1. Accuracy in Real-Time Estimation of Travel Times Galen McGill, Kristin Tufte, Josh Crain, Priya Chavan, Enas Fayed 15 th World Congress on Intelligent Transportation Systems November 18, 2008

2. Project Summary Initial Project Phase –Collected 500 ground truth runs –Analyzed travel time estimation accuracy Second Phase –Addressing primary causes of error –Algorithmic adjustments –Analysis of actual DMS message accuracy

3. Study Area and Data Collection 544 Ground truth probe runs GPS-enabled vehicles (Garmin iQue ®) Detector data from 500 dual loop detectors on Portland-area freeways I-5 North of Downtown Map of Study Area I-84 I-205 I-5 South of Downtown OR- 217 US-26 Downtown Portland

4. Initial Project Results Overall average absolute percent error 11% (SDPE 18%) –15% of runs had absolute percent errors larger than 20% Accuracy varied between segments Primary causes of error –Malfunctioning detectors –Large detector spacing –Changing traffic conditions

5. Overall Estimation Accuracy Average Percent Error – All Runs

6. Outline Malfunctioning Detectors –Critical detectors Large Detector Spacing –Prioritizing addition of detectors How significant are changing traffic conditions? Is congestion correlated with error? Historical average period DMS message accuracy

7. Malfunctioning Detectors 50% of the runs had one or more detector stations malfunctioning High error when certain critical detectors failed (i.e. near recurrent bottlenecks) Identify set of critical detectors –Prioritize detector maintenance –May not provide travel time when a critical detector has failed

8. Critical Detectors on I-5 NB I-5 and I-405 JunctionI-5 NB Columbia River Crossing Bottleneck~ Critical Detectors Bottlenecks

9. Large Detector Spacing Where should detection be added? –Prioritize locations of additional detection –Understand implications of detector location Detectors simulated with ‘virtual detectors’ using probe vehicle speeds Compared ‘real-time’ travel time estimates

10. Additional Detection Locations Terwilliger Curves (mp 298) Marquam Bridge (mp 300.5) I-5 NB I-5 SB/OR 217 Junction (mp 292)

11. Addition of Detectors

12. Additional Detection - Conclusions Developed methodology for prioritization of new detector locations Detection recommended at several locations on I-5

13. Changing Traffic Conditions Travel time estimate provided at start of segment (DMS), but traffic conditions may change as a vehicle drives through segment

14. Changing Traffic Conditions Traffic flow DMS End of segment (DMS predicts travel time to this location) Vehicle (60 mph) Congestion Wave (?? mph) Location where vehicle encounters congestion Travel time estimation incorrect in this section Estimation error depends on speed of congestion wave (faster wave = more error).

15. Congestion Wave Speed and Error Analyzed four bottlenecks –Average congestion wave speed ranged from 6.5 mph to 9.7 mph Effect on error –7.5 mph congestion wave; 25 mph speed during congestion gives max error of 13.5% Traffic flow Traffic Speed 60 mph Congestion Wave (7.5 mph) Traffic Speed 25 mph

16. Maximum Error by Wave Speed

17. Is Congestion Correlated with Error? Little to no correlation for All Runs Some correlation on I-5 SB SoD Loop Speed vs. Error – All Runs Loop Speed vs. Error – I-5 SB SoD

18. Is Congestion Correlated with Error? Tried to correlate variables with error –Average loop speed –Average probe speed –Standard deviation probe speed –Estimated travel time –Minimum loop speed No significant correlation pattern found –Some segments correlated; no pattern across all runs

19. Effect of Historical Average Period Travel time estimation algorithms use a speed average (i.e. 3-minute, 5-minute) for travel time calculation 5-minute had lowest error, but was slightly biased towards underestimation 3-minute also had low error and was less biased Conclusion: 3-minute or 5-minute average is reasonable

20. DMS Travel Time Accuracy Ground truth vs. posted DMS travel times Expected to be fairly accurate, but… Carman DMS Prediction Ground Truth Travel Time < 10 min 10-12 min 12-15 min > 15 min < 10 min666 10-12 min2365 12-15 min 13 > 15 min Potential problem??

21. DMS Travel Time Accuracy Study showed ODOT’s estimation algorithm was fairly accurate DMS travel time messages were much less accurate –No messages “> 15 minutes” ever posted Issue reported to ODOT Staff Configuration error in the ATMS database was discovered and corrected

22. Conclusions Current algorithm accuracy relatively good Critical detectors and additional detection to address high error Effect of changing conditions may not be significant 3-5 minute average window is reasonable Need to verify actual DMS messages

23. Acknowledgments Oregon Department of Transportation –Dennis Mitchell, Jack Marchant At Portland State University –Robert L. Bertini, Sirisha Kothuri Oregon Transportation, Research and Education Consortium (OTREC)

24. Questions? Thank You! portal.its.pdx.edu www.its.pdx.edu Thank You! portal.its.pdx.edu www.its.pdx.edu