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Congestion Management Innovations in Oregon

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

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Congestion Management Innovations in Oregon

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

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

  3. Portland, Oregon - USA

  4. Portland, Oregon - USA

  5. Portland, Oregon - USA 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 Bus Data 1 year stop level data 140,000,000 rows Weather Data Every day since 2004 001590 Days Since July 2004 About +700 GB 6.9 Million Detector Intervals VMS Data 19 VMS since 1999 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

  13. Lyman and Bertini, 2007

  14. Systematically Identifying Bottlenecks

  15. Systematically Identifying Bottlenecks

  16. 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. Midpoint Method Using 5-Minute Data Signalized Intersections

  21. Adjust Influence Areas Manually Signalized Intersections

  22. Bus Data Confirms Adjustment Signalized Intersections

  23. Reveals Gaps in Detection Signalized Intersections

  24. New Occupancy Map From Combined Sources Signalized Intersections

  25. An Improvement Over Mid-Point Method Signalized Intersections

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

  27. 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

  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(13th <-> 53rd ) 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: • Emissions Estimates • Fuel Consumption • Cost of Delay • Person Mobility (PMT, PHT, PHD)

  35. Emissions Measure Methodology

  36. Hourly CO2 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. Thank You! www.its.pdx.edu

  41. Extra slides – no translation past this slide

  42. MOBILE 6.2 Improvements and caveats • New facility-specific drive cycles recorded in modern American cities • Updated vehicles, emissions rates, regulatory programs, and driver behaviors • Fuel consumption and CO2 estimates not speed-dependent (only based on fuel and fleet data) • Non-specified parameters default to national averages (many county-specific data available from the EPA)

  43. Average Speed Emissions Models • Model Development Process:

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