1. Assessment and Refinement of Real-Time Travel Time Algorithms for Use in Practice Nov 8, 2006

2. Outline Ramp Meter Data Fidelity Assessment Inrix Data Update Data Collection Plan Travel Time Best Practices Results Schedule update

3. Ramp Meter Data Fidelity Assessment Impacts of Various Factors on Travel Time Estimation Accuracy  Algorithms  Detector Spacing  Data Quality

4. Algorithm Comparison: Uncongested Runs I-5 N (217-405) I-5 S (Bridge-84) I-5 S (405-217) 217 SI-205 S (84-O.City) I-84 E (5-205)

5. Algorithm Comparison: Congested Runs I-5 SB (405-217) 217 N217 SI-205 N (5-O.City) I-5 NB (84-Bridge) I-5 S (Bridge-84) I-84 E (5-205) Large detector spacing Some probe runs encountered an incident Significant recurring congestion

6. Algorithms: Trajectory Comparison

7. Conclusions - Algorithms FHWA says 90% accuracy is ideal, accuracy must be no less than 80% (Agrees with what we discussed last time) No algorithm is consistently better and consistently < 10% Many runs have error > 10%  Appears to be associated with large detector spacing and incidents  Need more data to verify impacts of algorithms, spacing, etc. Moderate impact from algorithm, but probably not enough to overcome infrastructure issues (more when we examine other states practices)

8. Detector Spacing Impacts-Analytical More detector stations => more data samples  Lower error due to more samples  If one detector has issues, others will mitigate that problem Shockwave Propagation  When an incident/bottleneck occurs far from a detector, it takes time for the congestion to reach the detector  Shockwave propagation 12-16 mph, 15 mph = 4 minutes/mile 2 miles 1.5 miles = approx. 6 minutes Detector 1 Detector 2 Bottleneck

9. Detector Spacing Impacts: Congested Conditions

10. I-205 NB – Stafford – MP 3.55 – Lane 2

11. I-5 NB – Delta Park – MP 306.51 – Lane 2

12. Conclusions – Detector Spacing and Data Quality Detector Spacing  Expect and think we see association with detector spacing  Need more data to verify  Are also creating an analytical model for detector spacing impacts Data quality  Suspect there is an impact  Need more data to verify  Would like to clarify speed calculation procedure

13. Inrix Data Provide flow and travel time data  XML data stream Data Sources  Current data is a processed version of the ODOT Region 1 loop detector data  As of mid-November, probe data will be included (transponder detectors from instrumented fleets (taxis, etc.))

14. Data Validation Inrix has validated accuracy of their data for three east-coast cities  The networks in these cities use probe data only  Validation not valid for Portland (different city, probe + detector data) Potential good source of data, but do not believe we can use it as ground truth without more validation We are getting sample data (NDA in process) Meeting with Inrix Technical Staff?

15. Ground Truth Data Collection – Phase 1 Initial Study to confirm methodology and process Issues:  Collection Process  Corridor selection - focus on two corridors for this phase  Number of Runs  Timing of Runs

16. Quality Counts Recommended by ODOT personnel $45/hour + mileage (currently ~$0.49/mile) We provide list of highways, hours, and a list of locations on the highways that we want timed They use a stopwatch and record the time when they pass each specified location

17. Corridor Selection Corridor Selection Criteria  Moderate-severe recurrent congestion  Variable loop detector spacing (some low some high) to allow evaluation of spacing effects  Some situations with high data quality  Construction Schedule – avoid times/areas when there is construction Propose:  OR 217 (‘good’ conditions)  I-205 or I-5 (more variable detector spacing) Credit to Sue Ahn for her ideas for corridor selection in SWARM

18. 217 N, Weekdays - April, 2006 traffic flow

19. 217 S, Weekdays - April, 2006 traffic flow

20. 217 Notes Congestion: moderate congestion both NB and SB  Congestion NB and SB in both AM and PM Peaks  PM congestion generally worse than AM  SB congestion generally worse than NB Detector Spacing: good  NB: 9 stations SB: 11 stations Length: ~7 miles Data Quality  217 N - ~1% disqualified data  217 S - ~2% disqualfied data

21. Timing & Cost Specifics – 217 PM Peak 217 S  Peak: 3:00-6:00 Min/Max/Avg TT: 14.3/25.0/20.5 min 217 N:  Peak: 4PM – 6PM Min/Max/Avg TT:10.2/14.2/12 min Average Round trip ~32 minutes Need ~50 runs for 5% error at 95% confidence  Start with 20 runs/corridor 2 runs/hr, 10 hrs = 20 runs = $450 ($45/hr) Gas cost: 20 runs * 7 miles * 0.5/mile = $70 Data from weekdays – April, 2006

22. Timing – 217 AM Peak AM Peak  217 N Peak: 7:30-8:15 Min/Max/Avg TT: 8.2/10.6/9.3 min  217 S: Peak: 7:00-9:00 Min/Max/Avg TT:12.5/20.7/16.1 min  Avg round trip time ~25 min  Data from weekdays – April, 2006  Similar costs $500 for 20 runs

23. Detector Locations - 217

24. I-205 N, Weekdays – October, 2006 traffic flow Detector spacing poor before mp 8

25. I-205 S, Weekdays – October, 2006 traffic flow Detector spacing poor after mp 8

26. Detector Locations I-205 SB Stark/Washington mp 20.34 SB Clackamas Hwy mp 12.67 SB Johnson Creek mp 16.24

27. I-205 Notes Detector spacing is poor for mileposts 0-8  Do not collect data on that portion of 205 – means can not capture the congestion that occurs there  Consider collecting mp 13 – mp 20 Congestion:  Some congestion on northern end of I-205 NB and SB, AM and PM peaks  NB AM congestion appears worst Detector Spacing:  See Map Data Quality  I-205 NB - ~1% disqualified data  I-205 SB - ~2% disqualified data

28. I-5 N Weekdays – October, 2006 traffic flow

29. I-5 S, Weekdays – October, 2006 traffic flow

30. Detector Locations I-5 S of Downtown NB, Nyberg, mp 289.4 NB, Macadam, mp 299.7

31. Detector Locations I-5 N of Downtown NB, Macadam, mp 299.7 SB, Swift/Marine, mp 307.35

32. I-5 Notes Congestion:  N of Portland: SB congestion in AM and PM peaks, NB congestion PM peak  S of Portland: Minimal SB congestion, NB congestion through curves in AM peak  NB PM congestion (going over the bridge) appears worst  More severe congestion than 205 Detector Spacing:  Variable - See Map Data Quality  I-5 NB - ~2% disqualified data  I-5 SB - ~4% disqualified data

33. Other States – Best Practices…

34. Milwaukee, WI Detector Spacing  0.25 miles in urban areas  2 miles in rural areas Data from detectors transmitted to TOC Center Freeway Traffic Management System (FTMS) Server  Travel Time = Known Distance/Average Speed Website updated every 3 minutes DMS signs updated every 1 minute No probe vehicle data; all detector derived travel times

35. San Antonio, TX Travel Times calculated from/to major interchanges Detectors  Loop Detectors  Video Detectors Point travel speeds used to calculate travel times from detector to detector  Segment travel speed is the lower of u/s and d/s speed  Point to point travel times are summation of segment travel times Travel times posted on TransGuide website use the same algorithm

36. Chicago, IL  DMS Travel Times From three sources (IPASS, RTMS, Loops)  GCM Webpage Only IPASS travel times  IPASS Data Travel times from toll plaza to toll plaza Based on toll transponder data collected by ETC system > 1.5 million users on tollways Significant number of probe vehicles provide time stamp and location Travel times calculated using location and time stamp information High quality of data  RTMS Data  IDOT Loop detector data

37. Houston, TX Vehicle Probes with transponder tags Readers collect data as vehicles pass  2-3 miles apart  Time  Location of probe Software  Average Speeds  Average Travel Times  Transtar website  DMS Updated every 10 minutes

38. Nashville, TN RTMS detectors  0.25 mile spacing  Speeds  Ensure data quality by regular calibration  CCTV cameras Travel Time verification Data Collection & Processing  Average speed from RTMS every 2 minutes  Travel time calculation average speed and distances between sensors  Travel Times automatically posted to the DMS by TMC software  Travel Times are only reported for segments < 5 miles

39. Atlanta, GA VDS Cameras  Monitoring and Video Detection Cameras  Fixed black and white cameras  Placed along all major freeways  Provide volumes and speeds Travel Times between 6 a.m. – 9 p.m.  Average speeds from Video Detection Cameras Software  Automatic message generation for DMS

40. San Francisco, CA Existing Caltrans System  Dual Loop Detectors Speeds New MTC System  Antennas to read FasTrak Toll Tags  Average Travel Times and speeds of all vehicles 511 System  Combination of data from both sources to calculate travel times

41. Other Cities www.smartroute.com  Real Time Traveler Information Boston Miami St. Louis North Carolina New Jersey

42. Summary Two main approaches for generating travel times  In house Loop Detectors  High Density (0.25 mile spacing) Video Detectors RTMS Toll Tags  Private providers Smartroute Systems Inrix

43. Schedule Update…