Presented by Gary Duncan Traffic Signal Performance Measures.

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Presentation transcript:

Presented by Gary Duncan Traffic Signal Performance Measures

Contributor to this talk Econolite (ASC 3 Data Logger ) –Gary Duncan –Eric Raamot –Brian Griggs INDOT (Infrastructure Support and Agency Perspective) –Jim Sturdevant –Jay Wasson –Ryan Gallagher Indiana LTAP –Neal Carboneau –Jay Grossman (Elkhart County) Purdue University –Chris Day –Tom Brennan –Ross Haseman –Darcy Bullock 2

Outline Introduction & Collaboration History Need for Performance Measures Seven (7) Draft Performance Measures Detailed discussion of Arterial Coordination Performance Measure Results of a Arterial Case Study Future Efforts by Industry 3

Discussion Points Brief summary of historic intersection performance measurement Overview of the successful Academic/DOT/Industry collaboration used to research enhancements on performance measurement Outline of Product enhancements derived from research

Historical Challenges Typical field data collection has been limited to: –Volume –Occupancy –Average speed –Data averaged over relatively large aggregation periods Typically minutes Controllers have provided some basic MOE logging: –Phase split duration –Reason for termination (Max/Gap/Force Off) –Pedestrian actuation –Transition begin/end –Preemption begin/end

15-minute Average Detector Occupancy 6

High Resolution Data Effective green intervals occur Vehicles move through detection zones 7 Internal data logger – 0.1 second resolution

High Resolution Data How can we use high resolution data to –Identify problems in an arterial network –Find ways to improve operations 8 Focus of Today Talk

Seven Recommended Performance Measures Culmination of 3 years of collaboration between a INDOT, Purdue University, and NEMA Vendors that participate in the Indiana Market (Econolite is one of them). Extensively validated by Purdue on Econolite ASC 3 controllers deployed along instrumented corridors in Noblesville, Elkhart County, West Lafayette, and Lafayette. Performance measures presented on subsequent slides are drawn from 250,000,000 record database at Purdue that has been archived using Econolite Data Loggers at 15 intersections.

FTP Harvesting and SQL Database Ingestion INTERNE T 8 Remote Indiana ASC/3 Locations Purdue Traffic Lab: Scheduled FTP of High Resolution Traffic Controller Data RAW *.dat Files Downloaded into Temp Folder Unique Traffic Signal Event Records ingested into SQL 2007 Database RAW *.dat Files Processed and ingested into SQL Database *.DAT successfully ingested into SQL are moved from TEMP to ARCHIVE folder 10

Outline Introduction & Collaboration History Need for Performance Measures Seven (7) Draft Performance Measures Detailed discussion of Arterial Coordination Performance Measure Results of a Arterial Case Study Future Efforts by Industry 11

Question Where (and when) are the opportunities to improve signal operations? 12 INDOT Signal Network INDOT: 2,600 signals in 300 systems Nationwide: 300,000 signals Globally: who knows?

Outline Introduction & Collaboration History Need for Performance Measures Seven (7) Draft Performance Measures Detailed discussion of Arterial Coordination Performance Measure Results of a Arterial Case Study Future Efforts by Industry 13

Highway Capacity Manual Delay Equation 14 Capacity Utilization (Volume-to-Capacity Ratio) Quality of Progression (Percent on Green) Oversaturation (Split Failures)

1. Cycle Length VCR Scheduling Evaluation

1. Cycle Length (Mismatch) Inconsistent cycle length in system VCR Scheduling Evaluation

2. Equivalent Hourly Flow Rate Are TOD breakpoints in appropriate locations?

3. Green Time Force off min green Early return

4. Volume to Capacity Ratio No split failures Many split failures

5. Split Failures Per Half Hour Number of times that v/c > 1 No split failures Many split failures

6. Purdue Coordination Diagram (PCD) Extensive Detail in subsequent section

7. Percentage of Phases with Pedestrians

Pedestrian Phasing 23 Concurrent Ped 4/8 Exclusive Ped Phase “9”

SuMTuWThFSaSuMTuWThFSa Missing data 11/13 and 11/14 (days 5/6 in BEFORE)

Outline Introduction & Collaboration History Need for Performance Measures Seven (7) Draft Performance Measures Detailed discussion of Arterial Coordination Performance Measure Results of a Arterial Case Study Future Efforts by Industry 25

Evaluating Arterial Progression using PCDs

High Resolution Data How can we use high resolution data to –Identify problems in an arterial network –Find ways to improve operations 27

Purdue Coordination Diagram Construction time Cycle begins 0 sec 12:00:00 Cycle endsGreen phase endsGreen phase begins 120 sec 12:02:00 90 sec50 sec 70 sec 12:01: time of day Time in cycle 0 12:00:00 12:02:0012:01:10 Green Red Cycle boundary Green window Coordination Loop Detection

30 minute view 29 c383c384c385c386c387c388c389c390c391c392c393c394c395c396 Phase 2 Green Clearance Phase 1 Phase 4 Phase 3 Phase 2 Red c397 green red Secondary platoon Primary platoon Arrivals in Green Arrivals in Red

24-hour view TOD Plan Time Period 20-pt. moving average b1 a1 a2 b2

Relationship between PCD and Flow Profile 31 Fig. 6.9

Saturday Performance (June 6, 2009) 32 good bad good Random arrivals No platoons

Closer look at three poor arrival patterns 33 Int Int Int “Anti-coordination”

Modeling Offset Changes 34 Fig. 6.6

Example Adjustment Northbound at 37/Pleasant is bad. The platoon arrives 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. 35 Int. Int POG = 40.1%POG = 80.2% 5069 arrivals on green ( )

Add 10 seconds at Int 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 36 POG = 55.4%POG = 77.8% 5589 arrivals on green Int. Int s

Add 20 seconds at Int 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 37 POG = 67.4%POG = 68.8% 5688 arrivals on green Int. Int s

Add 30 seconds at Int 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 38 POG = 73.4%POG = 57.5% 5446 arrivals on green Int. Int s

Offset Optimization 39

Outline Introduction & Collaboration History Need for Performance Measures Seven (7) Draft Performance Measures Detailed discussion of Arterial Coordination Performance Measure Results of a Arterial Case Study Future Efforts by Industry 40

41 Before good bad good OK Random Fig. 6.5

42 Predicted Still OK better good better Random Fig. 6.7

43 ACTUAL OK good better Random Fig. 6.8

44 Change in Arrivals on Green Table 6.2

Before (Sample size=4797)

After (Sample size=5401)

Cumulative Travel Time – Bluetooth MAC Address Matching 47 ~1.9 min Travel Time Reduction T-value = P-value > NorthboundSouthbound Independent verification of effectiveness of the operational changes

Outline Introduction & Collaboration History Need for Performance Measures Seven (7) Draft Performance Measures Detailed discussion of Arterial Coordination Performance Measure Results of a Arterial Case Study Future Efforts by Industry 48

Future Efforts and Concluding Comments Partnership between Industry, State Agencies, Purdue, and most recently local agencies has resulted in a research result that –Has had measurable impact on arterial operation. –Provided a forum for vetting pictorial performance measures that are understandable by a broad cross section of stake holders –Integrated stake holders in the development process to facilitate rapid deployment by signal vendors –Incorporation of these into specification for a system to be delivered in