National Academies Press: OpenBook

Performance-Based Management of Traffic Signals (2020)

Chapter: Chapter 2 - Performance Measure Selection

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Suggested Citation:"Chapter 2 - Performance Measure Selection." 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 2 - Performance Measure Selection." 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 2 - Performance Measure Selection." 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 2 - Performance Measure Selection." 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 2 - Performance Measure Selection." 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 2 - Performance Measure Selection." 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 2 - Performance Measure Selection." 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 2 - Performance Measure Selection." 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 2 - Performance Measure Selection." 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 2 - Performance Measure Selection." 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 2 - Performance Measure Selection." 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 2 - Performance Measure Selection." 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 2 - Performance Measure Selection." 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 2 - Performance Measure Selection." 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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CHAPTER 2 PERFORMANCE MEASURE SELECTION 2.1 DEFINE THE OPERATING ENVIRONMENT 15 2.2 IDENTIFY USERS OF THE TRANSPORTATION SYSTEM 16 2.3 ESTABLISH USER AND MOVEMENT PRIORITIES 17 2.4 SELECT OBJECTIVES 17 2.5 SELECT SIGNAL PERFORMANCE MEASURES 19 2.6 DETERMINE IMPLEMENTATION SCALE 21 2.7 ADDITIONAL RESOURCES 22 2.8 REFERENCES 24 LIST OF EXHIBITS EXHIBIT 2-1. APPLYING THE STM2 OUTCOME- BASED PROCESS TO SIGNAL PERFORMANCE MEASURES 14 EXHIBIT 2-2. OBJECTIVE-BASED CATEGORIES FOR SIGNAL PERFORMANCE MEASURES 18 EXHIBIT 2-3. AGGREGATION OPTIONS FOR SIGNAL PERFORMANCE MEASURES 20 EXHIBIT 2-4. IMPLEMENTATION SCALE OPTIONS 21

CHAPTER FOCUS Integrating signal performance measures into the management of a traffic signal system can follow a process that is similar to the outcome-based process introduced in NCHRP Report 812: Signal Timing Manual (STM2) (Urbanik et al. 2015). There are a number of ways to time a traffic signal, but the “best” way will depend significantly on what an agency is trying to accomplish. The same is true for signal performance measures. It is important for an agency to prioritize signal performance measures that align with their desired outcomes. The number of available metrics makes it difficult to effectively monitor all of them, making a focused approach necessary. This chapter focuses on the first five steps of the outcome-based process (as illustrated in EXHIBIT 2-1), which lead a practitioner through selecting performance measures that will match their goals, objectives, and methods of signal system management. More details about each step of the outcome-based process can be found in STM2 Chapter 1 and STM2 Exhibit 3-1. Subsequent chapters in this guidebook describe how the steps in the STM2 relate to the use of signal performance measures. EXHIBIT 2-1. APPLYING THE STM2 OUTCOME-BASED PROCESS TO SIGNAL PERFORMANCE MEASURES SIGNAL TIMING MANUAL OUTCOME- BASED PROCESS STEP 1 Define the Operating Environment STEP 2 Identify Users STEP 3 Establish User and Movement Priorities STEP 4 Select Operational Objectives STEP 5 Establish Performance Measures STEP 6 Develop Timing Strategies and Timing Values STEP 7 Implement and Observe STEP 8 Monitor and Maintain SIGNAL PERFORMANCE MEASURES INTEGRATION STEP 1 Chapter 2 Performance Measure Selection STEP 2 Chapter 3 Performance Measure Details STEP 3 Chapter 4 System Needs for Performance Measures STEP 4 Chapter 5 Implementation of Performance Measures STEP 5 Chapter 6 Integration into Agency Practice 14 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

2.1 DEFINE THE OPERATING ENVIRONMENT The STM2 includes a comprehensive discussion of signal system operating environments (Urbanik et al. 2015). As the first step in the outcome-based process, defining the operating environment directly impacts the priorities and objectives an agency will use to select performance measures. This section summarizes different operating environment elements introduced in the STM2 and highlights considerations specific to the selection of signal performance measures. Note that in this case, the operating environment goes beyond physical characteristics and includes goals of the local operating agency and its regional stakeholders. Multi-jurisdictional impacts. Many signalized roadway networks cross jurisdictional boundaries. As a result, multiple agencies may be interested in signal performance measures at the same intersections and along the same corridors. By sharing a signal performance measure system, multiple agencies can share the costs (e.g., equipment, software, staff time) as well as the benefits (e.g., available data, performance measure reports). Such a scheme could reduce the cost of implementation and elevate collaboration among the agencies. However, it may also require the agencies to develop new agreements. Location and associated environment. Operational objectives (and the resulting performance measurement needs) will vary depending on whether traffic signals are located in an area that is predominately urban, suburban, or rural. For example, performance measures related to vehicles may take precedence in a rural environment, whereas performance measures that track pedestrian activity may be the primary focus in an urban area. Additionally, growth may influence the selection of performance measures. For example, if areas near a signalized corridor are developing quickly, traffic volumes may be an important performance measure to collect for planners who are modeling changing conditions. Roadway classification. There are different user expectations on freeways, arterials, collectors, and local streets, and each type of roadway will have different operational priorities depending on the level of mobility and accessibility. For example, the priority of vehicle throughput on an arterial may lead an agency to emphasize performance measures related to progression quality, whereas the priority of pedestrians on a local street may lead to measuring pedestrian delay. Transportation network characteristics. In addition to operational differences between isolated intersections and coordinated corridors, the degree of signal density may influence the means of implementing performance measures. For instance, an agency that maintains traffic signals concentrated in a small geographic area may be able to easily establish communication to each intersection, whereas an agency that maintains traffic signals located at greater distances might need to consider alternatives to wired communication (e.g., cellular modems or periodic field visits to collect performance measure data). Traffic patterns. Traffic conditions change by time of day, day of week, and seasonally. Some locations may have very predictable patterns that remain consistent year after year, but others may have less predictable patterns that change as a result of special events, tourism, or regional growth. Performance measures can quantify the degree of variability in those patterns and help determine whether retiming, special timing plans, or other investments might be beneficial.AN OUTCOME-BASED PROCESS results in signal timing that is based on the operating environment, users, user priorities by movement, and local operational objectives. PERFORMANCE MEASURE SELECTION 15

Policies and visions. Many transportation agencies have goals related to mobility, reliability, and safety, and performance measures can support the resulting policies and visions driven by those goals. Performance measures provide an agency with the opportunity to inform leadership about the health and operation of the system. For example, an agency that has a goal of improving mobility may want to measure and track the number of vehicles arriving on green, so that they can report on progress made toward a transportation system with fewer stops. 2.2 IDENTIFY USERS OF THE TRANSPORTATION SYSTEM It is important for the roadway environment to balance the needs of all transportation system users. Agencies should select performance measures considering the mix of users at their intersections. Pedestrians. Practitioners can use performance measures to identify intersections with high pedestrian delay, high pedestrian demand, and high conflicting demand between vehicles and pedestrians. • High pedestrian delay can help an agency identify if cycle lengths are too long to meet pedestrian priorities. • High pedestrian demand during peak times can indicate that an agency needs splits that can accommodate full pedestrian clearance times every cycle. A location with low pedestrian demand can operate acceptably with programmed splits that are lower than the pedestrian clearance times if allowed by the controller (resulting in a possible temporary loss of coordination when there is pedestrian demand). • High conflicting demand can help an agency determine if a priority treatment should be considered, such as a leading pedestrian interval or exclusive pedestrian phase. Bicycles. Signal timing parameters related to phase initiation and phase termination (i.e., minimum green, yellow change, and red clearance) are critical to bicycles. Practitioners may need to adjust parameters to account for the bicyclists’ lower speeds and acceleration characteristics. Practitioners can use performance measures to make adjustments at intersections with high bicycle volumes. Light vehicles. The number of light vehicles (i.e., passenger cars and light trucks) using an intersection will impact many signal timing parameters, including cycle length, the time allocated to each phase, and the order of the phases. Practitioners can monitor intersection- and system-level performance measures to ensure that they allocate green time appropriately and offsets are progressing traffic effectively. Heavy vehicles. Large trucks require longer acceleration and deceleration times. This requirement impacts queue storage, discharge rates, and detector configurations. If practitioners program detection to count heavy vehicles, an agency can determine if they should consider priority treatments based on high volumes of trucks. Additionally, practitioners can track the frequency of truck priority requests and determine how often priority is serviced as well as the impacts to other users. Transit vehicles. Transit vehicles may justify special phasing and preferential treatment through the use of priority. Similar to truck priority, performance measures can identify the frequency of priority requests, priority service, and impacts to other users. Emergency vehicles. Emergency vehicles often use preemption to prioritize their movements at signalized intersections. Practitioners can use performance measures to monitor the number of preemption requests, preemption service, and impacts to other users during and after events. 16 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

Rail (heavy, light, or streetcars using exclusive right-of-way). Railway crossings near signalized intersections often require preemption to clear the tracks of other transportation system users prior to the arrival of trains. Practitioners can use performance measures to monitor the number of preemption requests, preemption service, and impacts to other users during and after events. 2.3 ESTABLISH USER AND MOVEMENT PRIORITIES The operating environment and mix of intersection users may result in competing priorities. For example, in an urban setting, vehicle throughput may be at odds with providing pedestrians additional time to cross. The users and movements an agency chooses to prioritize will directly affect which performance measures are the most important to track. For example, if an agency decides that vehicle throughput is the priority, the most relevant performance measure may be arrivals on green on the major street versus tracking pedestrian delay. With the number of performance measures currently available (and more likely to be developed in the future), it is essential that an agency identify user and movement priorities to guide the selection of objectives and ultimately performance measures. 2.4 SELECT OBJECTIVES An agency’s signal system objectives should reflect the operating environment, mix of transportation system users, and priorities. Once an agency has identified what they want to accomplish with their traffic signal system, they should match the objectives to relevant signal performance measures. Throughout the remainder of this guidebook, signal performance measures will be grouped into five objective-based categories (as illustrated in EXHIBIT 2-2): communication, detection, intersection/ uncoordinated timing, system/coordinated timing, and advanced systems and applications. Communication is often installed to maintain coordination between signalized intersections. Although communication is not required for a traffic signal to function, it is essential for scalable, system-wide performance-based management. While data can be collected during periodic field visits, communication to a central location allows data to be collected automatically and in real-time. Performance measures can be used to determine the health of the communication system (i.e., number of intersections that are communicating). Detection can be used to improve intersection operations, but it is not a requirement for signalized intersections. Intersections without detection operate in a pre-timed mode in which each movement receives the same amount of green every cycle. However, for signal performance measures, detection is needed to compare traffic patterns to signal events. Without robust detection, only a subset of signal performance measures will be available. Performance measures can be used to determine the health of the detection system (i.e., number of functioning detectors). In addition to equipment health, signal performance measures can be used to report on intersection and system- wide operations. Basic intersection/ uncoordinated timing is required for every traffic signal; it is programmed to serve different transportation system users efficiently and safely. Performance measures can be used to determine the delay and safety experienced by different modes at individual intersections. If a signalized intersection is located in a system, it may be synchronized with other intersections to provide coordinated control. Performance measures can be used to determine how well the signal system is progressing traffic along the corridor and to identify impacts of high volumes, which can involve multiple intersections. PERFORMANCE MEASURE SELECTION 17

EXHIBIT 2-2. OBJECTIVE-BASED CATEGORIES FOR SIGNAL PERFORMANCE MEASURES CATEGORY OBJECTIVE(S) 1 COMMUNICATION • Maximize number of connected intersections 2 DETECTION • Maximize number of functioning detectors 3 INTERSECTION / UNCOORDINATED TIMING • Minimize delay for transportation system users (e.g., vehicles, bicycles, pedestrians) • Improve safety 4 SYSTEM /COORDINATED TIMING • Improve priority movements (i.e., progression) 5 ADVANCED SYSTEMS AND APPLICATIONS • Minimize delay for modes with preferential treatment (e.g., rail, emergency vehicles, transit, trucks) • Manage traffic variability Note: Exhibit modified from Integrating Traffic Signal Performance Measures into Agency Business Processes (Day et al. 2015). Finally, if there are modes with preferential treatment or if traffic is unpredictable, practitioners can apply advanced systems and applications to better manage the intersection and system control. For additional information on advanced systems and applications (e.g., adaptive, traffic responsive systems, transit signal priority), refer to STM2 Chapters 9 and 10. Performance measures can be used to assess the delay experienced by modes with preferential treatment as well as whether the advanced systems and applications are managing variability in traffic conditions. 18 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

2.5 SELECT SIGNAL PERFORMANCE MEASURES Practitioners can use performance measures at different levels of aggregation. Agencies should consider not only the individual performance measures they will assess, but also how they can combine those measures to provide information about their entire signal system. For example, the number of vehicles arriving on green can be reviewed for a particular phase at a particular intersection to inform an offset adjustment. At a different level of aggregation, the total arrivals on green can be calculated for the entire corridor to assess overall progression. An agency may have an existing system that allows them to obtain many signal performance measures. However, practitioners may quickly find themselves overloaded with data if they try to evaluate all of these measures. Even if an agency is able to obtain all of the signal performance measures discussed in this guidebook, this section (along with the information in Chapter 3) will help an agency focus the available time on the signal performance measures that best match their needs. EXHIBIT 2-3 groups individual performance measures into the five objective-based categories and summarizes how they can be aggregated to provide status reports. Chapter 3 includes detailed descriptions and example applications for each performance measure. Vendors continue to create new performance measures and new ways to visualize existing measures, and technological advances will lead to even more performance measures in the future. As connected vehicles make their way into the fleet, trajectory data will be an important data source for performance- based management. Advances in predictive tools (such as offset optimization) could lead to controllers adjusting signal timing based on high-resolution data. These developments might be accelerated with the introduction of machine learning and artificial intelligence. The signal performance measures described in this guidebook are only the beginning of performance-based management for traffic signal systems.  PERFORMANCE MEASURE SELECTION 19

EXHIBIT 2-3. AGGREGATION OPTIONS FOR SIGNAL PERFORMANCE MEASURES OBJECTIVE AGGREGATED STATUS REPORT(S) INDIVIDUAL SIGNAL PERFORMANCE MEASURE(S) WITH CHAPTER 3 REFERENCES 1 Communication Equipment Health • Percent of connected signals communicating • 3.1 Communication Status • 3.2 Flash Status • 3.3 Power Failures 2 Detection Equipment Health • Percent of detectors fully functional by mode (e.g., vehicle, pedestrian, bicycle, rail, emergency vehicle, transit, truck) • 3.4 Detection System Status • 3.5 Vehicle Volumes (high volumes during low-traffic times) • 3.6 Phase Termination (high number of max-outs, force-offs, and/or pedestrian calls during low-traffic times) • 3.12 Pedestrian Volumes (high volumes during low-traffic times) • 3.13 Pedestrian Phase Actuation and Service (high number of pedestrian calls during low-pedestrian times) • 3.25 Preemption Details (high number of requests) • 3.26 Priority Details (high number of requests) 3 Vehicle Delay • Average vehicle delay • Percent of cycles with unserved vehicles • Percent of movements at or near capacity • 3.5 Vehicle Volumes (applied to capacity analysis) • 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 • 3.18 Effective Cycle Length (for delay estimation) • 3.24 Time-Space Diagram Pedestrians • Minimum, maximum, and average pedestrian delay • Percent of movements with high pedestrian activity • 3.12 Pedestrian Volumes • 3.13 Pedestrian Phase Actuation and Service • 3.14 Estimated Pedestrian Delay • 3.15 Estimated Pedestrian Conflicts • 3.18 Effective Cycle Length (for delay estimation) Bicycles • Minimum, maximum, and average bicycle delay • Percent of movements with high bicycle activity • 3.5 Vehicle Volumes (applied to capacity analysis if bicycle detection is separate) • 3.9 Estimated Vehicle Delay (if bicycle detection is separate) • 3.18 Effective Cycle Length (for delay estimation) Safety • Percent of movements with queues that exceed storage • Percent of vehicles entering on red • Number of conflicting movements • 3.10 Estimated Queue Length • 3.11 Oversaturation Severity Index • 3.15 Estimated Pedestrian Conflicts • 3.16 Yellow/Red Actuations • 3.17 Red-Light-Running (RLR) Occurrences 4 Vehicle Progression • Percent of vehicles arriving on green • Percent of vehicles arriving on red • 3.5 Vehicle Volumes (assess TOD plans) • 3.10 Estimated Queue Length • 3.11 Oversaturation Severity Index • 3.18 Effective Cycle Length (confirm coordination) • 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 5 Rail • Percent of preempt calls received at design value or no more than specified value • Percent of track clearance green intervals completed before train arrival • Average delay due to preemption • 3.25 Preemption Details (percent of false calls as well as minimum, maximum, and average preemption times – advance and/or simultaneous) Emergency Vehicles • Percent of emergency vehicles arriving on green • Percent of emergency vehicles arriving on red • 3.25 Preemption Details (delay per emergency vehicle and number of requests) Transit • Percent of transit vehicles arriving on green • Percent of transit vehicles arriving on red • 3.26 Priority Details (delay per transit vehicle and number of requests) Trucks • Percent of trucks arriving on green • Percent of trucks arriving on red • 3.26 Priority Details (delay per truck and number of requests) 20 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

2.6 DETERMINE IMPLEMENTATION SCALE Once an agency has selected signal performance measures, they must decide the best approach to acquire required resources, install upgrades, and integrate signal performance measures into the management of their signal system. Agencies have taken different approaches to implementing signal performance measures, as summarized in EXHIBIT 2-4. EXHIBIT 2-4. IMPLEMENTATION SCALE OPTIONS PILOT PROJECT SYSTEMATIC UPGRADES SYSTEM-WIDE IMPLEMENTATION Description • Test performance measures at a select number of intersections • Upgrade equipment as other projects are completed (either to accommodate future use of performance measures or add intersections to an existing performance measure system) • Deploy performance measures at all intersections Potential Resources and Needs • Equipment upgrades (e.g., controllers, detection, communication) at a select number of intersections • Central equipment or a cloud-based solution to store and process data • Training for staff • Equipment upgrades (e.g., controllers, detection, communication) as part of other projects • Revised design standards to promote desired performance measures • Potential equipment upgrades (e.g., controllers, detection, communication) at all intersections • Central equipment or a cloud-based solution to store and process data • Training for staff Advantages • Can test performance measures at intersections that already have upgraded equipment • Limited number of intersections reduces time required for setup and verification • Reduced data storage needs • Facilitates value assessment prior to a system-wide implementation • Limited upfront investment • Allows “easing-in” of eventual ATSPM deployment and thorough assessment of gaps • Can track performance measures across the system • Equipment and software consistency due to initial upfront procurement Challenges • Requires installation of central data storage • Limited number of locations with performance measures • No performance measures until data storage and performance measure software are installed • Upgraded equipment may be inconsistent over time • May be costly to upgrade field infrastructure at all intersections • Increased time for setup and verification • Increased data storage needs • Increased data requires a plan for effective application and funding improvements Examples • Oregon Department of Transportation (ODOT): Deployed ATSPMs at seven traffic signals on US 101 in Lincoln City, Oregon • Virginia Department of Transportation (VDOT): Deployed ATSPMs at 15 intersections along Route 29 near Charlottesville, Virginia • Pennsylvania Department of Transportation (PennDOT): Tested ATSPMs along a pilot corridor in Cranberry Township in the Pittsburgh area • Indiana Department of Transportation (INDOT): Adjusted design specifications and approved materials list to facilitate incremental addition of intersections using upgrades accounted for in the maintenance budget • City of Portland, Oregon: In addition to a small pilot project, updated detection standards to accommodate future use of performance measures • Utah Department of Transportation (UDOT) • Georgia Department of Transportation (GDOT) • Clark County, Washington • College Station, Texas PERFORMANCE MEASURE SELECTION 21

While some agencies have completed system-wide updates, most build out their systems through smaller projects, either by upgrading all required equipment at a select number of intersections to get to a fully functional pilot project or by upgrading equipment systematically in preparation for future use of performance measures. At first glance it may seem that signal performance measures require significant equipment upgrades (explained in detail in Chapter 4), but most investments made to facilitate signal performance measures will also improve intersection operations. For example, upgrades to communication, detection, and controllers can result in fewer equipment malfunctions and signal timing that is responsive to intersection users. There are benefits to completing a large-scale implementation. Large implementations will likely require more equipment upgrades and staff time for installation, but they also give agencies the ability to make system-wide decisions based on performance measures. If an agency implements ATSPMs at all their intersections, they have an opportunity to identify the most-critical signalized intersections (based on their signal system objectives) and to make operations and maintenance improvements at those locations. Performance-based management applied at the system level will result in the largest benefits. Regardless of how an agency chooses to implement signal performance measures, they should use a systematic process to identify performance measures that match their operational objectives. Different implementation scales do not negate the necessity to choose signal performance measures based on needs, capabilities, and constraints. 2.7 ADDITIONAL RESOURCES As introduced in Chapter 1, most of the performance measures presented in this guidebook are automated traffic signal performance measures (ATSPMs) based on high-resolution data. There are other signal performance measures available, but the use of high-resolution data (further described in Chapter 4) allows for continuous monitoring of traffic signals and provides a scalable solution to signal system management. Several existing resources, listed in the next section, have previously defined many of the measures presented in Chapter 3. All of these reports are available for download online at no cost. 2.7.1 POOLED FUND STUDY REPORTS From 2014 to 2017, a group of 10 state departments of transportation (DOTs) and the City of Chicago sponsored a pooled fund study on signal performance measures. This study led to the development of two reports that included extensive documentation of different performance measures based on high-resolution data and travel time data sets. The first report discussed individual performance measures in detail, compiling previous work, and the second report provided further illustrations of their use. Presentations from a January 2016 workshop on ATSPMs are also available at the link provided for the workshop. • 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 • 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 • January 2016 Automated Traffic Signal Performance Measures Workshop presentations. http://docs.lib.purdue.edu/ atspmw/2016/Presentations/ 22 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

2.7.2 HIGH-RESOLUTION DATA ENUMERATIONS The Indiana Department of Transportation (INDOT) sponsored research into signal performance measures for several years. That research facilitated the development of the current de facto “standard” for high- resolution data—the “enumerations” that list the individual event codes and their meanings. • Sturdevant, J.R., T. Overman, E. Raamot, R. Deer, D. Miller, D.M. Bullock, C.M. Day, T.M. Brennan, H. Li, A. Hainen, and S.M. Remias. 2012. Indiana Traffic Signal Hi Resolution Data Logger Enumerations. Indiana Department of Transportation and Purdue University, West Lafayette, IN. http:// dx.doi.org/10.4231/K4RN35SH Although this document serves as a de- facto standard, there is a need to update the enumeration list to reflect enhancements that are emerging from vendors to accommodate new features requested by agencies. 2.7.3 UTAH DOT OPEN SOURCE SOFTWARE The Utah Department of Transportation (UDOT) developed an open source system for downloading high-resolution data, calculating ATSPMs, and publishing reports and charts online. UDOT’s web page (with live field data) is publicly accessible, and the source code for the website is available at the second link in this section. The third link allows developers to track changes made to the open source code while UDOT reviews and updates the version released on the Open Source Application Development Portal (OSADP). The UDOT web page also contains links to numerous presentations on the use of ATSPMs. • Utah Department of Transportation (UDOT). (n.d.-a). Automated Traffic Signal Performance Measures website. http:// udottraffic.utah.gov/atspm/ • Federal Highway Administration. Open Source Application Development Portal ATSPM source code. https://www.itsforge. net/index.php/community/explore- applications#/30/133 • Utah Department of Transportation (UDOT). (n.d.-b). ATSPM GitHub development website. https://github.com/ udotdevelopment/ATSPM 2.7.4 NCHRP PROJECT 03-90, "OPERATION OF TRAFFIC SIGNALS IN OVERSATURATED CONDITIONS" NCHRP Project 03-90 developed guidance for agencies operating traffic signals with traffic demand above saturation levels. This project also facilitated the development of performance measures for oversaturated conditions. • Gettman, D., M. Abbas, H. Liu, and A. Skabardonis. 2012. NCHRP Web- Only Document 202: Operation of Traffic Signal Systems in Oversaturated Conditions, Volume 1: Practitioner Guidance. Transportation Research Board, Washington, DC. http://dx.doi. org/10.17226/22290 • Gettman, D., G. Madrigal, S. Allen, T. Boyer, S. Walker, J. Tong, S. Phillips, H. Liu, X. Wu, H. Hu, M. Abbas, Z. Adam, and A. Skabardonis. 2012. NCHRP Web-Only Document 202: Operation of Traffic Signal Systems in Oversaturated Conditions, Volume 2: Final Report. Transportation Research Board, Washington, DC. http:// dx.doi.org/10.17226/22289 PERFORMANCE MEASURE SELECTION 23

2.7.5 AASHTO INNOVATION INITIATIVE AASHTO selected ATSPMs as a focus technology for the Innovation Initiative in 2013. ITE produced and delivered a series of webinars in 2014; further information is available on the AASHTO website. • Automated Traffic Signal Performance Measures webinars. http://aii.transportation.org/Pages/ AutomatedTrafficSignalPerformance Measures.aspx 2.7.6 FHWA EVERY DAY COUNTS In 2015, the Every Day Counts Initiative update, EDC-4, identified ATSPMs as an important tool “to support objectives and performance-based maintenance and operations strategies that improve safety and efficiency while cutting congestion and cost.” Additional information is available on the FHWA EDC website. • Federal Highway Administration. EDC-4: Automated Traffic Signal Performance Measures (ATSPMs) website. https://www. fhwa.dot.gov/innovation/everydaycounts/ edc_4/atspm.cfm 2.8 REFERENCES 1. "Automated Traffic Signal Performance Measures" webpage. http://aii.transportation.org/Pages/ AutomatedTrafficSignalPerformance Measures.aspx 2. 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 3. 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 4. Federal Highway Administration. EDC-4: Automated Traffic Signal Performance Measures (ATSPMs) website. https:// www.fhwa.dot.gov/innovation/ everydaycounts/edc_4/atspm.cfm 5. Federal Highway Administration. Open Source Application Development Portal ATSPM source code. https://www. itsforge.net/index.php/community/ explore-applications#/30/133 6. Gettman, D., M. Abbas, H. Liu, and A. Skabardonis. 2012. NCHRP Web- Only Document 202: Operation of Traffic Signal Systems in Oversaturated Conditions, Volume 1: Practitioner Guidance. Transportation Research Board, Washington, DC. http://dx.doi. org/10.17226/22290 7. Gettman, D., G. Madrigal, S. Allen, T. Boyer, S. Walker, J. Tong, S. Phillips, H. Liu, X. Wu, H. Hu, M. Abbas, Z. Adam, and A. Skabardonis. 2012. NCHRP Web-Only Document 202: Operation of Traffic Signal Systems in Oversaturated Conditions, Volume 2: Final Report. Transportation Research Board, Washington, DC. http://dx.doi. org/10.17226/22289 8. January 2016 Automated Traffic Signal Performance Measures Workshop presentations. http://docs.lib.purdue. edu/atspmw/2016/Presentations/ 9. Sturdevant, J.R., T. Overman, E. Raamot, R. Deer, D. Miller, D.M. Bullock, C.M. Day, T.M. Brennan, H. Li, A. Hainen, and S.M. Remias. 2012. Indiana Traffic Signal Hi Resolution Data Logger Enumerations. Indiana Department of Transportation and Purdue University, West Lafayette, IN. http://dx.doi.org/10.4231/K4RN35SH 24 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

10. Urbanik, T., et al. 2015. NCHRP Report 812: Signal Timing Manual, 2nd ed. Transportation Research Board of the National Academies, Washington, DC. 11. Utah Department of Transportation (UDOT). (n.d.-b). ATSPM GitHub development website. https://github. com/udotdevelopment/ATSPM 12. Utah Department of Transportation (UDOT). (n.d.-a). Automated Traffic Signal Performance Measures website. http://udottraffic.utah.gov/atspm/ PERFORMANCE MEASURE SELECTION 25

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Performance-Based Management of Traffic Signals Get This Book
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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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