1. Robert L. Bertini Sirisha M. Kothuri Kristin A. Tufte Portland State University Soyoung Ahn Arizona State University 9th International IEEE Conference on Intelligent Transportation Systems Toronto, Canada September 20, 2006 Development of an ITS Data Archive Application for Improving Freeway Travel Time Estimation

2. 2 Outline  Introduction  Study Area  Data Sources  Data Analysis  Conclusions  Next Steps

3. 3 Project Goals 1.Evaluation of Oregon Department of Transportation (ODOT) travel time estimating and reporting capabilities 2.Identify travel time algorithms for real time applications and historical analysis

4. 4  FHWA policy  Variety of technologies  Inductive loop detectors  Microwave radar  Automatic vehicle tag matching  Video detection  License plate matching  Cell phone matching  Past research  General accuracy in free-flow conditions  Recurring congestion & incidents more challenging Real-time Travel Time Estimates

5. 5 Portland ATMS  Freeway Surveillance  485 inductive loop detectors  (175 stations)  Dual loop (act as single loop)  Mainline lanes  Upstream of on-ramps  135 ramp meters  98 CCTV  ATIS  www.TripCheck.com  Real-time speed map  Static CCTV images  18 dynamic message signs (DMS)  3 display travel times

6. 6  15 directional freeway links  I-5 (6)  I-205 (3)  I-84 (2)  US-26 (2)  OR-217 (2)  87 probe runs  516 miles driven by 12 drivers  15 hours of data collected Travel Time Study Area

7. 7 PORTAL  National ITS Architecture ADUS  Funded by NSF  Direct fiber-optic connection between ODOT and PSU  20-second data  Occupancy  Volume  Speed  Customized travel time area  Conforms to TMOC (Portland Regional Transportation Archive Listing) www.portal.its.pdx.edu

8. 8 Ground Truth Data  Hardware  Palm handheld computers  Magellan GPS devices  Software  ITS-GPS  Available at www.its.pdx.edu  Individual runs and groups of probe vehicles  Variety of traffic conditions  45 percent congested  2 notable incidents

9. 9 Travel Time – Midpoint Algorithm Influence Area 4  Travel Time 4 (at t = 0)  Travel Time 1 Influence Area 1  Travel Time 3 (at t = 0) Influence Area 3  Travel Time 2 (at t = 0) Influence Area 2 Link Travel Time (TT1 + TT2 + TT3 + TT4)

10. 10 Travel Time - Coifman Algorithm

11. 11 Travel Time - Coifman Algorithm Time Distance

12. 12 Analysis – Six Testing Scenarios  Coifman algorithm using speeds from upstream detectors only  Coifman algorithm using speeds from downstream detectors only  Coifman algorithm using speeds from both upstream and downstream detectors along with the midpoint influence areas

13. 13 Analysis – Testing Scenarios Contd..  Coifman algorithm using speeds from upstream and downstream detectors weighted in the ratio of distance of the hypothetical vehicle from each detector  Midpoint algorithm (based on influence areas)  Midpoint algorithm using speed at time (t = 0) that is an average of the upstream and downstream detector readings

14. 14 Analysis – Probe TT & TT Estimates

15. 15 Analysis – Variance Comparisons

16. 16 Analysis – Free Flow Travel Times

17. 17 Analysis – Incident Travel Times

18. 18 Analysis – Large Detector Spacing

19. 19 Analysis – Detector Spacing

20. 20 Bus Probes

21. 21 Analysis - Bus TT & TT Estimates

22. 22 Analysis – Variance Comparisons

23. 23 Conclusions  Travel times estimated by Coifman algorithm are more accurate than midpoint travel times.  The accuracy of travel times depends on  Location and density of detectors  Location, formation and dissipation of queue  Both algorithms misestimate when incidents are encountered.  Coifman algorithm more suited for historical analysis in its current form.

24. 24 Next Steps  More probe data  ITS data fidelity and its effect on travel time estimates  Assessment of performance of algorithms with additional ground truth data  Sensitivity analysis  Refinement of algorithms

25. 25 Acknowledgements  Dr. Chris Monsere  Stacy Shetler  Aaron Breakstone  Dean Deeter  Galen McGill  ODOT  TriMet  Castle Rock Consultants  PORTAL Team  Peter Bosa  Volunteer Drivers