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Performance-Based Management of Traffic Signals (2020)

Chapter: Chapter 5 - Implementation of Performance Measures

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Suggested Citation:"Chapter 5 - Implementation of Performance Measures." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 5 - Implementation of Performance Measures." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 5 - Implementation of Performance Measures." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 5 - Implementation of Performance Measures." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 5 - Implementation of Performance Measures." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 5 - Implementation of Performance Measures." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 5 - Implementation of Performance Measures." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 5 - Implementation of Performance Measures." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 5 - Implementation of Performance Measures." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 5 - Implementation of Performance Measures." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 5 - Implementation of Performance Measures." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 5 - Implementation of Performance Measures." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 5 - Implementation of Performance Measures." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 5 - Implementation of Performance Measures." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 5 - Implementation of Performance Measures." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 5 - Implementation of Performance Measures." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 5 - Implementation of Performance Measures." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 5 - Implementation of Performance Measures." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 5 - Implementation of Performance Measures." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 5 - Implementation of Performance Measures." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 5 - Implementation of Performance Measures." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 5 - Implementation of Performance Measures." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 5 - Implementation of Performance Measures." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 5 - Implementation of Performance Measures." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 5 - Implementation of Performance Measures." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 5 - Implementation of Performance Measures." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 5 - Implementation of Performance Measures." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 5 - Implementation of Performance Measures." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 5 - Implementation of Performance Measures." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 5 - Implementation of Performance Measures." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 5 - Implementation of Performance Measures." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 5 - Implementation of Performance Measures." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 5 - Implementation of Performance Measures." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 5 - Implementation of Performance Measures." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 5 - Implementation of Performance Measures." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 5 - Implementation of Performance Measures." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 5 - Implementation of Performance Measures." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 5 - Implementation of Performance Measures." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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5.1 CONFIGURATION 154 5.2 CONFIGURATION VERIFICATION 155 5.3 DATA VERIFICATION 160 5.4 VALIDATION 163 5.5 INTERSECTION/ UNCOORDINATED TIMING VALIDATION 165 5.6 SYSTEM/COORDINATED TIMING VALIDATION 174 5.7 ADVANCED SYSTEMS AND APPLICATIONS VALIDATION 179 5.8 EQUIPMENT MAINTENANCE VALIDATION 182 5.9 PREDICTIVE TOOLS 184 5.10 MONITORING THROUGH AUTOMATED ALERTS 185 5.11 AGGREGATED REPORTS 187 5.12 REFERENCES 190 CHAPTER 5 IMPLEMENTATION OF PERFORMANCE MEASURES

LIST OF EXHIBITS EXHIBIT 5-1. INTERSECTION CONFIGURATION REQUIREMENTS 155 EXHIBIT 5-2. DATA AVAILABILITY EXAMPLE (COURTESY VIRGINIA DEPARTMENT OF TRANSPORTATION) 156 EXHIBIT 5-3. TIMESTAMPS EXAMPLE (COURTESY VIRGINIA DEPARTMENT OF TRANSPORTATION) 157 EXHIBIT 5-4. INTERSECTION CONFIGURATION EXAMPLE: PHASE TERMINATIONS (COURTESY UTAH DEPARTMENT OF TRANSPORTATION) 158 EXHIBIT 5-5. INTERSECTION CONFIGURATION EXAMPLE: CYCLE LENGTHS (COURTESY VIRGINIA DEPARTMENT OF TRANSPORTATION) 158 EXHIBIT 5-6. DETECTOR CONFIGURATION EXAMPLE: APPROACH VEHICLE VOLUMES (COURTESY UTAH DEPARTMENT OF TRANSPORTATION) 159 EXHIBIT 5-7. DETECTOR CONFIGURATION EXAMPLE: LANE-BY-LANE VEHICLE VOLUMES (COURTESY VIRGINIA DEPARTMENT OF TRANSPORTATION) 159 EXHIBIT 5-8. DATA VERIFICATION EXAMPLE: TRAFFIC COUNT COMPARISON OF LOOP DETECTOR ACTUATIONS VERSUS VISUAL COUNTS (DAY ET AL. 2014) 160 EXHIBIT 5-9. VIDEO RECORDING EXAMPLE (BULLOCK 2016) 161 EXHIBIT 5-10. TRAVEL TIME AND AVERAGE SPEED EXAMPLE: AVERAGE, 5TH PERCENTILE, AND 95TH PERCENTILE SPEEDS (COURTESY VIRGINIA DEPARTMENT OF TRANSPORTATION) 162 EXHIBIT 5-11. TRAVEL TIME AND AVERAGE SPEED EXAMPLE: PURDUE COORDINATION DIAGRAM (COURTESY VIRGINIA DEPARTMENT OF TRANSPORTATION) 162 EXHIBIT 5-12. PERFORMANCE MEASURE VALIDATION APPLICATIONS 164 EXHIBIT 5-13. YELLOW CHANGE EXAMPLE: APPROACH SPEED (COURTESY UTAH DEPARTMENT OF TRANSPORTATION) 165 EXHIBIT 5-14. RED CLEARANCE EXAMPLE: YELLOW/RED ACTUATIONS (COURTESY UTAH DEPARTMENT OF TRANSPORTATION) 166 EXHIBIT 5-15. MINIMUM GREEN EXAMPLE: BICYCLE VOLUMES (COURTESY UTAH DEPARTMENT OF TRANSPORTATION) 167 EXHIBIT 5-16. MAXIMUM GREEN EXAMPLE: PHASE TERMINATION (COURTESY UTAH DEPARTMENT OF TRANSPORTATION) 168 EXHIBIT 5-17. PASSAGE TIME EXAMPLE: INCREASED PASSAGE TIME RESULTS IN PHASE UTILIZING PROGRAMMED SPLIT (MACKEY 2017) 169 EXHIBIT 5-18. PEDESTRIAN INTERVAL EXAMPLE: PEDESTRIAN DELAY (COURTESY UTAH DEPARTMENT OF TRANSPORTATION) 170 EXHIBIT 5-19. RECALL EXAMPLE: MAXIMUM RECALL SETTING (COURTESY VIRGINIA DEPARTMENT OF TRANSPORTATION) 172 EXHIBIT 5-20. TIME-OF-DAY PLANS EXAMPLE: WEEKLY VOLUME PROFILES (COURTESY UTAH DEPARTMENT OF TRANSPORTATION) 173 EXHIBIT 5-21. CYCLE LENGTH EXAMPLE: DIFFERENT EFFECTIVE CYCLE LENGTHS AT NEIGHBORING INTERSECTIONS (COURTESY CHRIS DAY, PURDUE UNIVERSITY) 175 EXHIBIT 5-22. SPLITS EXAMPLE: SPLIT FAILURES BEFORE AND AFTER SPLIT ADJUSTMENT (DAY ET AL. 2015) 176 EXHIBIT 5-23. OFFSETS EXAMPLE: PURDUE COORDINATION DIAGRAM BEFORE AND AFTER OFFSET OPTIMIZATION (DAY ET AL. 2010) 178 EXHIBIT 5-24. ADVANCED SIGNAL SYSTEMS EXAMPLE: PURDUE COORDINATION DIAGRAMS BEFORE AND AFTER DEPLOYMENT OF AN ADAPTIVE SYSTEM (COURTESY CLACKAMAS COUNTY, OR) 180 EXHIBIT 5-25. PREEMPTION DETAILS EXAMPLE: PREEMPT SERVICE (COURTESY UTAH DEPARTMENT OF TRANSPORTATION) 181 EXHIBIT 5-26. COMMUNICATION SYSTEM STATUS EXAMPLE: DAY-TO-DAY DATA AVAILABILITY BY INTERSECTION OVER 2 MONTHS (COURTESY LUCY RICHARDSON, PURDUE UNIVERSITY) 182 EXHIBIT 5-27. VEHICLE DETECTION EXAMPLE: PHASE TERMINATIONS OVER 3 DAYS BEFORE AND AFTER A DETECTOR SPLICE FIX (COURTESY VIRGINIA DEPARTMENT OF TRANSPORTATION) 183 EXHIBIT 5-28. VEHICLE DETECTION EXAMPLE: VEHICLE VOLUMES OVER 3 DAYS BEFORE AND AFTER A DETECTOR SPLICE FIX (COURTESY VIRGINIA DEPARTMENT OF TRANSPORTATION) 183 EXHIBIT 5-29. PEDESTRIAN DETECTION EXAMPLE (COURTESY VIRGINIA DEPARTMENT OF TRANSPORTATION) 184 EXHIBIT 5-30. PURDUE LINK PIVOT RESULTS FOR ONE LINK (COURTESY UTAH DEPARTMENT OF TRANSPORTATION) 185 EXHIBIT 5-31. UDOT ALERTS AND THRESHOLDS (MACKEY 2017) 186 152 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

EXHIBIT 5-32. COMPARISON OF DETECTOR CALLS VERSUS HISTORICAL AVERAGE WITH ERRORS DETERMINED THROUGH STATISTICAL ANALYSIS (GROSSMAN 2017) 186 EXHIBIT 5-33. AGGREGATED REPORT FOR SPLIT FAILURES (MACKEY 2018) 187 EXHIBIT 5-34. MONTHLY DASHBOARD (DAVIS AND HARRIS 2018) 188 EXHIBIT 5-35. SUMMARY TABLE FOR PERFORMANCE MEASURE TARGETS (UDOT 2018) 189 EXHIBIT 5-36. PERCENT CONNECTED SIGNALS THAT ARE COMMUNICATING COMPARED TO TARGET (UDOT 2018) 189 IMPLEMENTATION OF PERFORMANCE MEASURES 153

CHAPTER FOCUS After an agency has procured and installed the necessary resources to deploy signal performance measures (as described in Chapter 4), they must program the intersections into the automated traffic signal performance measure (ATSPM) system and verify that it is reporting accurate information. This chapter describes ways to check that intersections have been configured correctly and how to use the information from the reports to make signal timing and maintenance adjustments. 5.1 CONFIGURATION EXHIBIT 5-1 summarizes several pieces of information that must be programmed in the ATSPM system prior to producing reports: signal ID, controller type, and detection (as described in Chapter 4). If using a vendor- supported ATSPM system, these elements may be programmed by the vendor, but the verification techniques discussed in Section 5.2: Configuration Verification should be applied regardless of how initial values are programmed. 154 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

EXHIBIT 5-1. INTERSECTION CONFIGURATION REQUIREMENTS INTERSECTION CONFIGURATION REQUIREMENTS DESCRIPTION/EXAMPLES Signal ID • Number (e.g., IP address) Controller Type • Controller type and firmware version must be identified to convert the high-resolution data into a standard database format. Detection Approach • Northbound • Southbound • Westbound • Eastbound Phase • Number (e.g., Phases 1–8) Channel • Number (e.g., Channels 1–96) Type • Stop Bar Presence • Stop Bar Count • Advance • AVI/AVL • Pedestrian • Speed Location (Advance Only) • Distance from stop bar (e.g., 400 feet) • Speed on approach (e.g., 40 mph) Lane Number (Typically Numbered from Inside to Outside) • Left 1, 2, n • Left-Through 1, 2, n • Through 1, 2, n • Through-Right 1, 2, n • Right 1, 2, n • Bicycle 1, 2, n 5.2 CONFIGURATION VERIFICATION There are verification techniques an agency can use to confirm that data are being collected appropriately and that intersections have been configured correctly in the ATSPM system. Most of these techniques require that a practitioner compare the programmed signal timing to the ATSPM data. The comparison can reveal issues with the ATSPM system and may also highlight signal timing parameters that have been mis-programmed in the controller. For example, a practitioner may be expecting that the coordinated timing plans end at 7:00 PM, but the ATSPM reports may show them ending at 10:00 PM. There could be an issue with the ATSPM system (e.g., the wrong intersection address may have been programmed or the timestamps may not be adjusted to the correct time zone) but it is also possible that the time-of-day plan has been mis-programmed in the controller. As a practitioner uses these verification techniques, he or she should reference up-to-date signal timing plans from the controller. IMPLEMENTATION OF PERFORMANCE MEASURES 155

EXHIBIT 5-2. DATA AVAILABILITY EXAMPLE (COURTESY VIRGINIA DEPARTMENT OF TRANSPORTATION) Data Request Was Retrieving and Deleting Data Files Off the Controller Resulting in the Active Data File Being Deleted Each Hour(A) Missing data before code updates Data Request Reprogrammed to Prevent Gaps in Data (B) No missing data after code updates Gap-Out Max-Out Force-Off Unknown Pedestrian Activity Gap-Out Max-Out Force-Off Unknown Pedestrian Activity 5.2.1 DATA AVAILABILITY POTENTIAL ISSUE Data are not being reported for some or all time periods. POTENTIAL CAUSE • Data are not being recorded by the data logger. There may be a setting that is preventing the high- resolution data from being logged, the active data file may be corrupt, or the data logger may not have adequate memory. • Data are not being downloaded from the data logger to the central office. There may be an issue with communication or the data request settings. • Data are not being translated from the unprocessed data files into the database. There may be an issue with the code used to normalize the data. • Data are not being aggregated correctly. There may be an issue with the functions used to calculate various metrics. • Data are not being displayed correctly in the reports. There may be an issue with the code used to generate graphics. EXAMPLE CHECK Are there gaps in data? At an intersection in Virginia, data were successfully downloaded from a traffic signal controller, but once the data set was processed, the phase termination report revealed gaps (as shown in EXHIBIT 5-2A). In this case, the data request was retrieving unprocessed data files and then deleting them off the controller to prevent duplication in the database. It was discovered that the active file was being retrieved and deleted in addition to the historical data files, which prevented new data from being recorded until another active data file was created at the beginning of each hour. The code used to request and transfer the data into the database was rewritten so that data did not have to be deleted from the controllers. After being collected, only the most recent data were transferred to the database, resulting in a full dataset as shown in EXHIBIT 5-2B. 156 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

5.2.2 TIMESTAMPS POTENTIAL ISSUE Data are not being reported at the correct times. POTENTIAL CAUSE • Enumerations timestamped using Coordinated Universal Time (UTC). • Enumerations timestamped without daylight savings time enabled. EXAMPLE CHECK Is data being reported at the correct times? Using a phase termination report provides several indicators that data are being reported at the correct timestamps. In the example illustrated in EXHIBIT 5-3, the coordination plans are listed at the top of the chart, so their start and end times can be directly compared to the programmed time-of-day (TOD) plan. However, termination types can also be indicative of coordinated times of day. For example, force-offs will be recorded during coordination plans while max-outs will be recorded during uncoordinated times of day. In this case, the first coordination plan and associated force-offs were shown starting at 1:00 AM in EXHIBIT 5-3A, which was several hours off from the programmed TOD plan. It was discovered that the controller was logging enumerations using UTC timestamps, so the timestamps had to be converted to the local time zone. Once the timestamps had been adjusted, the same phase termination report in EXHIBIT 5-3B showed the first coordination plan and associated force-offs starting at 6:00 AM. EXHIBIT 5-3. TIMESTAMPS EXAMPLE (COURTESY VIRGINIA DEPARTMENT OF TRANSPORTATION) Plan 1 Shown Starting at 1:00 AM Because Data Are Recorded Using UTC (A) Before time zone adjustment Timestamps Adjusted to Local Time Zone So Plan 1 Shown Starting at 6:00 AM (B) After time zone adjustment Gap-Out Max-Out Force-Off Unknown Pedestrian Activity Gap-Out Max-Out Force-Off Unknown Pedestrian Activity Force-Offs (Indicative of Coordination) Shown Starting at 1:00 AM Force-Offs (Indicative of Coordination) Shown Starting at 6:00 AM IMPLEMENTATION OF PERFORMANCE MEASURES 157

5.2.3 INTERSECTION CONFIGURATION POTENTIAL ISSUE Data are being reported from a different intersection than intended. If a signal ID is mis-programmed, data from an intersection may be tied to the wrong intersection name or detector configuration. POTENTIAL CAUSE • Mis-programmed signal ID. EXAMPLE CHECK 1 Do phase terminations match programmed phases? In the example in EXHIBIT 5-4, the phase termination report provides several pieces of information about the operation of the intersection. • Data are reported for Phases 1, 2, 4, 5, 6, and 8, but not for Phases 3 and 7. • There is a pedestrian phase programmed on Phase 2 only. • The intersection has one coordination plan that operates during the day and then runs free at night. If a practitioner expected data on eight phases (e.g., if protected left turns were present on all approaches), pedestrian phase service on all approaches (e.g., Phases 2, 4, 6, and 8), or multiple coordination plans, this report could indicate that the data came from a different intersection than intended. EXHIBIT 5-4. INTERSECTION CONFIGURATION EXAMPLE: PHASE TERMINATIONS (COURTESY UTAH DEPARTMENT OF TRANSPORTATION) Detector Activation Change to Green Change to Yellow Change to Red Gap-Out Max-Out Force-Off Unknown Pedestrian Activity Coordination Plan Between 7:30 AM and 7:30 PM Pedestrian Phase Service on Phase 2 Phases 3 and 7 Are Not Active EXAMPLE CHECK 2 Do cycle lengths match programmed values? Although Purdue Coordination Diagrams report a variety of progression information, they can also be used to quickly assess cycle lengths to ensure they match those programmed in the coordination plans. EXHIBIT 5-5 is an example that illustrates cycle lengths ranging between 115 seconds and 130 seconds. If a practitioner expected cycle lengths between 90 seconds and 110 seconds, this report could indicate that the data are from a different intersection than intended. Note that there may be some variability in cycle lengths during coordination plans, depending on actuated-coordinated operations, pedestrian timing that is not accommodated within the coordinated cycle length, and preemption or priority events. EXHIBIT 5-5. INTERSECTION CONFIGURATION EXAMPLE: CYCLE LENGTHS (COURTESY VIRGINIA DEPARTMENT OF TRANSPORTATION) Cycle Lengths Range Between 115–130 Seconds; From 7:00 PM to 11:00 PM, the Cycle Length Is 120 Seconds 158 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

Lane 1 Lane 2 Lane 3 5.2.4 DETECTOR CONFIGURATION POTENTIAL ISSUE Volumes are reported higher or lower than expected conditions based on directionality, peak periods, or values compared to capacity. POTENTIAL CAUSE • Mis-programmed signal ID. • Mis-programmed detector channel (e.g., number, phase). • Mis-programmed detector location (e.g., approach, lane, movement). • Mis-programmed detector type (e.g., stop bar, advance, speed). • Malfunctioning detector. EXAMPLE CHECK 1 Do volume profiles match expected conditions? EXHIBIT 5-6 shows 15-minute flow rates for a 24-hour period. This report can be used to verify that the correct intersection has been programmed and whether detectors have been configured correctly. In this example, there are higher southbound volumes in the morning and higher northbound volumes in the evening. If the intersection was located on a corridor with the opposite commute patterns, this report could indicate that the data are from a different intersection than intended or that the detectors have been programmed incorrectly (i.e., mis-programmed detector channel, location, or detector type). EXAMPLE CHECK 2 Does the lane distribution match expected conditions? EXHIBIT 5-7 shows 15-minute flow rates for three southbound through lanes. Lanes are numbered from inside to outside, so this example shows the inside through lane has lower volumes than the middle and outside lanes. If most vehicles are making a southbound left turn downstream of the intersection, this volume distribution might indicate that the detector channels have not been configured for the correct approach or lane. Higher Southbound Volumes During the AM Peak Higher Northbound Volumes During the PM Peak Lane 1 (Inside) Has Lower Volumes Than Lane 2 (Middle) or Lane 3 (Outside) EXHIBIT 5-7. DETECTOR CONFIGURATION EXAMPLE: LANE-BY-LANE VEHICLE VOLUMES (COURTESY VIRGINIA DEPARTMENT OF TRANSPORTATION) EXHIBIT 5-6. DETECTOR CONFIGURATION EXAMPLE: APPROACH VEHICLE VOLUMES (COURTESY UTAH DEPARTMENT OF TRANSPORTATION) IMPLEMENTATION OF PERFORMANCE MEASURES 159

5.3 DATA VERIFICATION Although data can be verified qualitatively based on knowledge of the intersection, high- resolution data should also be verified quantitatively using a secondary source. 5.3.1 TRAFFIC COUNTS DESCRIPTION Traffic counts can be collected using a traditional method, such as permanent count stations or manual turning movement counts, and then compared against detector actuations from high-resolution data. EXAMPLE CHECK EXHIBIT 5-8 compares loop detector actuations against a visual count obtained from a video recording. EXHIBIT 5-8A and EXHIBIT 5-8B show that the westbound detectors are reporting accurate volumes, EXHIBIT 5-8C shows the southbound detector in the inside lane over-counts slightly, and EXHIBIT 5-8D shows the southbound detector in the outside lane reports twice as many vehicles as are observed. This type of analysis can help an agency determine the accuracy of volumes being reported and could be used to adjust the high- resolution data. (A) Westbound left (stop bar) (C) Southbound inside lane (advance) (B) Westbound through (stop bar) (D) Southbound outside lane (advance) EXHIBIT 5-8. DATA VERIFICATION EXAMPLE: TRAFFIC COUNT COMPARISON OF LOOP DETECTOR ACTUATIONS VERSUS VISUAL COUNTS (DAY ET AL. 2014) Visual Count Loop Detector Detector Reporting Accurate Volumes Detector Reporting High Volumes 160 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

Track Detector Occupancy Using Video Track Signal Display Outputs Using Video 5.3.2 VIDEO RECORDING DESCRIPTION Video recordings can be used to verify any signal performance measure. Agencies can directly compare events recorded by the controller and events seen on the video for a selected time period. This information can be used to compare the accuracy of different detection systems or ATSPM systems. EXAMPLE CHECK EXHIBIT 5-9 is an example of a screen capture from a video recording. In this case, the video was used to verify split failures. A practitioner used the video to observe green occupancies and red occupancies in order to identify if split failures were being reported accurately through the high-resolution data. EXHIBIT 5-9. VIDEO RECORDING EXAMPLE (BULLOCK 2016) IMPLEMENTATION OF PERFORMANCE MEASURES 161

Lower Midday Speeds EXHIBIT 5-11. TRAVEL TIME AND AVERAGE SPEED EXAMPLE: PURDUE COORDINATION DIAGRAM (COURTESY VIRGINIA DEPARTMENT OF TRANSPORTATION) EXHIBIT 5-10. TRAVEL TIME AND AVERAGE SPEED EXAMPLE: AVERAGE, 5TH PERCENTILE, AND 95TH PERCENTILE SPEEDS (COURTESY VIRGINIA DEPARTMENT OF TRANSPORTATION) Detector Activation Change to Green Change to Yellow Change to Red Majority of Vehicles Arriving on Red During Midday Period 5.3.3 TRAVEL TIME AND AVERAGE SPEED DESCRIPTION Travel times and average speeds collected by probe data can be compared to progression quality metrics. For example, intersections with poor progression (e.g., high arrivals on red along a corridor) should generally correlate to longer corridor travel times and slower speeds. EXAMPLE CHECK EXHIBIT 5-10 shows average, 5th percentile, and 95th percentile speeds collected using Bluetooth data on a route operated by the Virginia Department of Transportation. Prior to a midday offset adjustment, the corridor experienced lower speeds. This correlated directly to fewer arrivals on green, which could be assessed using a report such as the Purdue Coordination Diagram in EXHIBIT 5-11. 162 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

5.4 VALIDATION ATSPMs allow an agency to collect robust and widespread signal performance data, which can help practitioners (1) find problems faster (e.g., mis-programmed parameters, malfunctioning equipment, and intersections that need to be retimed) and (2) quickly identify the impact of adjustments. This section discusses ways in which ATSPMs can be used by agencies for validating their signal systems, as summarized in EXHIBIT 5-12. The following content is organized using NCHRP Report 812: Signal Timing Manual, 2nd ed. (STM2) categories (Urbanik et al. 2015): (1) intersection/uncoordinated timing, (2) system/coordinated timing, (3) advanced systems and applications, and (4) equipment maintenance. IMPLEMENTATION OF PERFORMANCE MEASURES 163

EXHIBIT 5-12. PERFORMANCE MEASURE VALIDATION APPLICATIONS PERFORMANCE MEASURE UNCOORDINATED COORDINATED ADV EQUIPMENT YE LL OW C HA NG E RE D CL EA RA NC E M IN IM UM G RE EN M AX IM UM G RE EN PA SS AG E TI M E PE D. IN TE RV AL S RE CA LL S TO D PL AN S CY CL E LE NG TH SP LI TS OF FS ET S AD VA NC ED S YS . PR EF . T RE AT M EN T CO M M UN IC AT IO N SI GN AL C AB IN ET VE HI CL E DE TE CT IO N PE D. D ET EC TI ON 3.1 COMMUNICATION STATUS X 3.2 FLASH STATUS X 3.3 POWER FAILURES X 3.4 DETECTION SYSTEM STATUS X X 3.5 VEHICLE VOLUMES X X X X X X X X 3.6 PHASE TERMINATION X X X X X X X X 3.7 SPLIT MONITOR X X X X X X X X 3.8 SPLIT FAILURES X X X X X 3.9 ESTIMATED VEHICLE DELAY X X X X X 3.10 ESTIMATED QUEUE LENGTH X X X X X X 3.11 OVERSATURATION SEVERITY INDEX X X X X X X 3.12 PEDESTRIAN VOLUMES X X X X X X X X X 3.13 PEDESTRIAN PHASE ACTUATION AND SERVICE X X X X X X X X X 3.14 ESTIMATED PEDESTRIAN DELAY X X X X X X 3.15 ESTIMATED PEDESTRIAN CONFLICTS X 3.16 YELLOW/RED ACTUATIONS X X X 3.17 RED-LIGHT-RUNNING (RLR) OCCURRENCES X X X 3.18 EFFECTIVE CYCLE LENGTH X X X 3.19 PROGRESSION QUALITY X X X 3.20 PURDUE COORDINATION DIAGRAM X X X X 3.21 CYCLIC FLOW PROFILE X X X 3.22 OFFSET ADJUSTMENT DIAGRAM X X 3.23 TRAVEL TIME AND AVERAGE SPEED X X X X 3.24 TIME-SPACE DIAGRAM X X 3.25 PREEMPTION DETAILS X 3.26 PRIORITY DETAILS X 164 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

5.5 INTERSECTION/UNCOORDINATED TIMING VALIDATION Intersection/uncoordinated timing parameters must be programmed at every signalized intersection. 5.5.1 YELLOW CHANGE DESCRIPTION The Manual on Uniform Traffic Control Devices (MUTCD) requires that the yellow change interval be determined using engineering practices (FHWA 2009). When determining the time for yellow change, a practitioner should consider perception-reaction time, approach speed, deceleration rate, and approach grade. These values are often estimated using defaults, but they can be adjusted based on measured data (e.g., speeds). If there are many vehicles entering the intersection on red, the yellow change interval should be investigated. It is possible that actual speeds are higher than those used to calculate the yellow change interval. If the yellow is too short, drivers will experience a decision zone. Note that yellow change is only one control parameter that influences whether vehicles enter an intersection on red. Detector placement and passage settings can impact the number of vehicles caught in the decision zone (i.e., forced to make a go/no-go decision) and overall signal timing can impact vehicle delay and progression quality, sometimes leading to more aggressive driving and more red-light-running occurrences. PERFORMANCE MEASURES • 3.16 Yellow/Red Actuations • 3.17 Red-Light-Running (RLR) Occurrences • 3.23 Travel Time and Average Speed STM2 REFERENCE • Section 6.1.1: Yellow Change EXAMPLE Using speed reports such as the example in EXHIBIT 5-13, an agency can confirm and/or adjust speeds used in yellow change calculations. Speed Limit 85% Speed Average Speed Posted Speed 85th Percentile Speed Average Speed EXHIBIT 5-13. YELLOW CHANGE EXAMPLE: APPROACH SPEED (COURTESY UTAH DEPARTMENT OF TRANSPORTATION) IMPLEMENTATION OF PERFORMANCE MEASURES 165

5.5.2 RED CLEARANCE DESCRIPTION Red clearance is a signal timing parameter that is applied differently across agencies. While not required, the purpose is to provide an interval when conflicting signal displays are red so that a vehicle that entered the intersection on yellow has enough time to cross the intersection without conflict. Using signal performance measures that track vehicles entering the intersection on red can help an agency identify whether red clearance should be applied, as well as the appropriate duration. PERFORMANCE MEASURES • 3.16 Yellow/Red Actuations • 3.17 Red-Light-Running (RLR) Occurrences • 3.23 Travel Time and Average Speed STM2 REFERENCE • Section 6.1.2: Red Clearance EXAMPLE Using yellow/red actuations (as shown in EXHIBIT 5-14), an agency can determine the frequency of violations (i.e., vehicles that entered the intersection on red) and “severe” violations (i.e., vehicles that entered the intersection well after the end of red clearance). If the report reveals a relatively high number of vehicles entering the intersection in the first few seconds after the end of the yellow change interval, an agency may use ATSPMs to evaluate progression and determine if an adjustment to the offset or green time could reduce the frequency of violations, or if additional red clearance is necessary. EXHIBIT 5-14. RED CLEARANCE EXAMPLE: YELLOW/RED ACTUATIONS (COURTESY UTAH DEPARTMENT OF TRANSPORTATION) Vehicles Entering the Intersection After Yellow Change Interval Detector Activation Red Red Clearance Yellow Clearance 166 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

5.5.3 MINIMUM GREEN DESCRIPTION Minimum green values should be set based on driver expectancy and to clear vehicle queues (depending on the detector configuration), but values should also consider other intersection users including pedestrians and roadway users with longer start-up times (i.e., bicycles, trucks, and transit). Practitioners can use volume reports to determine if there are high numbers of those users at a particular intersection or on a particular approach. PERFORMANCE MEASURES • 3.5 Vehicle Volumes (with classification information for bicycles, trucks, and transit) • 3.10 Estimated Queue Length • 3.12 Pedestrian Volumes • 3.13 Pedestrian Phase Actuation and Service STM2 REFERENCE • Section 6.1.3: Minimum Green EXAMPLE If there are high bicycle volumes, the minimum green time should accommodate cyclists’ slower acceleration. EXHIBIT 5-15 illustrates a bicycle volume report. EXHIBIT 5-15. MINIMUM GREEN EXAMPLE: BICYCLE VOLUMES (COURTESY UTAH DEPARTMENT OF TRANSPORTATION) Bicyclists Have Longer Start-Up Times and Can Benefit from Longer Minimum Green Times IMPLEMENTATION OF PERFORMANCE MEASURES 167

5.5.4 MAXIMUM GREEN DESCRIPTION Maximum green is the longest duration a green signal indication can be displayed in the presence of conflicting demand. If it is too short, vehicles will remain unserved at the end of green. If it is too long, other movements will experience delay. Maximum green is typically programmed long enough to be able to accommodate pedestrian intervals. Vehicle volumes are often used to initially distribute green time. Once maximum green values have been programmed, practitioners can use phase termination information to assess whether vehicles are unserved at the end of green. If it is found that a phase is experiencing a high number of max-outs while other competing phases are experiencing a low number of max-outs, there may be opportunity to adjust maximum green values to limit the number of occurrences of unserved vehicles. Delay and queues should also be investigated, if available, when evaluating maximum green times. If using phase termination information to estimate split failures, a practitioner should investigate all potential causes of max-outs, which could include: • Malfunctioning detection that is causing a continuous call for service. • Mis-programmed maximum recall. • Passage time that is set too high, preventing the phase from gapping out even during low flow rates. PERFORMANCE MEASURES • 3.5 Vehicle Volumes • 3.6 Phase Termination • 3.7 Split Monitor • 3.8 Split Failures • 3.9 Estimated Vehicle Delay • 3.10 Estimated Queue Length • 3.11 Oversaturation Severity Index • 3.12 Pedestrian Volumes • 3.13 Pedestrian Phase Actuation and Service • 3.14 Estimated Pedestrian Delay STM2 REFERENCE • Section 6.1.4: Maximum Green EXAMPLE EXHIBIT 5-16 provides an example in which Phase 2 maxed out almost every cycle between 7:00 AM and 9:00 PM. These max-out occurrences indicate a potential opportunity to adjust green times. EXHIBIT 5-16. MAXIMUM GREEN EXAMPLE: PHASE TERMINATION (COURTESY UTAH DEPARTMENT OF TRANSPORTATION) Phase 2 Maxing Out Frequently Gap-Out Max-Out Force-Off Unknown Pedestrian Activity 168 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

5.5.5 PASSAGE TIME DESCRIPTION Passage time (also known as unit extension or gap time) is a parameter that is used to terminate the current phase when a gap in traffic is identified based on a particular flow rate. If it is too short, the green interval may end prematurely before the queue is fully served. If it is too long, the controller may extend the green unnecessarily, resulting in wasted green time and delay for conflicting phases. Similar to the maximum green assessment, a practitioner can use split failures and/or phase termination information to assess whether passage time is set appropriately. However, it is also important to understand how the size of the detection zone impacts the efficiency of phase terminations. Reference the STM2 for more information (Urbanik et al. 2015). Passage time is also often used in combination with advance detection to limit the number of vehicles caught in the decision zone (i.e., having to make a decision whether to stop or go when the signal display turns yellow). If there are many vehicles entering the intersection on red, passage time is one parameter that should be investigated. It is possible that the phase is not being extended long enough after vehicles are detected for drivers to enter the intersection before the phase terminates. PERFORMANCE MEASURES • 3.6 Phase Termination • 3.7 Split Monitor • 3.8 Split Failures • 3.9 Estimated Vehicle Delay • 3.11 Oversaturation Severity Index • 3.16 Yellow/Red Actuations • 3.17 Red-Light-Running (RLR) Occurrences STM2 REFERENCE • Section 6.1.5: Passage Time (Unit Extension or Gap Time) EXAMPLE Split monitor charts can be used to assess actual phase duration compared to the programmed split. In this example, an engineer received a public service request that a particular movement was terminating before traffic was served. While an initial reaction may be to increase the green time for that movement, review of the split monitor in EXHIBIT 5-17A indicated that Phase 1 gapped out during nearly every cycle. The engineer identified that there was a 1-second passage time set on a 15-foot detection zone, which resulted in frequent gap-outs. After increasing the passage time, the issue was resolved with the phase utilizing most of its programmed split during the PM peak (as shown in EXHIBIT 5-17B). EXHIBIT 5-17. PASSAGE TIME EXAMPLE: INCREASED PASSAGE TIME RESULTS IN PHASE UTILIZING PROGRAMMED SPLIT (MACKEY 2017) Phase Gapping Out Frequently During PM Peak (A) Before passage time adjustment (B) After passage time adjustment Phase Using Most of the Programmed Split with Increased Passage Time Programmed Split Gap-Out Max-Out Programmed Split Gap-Out Max-Out Force-Off Pedestrian Activity Unknown Termination Cause Force-Off Pedestrian Activity Unknown Termination Cause IMPLEMENTATION OF PERFORMANCE MEASURES 169

5.5.6 PEDESTRIAN INTERVALS DESCRIPTION The pedestrian phase consists of the Walk, Flashing Don't Walk, and Steady Don't Walk intervals. While the Manual on Uniform Traffic Control Devices (MUTCD) sets minimum requirements for the Walk and pedestrian clearance intervals based on intersection geometry (FHWA 2009), the time can be increased based on pedestrian volumes or characteristics. For example, a longer Walk interval, Rest-in-Walk mode, or Extended- Walk mode may be used at a location with high pedestrian volumes. Using pedestrian volumes (or the number of pedestrian actuations), an agency can evaluate the level of pedestrian demand at an intersection. Other pedestrian treatments that are often considered at locations with high pedestrian demand or a high number of potential conflicts between pedestrians and other roadway users include Leading Pedestrian intervals and exclusive pedestrian phases. Pedestrian volumes can also influence other timing parameters. For example, a practitioner may have decided not to accommodate the pedestrian time within the programmed split. This allows a shorter green to time during cycles without a pedestrian call. However, if there are pedestrian actuations most cycles, it may cause fewer disruptions to program a split time that is long enough to serve the full pedestrian time. High levels of pedestrian delay indicate an agency should consider adopting a shorter cycle length or pedestrian re-service, which allows pedestrian calls to bring up the Walk interval after the phase has already started. Although this is not a standard feature, practitioners can use overlaps and/or specific flags in many cases. PERFORMANCE MEASURES • 3.12 Pedestrian Volumes • 3.13 Pedestrian Phase Actuation and Service • 3.14 Estimated Pedestrian Delay • 3.15 Estimated Pedestrian Conflicts STM2 REFERENCE • Section 6.1.6: Pedestrian Intervals EXAMPLE EXHIBIT 5-18 provides an example report for pedestrian delay, which also provides information about the number of cycles with pedestrian actuations. Although this report only presents whether a pedestrian actuation was received during a cycle (and not the total number of pedestrians or the number of times the button was pushed), it does provide a snapshot of pedestrian demand. A pedestrian call occurred during nearly every cycle between 10:00 AM and 6:00 PM, indicating heavy pedestrian activity. In this case, it may be useful to program a split that can accommodate the time required to serve pedestrians between 10:00 AM and 6:00 PM. Outside of those hours, using a shorter split may be more efficient for overall intersection operations. EXHIBIT 5-18. PEDESTRIAN INTERVAL EXAMPLE: PEDESTRIAN DELAY (COURTESY UTAH DEPARTMENT OF TRANSPORTATION) Pedestrian Actuations Nearly Every Cycle Between 10:00 AM and 6:00 PM Pedestrian Delay by Actuation 170 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

5.5.7 RECALLS DESCRIPTION Recalls place a call automatically for a specified phase regardless of any detector actuations (except for soft recall which only places a call in the absence of calls on other phases). There are four different types of recalls: minimum, maximum, soft, and pedestrian. Signal performance measures are most effective for evaluating the operation of maximum and pedestrian recalls but can also be used to determine which type of recall to apply. Maximum recall places a continuous call, which results in the phase timing its maximum green every cycle. It is not typically used at locations with detection; if it is mis-programmed, it can result in wasted time at the intersection and long delays for conflicting movements. Using phase termination information, a practitioner can quickly identify if maximum recall is set on one or multiple phases. Pedestrian recall places a continuous pedestrian call, which results in the pedestrian phase timing every cycle. Even at intersections with pedestrian detection, it may be programmed during times of day with heavy pedestrian demand. Similar to maximum recall, phase termination information can be used to determine times of day with pedestrian recall set on one or multiple phases. Vehicle and pedestrian volumes can be used to determine which type of recall to program during different times of day. Minimum recall is typically applied to major movements with steady traffic, soft recall during low- traffic periods, and pedestrian recall at locations with heavy pedestrian demand. PERFORMANCE MEASURES • 3.5 Vehicle Volumes • 3.6 Phase Termination • 3.7 Split Monitor • 3.12 Pedestrian Volumes • 3.13 Pedestrian Phase Actuation and Service STM2 REFERENCE • Section 6.1.8: Recalls and Memory Modes EXAMPLE EXHIBIT 5-19 summarizes phase terminations before and after a recall adjustment. At this intersection, max-outs were being logged all night on Phases 2 and 6 (as shown in EXHIBIT 5-19A). After reviewing the programmed signal timing, it was discovered that a maximum recall had been set for those phases inadvertently. The maximum recall was removed, and Phases 2 and 6 were observed gapping out throughout the night (as shown in EXHIBIT 5-19B). This allows for more efficient transitions between Phases 2 and 6 and conflicting phases. When a vehicle arrives on a conflicting phase, it no longer has to wait the duration of maximum green if there is no demand on Phases 2 and 6. IMPLEMENTATION OF PERFORMANCE MEASURES 171

Phases 2 and 6 Gapping Out During the Night After Recall Adjustment (B) After recall setting adjustment Phases 2 and 6 Maxing Out During the Night Because of Maximum Recall (A) Before recall setting adjustment EXHIBIT 5-19. RECALL EXAMPLE: MAXIMUM RECALL SETTING (COURTESY VIRGINIA DEPARTMENT OF TRANSPORTATION) Gap-Out Max-Out Force-Off Unknown Pedestrian Activity Gap-Out Max-Out Force-Off Unknown Pedestrian Activity 172 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

5.5.8 TIME-OF-DAY (TOD) PLANS DESCRIPTION Time-of-day plans allow a controller to apply different signal timing parameter values during different times of day or days of the week. Practitioners typically program the TOD plans to match traffic conditions, but they can also use the plans to vary operations based on the time of year or for special events. ATSPMs provide the unique benefit of allowing agencies to track volumes over longer periods of time than traditional traffic count methods. Practitioners can use volume reports to determine how many timing plans should be programmed throughout the day, week, or year. After TOD plans are programmed, effective cycle length reports or Purdue Coordination Diagrams can be used to confirm that TOD plan transitions are occurring as intended (i.e., correct cycle lengths are being reported for correct times of day). PERFORMANCE MEASURES • 3.5 Vehicle Volumes • 3.12 Pedestrian Volumes • 3.13 Pedestrian Phase Actuation and Service • 3.18 Effective Cycle Length • 3.20 Purdue Coordination Diagram STM2 REFERENCE • Section 6.3: Time-of-Day Plans EXAMPLE EXHIBIT 5-20 depicts northbound and southbound flow rates (calculated using 15-minute volumes) over 1 week. This location has different volume profiles on weekdays than on weekends, so different timing plans should likely be applied Monday through Friday versus Saturday and Sunday. EXHIBIT 5-20. TIME-OF-DAY PLANS EXAMPLE: WEEKLY VOLUME PROFILES (COURTESY UTAH DEPARTMENT OF TRANSPORTATION) Weekday Different Volume Profiles on Weekdays and Weekends Indicate Different TOD Plans Should Be Applied Weekend Northbound Southbound IMPLEMENTATION OF PERFORMANCE MEASURES 173

5.6 SYSTEM/COORDINATED TIMING VALIDATION System/coordinated signal timing parameters include cycle lengths, splits, and offsets that are used to progress vehicles. 5.6.1 CYCLE LENGTH DESCRIPTION Cycle length is the time required for a complete sequence of signal phases at an intersection. Along a coordinated corridor, all the intersections should have the same cycle length to maintain synchronization. One exception is “double cycling,” when an intersection has half the cycle length used at other intersections and serves phases twice as often. Cycle length is often determined based on the critical (or highest volume) intersection in a group of coordinated signals. To initially select a cycle length that meets demand, a practitioner can use vehicle volumes for a critical movement analysis (CMA) or similar approach (Urbanik et al. 2015). Splits should also be chosen considering pedestrian volumes; if there is high pedestrian demand, splits should generally be higher than the time required to serve pedestrians. For cycle lengths already programmed in the field, practitioners can use split failures and/or phase termination information at the critical (or highest-volume) intersection to assess whether splits are long enough to accommodate demand. If all phases are experiencing a high number of split failures, it may be beneficial to increase the cycle length. However, long cycle lengths may not provide optimal operations for all roadway users. If there is high pedestrian demand, a practitioner should consider keeping cycle lengths low to reduce pedestrian delay. Queues should also play a critical role in cycle length selection. Longer cycle lengths can result in increased congestion if there are oversaturated movements. Vehicle volumes and arrival-on-green information can also be used to determine if free operations should be considered at an intersection, particularly during low-volume periods. PERFORMANCE MEASURES • 3.5 Vehicle Volumes • 3.6 Phase Termination • 3.7 Split Monitor • 3.8 Split Failures • 3.9 Estimated Vehicle Delay • 3.10 Estimated Queue Length • 3.11 Oversaturation Severity Index • 3.12 Pedestrian Volumes • 3.13 Pedestrian Phase Actuation and Service • 3.14 Estimated Pedestrian Delay • 3.18 Effective Cycle Length • 3.19 Progression Quality • 3.20 Purdue Coordination Diagram • 3.21 Cyclic Flow Profile STM2 REFERENCE • Section 7.3.2: Cycle Length • Section 7.4.2: Cycle Length Guidance • Section 7.6.5: Critical Intersection Control • Section 12.3.1.2: Cycle Length Increase (Chapter 12: Oversaturated Conditions) EXAMPLE EXHIBIT 5-21 shows effective cycle lengths for two neighboring intersections on a corridor in Indiana. From 6:00 AM to 9:00 AM, the two intersections both operate with a 120-second cycle length. After 9:00 AM, Intersection 2 drops down to an 80-second cycle length for the rest of the day. In this case, the difference in cycle lengths is by design, but the example demonstrates the potential of such a graphic to identify anomalies that may be related to programmed values or a detection malfunction. 174 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

EXHIBIT 5-21. CYCLE LENGTH EXAMPLE: DIFFERENT EFFECTIVE CYCLE LENGTHS AT NEIGHBORING INTERSECTIONS (COURTESY CHRIS DAY, PURDUE UNIVERSITY) 120-Second Effective Cycle Length at Intersection 1 80-Second Effective Cycle Length at Intersection 2 5.6.2 SPLITS DESCRIPTION Splits are the portion of the coordinated cycle allocated to each phase (including the green, yellow change, and red clearance intervals). Practitioners often select splits by distributing available green time in proportion to estimated demand. When initially programming splits, practitioners can use vehicle volumes to estimate demand and the time required to serve it. Pedestrian volumes will also impact the time allocated to splits. If there is high pedestrian demand, splits should generally be programmed to accommodate the time required to serve pedestrians. During periods with fewer pedestrians, shorter splits may be more efficient for overall intersection operations. If a practitioner has already programmed splits in the field, he or she can use phase termination information and split failures to determine how much of the programmed split the phase used and whether there were vehicles left unserved. If some phases are experiencing high split failures while others are not (or a high number of force-offs while others are gapping out), there may be an opportunity to reallocate green time between phases. Delay and queues should also be investigated, if available, when evaluating splits. If using phase termination information to estimate split failures, a practitioner should investigate all potential causes of force-offs, which could include: • Malfunctioning detection that is causing a continuous call for service. • Mis-programmed coordinated phases causing them to be shown as force-offs every cycle. • Passage time that is set too high, preventing the phase from gapping out even during low flow rates. PERFORMANCE MEASURES • 3.5 Vehicle Volumes • 3.6 Phase Termination • 3.7 Split Monitor • 3.8 Split Failures • 3.9 Estimated Vehicle Delay • 3.10 Estimated Queue Length • 3.11 Oversaturation Severity Index • 3.12 Pedestrian Volumes • 3.13 Pedestrian Phase Actuation and Service • 3.14 Estimated Pedestrian Delay STM2 REFERENCE • Section 7.3.3: Splits • Section 7.4.3: Splits Guidance • Section 12.3.1.1: Split Reallocation (Chapter 12: Oversaturated Conditions) Epler Avenue Thompson Road IMPLEMENTATION OF PERFORMANCE MEASURES 175

EXAMPLE EXHIBIT 5-22 shows green and red occupancy ratios at an intersection before and after split adjustments. If the green and red occupancy ratios are both above 80%, the cycle is logged as having a split failure. The example shows a high number of split failures occurring on Phase 3 compared to the other conflicting phases in EXHIBIT 5-22A. A split adjustment resulted in reduced split failures on Phase 3 and for the intersection overall (as shown in EXHIBIT 5-22B), although split failures increased slightly on some of the conflicting phases. EXHIBIT 5-22. SPLITS EXAMPLE: SPLIT FAILURES BEFORE AND AFTER SPLIT ADJUSTMENT (DAY ET AL. 2015) High Number of Split Failures on Phase 3 Compared to Conflicting Phases (A) Before split adjustment Reduced Split Failures on Phase 3 (B) After split adjustment Green Occupancy Ratio Green Occupancy Ratio R ed O cc u p an cy R at io R ed O cc u p an cy R at io 176 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

5.6.3 OFFSETS DESCRIPTION Offsets define the time relationship between the “system” clock and the “local” clock at individual intersections, thereby controlling the time relationship between intersections based on the actual or desired travel speed. Ideally, they allow platoons of vehicles to arrive on green (i.e., leave an upstream intersection at the start of green and arrive at a downstream intersection at the start of green). There are some complexities with progression that can be investigated using ATSPMs including: • Phase sequence. • Early return to green. • Heavy minor street volumes. • Queues on oversaturated movements. Practitioners have used high-resolution data to automatically identify offset adjustments by applying a simple prediction model to the underlying data (Day and Bullock 2011). In most cases, practitioners can predict the impacts of an offset adjustment by a linear adjustment of the arrival times at the approaches of the local intersection and at neighboring intersections. This process yields predicted metrics such as arrivals on green. One method of systematically adjusting intersections along an arterial corridor is the “Link Pivot” algorithm (Day et al. 2011). PERFORMANCE MEASURES • 3.10 Estimated Queue Length • 3.11 Oversaturation Severity Index • 3.19 Progression Quality • 3.20 Purdue Coordination Diagram • 3.21 Cyclic Flow Profile • 3.22 Offset Adjustment Diagram • 3.23 Travel Time and Average Speed • 3.24 Time-Space Diagram STM2 REFERENCE • Section 7.3.9: Offsets • Section 7.4.9: Offsets Guidance • Section 7.6: Complexities • Section 12.3.2.3 Offset Strategies (Chapter 12: Oversaturated Conditions) EXAMPLE A practitioner can use the Purdue Coordination Diagram to assess when vehicles are arriving during the cycle. Although other progression metrics are better for assessing offsets corridor-wide, this type of report can help confirm the value and direction of an offset adjustment. EXHIBIT 5-23 shows Purdue Coordination Diagrams (A) before and (B) after an offset adjustment. In this case, the adjustment resulted in vehicle platoons arriving on green much more frequently throughout the day. IMPLEMENTATION OF PERFORMANCE MEASURES 177

Most Vehicles Arriving on Red Before Offset Adjustment (A) Before offset optimization Most Vehicles Arriving on Green After Offset Adjustment (B) After offset optimization Detection Activation Change to Green Change to Yellow Change to Red Detection Activation Change to Green Change to Yellow Change to Red EXHIBIT 5-23. OFFSETS EXAMPLE: PURDUE COORDINATION DIAGRAM BEFORE AND AFTER OFFSET OPTIMIZATION (DAY ET AL. 2010) 178 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

5.7 ADVANCED SYSTEMS AND APPLICATIONS VALIDATION Advanced systems are often implemented to address unpredictable traffic conditions. Objectives for these systems will vary based on agency needs. 5.7.1 ADVANCED SIGNAL SYSTEMS (TRAFFIC RESPONSIVE AND ADAPTIVE SYSTEMS) DESCRIPTION Traffic responsive and adaptive systems adjust signal timing parameters (often cycle lengths, splits, and offsets) in response to traffic demand. A practitioner can apply the reports discussed in Section 5.6: System/ Coordinated Timing Validation to assess how often traffic responsive and adaptive systems are making adjustments and whether the adjustments are improving operations. PERFORMANCE MEASURES • 3.5 Vehicle Volumes • 3.6 Phase Termination • 3.7 Split Monitor • 3.8 Split Failures • 3.9 Estimated Vehicle Delay • 3.10 Estimated Queue Length • 3.11 Oversaturation Severity Index • 3.12 Pedestrian Volumes • 3.13 Pedestrian Phase Actuation and Service • 3.14 Estimated Pedestrian Delay • 3.18 Effective Cycle Length • 3.19 Progression Quality • 3.20 Purdue Coordination Diagram • 3.21 Cyclic Flow Profile • 3.22 Offset Adjustment Diagram • 3.23 Travel Time and Average Speed • 3.24 Time-Space Diagram STM2 REFERENCE • Chapter 9: Advanced Signal Systems EXAMPLE An adaptive traffic signal control (ATSC) system was deployed along a corridor in Oregon. The system adjusted cycle lengths, splits, and offsets. As shown in EXHIBIT 5-24, one method for validating the system was reviewing arrivals on green (A) before and (B) after deployment using Purdue Coordination Diagrams. After implementation of the adaptive system, offset adjustments improved vehicle arrivals on green during the AM peak. Cycle length adjustments could also be evaluated using this report to confirm that the minimum and maximum values were within the intended range. IMPLEMENTATION OF PERFORMANCE MEASURES 179

EXHIBIT 5-24. ADVANCED SIGNAL SYSTEMS EXAMPLE: PURDUE COORDINATION DIAGRAMS BEFORE AND AFTER DEPLOYMENT OF AN ADAPTIVE SYSTEM (COURTESY CLACKAMAS COUNTY, OR) Most Vehicles Arriving on Red During AM Peak Before Adaptive System TOD Cycle Length (A) Before adaptive system deployment Most Vehicles Arriving on Green During AM Peak After Adaptive System Adaptive Cycle Length (B) After adaptive system deployment Detector Activation Change to Green Change to Yellow Change to Red Detector Activation Change to Green Change to Yellow Change to Red 180 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

5.7.2 PREFERENTIAL TREATMENT (PREEMPTION AND PRIORITY) DESCRIPTION Practitioners can use preferential treatment to alter normal operations for a preferred vehicle. If a signalized intersection is located near a rail crossing, preemption is used to clear the space between the traffic signal and the tracks before the arrival of a train. Emergency vehicles typically use preemption to terminate normal operations and serve the approach with the emergency vehicle. Transit and truck priority differs slightly because it gives preference to transit vehicles and trucks without interrupting coordination. ATSPMs can provide information about when a preempt or priority call was received, when it was serviced, and how the intersection operated during the event. For preemption, those metrics can include entry delay, track clearance, gate down, dwell, time to service, max-out, and preempt input on/off. For rail preemption, an island circuit is required for the most meaningful signal performance measures because it provides information about when the train arrived at the crossing. For priority, ATSPMs can record TSP check in, adjustment to early green, adjustment to extend green, and TSP check out. A practitioner can identify how often preemption and priority events are occurring and whether they are operating as anticipated. This can help troubleshoot mis-programmed signal timing parameters and preferential-treatment-specific equipment that is malfunctioning. Using a combination of signal performance measures, a practitioner can also identify the experience of other intersection users during a preferential treatment event and may be able to adjust phasing or signal timing to reduce delay for other users. PERFORMANCE MEASURES • 3.25 Preemption Details • 3.26 Priority Details STM2 REFERENCE • Chapter 10: Preferential Treatment EXAMPLE EXHIBIT 5-25 shows preemption details for a signalized intersection near an at-grade railroad crossing with a relatively low frequency of trains (typically two trains per day, 3 days per week). Over several years, there were a number of public service requests at this location. Before any adjustments were made, EXHIBIT 5-25A revealed that the signalized intersection was experiencing a high number of short preemption events. The problem was due to false inputs caused by railroad switching operations occurring on nearby tracks, which did not ultimately result in a train using the at-grade crossing. Documentation of the frequency of false calls was helpful during discussions with the railroad. The railroad confirmed the problem and isolated the tracks at the at-grade crossing from the tracks that were generating the false calls. EXHIBIT 5-25B shows that the number of preemption calls was reduced dramatically after adjustments were made to the tracks. EXHIBIT 5-25. PREEMPTION DETAILS EXAMPLE: PREEMPT SERVICE (COURTESY UTAH DEPARTMENT OF TRANSPORTATION) (A) Before track adjustments corrected false calls (B) After track adjustments corrected false calls High Number of False Preempt Requests Due to Railroad Switching Reduced Number of Preempt Requests After Track Adjustments Gate Down Input Off Input On Call Max-Out Dwell Time Track Clear Time to Service Delay X IMPLEMENTATION OF PERFORMANCE MEASURES 181

5.8 EQUIPMENT MAINTENANCE VALIDATION This section discusses performance measures related to the maintenance of traffic signal equipment. 5.8.1 COMMUNICATION DESCRIPTION Communication is critical to a scalable monitoring system as well as keeping intersections coordinated. If an intersection is missing data or the central system is unable to communicate with a controller (i.e., “ping”), the communication infrastructure may be malfunctioning. Practitioners can track communication outages by location, type of communication equipment, or age of communication equipment, so they can identify the highest priority locations for future communication equipment upgrades. PERFORMANCE MEASURES • 3.1 Communication Status STM2 REFERENCE • Section 4.4: Signalized System Design EXAMPLE EXHIBIT 5-26 shows the availability of data by intersection along three corridors. Each dot represents a day when data was present; each row represents an individual intersection. Over time, communication to the US-36 and SR-37 corridors was restored, but along all three corridors there remained some individual intersections with sporadic communication. This chart was used to validate communication upgrades. EXHIBIT 5-26. COMMUNICATION SYSTEM STATUS EXAMPLE: DAY-TO-DAY DATA AVAILABILITY BY INTERSECTION OVER 2 MONTHS (COURTESY LUCY RICHARDSON, PURDUE UNIVERSITY) 5.8.2 SIGNAL CABINET EQUIPMENT DESCRIPTION Practitioners can track flash status and power failures to ensure the signal cabinet equipment is functional. By assessing the frequency of outages by location, equipment type, and equipment age, an agency can identify high-priority intersections for maintenance upgrades. In addition, those metrics can help identify locations where agencies should consider back-up power supply (BPS) systems. PERFORMANCE MEASURES • 3.2 Flash Status • 3.3 Power Failures STM2 REFERENCE • Section 4.2: Signal Cabinet Equipment Each Row Reports Availability of Data for One Intersection SR-37 Intersection Experienced Gaps in Communication 182 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

5.8.3 VEHICLE DETECTION DESCRIPTION Although controllers sometimes log detector failures as alarms, practitioners can also use phase termination information and vehicle volumes to identify detector failures. If phases are consistently maxing out during low-volume periods (e.g., late at night), it may be an indication that a detector is not functioning properly and placing a constant call. When this occurs, particularly during free operations, phases may time that otherwise would not, and vehicles on conflicting approaches may be experiencing unnecessary delays. PERFORMANCE MEASURES • 3.4 Detection System Status • 3.5 Vehicle Volumes • 3.6 Phase Termination • 3.7 Split Monitor STM2 REFERENCE • Section 4.1: Detection EXAMPLE At an intersection in Virginia, phase termination reports revealed a high number of max-outs on Phase 6 during the night, as shown in EXHIBIT 5-27. After reviewing lane-by-lane vehicle flow rates (calculated using 15-minute volumes), as shown in EXHIBIT 5-28, it was discovered that one of the southbound detectors associated with Phase 6 was not reporting any volumes. The detector had a bad splice and was defaulting to a constant call. After the splice was fixed, the detector began reporting volumes that were consistent with the other lanes on the southbound approach, and Phase 6 (and Phase 2 by association) was able to gap-out during the night. EXHIBIT 5-27. VEHICLE DETECTION EXAMPLE: PHASE TERMINATIONS OVER 3 DAYS BEFORE AND AFTER A DETECTOR SPLICE FIX (COURTESY VIRGINIA DEPARTMENT OF TRANSPORTATION) Gap-Out Max-Out Force-Off Unknown Pedestrian Activity High Number of Max-Outs Overnight Phase 6 Gapping Out After Bad Detector Splice Fixed EXHIBIT 5-28. VEHICLE DETECTION EXAMPLE: VEHICLE VOLUMES OVER 3 DAYS BEFORE AND AFTER A DETECTOR SPLICE FIX (COURTESY VIRGINIA DEPARTMENT OF TRANSPORTATION) Lane 3 Detector Not Reporting Volumes Until Bad Splice Fixed Lane 1 Lane 2 Lane 3 Lane 4 IMPLEMENTATION OF PERFORMANCE MEASURES 183

5.8.4 PEDESTRIAN DETECTION DESCRIPTION Practitioners can use performance measures to identify locations with malfunctioning pedestrian detection. The phase termination and pedestrian delay metrics report pedestrian actuations. Like vehicle detectors, broken pedestrian push buttons default to a constant call, which can cause pedestrian phases to time when there are no pedestrians present, resulting in delays for other intersection users. PERFORMANCE MEASURES • 3.4 Detection System Status • 3.6 Phase Termination • 3.7 Split Monitor • 3.12 Pedestrian Volumes • 3.13 Pedestrian Phase Actuation and Service • 3.14 Estimated Pedestrian Delay STM2 REFERENCE • Section 4.1: Detection EXAMPLE EXHIBIT 5-29 highlights a high number of pedestrian actuations (i.e., calls every cycle) during a low-volume period (i.e., late at night) over the course of several days. In such cases, agencies should check the pedestrian detection for failure. EXHIBIT 5-29. PEDESTRIAN DETECTION EXAMPLE (COURTESY VIRGINIA DEPARTMENT OF TRANSPORTATION) Continuous Pedestrian Calls During the Night Pedestrian Recall During the Day Pedestrian Delay by Actuation 5.9 PREDICTIVE TOOLS There are some tools available that use high-resolution data to develop predictive models. A practitioner can use these tools to predict improvements to signal timing settings based on the desired objective. One example of this type of tool is Purdue Link Pivot Analysis, which predicts offset adjustments that will improve arrivals on green. This methodology assesses whether arrivals on green would likely increase or decrease if the offset at an intersection shifted. Once the tool identifies recommended offset adjustments for all of the intersections along a specified route, it calculates programmable offset values based on the existing offset relationships between intersections (Day et al. 2011). See EXHIBIT 5-30 for an example of existing and predicted arrivals on green for one link based on Purdue Link Pivot Analysis. 184 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

Total AOG Downstream AOG Upstream AOG EXHIBIT 5-30. PURDUE LINK PIVOT RESULTS FOR ONE LINK (COURTESY UTAH DEPARTMENT OF TRANSPORTATION) UPSTREAM INTERSECTION DOWNSTREAM INTERSECTION TOTAL LINK Existing AOG (#, %) Predicted AOG (#, %) Existing AOG (#, %) Predicted AOG (#, %) Existing AOG (#, %) Predicted AOG (#, %) 1225 (86%) 1277 (90%) 833 (71%) 989 (84%) 2058 (79%) 2266 (87%) 108-Second Offset Adjustment Recommended Based on Maximum Arrivals on Green 5.10 MONITORING THROUGH AUTOMATED ALERTS Agencies can program reports and dashboards to automatically flag issues and alert technicians of issues or inefficiencies in the system. This can help agencies identify problems faster and validate operations noted by the public in service requests. In order to develop a robust program that limits “false alerts,” agencies need to determine the appropriate ATSPM thresholds for their system. The Utah Department of Transportation (UDOT) uses ATSPM automated alerts regularly to create system-wide email notifications; EXHIBIT 5-31 summarizes their available alerts and typical thresholds. Initial Percent Arrival on Green (AOG) Increase in Percent Arrival on Green Decrease in Percent Arrival on Green Adjustment (seconds) Max Arrivals On Green By Second A rr iv al s O n G re en IMPLEMENTATION OF PERFORMANCE MEASURES 185

EXHIBIT 5-31. UDOT ALERTS AND THRESHOLDS (MACKEY 2017) CATEGORY ALERT DESCRIPTION THRESHOLD TIME PERIOD Communication Data Entry Intersections with a low number of records in the database Less than 500 records 12:00 AM to 11:59 PM Detection Max-Out Phases with a high number of max-outs during low-volume periods 90% or more max- outs in at least 50 activations 1:00 AM to 5:00 AM Detection Pedestrian Call Phases with a high number of pedestrian actuations during low- volume periods More than 200 actuations 1:00 AM to 5:00 AM Detection Detector Count Advance detectors reporting low volumes during high-volume periods Less than 100 actuations 5:00 PM to 6:00 PM System / Coordinated Timing Force-Off Phases with a high number of force-offs during low-volume periods 90% or more force-offs in at least 50 activations 1:00 AM to 5:00 AM Agencies can also program automated alerts based on how current conditions compare to historical data. For example, EXHIBIT 5-32 shows hourly detector calls compared to a historical average compiled from previous weeks. A statistical comparison between the current and historical data revealed hours when the current count was more than one standard deviation away from the historical average (highlighted with red circles). Note that during the 3 days in the middle of the week, there were many times when the counts dropped to zero during overnight periods. The cold overnight January temperatures caused a cable to contract and ultimately disconnected the detector. As a result, the system placed a constant call on the associated phase overnight, although it operated normally during the day. Technicians corrected the problem before a citizen made a public service request. EXHIBIT 5-32. COMPARISON OF DETECTOR CALLS VERSUS HISTORICAL AVERAGE WITH ERRORS DETERMINED THROUGH STATISTICAL ANALYSIS (GROSSMAN 2017) Number of Calls More Than One Standard Deviation from Historical Average Calls Average >stDev 186 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

5.11 AGGREGATED REPORTS As of writing, aggregated reports are being incorporated into the open source code and several vendor products. Aggregated reports provide several benefits for signal timers and other stakeholders. They can: Identify “hot spots” either amongst intersections or the movements at an intersection. Compare to historical data for trend analysis and quantitative performance tracking. Produce shareable reports (i.e., summary tables) that can be given to the public and decision-makers. The following example reports aggregate data at different levels for different audiences. 5.11.1 AGGREGATED CHARTS Aggregated charts sum (or average) performance measures by intersection and time of day for various intervals (e.g., 15-minute intervals, 30-minute intervals, etc.). These are useful for signal timers trying to identify intersections or movements that require signal timing and maintenance adjustments. The example in EXHIBIT 5-33 illustrates the number of split failures by hour of the day for different movements at an intersection. In this case, the northbound left-turn phase is experiencing the highest number of split failures throughout the day. EXHIBIT 5-33. AGGREGATED REPORT FOR SPLIT FAILURES (MACKEY 2018) WBL Ph(8) EBL Ph(4) WBT Ph8 EBT Ph4 SBT Ph6 SBL Ph1(2) NBL Ph5(6) NBT Ph2 Note: Phase numbers outside parentheses are protected phases, and phase numbers inside parentheses are permitted phases. IMPLEMENTATION OF PERFORMANCE MEASURES 187

5.11.2 DASHBOARDS Dashboards can be used to compare current conditions to historical data. This type of summary is useful for both signal timers and decision-makers to track progress. The example in EXHIBIT 5-34 shows the status of various performance measures along with the percent change from the previous month. EXHIBIT 5-34. MONTHLY DASHBOARD (DAVIS AND HARRIS 2018) 188 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

5.11.3 SUMMARY TABLES AND CHARTS Summary tables and charts aggregate data up to a high level so that performance measures can be shared with the public and decision-makers. It is most useful to report performance measures against EXHIBIT 5-35. SUMMARY TABLE FOR PERFORMANCE MEASURE TARGETS (UDOT 2018) targets. EXHIBIT 5-35 is a summary table of performance measure targets used by UDOT, and EXHIBIT 5-36 is a summary chart that illustrates communication status over several years compared to the targets. On a public website, UDOT provides similar summary charts for each performance measure listed in EXHIBIT 5-35. EXHIBIT 5-36. PERCENT CONNECTED SIGNALS THAT ARE COMMUNICATING COMPARED TO TARGET (UDOT 2018) Target is above 97.5% Region 1 Region 2 Region 3 Region 4 IMPLEMENTATION OF PERFORMANCE MEASURES 189

5.12 REFERENCES 1. Bullock, D. 2016. “Traffic Signal Performance Measures Workshop.” Presented at Automated Traffic Signal Performance Measures Workshop. http:// dx.doi.org/10.5703/1288284316016 2. Davis, A and S. Harris. 2018. “ATSPM – GDOT Experience.” Presented at February 2018 FHWA ATSPM Webinar. 3. Day, C.M., R. Haseman, H. Premachandra, T.M. Brennan, J.S. Wasson, J.R. Sturdevant, and D.M. Bullock. 2010. “Evaluation of arterial signal coordination: methodologies for visualizing high-resolution event data and measuring travel time.” Transportation Research Record, No. 2192, pp. 37-49. 4. Day, C.M., T.M. Brennan, A.M. Hainen, S.M. Remias, H. Premachandra, J.R. Sturdevant, G. Richards, J.S. Wasson, and D.M. Bullock. 2011. "Reliability, flexibility, and environmental impact of alternative objective functions for arterial offset optimization." Transportation Research Record, No. 2259, pp. 8-22. 5. Day, C.M. and D.M. Bullock. 2011. "Computational efficiency of alternative algorithms for arterial offset optimization." Transportation Research Record, No. 2259, pp. 37-47. 6. Day, C.M., D.M. Bullock, H. Li, S.M. Remias, A.M. Hainen, R.S. Freije, A.L. Stevens, J.R. Sturdevant, and T.M. Brennan. 2014. Performance Measures for Traffic Signal Systems: An Outcome- Oriented Approach. Purdue University, West Lafayette, IN. http://dx.doi. org/10.5703/1288284315333 7. Day, C.M., D.M. Bullock, H. Li, S. Lavrenz, W.B. Smith, and J.R. Sturdevant. 2015. Integrating Traffic Signal Performance Measures into Agency Business Processes. Purdue University, West Lafayette, IN. http://dx.doi. org/10.5703/1288284316063 8. Federal Highway Administration (FHWA). 2009. Manual on Uniform Traffic Control Devices, 2009 Edition with Revision 1 Dated May 2012 and Revision 2 Dated May 2012. U.S. Department of Transportation, Washington, D.C. 9. Grossman, J. 2017. Development, Testing, and Implementation of Traffic Signal Performance Measures at a Local Governmental Agency. PhD Thesis, Purdue University, West Lafayette, IN. 10. Mackey, J. 2017. “UDOT Automated Traffic Signal Performance Measures Configuration Utility.” Presented at UDOT ATSPM Train-the-Trainer Workshop, Salt Lake City, UT. http://udottraffic.utah.gov/ ATSPM/Images/TTTJamieMackey.pdf 11. Mackey, J. 2018. “UDOT ATSPM 4.2.” Presented at January 2018 FHWA ATSPM Webinar. 12. Urbanik, T., et al. 2015. NCHRP Report 812: Signal Timing Manual, 2nd ed. Transportation Research Board of the National Academies, Washington, DC. 13. Utah Department of Transportation (UDOT). 2018. Traffic Management Tactical Measures website. https:// dashboard.udot.utah.gov/stories/s/ Traffic-Management-Tactical-Measures/ w8up-dkii 14. Utah Department of Transportation (UDOT). (n.d.-a). Automated Traffic Signal Performance Measures website. http://udottraffic.utah.gov/atspm/ 190 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

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Management of traffic signal systems is a critical function for many transportation agencies. Thanks to advancements in technology, it is now possible to collect large amounts of data at signalized intersections, leading to the development of dozens of performance measures.

The TRB National Cooperative Highway Research Program's NCHRP Research Report 954: Performance-Based Management of Traffic Signals provides information to help agencies invest in signal performance measures as part of a comprehensive approach to performance-based management.

Supplementary materials to the report include a data dictionary and a PowerPoint presentation.

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