A Methodology and Case Study: Evaluating the Benefits and Costs of Implementing Automated Traffic Signal Performance
Chapter 2. Relevant Prior Work
This chapter presents background on signal monitoring concepts that enabled automated traffic signal performance measures (ATSPM) to be developed. It also provides an overview of previous studies in which the performance measures were developed and their utility was demonstrated through applications to various real-world use cases.
Event Data Concept
The core data used in ATSPMs is high-resolution data, which is a digital record of events occurring within the signal controller, such as times when signal outputs changed (e.g., transitions between green, yellow, and red for phases or overlaps), detector states changed from "on" to "off" or vice versa, and other control events. In theory, any change in information within a signal controller could be registered as an event. The present enumerations list 101 types of events, and some vendors have independently added more.
In the past, limitations of hardware and communication used in traffic signal systems restricted the availability of detailed data-collection capabilities. Some advanced technologies, such as adaptive signal control, required collection of similar types of data. However, most adaptive systems did not report this internal data. One exception was ACS Lite, whose records of signal and detector state data could be extracted in raw form (Gettman 2007).
In the early 2000s, researchers began to explore use cases in signal control evaluations that required logging of real-time detector and phase states. One use case was the evaluation of new detection systems. The measurement of detector on-time latency made it necessary to record state changes at time resolutions less than 1 second; this led to the creation of an intersection testbed where state changes were logged in real time. Researchers soon realized the data used for evaluating detectors contained useful information for measuring intersection performance (Smaglik 2007; National Transportation Operations Coalition 2012). Around the same time, researchers in Minnesota began to log intersection data in a similar manner, with the goals of evaluating corridor progression by estimating queue lengths to predict travel times of virtual probe vehicles (Liu 2009).
Figure 6 illustrates an example of high-resolution data. In this example, an instance of phase 2 (Φ2) is shown. The time when the controller is managing the timing of phase 2 includes its green, yellow, and red clearance intervals. The start and end of each interval is marked by a corresponding event. The input states of two detector channels are also shown; transitions from an "on" to "off" state, and vice versa, are recorded by events. From these data, assuming the two detector channels represent stop-bar detectors, it's possible to determine when vehicles are waiting for the phase to begin (when the detector turns on), and when the phase is finished serving vehicles (when the detectors turn off).
Performance Measures and Use Cases
Research into signal performance measures using high-resolution data has been ongoing for more than a decade, and these data have been applied to numerous use cases. A functional classification of use cases for signal performance measures was suggested by researchers (Day 2015), based on the idea that interdependencies of system elements suggest different roles of performance measurement. An illustration of this hierarchy is presented in figure 7; this is slightly modified from earlier representations, reflecting input received from practitioners.
From bottom to top, the five layers are:
The top layer is entitled “Advanced Applications”, and shows arbitrary statistical charts in the background. The second layer is entitled “Coordination”, and shows vehicles moving through a series of traffic signals in the background. The third layer is entitled “Communication”, and shows blue electrical cables in the background. The fourth layer is entitled “Detection”, and shows rectangular detection zones (near an intersection approach stop line) in the background. The bottom layer is entitled “Local Control”, and shows the inside of a traffic signal controller cabinet in the background.
Each layer has its own maintenance or operation concerns. At a basic level, it is helpful to ensure the system components are functional, such as detection and communication systems. Many agencies invest a lot of time in keeping these systems up and running. The impact of signal timing is also important to quantify; the quality of that service can be described in many different ways, depending on the type of objective of the operation. For example, the distribution of green times affects the service of individual movements; inadequate green time leads to the buildup of vehicle queues; excessive green times on other phases during the same cycles indicate an inefficiency that likely could be corrected.
Table 3 presents a list of objectives for each functional hierarchy layer, with examples of performance measures that have been applied in literature to assist in achieving these objectives. Example studies are highlighted where quantitative user benefits have been demonstrated as an outcome of using performance measures to manage the system. There are only a handful of studies where this is the case; these used external data sources to measure travel times, and converted the change in travel time to annualized user benefits. A few other studies included changes in delay and other similar metrics, but did not include user benefit documentation.
The lack of documentation of monetized user benefits does not mean that benefits do not exist. However, for the most part, the extra steps necessary to obtain a quantitative record of such changes is beyond the scope of most studies or agency resources. Directly measuring these changes can be expensive, in that some external method of obtaining travel times is generally needed. It is possible the data itself could provide such estimates, but this has not yet been applied in a conversion to user benefits. Table 4 provides a list of studies that have documented user benefits from studies that provided benefits as an annualized dollar amount.
Activities promoting implementation of traffic signal performance measures were elements of research and development from an early stage. Research in both Indiana and Minnesota led to the development of scalable systems for bringing in field data. In Minnesota, this led to the development of a commercial system, while in Indiana, a prototype system was cooperatively developed between Purdue University and the Illinois Department of Transportation (IDOT). In 2012, Utah Department of Transportation (UDOT) began developing its own system, taking inspiration from the work done in Indiana, and investing considerable resources to develop a stable software package. The code was made available as an open-source license, and offered under the Federal Highway Administration's (FHWA) Open Source Application Development Portal (OSADP) in 2017, when FHWA's fourth Every Day Counts program started. Around this time, UDOT named the overall data and performance measure methodology "automated traffic signal performance measures" (ATSPM).
Indiana's research included outreach to traffic signal vendors. Through collaborative discussions between academia and industry, State, and local agencies, a common data format was agreed upon and became a de facto standard for high-resolution data. By 2016, all major controller manufacturers in the U.S. had implemented high-resolution data collection in at least one model. In addition, third-party vendors had introduced products that could collect high-resolution independent of the controller. Many of these vendors also began to develop their own systems for delivering ATSPMs to the user. At the time of this writing, there are many commercial choices for implementation in addition to the open-source software.
The growth of interest in and development of tools was accelerated in 2014 by the selection of ATSPMs as an American Association of State Highway and Transportation Officials (AASHTO) Innovation Initiative focus technology, and the FHWA's fourth Every Day Counts program in 2017–2018. These programs made hosting numerous workshops possible across the U.S., which increased awareness and implementations of ATSPMs. As of December 2018 (see figure 9), the technology has been implemented by at least 39 State departments of transportation (DOTs) at a demonstration stage, or higher, and institutionalized in four States. Many local agencies have also implemented ATSPMs, or are in the process of doing so. Presently, most of these systems are still in the early phases, many with pilot intersections or corridors with ATSPMs available, but over time the number of intersections has been growing. With ATSPMs themselves having transformed from a research topic to a tool available to practitioners by various means, an emerging focus for new research is utilization of the measures to facilitate performance-based management, as described in Chapter 1: Introduction and Concepts.
The following five categories of implementation are defined in a color-coded legend: INSTITUTIONALIZED: The State has adopted the innovation as a standard process or practice and uses it regularly on projects. ASSESSMENT STAGE: The State is assessing the performance of and process for carrying out the innovation and making adjustments to prepare for full deployment. DEMONSTRATION STAGE: The State is testing and piloting the innovation. DEVELOPMENT STAGE: The State is collecting guidance and best practices, building support with partners and stakeholders, and developing an implementation process. NOT IMPLEMENTING: The State is not currently using the innovation anywhere in the State and is not interested in pursuing the innovation. The following States are classified as INSTITUTIONALIZED: Utah, Georgia. The following States are classified as ASSESSMENT STAGE: Wisconsin, Indiana, Rhode Island. The following States are classified as DEMONSTRATION STAGE: Oregon, Minnesota, Michigan, Alabama, Virginia, Pennsylvania. The following States are classified as DEVELOPMENT STAGE: Washington DC, Washington State, Idaho, Arizona, Montana, Wyoming, Colorado, New Mexico, Texas, South Dakota, Louisiana, Arkansas, Missouri, Iowa, Kentucky, Tennessee, Florida, North Carolina, Ohio, New York, New Jersey, Delaware, Massachusetts, Vermont, New Hampshire, Maine. The following States are classified as NOT IMPLEMENTING: California, Nevada, Alaska, North Dakota, Nebraska, Kansas, Oklahoma, Illinois, Mississippi, West Virginia, South Carolina, Maryland, Connecticut, Federal Lands Highway, Puerto Rico, US Virgin Islands.
The following five categories of implementation are defined in a color-coded legend: INSTITUTIONALIZED: The State has adopted the innovation as a standard process or practice and uses it regularly on projects. ASSESSMENT STAGE: The State is assessing the performance of and process for carrying out the innovation and making adjustments to prepare for full deployment. DEMONSTRATION STAGE: The State is testing and piloting the innovation. DEVELOPMENT STAGE: The State is collecting guidance and best practices, building support with partners and stakeholders, and developing an implementation process. NOT IMPLEMENTING: The State is not currently using the innovation anywhere in the State and is not interested in pursuing the innovation. The following States are classified as INSTITUTIONALIZED: Utah, Georgia, Wisconsin, Indiana. The following States are classified as ASSESSMENT STAGE: Oregon, Wyoming, Colorado, New Mexico, Arizona, Minnesota, Alabama, Tennessee, Virginia, New Jersey, Florida. The following States are classified as DEMONSTRATION STAGE: Michigan, Pennsylvania, Washington State, Idaho, Texas, Louisiana, Missouri, Iowa, Kentucky, Ohio, North Carolina, Massachusetts, Vermont, New Hampshire, Maine, Connecticut. The following States are classified as DEVELOPMENT STAGE: Rhode Island, Washington DC, Montana, South Dakota, Arkansas, New York, Delaware, North Dakota, West Virginia. The following States are classified as NOT IMPLEMENTING: California, Nevada, Alaska, Nebraska, Kansas, Oklahoma, Illinois, Mississippi, South Carolina, Maryland, Federal Lands Highway, Puerto Rico, US Virgin Islands.
United States Department of Transportation - Federal Highway Administration