1. 1 Using Automatic Vehicle Location Data to Determine Detector Placement Robert L. Bertini, Christopher Monsere, Michael Wolfe and Mathew Berkow Portland State University 2008 CITE District and Quad Regional Conference April 28, 2008 Using Automatic Vehicle Location Data to Determine Detector Placement

2. 2 Objective  Develop an automated way to report speeds, travel times and performance measures using:  Existing ITS signal infrastructure  Automatic Vehicle Locator (AVL) data  Expand PORTAL to include arterial data  Develop an automated way to report speeds, travel times and performance measures using:  Existing ITS signal infrastructure  Automatic Vehicle Locator (AVL) data  Expand PORTAL to include arterial data

3. 3 Using Automatic Vehicle Location Data to Determine Detector Placement Selected Arterial Performance Measures

4. 4 Using Automatic Vehicle Location Data to Determine Detector Placement Inspiration – Signal System Data Only

5. 5 Using Automatic Vehicle Location Data to Determine Detector Placement Inspiration – Bus AVL System Data Only I-5 I-205 US 26 SR 217 Powell Blvd.

6. 6 Using Automatic Vehicle Location Data to Determine Detector Placement This Project: Combine Signal and Bus AVL

7. 7 Using Automatic Vehicle Location Data to Determine Detector Placement Signal System Data

8. 8 Using Automatic Vehicle Location Data to Determine Detector Placement DD Signal System Data: Portland’s Detection Infrastructure Data Aggregation  Count Station 5 min  Other Detector 15 min  7 Day Sample

9. 9 Using Automatic Vehicle Location Data to Determine Detector Placement Case Study: Barbur Blvd. Speed Map Sheridan Hooker Hamilton 3rd Terwilliger Bertha 19th I-5 Off-ramp 30th Park & Ride N

10. 10 Using Automatic Vehicle Location Data to Determine Detector Placement Detectors at Barbur and Bertha

11. 11 Using Automatic Vehicle Location Data to Determine Detector Placement Density vs. Occupancy   Density = number of vehicles per distance   Occupancy = percent of time with a vehicle on the sensor   Density = Occupancy X 1/(vehicle length + sensor length) Density = 2 vehicles / 45 feet =.044 45 ’ 12 ’ 6’6’ Density =.80 * 1 / (12 + 6) =.044 Occupancy = 80%

12. 12 Using Automatic Vehicle Location Data to Determine Detector Placement Flow vs. Occupancy: 5 Minute Data

13. 13 Using Automatic Vehicle Location Data to Determine Detector Placement 5 Minute Speed and Occupancy (at Hamilton)

14. 14 Using Automatic Vehicle Location Data to Determine Detector Placement Barbur Northbound Contour Plot

15. 15 Using Automatic Vehicle Location Data to Determine Detector Placement AM Peak Speed Map From Detector Data

16. 16 Using Automatic Vehicle Location Data to Determine Detector Placement Conclusions   Lack of Access to Real Time Data   Limited Detection   Very Limited Aggregation (5 Minute Won’t Work)   Detector Spacing   Lack of Access to Real Time Data   Limited Detection   Very Limited Aggregation (5 Minute Won’t Work)   Detector Spacing

17. 17 Using Automatic Vehicle Location Data to Determine Detector Placement Bus AVL System Data

18. 18 Using Automatic Vehicle Location Data to Determine Detector Placement TriMet Archived AVL Data  Route Number  Vehicle Number  Service Date  Actual Leave Time  Scheduled Stop Time  Actual Arrive Time  Operator ID  Direction  Trip Number  Bus Stop Location  Dwell Time  Door Opened  Lift Usage  Ons & Offs (APCs)  Passenger Load  Maximum Speed on Previous Link  Distance  Longitude  Latitude

19. 19 Using Automatic Vehicle Location Data to Determine Detector Placement 0 0.5 1 1.5 2 2.5 3 1:04:00 PM1:09:00 PM1:14:00 PM Time Distance (miles) Ross Island Bridge Test Vehicle Bus Hypothetical Bus Pseudo Bus Modified Pseudo Bus Bus Hypo Pseudo Modified Pseudo Vehicle Powell Blvd. Corridor Study

20. 20 Using Automatic Vehicle Location Data to Determine Detector Placement Building on Powell Blvd. Study  Begin with limited signal system data.  Gather archived TriMet AVL data.  Merge two data sources to examine synergies due to data fusion.  Use AVL data to calibrate influence areas from loops.

21. 21 Using Automatic Vehicle Location Data to Determine Detector Placement Buses Inform Detector Readings – 2/12/07

22. 22 Using Automatic Vehicle Location Data to Determine Detector Placement Buses Inform Detector Readings – 2/15/07

23. 23 Using Automatic Vehicle Location Data to Determine Detector Placement Midpoint Method Using 5-Minute Data

24. 24 Using Automatic Vehicle Location Data to Determine Detector Placement Adjust Influence Areas Manually

25. 25 Using Automatic Vehicle Location Data to Determine Detector Placement Bus Data Confirms Adjustment

26. 26 Using Automatic Vehicle Location Data to Determine Detector Placement Reveals Gaps in Detection

27. 27 Using Automatic Vehicle Location Data to Determine Detector Placement New Occupancy Map From Combined Sources

28. 28 Using Automatic Vehicle Location Data to Determine Detector Placement An Improvement Over Mid-Point Method

29. 29 Using Automatic Vehicle Location Data to Determine Detector Placement Average Link Travel Times Northbound - Mean Travel Time Actual Hypo- theticalPseudo Modified Pseudo Loop Detectors Pseudo * 1.25 Signal + Bus Weekday Morning Peak (n = 38)15.316.117.719.8914.909.6410.79 Weekday Midday Off-Peak (n = 132)12.847.027.308.9411.099.127.97 Weekday Evening Peak (n = 46)13.837.957.809.5413.149.758.36 Northbound - Mean Speed Actual Hypo- theticalPseudo Modified Pseudo Loop Detectors Pseudo * 1.25 Signal + Bus Weekday Morning Peak (n = 38)17.6344.1835.0027.2918.1228.0025.03 Weekday Midday Off-Peak (n = 132)21.0338.4537.0130.2124.3429.6033.87 Weekday Evening Peak (n = 46)19.5233.9434.6028.3020.5527.6832.29

30. 30 Using Automatic Vehicle Location Data to Determine Detector Placement Average Link Travel Times – AM Peak Travel Time Observations (95% CI) Link Travel Time (Min) ActualActualHypoHypoPseudoPseudo Mod. Pseudo Loop Detectors Psuedo * 1.25 Signal-BusSignal-Bus

31. 31 Using Automatic Vehicle Location Data to Determine Detector Placement Conclusions and Next Steps   TriMet Buses Can Be Probes   Detailed AVL Data (Stop Level) Not Available in Real Time (?)   No Access to Real Time Data (Transit Tracker)   Travel Times Limited by Detector Data   TriMet Buses Can Be Probes   Detailed AVL Data (Stop Level) Not Available in Real Time (?)   No Access to Real Time Data (Transit Tracker)   Travel Times Limited by Detector Data

32. 32 Using Automatic Vehicle Location Data to Determine Detector Placement Acknowledgements   TransPort Members   FHWA: Nathaniel Price   ODOT: Galen McGill   PSU & OTREC (Local Matching Funds)   City of Portland: Bill Kloos, Willie Rotich   TriMet: David Crout, Steve Callas   JPACT and Oregon Congressional Delegation   ITS Lab: John Chee, Rafael Fernandez   TransPort Members   FHWA: Nathaniel Price   ODOT: Galen McGill   PSU & OTREC (Local Matching Funds)   City of Portland: Bill Kloos, Willie Rotich   TriMet: David Crout, Steve Callas   JPACT and Oregon Congressional Delegation   ITS Lab: John Chee, Rafael Fernandez

33. 33 Using Automatic Vehicle Location Data to Determine Detector Placement Thank You! www.its.pdx.edu