1. Congestion Management Innovations in Oregon Christopher Monsere Assistant Professor Portland State University Civil and Environmental Engineering Director, Intelligent Transportation Systems Laboratory ITS

2. Outline Portland, Oregon Regional Approach Freeway Performance Arterial Performance Environmental Performance

3. Portland, Oregon - USA

5. Population 2.2 million

6. A Regional Approach TransPort ITS Coordinating Committee

7. PORTAL -- The Portland Region’s Archived Data User Service (ADUS)

8. What’s in the PORTAL Database? Loop Detector Data 20 s count, lane occupancy, speed from 500 detectors (1.2 mi spacing) Incident Data 140,000 since 1999 Weather Data Every day since 2004 VMS Data 19 VMS since 1999 Days Since July 2004 About +700 GB 6.9 Million Detector Intervals Bus Data 1 year stop level data 140,000,000 rows 001590 WIM Data 22 stations since 2005 30,026,606 trucks Crash Data All state-reported crashes since 1999 - ~580,000

9. Freeway Performance

10. Performance Measures Used Volume Speed Occupancy Vehicle Miles Traveled Vehicle Hours Traveled Travel Time Delay Reliability

11. Interstate 5 Northbound About 38.6 kilometers

12. Lyman and Bertini, 2007

14. Systematically Identifying Bottlenecks

17. Arterial Performance

18. Objective  Develop an automated way to report  Speeds  Travel times  Performance measures  Using  Existing ITS signal infrastructure  Automatic Vehicle Locator (AVL) data

19. Speed Map Generated from TriMet Bus AVL System Data Only

20. ITS Midpoint Method Using 5-Minute Data Signalized Intersections

21. ITS Adjust Influence Areas Manually Signalized Intersections

22. ITS Bus Data Confirms Adjustment Signalized Intersections

23. ITS Reveals Gaps in Detection Signalized Intersections

24. ITS New Occupancy Map From Combined Sources Signalized Intersections

25. ITS An Improvement Over Mid-Point Method Signalized Intersections

26. ITS Obstacles System Signal Detector – Very Limited Aggregation – Access to Real Time Data – Limited Detection & Spacing Bus – Access to Real Time Data

27. ITS Next Step System Signal Detector – Cycle level data (Gresham, OR – SCATS) Bus – TriMet Buses Can Be Probes – Extensive Network Coverage – Opportunity to Evaluate Multiple Routes on Same Arterial

28. Glossary MAC Address: a 48 bit (>28 trillion) unique address assigned to a device by its manufacturer. Bluetooth: a wireless protocol utilizing short-range communications technology facilitating data transmission over short distances from fixed and/or mobile devices Class Maximum Power Operating Range Class 1 100mW (20dBm) 100 meters Class 2 2.5mW (4dBm) 10 meters Class 3 1mW (0dBm) 1 meter

29. Estimated Travel Time Example Not always a trivial distinction…some thought needs to be given to geometrics/physics

30. Powell Blvd Corridor Bluetooth reader locations

31. Travel Times (13 th 53 rd ) Eastbound TT (Min) West bound TT (Min)

32. Environmental Performance

33. Arterial Fusion Project Create framework to fuse – Bus Probe Data – Matched Vehicle Probe Data – Adaptive Signal System Data – Private Sector Data? In to one complete picture

34. Sustainability Performance Measures Using Archived ITS Data: 1.Emissions Estimates 2.Fuel Consumption 3.Cost of Delay 4.Person Mobility (PMT, PHT, PHD)

35. Emissions Measure Methodology MOBILE inputs generated from PORTAL and gathered local dataMOBILE model run for locations and time periods of interestMOBILE output database processed to establish emissions rates Emissions rates combined with PORTAL travel data (VMT) to determine freeway segment emissions

36. Hourly CO 2 Estimate I-5 MP 302.5 (1.4 mile section)

37. CO Emissions From Congestion I-5 MP 302.5 (1.4 mile section)

38. Acknowledgments R.L. Bertini - ITS Lab and PORTAL founder Colleagues – Kristin Tufte, Miguel Figliozzi, Ashley Haire, Portland State University Peter Koonce, Shaun Quayle Kittelson and Associates Darcy Bullock, Purdue University Willie Rotich and Paul Zabell, Portland Bureau of Transportation Sponsors - National Science Foundation Oregon Department of Transportation Federal Highway Administration TransPort ITS Coordinating Committee City of Portland, Office of Transportation TriMet Oregon Engineering and Technology Industry Council Students

39. References MAC Address Tracking Wasson, J.S., J.R. Sturdevant, D.M. Bullock, “Real-Time Travel Time Estimates Using MAC Address Matching,” Institute of Transportation Engineers Journal, ITE, Vol. 78, No. 6, pp. 20-23, June 2008. Bullock, D.M., C.M. Day; J.S. Sturdevant, ”Signalized Intersection Wasson J.S., S.E. Young, J.R. Sturdevant, P.J. Tarnoff, J.M. Ernst, and D.M. Bullock,, “Evaluation of Special Event Traffic Management: The Brickyard 400 Case Study,” under review. Cycle by cycle and Movement based Performance Measures Performance Measures for Operations Decision Making,” Institute of Transportation Engineers Journal, ITE, Vol. 78, No. 8, pp. 20-23, August 2008. Hubbard, S.M.L., D.M. Bullock, and C. Day “Opportunities to Leverage Existing Infrastructure To Integrate Real-Time Pedestrian Performance Measures Into Traffic Signal System Infrastructure,” Paper ID: 08-1392, submitted July 2007, revised October 2007, in press. Day, C., E. Smaglik, D.M. Bullock, and J. Sturdevant, ”Quantitative Evaluation of Actuated Versus Nonactuated Coordinated Phases,” Paper ID: 08-0383, submitted July 2007, revised October 2007, in press. Smaglik E.J., A. Sharma, D.M. Bullock, J.R. Sturdevant, and G. Duncan, “Event-Based Data Collection for Generating Actuated Controller Performance Measures," Transportation Research Record, #2035, TRB, National Research Council, Washington, DC, pp.97-106, 2007.

40. ITS Thank You! www.its.pdx.edu

41. Extra slides – no translation past this slide

42. MOBILE 6.2 1.New facility-specific drive cycles recorded in modern American cities 2.Updated vehicles, emissions rates, regulatory programs, and driver behaviors 3.Fuel consumption and CO 2 estimates not speed- dependent (only based on fuel and fleet data) 4.Non-specified parameters default to national averages (many county-specific data available from the EPA) Improvements and caveats

43. Average Speed Emissions Models Model Development Process: Record Drive Cycles Probe vehicles on complete trips Representative set of conditions Key to accuracy of model Test Vehicles Run vehicles through drive cycles on a dynamometer Representative set of vehicles from roadway fleet Important to capture range of conditions, size, age, etc. Avg. Speed Emission Rates Link emissions to vehicle classes at average drive cycle speeds Facility-specific drive cycles can capture congestion effects Calculate Emissions with rates and travel Uses VMT and emissions rates Emissions rates can be modified by other inputs (weather, fuel programs, etc.)