=yes" FOR OTP ROUTING:

23. P EDESTRIAN S IGNALS & C ROSSINGS How to support implicit coding routing, and locations where explicit/implicit codings merge? 23

24. O PEN I SSUES – C ROWD - SOURCING L EVEL OF S ERVICE Having traffic characteristics for roads would help in pedestrian/biking routing decisions However, traditional road traffic metrics (i.e., traffic volume, width of lanes) are difficult/dangerous to crowd-source Need better objective metrics to define bike and walk "level-of-service" (i.e., how "good" an OSM way is for walking or biking) that can easily be recorded by a casual observer 24

25. O PEN I SSUES – P ERSONALIZING B IKING D IRECTIONS Level-of-service metrics must translate to subjective judgments for whether a cyclist would be comfortable riding on a specific road Different for every cyclist: – Some expert cyclists would be comfortable riding on high traffic roads where other beginner cyclists would not – Also depends on presence of bike lanes, shoulder, etc. What does an “ideal” user interface look like to meet everyone’s needs, but not be overwhelming? Should we customize based on some self- assessment of skill/comfort level? 25

26. O PEN I SSUES – S PARSENESS OF OSM D ATA Many areas of U.S. are still sparsely populated in OSM We believe OTP is a “game-changer” – now OSM contributors can see direct benefits of their work in OTP routing What are the motivations/profiles of current U.S. contributors? How can we leverage this knowledge, and visibility of benefits in OTP, to motivate a larger crowd of OSM contributors? 26

27. GO-Sync A Software Tool to Synchronize Transit Agency GTFS Datasets with OpenStreetMap Coded by Khoa Tran

28. GO-S YNC M OTIVATION Shortcomings of official transit GTFS datasets – Inaccurate bus stop locations Lack of transit data in OSM for many U.S. cities Goal – create a tool that can: – Share transit agency data with OpenStreetMap community – Leverage social mapping model to improve bus stop inventory, and allow agency to retrieve these improvements

29. C HALLENGES Need to respect work by other OSM users – Avoid overwriting existing OSM data Lack of a strict tagging system in OSM – Ex: “route”, “routes”, “route_id”  “route_ref” Need to avoid duplicating OSM data Ongoing updates to GTFS data Integration of crowd-sourced data into transit agency internal datasets

30. GO-S YNC General Transit Feed Specification (GTFS) – OpenStreetMap (OSM) Synchronization – http://code.google.com/p/gtfs-osm-sync/ http://code.google.com/p/gtfs-osm-sync/ – Open-source, under Apache 2.0 GO-Sync is an open-source tool that can synchronize GTFS datasets with OSM – Performs “Point-conflation”, or merging, for bus stops in OSM

31. 1) Input GTFS data and Agency Info

32. GO-Sync analysis, allowing user changes before upload

33. 33

34. 34

35. E VALUATION IN T AMPA On July 2010, 3,812 new HART stops uploaded (133 stops previously existed) By January 2011, 173 modifications were made Example: moved

36. E VALUATION IN T AMPA

37. GO-S YNC S UMMARY GO-Sync can help you leverage crowd-sourced edits for your bus stop inventory Available to download from Google Code – http://code.google.com/p/gtfs-osm-sync/ http://code.google.com/p/gtfs-osm-sync/ Caveats: – Must have the GTFS owner’s permission before upload!!! – It’s a prototype – read the instructions carefully!! – May not be appropriate for all transit agencies – Knowledge of OSM is highly suggested – Respect others work! We would welcome improvements by other contributors! 37

38. CONCLUSIONS What should I take away from today’s presentation? 38

39. T AKEAWAYS Open-source multimodal trip planners are a reality Get your GIS data together for your community – GTFS – OpenStreetMap – Local GIS Think about multimodal data connections – Bike/walk is part of trip, not whole trip – Park-and-Ride lots, carsharing, bikesharing – Intersection data How might you benefit from crowd-sourced data? Benefits of open software/data – No vendor lock-in – Community add-ons (USF students created OTP Android app, USF BullRunner GTFS data)

40. C ONTACT I NFORMATION Project Website: – http://www.locationaware.usf.edu/ongoing- research/projects/open-transit-data/ http://www.locationaware.usf.edu/ongoing- research/projects/open-transit-data/ OpenTripPlanner Tampa Demo: – Opentripplanner.usf.edu Sean Barbeau, M.S. (OpenTripPlanner/Android) barbeau@cutr.usf.edu (813) 974-7208 Ed Hillsman, Ph.D.(OpenStreetMap) hillsman@cutr.usf.edu (813) 974-2977 40