1. Center for Urban Transportation Research | University of South Florida Technology Quick Check Sean J. Barbeau, Ph.D.

2. 2 Overview Mobile Tracking Technology Monitoring Carsharing Behavior Using Mobile Tracking Technology Cost-Effective Multimodal Trip Planners

3. 3 MOBILE TRACKING TECHNOLOGY The nuts and bolts

4. 4 Problem Past vehicle-based GPS tracking give low- resolution view of daily travel behavior Are these GPS fixes: – Points-of-interest? – Stops in traffic? Difficult to extract info: – Distance traveled – Origin-Destination pairs Misses non-vehicle trips

5. 5 Innovation Mobile phone apps can capture “high- definition” view of travel behavior Much easier to determine: – Path, distance traveled – Origin-Destination pairs – Avg. speeds Can capture transit/bike/walk trips Sprint CDMA EV-DO Rev. A network

6. 6 New Problem We can record GPS fixes as frequently as once per second and send to our server However, frequent GPS fixes come at great cost to: – battery energy – data transfer over network Both battery life and cell network data transfer are very limited resources

7. 7 Sprint CDMA EV-DO Rev. A network Sprint CDMA EV-DO Rev. A network One-day Requirement

8. 8 Let’s decrease the GPS recalculation rate when stationary!

9. 9 What is “Stationary”? Detecting User Movement MovingStopped d GPS noise causes uncertainty in states Many false transitions waste battery energy 4 second GPS sampling 5 minute GPS sampling

10. 10 Auto-Sleep to Reduce Energy Consumption 4 second GPS sampling 5 minute GPS sampling US Patent 8,036,679 October 11, 2011 Dynamically change the GPS sampling interval on the phone MovingStopped

11. 11 Evaluation – Summary of 30 tests Approx. 88% mean accuracy in state tracking Avg. doubling of battery life (based on TRAC-IT tests)

12. 12 Monitoring Carsharing Behavior Using Mobile Tracking Technology

13. 13 Case Study - Carsharing Summary Provided flip-phones for test and control subjects with TRAC-IT mobile app Carried phone for all trips – Passive data collection Varied hourly price in peak to shift time of rentals Provided daily summary and map of trips via email Collected data for two 3-week data collection periods – Data instantly transmitted to us “This is my trip to campus, via the Bull Runner, to pick up the WeCar. I then drove the WeCar to the CVS on Fowler to pick up medication and then drove to the grocery store on Bears Ave. After shopping, I dropped the groceries off at home and then drove back to campus to return the WeCar. I then took the Bull Runner back home.”

14. 14 Measuring Spatial Patterns of Activity-Travel Trip Length (miles)SDE (square miles) User TypeAverage Carsharing Trip Non Carsharing Average Carsharing Trip Non-Carsharing Trip Carsharing 2.68.01.70.50.20.5 Non-Carsharing4.2- 7.8- ActivitiesMean Center SDC Minor Axis Major Axis SDE Y X Y X

15. 15 Lessons Learned Pluses Providing phone: – Reduced need to test on multiple platforms – Povided additional privacy protection Continuous tracking while moving without running out of battery energy Passive collection with free-text self-validation worked well with extended period of data collection Phone instantly provides data to identify problems quickly Virtually limitless length of field deployment Minuses Need to carry a second phone/charger Providing cell phones and data plans Data post-processing More work needed to differentiate “points of interest” from stuck in traffic when passively collecting data – A current research focus

16. 16 COST-EFFECTIVE MULTIMODAL TRIP PLANNERS Open source, open data

17. 17 Mobility and Travel Choices Mobility and travel choices mean multiple travel options for getting around – Not being car-, bus-, bike-, or walk- dependent – Being able to mix and match modes to meet needs

18. 18 Why 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 – For bike/walk, path is very important

19. 19 Free, open-source software – opentripplanner.org Initial development led by TriMet and OpenPlans Available for anyone to download, deploy, and modify Companies such as Conveyal can provide installation, customization, maintenance support

20. 20 OpenTripPlanner = Multimodal USF’s OTP Demo for Tampa, Fl - http://opentripplanner.usf.edu – Example: Bike->Bus->Bike

21. 21 TriMet – Portland, OR Primary motivation was to merge separate transit and bike trip planners – http://rtp.trimet.org/ Launched beta version Oct. 2011 Switched to OTP Summer 2012

22. 22 Pune Bus Guide, India Production deployment of OpenTripPlanner – http://punebusguide.org/guide/ Translated to Devanagari script, including right-to- left interface

23. 23 Businfo, Tel Aviv, Israel Production deployment of OpenTripPlanner – http://businfo.co.il/ Translated to Hebrew – Also uses right-to-left interface Funded by regional transportation authority after reorganization of regional transit routes

24. 24 goEuropa, Poznan, Poland Production deployment of OpenTripPlanner – http://iplaner.pl/iPlaner2/ Translated to Polish Customized website interface, uses OTP to calculate routes on server

25. 25 Mobile OpenTripPlanner CUTR team is working on open-source Android app Can interface with any OTP server iPhone app source code also available from OpenPlans

26. 26 Why don’t we just use Google Maps? At USF, Google Maps can’t find USF building names or abbreviations Google Maps gives walking directions on Alumni Dr. (no sidewalks) and using a cross-street (instead of the nearby crosswalk) Google Maps OpenTripPlanner © 2011 Google – Map data © 2011 Google Data CC-By-SA OpenStreetMap

27. 27 Can Add New Transit Systems HART USF Bull Runner

28. 28 Bike Routing Options OTP bike routing supports mix of multiple options: – Time (fastest) – Hills (flatest) – Safety (dedicated bike lanes) Still open research area

29. 29 Shortest Route (with stairs) Wheelchair-accessible routing stairs

30. 30 Route with no stairs Wheelchair-accessible routing stairs

31. 31 Open Data Sources - Transit General Transit Feed Specification (GTFS) – Over 200 agencies in US have transit data in GTFS, more than 447 world-wide – See “GTFS Data Exchange” for list of agencies with “open” GTFS data: – http://www.gtfs-data-exchange.com/ – Challenges: – Not all agencies openly share their GTFS data – See City-Go-Round for list of “closed” transit agencies: » http://www.citygoround.org/ http://www.citygoround.org/ Some agencies need help organizing data

32. 32 Road/Bike/Walk - OpenStreetMap.org – “Wikipedia for geographic data” – Users contribute data under Creative Commons license – Edit online, tracing GPS or donated imagery, or via code – Anyone can download and use the data – Challenge – Coverage is still sparse in some areas

33. 33 CONCLUSIONS The takeaways

34. 34 Conclusions Mobile phones are new multimodal survey tool, can provide wealth of GPS and other data – Battery life, data processing still largest challenges OpenTripPlanner is cost-effective, customizable multimodal trip planner – Pedestrian/bike data from OpenStreetMap may be sparse in some communities

35. 35 Thanks! Sean J. Barbeau, Ph.D. barbeau@cutr.usf.edu 813.974.7208 Principal Mobile Software Architect for R&D Center for Urban Transportation Research University of South Florida