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KAI - Arterial Performance Measures 02-03-10
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KAI - Arterial Performance Measures 02-03-10

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This presentation focuses on arterial performance measures, reviewing two successful case studies: …

This presentation focuses on arterial performance measures, reviewing two successful case studies:

- KAI’s validation of Bluetooth MAC readers and their measurement of signal timing changes using MAC readers along Tualatin-Sherwood Road
- Purdue University’s research, led by Dr. Darcy Bullock to field measure quality of signal timing offsets and vehicle arrivals on green versus red using local controller software
- Peter Koonce provided an overview of arterial performance within the City of Portland and a regional vision for next steps, particularly focused on multi-modal and emergency management applications of the arterial data currently collected and to be collected in the future.

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  • Traditional = Bus AVL, manual TT or OD Next Gen = Bluetooth, Adv. Signal Software, Cellular, Freight AVL, Wireless Magnetometers/Loop Signature
  • Transcript

    • 1. Arterial Performance Measures Workshop Transportation Education Series Hosted by Kittelson & Associates, Inc. February 4 th , 2010 Peter Koonce, P.E.; City of Portland Dr. Darcy Bullock, P.E.; Purdue University Shaun Quayle, P.E.; Kittelson & Associates, Inc.
    • 2. Presentation Overview
      • Defining Arterial Performance
      • Tualatin-Sherwood Road Probe Data Example
      • Purdue University Arterial Performance Research Case Study
      • Regional Perspective, where to go from here?
      • Discussion
    • 3. Arterial Performance Big Picture
      • Why monitor arterial performance?
        • Active Response
        • Optimize & react!
        • $$$ & environmental benefits
      • Many forms of data
        • Point-based vs. Corridor-based
      • Many ways of collecting it
        • Traditional vs. next generation data & monitoring
    • 4. Tualatin-Sherwood Probe Data Study
      • MAC Reader Technology Overview
      • Study Background
      • Study Focus
        • Validate MAC Readers compared to Manual Methods
        • MAC-based travel time comparison
          • Impact of spring break
          • Impact of signal timing changes
    • 5. Tualatin-Sherwood Road Study Area
      • Tualatin-Sherwood Road (SW Portland Suburbs)
      • ~2.5 miles in length
      • ~40,000 ADT – 4 lane commercial arterial
      MAC reader locations AVERY STREET FRED MEYER ACCESS NYBERG / 65th MANUAL FLOATING CAR TRAVEL TIME RUNS
    • 6. MAC Reader Technology Overview
      • Media Access Control (MAC) = unique identifier by manufacturer, 48 bit (>28 trillion) characters
      • Bluetooth TM = common name for wireless radio frequency communication protocol between electronic devices
      00:1E:3D:AF:DA:C5 Auto Pedestrian Bike Bus/LRT TEXT FILE MS ACCESS FILE PERSONAL DEVICE FIELD RADIO & ANTENNA MINI COMPUTER (IN CABINET) OUTPUT KAI CUSTOM SOFTWARE Truncated MAC Address
    • 7. MAC Reader Background – Address Matching STATION 1 STATION 2 First to First First to First Last to Last Last to Last
    • 8. MAC Probe Technique Benefits
      • Higher amounts of collected data
        • Highway & arterial travel times, running speeds, and origin-destination
        • Off-peak & weekend; 7 days a week
      • Permanent or temporary deployment
        • Future communications infrastructure integration
      • Cost Effectiveness
        • Easily attainable data for relatively low costs compared to existing technologies and techniques
          • Compare to GPS travel time runs
          • Compare to license plate surveys
    • 9. MAC Probe Data Opportunities
      • Arterial performance measurement
        • Corridor studies and signal timing updates
        • Real-time measurement  ITS
        • Traveler information dissemination
      • Travel demand models
        • Origin-destination sample data
        • Possible mode evaluation & route selection
      • Validating/complementing other technologies
        • System detectors
        • Advanced signal software MOE’s
        • Transit AVL / Transit Signal Priority
    • 10. MAC Probe Data Challenges
      • Travel Time Outliers (pass-by trips, peds, etc.)
        • Good data vs. bad data
        • “ Average” travel times
        • Multiple modes
      • Fidelity
        • Data is macroscopic
        • No “stop” data
      • Strategic placement
        • Influences of variable traffic conditions
        • Mid-block is preferred, but not always available
    • 11. MAC Reader Corridor Test 1
      • Objectives
        • Compare manual vs. MAC travel times
        • Prove longer-term field concept
      • Manual floating car GPS travel time runs
        • Approximately 12 two-way trips (3 drivers)
        • Sample of ~<1%, only in peak hours
        • Wednesday, 2/11/09
      • MAC Reader Technology Deployment
        • 24 hours/7 days a week
        • 9 days (Tuesday, 2/10/09 to Thursday, 2/19/09)
        • Capture rate ~ 3 to 4% of ADT
    • 12. Travel Time Profile (~9 days)
    • 13. Travel Time: Manual vs. MAC Manual trend shows increasing travel time, while MAC shows end of congestion MAC points match manual points
    • 14. Travel Time Profile (2 days) ?
    • 15. Large sample size during peak and off-peak Free 95 105 100 Free Cycle Length (sec) 105
    • 16. MAC Readers to Measure Signal Timing Impacts
      • February travel times confirmed issues with signal timing
      • Signal Timings Modified
        • Correct RR preempt recovery routing
        • Bring a signal back into coordination due to communications issue
      • Redeploy MAC readers in March to gauge impact of changes
    • 17. Before (Orange) and After (Blue) Timing Change
      • Significant reduction in travel time and variability
      Westbound Eastbound
    • 18. Before (Orange) and After (Blue) Timing Change Westbound
    • 19. Impact of Signal Timing Change 0.2 0.7 -4 -23 Difference 18.8 19.4 366 336 March 19.0 20.1 371 360 February PM 2.9 4.3 -40 -57 Difference 23.9 25.1 291 278 March 21.0 20.8 332 335 February AM West East West East   Speed (mph) Travel Time (sec.)  
    • 20. MAC Travel Time  Incident Tracking Suspected Incidents Travel Time (s) Travel Speed (mph)
    • 21. MAC Probe Data – Origin-Destination Sampling
      • Regional travel demand model comparisons
      • Route selection
      • Progression analyses
    • 22. MAC Probe Data vs. Automatic License Plate
      • Approximately within 5%
      Figure 8: Station 1-6 O-D ANPR and MAC Data (Wednesday 18 th of September 2009)
    • 23. MAC Probe Data – Origin-Destination Sampling
    • 24. MAC Probe Data – Origin-Destination Sampling
    • 25. KAI Probe MAC Data Projects
    • 26.
      • Start Darcy’s Presentation
    • 27. Arterial Signal Based Data - Voyage
      • Insert NWS Graphic
    • 28. Regional Perspective on Arterial Performance
      • Operations monitoring
      • Real-time traveler information and routing choices
      • Multimodal applications
      • Policy assessments
    • 29. Operations monitoring
      • Application on key corridors to aid decision making
        • Prioritizing annual signal retiming
        • Managing the arterial street system
          • Special or weather events
    • 30. State of the Practice: Cameras
      • Cameras at key locations
        • Provides data, not information
        • Point data
        • Not always useful
      • Linked to action?
        • Sometimes
    • 31. State of the Practice: Controller Data
    • 32. Real-time Traveler Information
      • Comprehensive traveler information on arterials would yield routing decisions for all users
      Planning mode choice (travel time estimates)
    • 33. Real-time routing for emergency services
      • During events we can use information to:
        • Manage emergency routes/services
        • Modify transit schedules
        • Change traffic signal timing and lane use assignment (Mall)
        • Dispatch snow equipment
      • During emergency response we can provide information to:
        • Reduce response time
        • Enable communication regarding railroad status
    • 34. Multimodal applications
      • By collecting information with MAC Addresses, we collect data from all modes
        • People on buses, bikes, in their cars, pedestrians
      • Data comparison (Arterial data fusion) will inform strategic investments
    • 35. Discussion?
    • 36. Thank you for attending/Contact Information
      • Peter Koonce
      • Portland Bureau of Transportation
      • [email_address]
      • Shaun Quayle
      • Kittelson & Associates, Inc.
      • [email_address]
      • Darcy Bullock
      • Purdue University
      • [email_address]