PERFORMANCE MEASURES FOR TRAFFIC SIGNAL SYSTEMS:AN OUTCOME-ORIENTED APPROACH This monograph is a synthesis of research carried out on traffic signal performance measures based on highresolution controller event data, assembled into a methodology for performance evaluation of traffic signal systems. High-resolution data consist of a log of discrete events such as changes in detector and signal phase states. A discussion is provided on the collection and management of the signal event data and on the necessary infrastructure to collect these data. A portfolio of performance measures is then presented, focusing on several different topics under the umbrella of traffic signal systems operation. System maintenance and asset management is one focus. Another focus is signal operations, considered from the perspectives of vehicle capacity allocation and vehicle progression. Performance measures are also presented for nonvehicle modes, including pedestrians, and modes that require signal preemption and priority features. Finally, the use of travel time data is demonstrated for evaluating system operations and assessing the impact of signal retiming activities.

Operations-oriented traffic signal performance measures are important for identifying the need for retiming to improve traffic signal operations. Currently, most traffic signal performance measures are obtained from high-resolution traffic signal controller event data, which provides information on an intersection-by-intersection basis and requires significant initial capital investment. Over 400 billion vehicle trajectory points are generated each month in the United States. This paper proposes using high-fidelity vehicle trajectory data to produce traffic signal performance measures such as: split failure, downstream blockage, and quality of progression, as well as traditional Highway Capacity Manual level of service. Geo-fences are created at specific signalized intersections to filter vehicle waypoints that lie within the generated boundaries. These waypoints are then converted into trajectories that are relative to the intersection. A case study is presented that summarizes the performance of an eight-intersection corridor with four different timing plans using over 160,000 trajectories and 1.4 million GPS samples collected during weekdays in July 2019 between 5:00 a.m. and 10:00 p.m. The paper concludes by commenting on current probe data penetration rates, indicating that these techniques can be applied to corridors with annual average daily traffic of ~15,000 vehicles per day for the mainline approaches, and discussing cloud-based implementation opportunities.

Signal offset optimization recently has been shown to be feasible with vehicle trajectory data at low levels of market penetration. Offset optimization was performed on two corridors with that type of data. A proposed procedure called “virtual detection” was used to process 6 weeks of trajectory splines and create vehicle arrival profiles for two corridors, comprising 25 signalized intersections. After data were processed and filtered, penetration rates between 0.09% and 0.80% were observed, with variations by approach. Then those arrival profiles were compared statistically with those measured with physical detectors, and most approaches showed statistically significant goodness of fit at a 90% confidence level. Finally, the arrival profiles created with virtual detection were used to optimize offsets and compared with a solution derived from arrival profiles obtained with physical detectors. Results demonstrate that virtual detection can produce good-quality offsets with current market penetration rates of probe data. In addition, a sensitivity analysis of the sampling period indicated that 2 weeks may be sufficient for data collection at current penetration rates.

Federal Highway Administration Office of Operations and Resource Center. Some material in this monograph was compiled from previous studies that were made possible under National Cooperative Highway Research Program project 3-79a, INDOT State Planning and Research (SPR) projects, Indiana LTAP projects, and USDOT through Small Business Innovation Research (SBIR) projects with Traffax, Inc., and through a joint research project with Marshall University. We would like to thank Rick Schuman and colleagues at Inrix, Inc., for provision of sample vehicle trajectory data in Chapter 7. We are grateful to these sponsors and research partners for their support over the years.The contents of this paper reflect the views of the authors, who are responsible for the facts and the accuracy of the data presented herein, and do not necessarily reflect the official views or policies of the sponsoring organizations. These contents do not constitute a standard, specification, or regulation. CopyrightCopyright 2015 by Purdue University. All rights reserved.Print ISBN: 978-1-62260-376-3 ePUB ISBN: 978-1-62260-377-0 ABSTRACT INTEGRATING TRAFFIC SIGNAL PERFORMANCE MEASURES INTO AGENCY BUSINESS PROCESSESThis report discusses uses of and requirements for performance measures in traffic signal systems facilitated by high-resolution controller event data. Uses of external travel time measurements are also discussed. The discussion is led by a high-level synthesis of the systems engineering concepts for traffic signal control, considering technical and nontechnical aspects of the problem. This is followed by a presentation of the requirements for implementing data collection and processing of the data into signal performance measures. The remaining portion of the report uses an example-oriented approach to show a variety of uses of performance measures for communication and detector system health, quality of local control (including capacity allocation, safety, pedestrian performance, preemption, and advanced control analysis), and quality of progression (including evaluation and optimization).

Extensive literature in the adaptive control field uses local detection available from the traffic controller as input to various control models to adjust splits, cycle lengths, and offsets. All these models have implicit control objectives, which include facilitated progression, minimized stops, minimized delay, and equitable allocation of green time. Enormous opportunities exist to incorporate probe data into the decision process with respect to when and where adaptive control can be used and which operating objectives are most applicable to a corridor as well as to an outcome assessment tool to evaluate the effectiveness of adaptive control. The research reported in this paper compared how probe data sources could be used to identify appropriate adaptive control objectives and to assess the performance of adaptive systems. Four case studies demonstrated how travel time data could be used to evaluate existing conditions, to evaluate the outcome of a traditional signal retiming, and to assess the feasibility of adaptive control opportunities. Currently, the richest probe data sets are provided by agency-installed equipment. Given the increasing penetration of crowd-sourced probe data devices and the onset of connected vehicle infrastructure, however, these sources could provide similarly rich data. This paper recommends that commercial data providers begin to develop more detailed base maps. These maps would provide richer probe data information, such as hour-by-hour statistical distributions and approach delay for signalized arterials for which the segments did not span multiple intersections. This recommendation should motivate agencies to develop more detailed specifications for probe data that will better serve their needs.

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The Federal Highway Administration (FHWA) reported between 2016 and 2017, fatal crashes in work zones increased by 3%, while fatal crashes outside of work zones decreased by 1.5%. The FHWA also reported that work zones account for approximately 10% of the nation’s overall congestion and 24% of unexpected interstate delays. This paper reports on a study of 23 construction work zones that covered approximately 150 centerline miles of Indiana interstate roadway in the summer of 2019. Approximately 50% of all interstate crashes for the period of May to September 2019 occurred within or in an approach upstream or downstream of one of these work zones. Commercially available vehicle hard-braking event data is used for the study and geofenced to the work zone approaches and limits. This research examined 196,215 hard-braking events over a 2-month period in the summer of 2019 and 3132 crashes over the same 2-month period in 2018 and 2019 for the 23 interstate work zones. The study found there were approximately 1 crash/mile for every 147 hard-braking events in and around a construction site. The R2 was approximately 0.85. The paper concludes by recommending that hard-braking event data be used by agencies to quickly identify emerging work zone locations that show relatively large number of hard-braking events for further evaluation.

Overview of Study LocationThe location used to demonstrate the use of the Abstract Graphical Performance Measures for Practitioners to Triage Split Failure Trouble CallsDetector occupancy is commonly used to measure traffic signal performance. Despite improvements in controller computational power, there have been relatively few innovations in occupancy-based performance measures or integration with other data. This paper introduces and demonstrates the use of graphical performance measures based on detector occupancy ratios to verify potential split failures and other signal timing shortcomings reported to practitioners by the public. The proposed performance measures combine detector occupancy during the green phase, detector occupancy during the first five seconds of the red phase, and phase termination cause (gap out or force off). These are summarized by time of day to indicate whether the phase is undersaturated, nearly saturated, or oversaturated. These graphical performance measures and related quantitative summaries provide a first-level screening and triaging tool for practitioners to assess user concerns regarding whether sufficient green times are being provided to avoid split failures. They can also provide outcome-based feedback to staff after making split adjustments to determine whether operation improved or worsened. The paper concludes by demonstrating how the information was used to make an operational decision to re-allocate green time that reduced the number of oversaturated cycles on minor movements from 304 to 222 during a Thursday 0900-1500 timing plan and from 240 to 180 during a Friday 0900-1500 timing plan.