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Darcy Bullock Presentation 2-3-10

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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.

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.

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  • Now we’ll show a couple applications for progression diagrams… These are based on our testbed at SR 37 in Noblesville. First, we’ll use progression diagrams to observe the impact of a controller failure…. In this case, one of the controllers in the arterial system failed and was swapped out with an older controller with an incorrect plan programmed.
  • Developing weekend plans is a challenge for most agencies because of the need to collect data on weekends. In this case, on SR 37 the weekend plan has not been updated for quite some time… so we felt it would be a good idea to take a look and see if there were any opportunities for improving it. Here are the progression diagrams… (talk through good/bad) Good progression at x, y, z shown by the distribution of vehicles coinciding with the green band. -Poor progression at a,b,c illustrated by the distribution of vehicles coinciding with the red. We have random arrivals at the northern entry point into the system… we also see what appear to be random arrivals occurring at 37 and Pleasant.
  • Here’s a closer look at the three coord phases with poor quality of progression. Each of these shows a clear example of a case where we could improve the offsets. The vehicles appear to arrive in regular platoons, but those platoons are arriving during the red phase.
  • To illustrate this concept in detail, we will focus on the 2 nd and 3 rd intersections on SR 37.
  • Here’s an example where the approach on one end of the intersection has very good progression (POG = 80%), while the other on the opposite has rather poor progression (40%). Although we can change the offset at either intersection, there is only one relative offset between the two intersections that strongly influences the progression quality at both intersections. This is a classic example of a case where a tradeoff has to be made between one direction and the other… We’ll use progression diagrams to estimate what will happen as we change the offset… keeping track of total vehicle arrivals on green as the quantity we are trying to maximize.
  • Now that we have found some optimal offsets, we can use progression diagrams to estimate what the impact will be. First, we’ll put up the current weekend offsets and the actual time when vehicles are arriving during cycle for each intersection.
  • If we adjust the arrival times of vehicles according to the proposed offsets, this is the estimated impact on the system. (flip back and forth) As we can see here, we expect these adjustments to cause the coord platoons that are currently arriving in red to be shifted into green. The other offsets that were formerly good have not been negatively affected. In this case, we predict that southbound at 37/Town and Country will suffer a little, but it should be offset by substantial improvements at the other approaches.

Transcript

  • 1. Operational Performance Measures and Outcome Based Assessment for Arterial Management using High Resolution Controller Data and Bluetooth Probes
    • Jay Wasson & Jim Sturdevant, Indiana Department of Transportation
      • Chris Day, Ross Haseman, Tom Brennan & Darcy Bullock Purdue University School of Civil Engineering
    A Picture Book Approach to Traffic Engineering
  • 2. References
    • Day, C.M., R.J. Haseman, H. Premachandra, T.M. Brennan, J.S. Wasson, J.R. Sturdevant, and D.M. Bullock, “Visualization and Assessment of Arterial Progression Quality Using High Resolution Signal Event Data and Measured Travel Time,” Transportation Research Board Paper ID:10-0039, January 2010.
    • Bullock, D.M., R.J. Haseman, J.S. Wasson, and R. Spitler, “Anonymous Bluetooth Probes for Airport Security Line Service Time Measurement: The Indianapolis Pilot Deployment,” Transportation Research Board Paper ID:10-1438, January 2010.
    • Haseman, R.J., J.S. Wasson, and D.M.Bullock, “Real Time Measurement of Work Zone Travel Time Delay and Evaluation Metrics,” Transportation Research Board Paper ID:10-1442, January 2010.
    • Day, C.M., J.R. Sturdevant, and D.M. Bullock, “ Outcome Oriented Performance Measures for Signalized Arterial Capacity Management ,” Transportation Research Board Paper ID:10-0008, January 2010.
    • 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.
    Multimodal with vehicle, shuttle, and ped. modes
  • 3. Multi-scale Performance Measure Concepts
    • Travel Time is a important performance measure for both arterials and freeways
    • Travel Time data can identify transportation links with performance challenges
    • High resolution traffic signal controller data is needed to identify corrective actions
  • 4. Seminar Message ~250,000 Traffic Signals…We need systematic procedures for identifying operational problems…and fixing them.
  • 5. Objectives
    • Understand how to leverage consumer electronics (Bluetooth) to measure link travel time to identify arterial problems
    • Understand how to analyze traffic signal controller data to identify corrective actions
    • Understand how to verify that corrective actions improved arterial performance .
  • 6. Traffic Signal Timing Process Today’s Talk
  • 7. Learning Objectives
    • Understand how to leverage consumer electronics (Bluetooth) to measure link travel time to identify arterial problems . These probe data concepts can be applied to othre technologies (Toll Tags, Signatures…)
    • Understand how to analyze traffic signal controller data to identify corrective actions
    • Understand how to verify that corrective actions improved arterial performance .
  • 8. Concept
    • Bluetooth : a wireless protocol utilizing short-range communications technology facilitating data transmission over short distances from fixed and/or mobile devices
    • MAC Address : a 48 bit (>28 trillion) unique address assigned to a device by its manufacturer.
  • 9. I-65 Construction Zone Travel Times Mile Markers 228 to 235 (Lake, Newton, Jasper)
  • 10. Construction Zone Close Up Mile Markers228 to 235 Exit 230 Exit 240
  • 11. Easter Weekend: Southbound Example 40 min travel time sample ~MM 244 ~MM 220 ~40 minutes Observed at 18:50 Observed at 18:10
  • 12. Easter Weekend Southbound Travel Time (~24 miles) Normal Travel Time ~ 22 minutes Approximately 1 hour of delay Diversions on State/Local Roads
  • 13. Easter Weekend Northbound Travel Time (~24 miles) Approximately 1 hour of delay
  • 14. INDOT Sign/Data Collection Unit Wireless Link Bluetooth Sensor Solar Power
  • 15. Real Time Implementation Commercial Wireless Internet Access Commercial Wireless Internet Access SQL Database Query every 5 minutes for median MAC and Observation Time
  • 16. Travel Delay Notification Field Test Sunday, April 26 th , 2009
  • 17. Sample SR 32 Arterial Data SR 32 Instrumented Arterial from SR 238 to SR 37
  • 18.  
  • 19. SR 32 @ SR 238 Bluetooth Data Logger Ethernet Switch Bluetooth Antenna Econolite ASC 3 with Indiana Data Logger Enabled
  • 20. Probe Monitoring Stations Long Term Installation with Real-Time SQL Based Travel Time Calc Short Term Installation with Real-Time SQL Based Travel Time Calc Short Term Battery Powered Device (Traffax)..Data post processed
  • 21. Look at this Sample Size from SR 238 to SR 32
  • 22. Perhaps Opportunity to improve on Saturday Perhaps Opportunity to improve on Saturday
  • 23. Learning Objectives
    • Understand how to leverage consumer electronics (Bluetooth) to measure link travel time to identify arterial problems
    • Understand how to analyze traffic signal controller data to identify corrective actions
    • Understand how to verify that corrective actions improved arterial performance .
  • 24. Coordination: Split, Cycle , Offset Cycle
  • 25. Coordination: Split , Cycle, Offset Split
  • 26. Coordination: Split, Cycle, Offset Offset
  • 27. Coordination: Split, Cycle, Offset Cycle Split Offset
  • 28. Purdue Coordination Diagram Construction 0 sec 12:00:00 Cycle ends Green phase ends Green phase begins 120 sec 12:02:00 90 sec 50 sec 70 sec 12:01:10 120 90 50 70 12:02:00 12:01:10 Green Red Cycle boundary Green window Coordination Loop Detection time Cycle begins time of day Time in cycle 0 12:00:00
  • 29. Purdue Coordination Diagram (15 minutes) Green window Phase 6 is red while phases 7, 8 are served Phase 6 is red while phase 5 is served Phase 6 beginning of green Phase 6 end of green/ beginning of red
  • 30. c383 c384 c385 c386 c387 c388 c389 c390 c391 c392 c393 c394 c395 c396 g f e d c b h i a Phase 2 Green Clearance Phase 1 Phase 4 Phase 3 Phase 2 Red c397
  • 31. 24 Hour Overview 1 2 3 4 5 8 6 7 Timing Plan Pattern 20-pt. moving average b1 a1 a2 b2
  • 32. Perhaps Opportunity to improve on Saturday NB @ SR 238 NB @ Pleasant Better Progression
  • 33. Application and Case Study
  • 34. Saturday Offset Adjustment good bad bad bad good good Random arrivals No platoons SR 32 Pleasant Town & Country Greenfield
  • 35. Three Poor Offsets NB @ 37/Pleasant SB @ 37/Greenfield NB @ 37/Greenfield
  • 36. Offset Adjustments on Middle Segment good bad bad bad good good Random arrivals No platoons SR 32 Pleasant Town & Country Greenfield
  • 37. Offset Adjustments on Middle Segment
    • Northbound at 37/Pleasant is bad. The platoon is captured in red.
    • However, any offset adjustments that we make will also impact Southbound progression at 37/Town and Country (intersection to south) by shifting arrivals.
    • We can mitigate any impacts at 32/37 (intersection to north) by adjusting its offset to keep it fixed relative to 37/Pleasant.
    NB @ 37/Pleasant SB @ 37/Town and Country POG = 40.1% POG = 80.2% 5069 arrivals on green (0600-2200)
  • 38. Add 10 seconds at 37/Pleasant
    • Green times will occur 10 seconds earlier at 37 & Pleasant
      • Equivalent to vehicles arriving 10 seconds later
    • Southbound vehicles will arrive 10 seconds earlier at 37 & Town and Country
    NB @ 37/Pleasant SB @ 37/Town and Country POG = 55.4% POG = 77.8% 5589 arrivals on green
  • 39. Add 20 seconds at 37/Pleasant
    • Green times will occur 20 seconds earlier at 37 & Pleasant
      • Equivalent to vehicles arriving 20 seconds later
    • Southbound vehicles will arrive 20 seconds earlier at 37 & Town and Country
    NB @ 37/Pleasant SB @ 37/Town and Country POG = 67.4% POG = 68.8% 5688 arrivals on green
  • 40. Add 30 seconds at 37/Pleasant
    • Green times will occur 30 seconds earlier at 37 & Pleasant
      • Equivalent to vehicles arriving 30 seconds later
    • Southbound vehicles will arrive 30 seconds earlier at 37 & Town and Country
    NB @ 37/Pleasant SB @ 37/Town and Country POG = 73.4% POG = 57.5% 5446 arrivals on green
  • 41. Comparison with original (actual “before” case) good bad bad bad good good SR 32 Pleasant Town & Country Greenfield
  • 42. Predicted Vehicle Distributions with Offset Adjustments SR 32 Pleasant Town & Country Greenfield Unchanged Better Better Better Still OK Still OK
  • 43. So..did it have an impact.
  • 44. Learning Objectives
    • Understand how to leverage consumer electronics (Bluetooth) to measure link travel time to identify arterial problems
    • Understand how to analyze traffic signal controller data to identify corrective actions
    • Understand how to verify that corrective actions improved arterial performance .
      • Signal Data
      • Travel Time Data
  • 45. Before
  • 46. After
  • 47. Predicted
  • 48. Change in Arrivals on Green
  • 49. Learning Objectives
    • Understand how to leverage consumer electronics (Bluetooth) to measure link travel time to identify arterial problems
    • Understand how to analyze traffic signal controller data to identify corrective actions
    • Understand how to verify that corrective actions improved arterial performance .
      • Signal Data
      • Travel Time Data
  • 50. Before (Sample size=4797)
  • 51. After (Sample size=5401)
  • 52. Lets Statistically Evaluate the Impact
  • 53. NB: June 6, 2009 0900-1200 Travel Time Histograms Travel Time Bin (Minutes)
  • 54. NB: July 18, 2009 0900-1200 Travel Time Histograms Travel Time Bin (Minutes)
  • 55. Before (6/20/09) After (7/25/09) ~1 min Travel Time Reduction
  • 56. Business Case: SR 37 Timing Improvements (Largest Cost Benefit/Reduction/Avoidance)
    • USER SAVINGS
    • Travel time tests for SR37 Corridor have improved Northbound Travel Time by ~ 1 Minute.
    • ~8,500 Cars per Day Are Effected by this benefit (NB).
    • ~0.17 Cents per minute ($10/hour) saved for each driver in fuel costs and time value.
    • ~1.0 Minutes are assumed saved on average for the intersection over 1-Year with improvements.
    • User benefit =
    • (8,500 Veh/Day * $0.17/min * 1-min/Veh * 2*52 Days/Year )= $150,000/year for a 1.6 mile stretch of roadway is realized.
    0600 -2200 (8,500 veh)
  • 57. Assessing Deployments is Essential and Possible….we just have to do it
  • 58. Closing message
    • Let’s stop modeling what we can measure .
    • Let’s continue to enhance controllers to help us manage our systems.
    • Let’s close the “system performance measurement” loop.
    • It is essential that university collaborate with agencies & vendors
      • Very grateful for support from INDOT and Econolite
  • 59. References
    • Day, C.M., R.J. Haseman, H. Premachandra, T.M. Brennan, J.S. Wasson, J.R. Sturdevant, and D.M. Bullock, “Visualization and Assessment of Arterial Progression Quality Using High Resolution Signal Event Data and Measured Travel Time,” Transportation Research Board Paper ID:10-0039, January 2010.
    • Bullock, D.M., R.J. Haseman, J.S. Wasson, and R. Spitler, “Anonymous Bluetooth Probes for Airport Security Line Service Time Measurement: The Indianapolis Pilot Deployment,” Transportation Research Board Paper ID:10-1438, January 2010.
    • Haseman, R.J., J.S. Wasson, and D.M.Bullock, “Real Time Measurement of Work Zone Travel Time Delay and Evaluation Metrics,” Transportation Research Board Paper ID:10-1442, January 2010.
    • Day, C.M., J.R. Sturdevant, and D.M. Bullock, “ Outcome Oriented Performance Measures for Signalized Arterial Capacity Management ,” Transportation Research Board Paper ID:10-0008, January 2010.
    • 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.
    Multimodal with vehicle, shuttle, and ped. modes
  • 60. Related Performance Measure Activities
    • Railroad Preempted Intersection Performance Measures (May 2007-present study).
    • Multimodal performance measures in airport facilities (May 2009 study).
    • Performance Measures to prioritize intersections with phase capacity problems for operational intervention (previous talks at KAI in 2008 and 2009).
  • 61. Operational Performance Measures and Outcome Based Assessment for Arterial Management using High Resolution Controller Data and Bluetooth Probes
    • Jay Wasson & Jim Sturdevant, Indiana Department of Transportation
      • Chris Day, Ross Haseman, Tom Brennan & Darcy Bullock Purdue University School of Civil Engineering
    A Picture Book Approach to Traffic Engineering
  • 62.  
  • 63. EXTRA
  • 64. January 23 Observed NB SB 32/37 37/Pleasant 37/T&C 37/Greenfield 32/37 37/Pleasant 37/T&C 37/Greenfield
  • 65. Observed – June 6, 2009 0 24 8 4 12 16 20 0 24 8 4 12 16 20 0 24 8 4 12 16 20 0 24 8 4 12 16 20 140 0 105 70 35 0 24 8 4 12 16 20 0 24 8 4 12 16 20 0 24 8 4 12 16 20 0 24 8 4 12 16 20 140 0 105 70 35 0 100 50 25 75 114 0 100 50 25 75 114 0 100 50 25 75 114 0 100 50 25 75 114 0% 100% 50% 75% 25% 3% 0% 2% 1% 0 100 50 25 75 114 0 100 50 25 75 114 0 100 50 25 75 114 0 100 50 25 75 114 0% 100% 50% 75% 25% 3% 0% 2% 1% Time in Cycle Time in Cycle Time in Cycle Time in Cycle Time in Cycle Time in Cycle Time in Cycle Time in Cycle Hour Hour Hour Hour Hour Hour Hour Hour Probability of Green Time in Cycle Probability of Green Time in Cycle Pct. of Vehicles Pct. of Vehicles NB @ Int. 1001 NB @ Int. 1002 NB @ Int. 1003 NB @ Int. 1004 SB @ Int. 1001 SB @ Int. 1002 SB @ Int. 1003 SB @ Int. 1004 NB @ Int. 1001 NB @ Int. 1002 NB @ Int. 1003 NB @ Int. 1004 SB @ Int. 1001 SB @ Int. 1002 SB @ Int. 1003 SB @ Int. 1004
  • 66. Predicted Optimized – June 6, 2009 Data 0 24 8 4 12 16 20 0 24 8 4 12 16 20 0 24 8 4 12 16 20 0 24 8 4 12 16 20 140 0 105 70 35 0 24 8 4 12 16 20 0 24 8 4 12 16 20 0 24 8 4 12 16 20 0 24 8 4 12 16 20 140 0 105 70 35 0 100 50 25 75 114 0 100 50 25 75 114 0 100 50 25 75 114 0 100 50 25 75 114 0% 100% 50% 75% 25% 3% 0% 2% 1% 0 100 50 25 75 114 0 100 50 25 75 114 0 100 50 25 75 114 0 100 50 25 75 114 0% 100% 50% 75% 25% 3% 0% 2% 1% Time in Cycle Time in Cycle Time in Cycle Time in Cycle Time in Cycle Time in Cycle Time in Cycle Time in Cycle Hour Hour Hour Hour Hour Hour Hour Hour Probability of Green Time in Cycle Probability of Green Time in Cycle Pct. of Vehicles Pct. of Vehicles NB @ Int. 1001 NB @ Int. 1002 NB @ Int. 1003 NB @ Int. 1004 SB @ Int. 1001 SB @ Int. 1002 SB @ Int. 1003 SB @ Int. 1004 NB @ Int. 1001 NB @ Int. 1002 NB @ Int. 1003 NB @ Int. 1004 SB @ Int. 1001 SB @ Int. 1002 SB @ Int. 1003 SB @ Int. 1004
  • 67. Observed (After Changes) – July 25, 2009 0 24 8 4 12 16 20 0 24 8 4 12 16 20 0 24 8 4 12 16 20 0 24 8 4 12 16 20 140 0 105 70 35 0 24 8 4 12 16 20 0 24 8 4 12 16 20 0 24 8 4 12 16 20 0 24 8 4 12 16 20 140 0 105 70 35 0 100 50 25 75 114 0 100 50 25 75 114 0 100 50 25 75 114 0 100 50 25 75 114 0% 100% 50% 75% 25% 3% 0% 2% 1% 0 100 50 25 75 114 0 100 50 25 75 114 0 100 50 25 75 114 0 100 50 25 75 114 0% 100% 50% 75% 25% 3% 0% 2% 1% Time in Cycle Time in Cycle Time in Cycle Time in Cycle Time in Cycle Time in Cycle Time in Cycle Time in Cycle Hour Hour Hour Hour Hour Hour Hour Hour Probability of Green Time in Cycle Probability of Green Time in Cycle Pct. of Vehicles Pct. of Vehicles NB @ Int. 1001 NB @ Int. 1002 NB @ Int. 1003 NB @ Int. 1004 SB @ Int. 1001 SB @ Int. 1002 SB @ Int. 1003 SB @ Int. 1004 NB @ Int. 1001 NB @ Int. 1002 NB @ Int. 1003 NB @ Int. 1004 SB @ Int. 1001 SB @ Int. 1002 SB @ Int. 1003 SB @ Int. 1004
  • 68. Audience Participation on Following Two Slides (Eye Doctor A/B)
  • 69. March 13, 2008
  • 70. SB on SR 37 on Jan 19 (Clear)
  • 71. SB on SR 37 on Jan 26 (Snow)
  • 72. Purdue Coordination Diagrams as Changes Were Implemented CR17 and Missouri Elkhart County, IN Ross Haseman
  • 73. PPD Before Change, Phase 6, 02/17/09 Start of Green Platoon End of Green Φ 1 Φ 6 Φ 5 Φ 8 Φ 3 Φ 7 Φ 4 Φ 2 N
  • 74. Predicted PPD After Change, Phase 6 02/17/09 Calc Offset 35s+19=54s Φ 1 Φ 6 Φ 5 Φ 8 Φ 3 Φ 7 Φ 4 Φ 2 N
  • 75. PPD After Change, Phase 6, 02/24/09 Time Change Was Implemented Φ 1 Φ 6 Φ 5 Φ 8 Φ 3 Φ 7 Φ 4 Φ 2 N
  • 76. PPD Phase 6, 02/25/09 Subsequently Fixed 54s offset ok with 60s cycle 54s offset fails with 50s cycle Φ 1 Φ 6 Φ 5 Φ 8 Φ 3 Φ 7 Φ 4 Φ 2 N
  • 77. PPD Phase 6, 03/07/09 Not set back to TOD after correction, fixed 3/09 Φ 1 Φ 6 Φ 5 Φ 8 Φ 3 Φ 7 Φ 4 Φ 2 N
  • 78. PPD Phase 6, 03/20/09 Φ 1 Φ 6 Φ 5 Φ 8 Φ 3 Φ 7 Φ 4 Φ 2 N
  • 79. Percent of Cycles with Ped Phases, Wednesday 2 4 8 6 Before (1/9/08) After (1/30/08) 0:00 12:00 24:00 0:00 12:00 24:00 0% 100% 50% 0% 100% 50%
  • 80. 24 Hour Counts by phase…dependent upon Cycle P1 P2 P3 P4 P6 P5 P7 P8 60 0 30 0:00 24:00 12:00 0:00 24:00 12:00 0:00 24:00 12:00 0:00 24:00 12:00 60 0 30 Time of Day Vehicle Detections per Cycle
  • 81. 24 Hour Green Time by phase P1 P2 P3 P4 P6 P5 P7 P8 90 0 45 0:00 24:00 12:00 0:00 24:00 12:00 0:00 24:00 12:00 0:00 24:00 12:00 90 0 45 Time of Day Green Time (sec)
  • 82. V/C Ratios by Phase, 24 Hours P1 P2 P3 P4 P6 P5 P7 P8 1.0 0.0 0.5 0:00 24:00 12:00 0:00 24:00 12:00 0:00 24:00 12:00 0:00 24:00 12:00 1.0 0.0 0.5 Time of Day Volume-to-Capacity Ratio
  • 83. 24-Hour Plot of Intersection Saturation Showing Critical Path
  • 84. 24-Hour Plot of Intersection Saturation With Split Failures Indicated
  • 85.