Active Traffic Management Strategies: A Planning and Evaluation Guide (2024)
Chapter: 10 Learning from ATM Deployments
CHAPTER 10
Learning from ATM Deployments
Chapter Highlights and Objectives
Within any discussion regarding the next steps in transportation planning and development, input from current operational deployments is a critical need. Indeed, a key concept behind active traffic management (ATM) is that continued capacity growth via new facilities is not possible and that the capacity of existing facilities must be maximized. To maximize the return from ATM deployments and to help plan future deployments, a thorough understanding of the performance and lessons learned from existing deployments must be obtained.
As detailed in Chapter 1, ATM is one aspect of the overall approach to transportation development and operations for which agencies are responsible. ATM is the freeway and arterial management component of the broader concept of active transportation and demand management (ATDM), which is itself part of the larger context of TSMO. ATM seeks to enhance system reliability as well as extend the service life of the existing infrastructure (FHWA 2012). Examining the key tenets of existing ATM deployments and applying these principles to new designs and deployments are also means to continually improve both the reliability and sustainability of ATM deployments.
Although the desire and need to examine ATM deployments are recognized, these strategies are relatively new to the United States, and detailed evaluations are limited. To better understand their impact and performance, in-depth analyses of ATM deployments are warranted to examine and refine design and operations and quantify benefits. This analysis approach has the additional benefit of helping inform administrators who may be reluctant to embark on lesser-known strategies that seek more efficient use of existing capacity instead of supporting new infrastructure construction. This practice is consistent with the systems engineering approach used across the transportation field. This process includes operations, maintenance, and refinement—all of which can be assisted by evaluation. This chapter, therefore, provides an overview of topics related to the evaluation of existing ATM deployments as well as considerations for data and technologies to continue to refine and enhance ATM solutions. The remainder of this chapter presents the following sections:
- Ongoing Monitoring and Longer-Term Evaluations. Briefly overviews short- and long-term monitoring as well as benefit-cost analyses and qualitative assessments.
- New Data Sources. Discusses newer data sources that are available to help in ATM evaluations.
- New Technologies. Examines some of the newer technologies (particularly on vehicles) that could help enhance ATM strategy deployments.
- Integrating ATM Needs into Agency Business Processes. Briefly reviews how ATM fits into an agency’s TSMO and planning processes, which can be backed by a capability maturity model (CMM).
- Final Remarks. Summarizes the importance of making certain that agencies institutionalize activities to ensure ATM operations continue to serve the needs of the traveling public.
- Chapter 10 References. Includes a list of all references cited within the chapter.
Ongoing Monitoring and Longer-Term Evaluations
Project evaluation is a systematic method for collecting, analyzing, and using information to answer questions about projects, policies, and programs—particularly regarding their effectiveness and efficiency. Although ATM deployments are increasing across the United States, systematic evaluation of their impact is still a developing process. Over time, however, evaluation experience has been gathered from multiple sources, including Minnesota (Hourdos et al. 2013; Turnbull et al. 2009; Turnbull et al. 2013), Washington (Burt et al. 2009), and the United Kingdom (Mott McDonald 2008; Mott McDonald 2011). Additionally, in 2017, FHWA published the Active Traffic Management (ATM) Implementation and Operations Guide (Kuhn et al. 2017) that offers guidance and examples from which agencies can learn. These examples describe the planning, data collection, and analysis efforts necessary to produce comprehensive evaluation reports as well as the challenges associated with those efforts (e.g., collection of sufficient, quality data).
The ITS Deployment Evaluation website offers guidance that can be used for evaluating ATM deployments.
FHWA has developed significant resources to support the evaluation of projects and to advance the concepts and lessons learned. In particular, the Intelligent Transportation Systems (ITS) Deployment Evaluation website (ITS JPO 2023) offers information on benefits, costs, deployment statistics, briefings, lessons learned, and decision support resources. While this website is a broader information repository that encompasses all types of ITS projects and not just ATM, the material contained therein can be valuable and informative.
ATM evaluation results can be used not only by the project partners for continual refinement of similar systems at other locations but also by others wanting to implement similar systems in the future. These results might also be utilized to justify continued funding for the operation of the existing system as well as new investments in other, similar ATM deployments.
Additionally, the FHWA Work Zone Intelligent Transportation Systems Implementation Guide (Ullman et al. 2014) is a useful reference for evaluation that can be extended to an ATM deployment because it encompasses ATM strategies as they apply to work zones, such as queue warning, dynamic merging, and dynamic speeds.
In general, after an ATM strategy is deployed, evaluation is performed to assess how well the goals and objectives of the project are being met. The goals and objectives of the ATM project should have been clearly stated during the project-planning process, and specific objectives should be measurable. Here, “monitoring” refers to ongoing or frequently recurring analyses, while “evaluation” is typically more in-depth, covering a longer period and including more performance measures. The purposes of conducting monitoring and evaluation activities are to
- Identify changes to fine-tune and improve existing system operations or design and improve performance to meet or exceed established goals and objectives.
- Identify and quantify the benefits of the ATM system to help justify the existing system and future investments in similar ATM systems.
- Document lessons learned.
With an analysis framework, an agency can plan data collection activities to support an effective evaluation that will look at conditions before and after the deployment of the ATM strategy to better measure the impacts of the strategy.
System evaluation and reporting will vary based on the scope of analysis, which should be set to meet the expectations of all groups that require feedback regarding the ATM deployment. Generally, due to the more active and dynamic nature of ATM deployments, more frequent analysis will be required
than with traditional ITS and operations to continuously improve performance. This requirement is especially true for agencies that are deploying an ATM system for the first time because both operators and drivers must acclimate to the new system. Thus, planning for monitoring and evaluation should begin by determining the appropriate scope of analysis. The scope, as well as the type of funds used to pay for the deployment, help establish the extent of the assessment. For example, these factors along with the complexity of the system and the scale of the project may establish the specific factors to be measured, level of effort and detail, frequency, and duration of evaluation efforts. An informal evaluation may be sufficient for simple ATM systems, while a more formal evaluation be needed for larger-scale deployments.
By developing an analysis framework, an agency can plan data collection activities, rather than piece together potentially incomplete data a year or more after the project is deployed. An effective evaluation will look at conditions before and after the deployment of the ATM strategy to better measure the impacts of the strategy. Any analysis that is conducted should trace back to the operational objectives and performance measures with the development of hypotheses, system performance objectives and performance measure targets, and measures of effectiveness. Performance measures presented in Chapter 5 may be used to assess the ATM deployment.
Planning is required to ensure that useful data are available, particularly for the “before” case. In general, the need exists for more data collection to better quantify benefits. Agencies deploying ATM strategies should keep in mind the reasons for conducting monitoring and evaluation when identifying the types of data to collect and the short- and long-term time frames for analysis.
Short-Term Monitoring
In the short term, monitoring should identify minor operational changes that may allow the existing ATM system to be more easily understood by drivers, improve the quality and timeliness of automated responses, and improve the overall efficiency of traffic flow. It is in the best interest of the operating agency to perform ongoing monitoring activities for a variety of reasons. At the very least, an agency will want to observe the performance of the ATM deployment in the field to ensure that it is functioning properly. As an example, thresholds for implementing various levels of a strategy may need to be revised once actual operations have been observed.
Longer-Term Evaluation
After 1 to 3 years of system deployment, a larger evaluation may be conducted that includes a larger set of mobility and safety data to identify significant trends. In particular, multiple years of crash data are necessary to effectively analyze safety impacts. A larger evaluation can also document lessons learned and overall benefits of the ATM deployment. It may also include the collection of some final qualitative data from stakeholders (from surveys or interviews). Analysis of all quantitative and qualitative data that have been collected should be conducted as described by the evaluation plan developed prior to deployment, which links back to the planning process and initial objectives of the project.
Both ongoing monitoring and longer-term evaluations should assess the impacts of the ATM deployment relative to the performance measures and objectives in the evaluation plan. Changes caused by exogenous factors that could affect the results—such as the price of fuel, unemployment rates, other highway improvements, and nearby work zones—also need to be considered. For example, improved mobility and safety impacts observed after the deployment of the ATM system might partially be the result of a work zone in place before the deployment of
the ATM system at the same location. Likewise, fewer vehicles are likely to travel on roadways when fuel prices and unemployment rates are higher, resulting in relative improvements in mobility and safety. Separating the impacts due to exogenous factors can be challenging, especially because the impacts of the ATM deployment may be smaller by comparison.
As a result, a longer before and/or after analysis period may be necessary to ascertain the relative impacts of the ATM deployment for some performance measures. However, this requirement also reflects the importance of balancing an evaluation with both quantitative and qualitative analyses to capture meaningful results from different perspectives. Not all performance measures are always easily measured due to exogenous factors, data availability, or data quality issues, for example.
MoDOT deployed enforceable VSLs on the I-270 and I-255 corridor around St. Louis in 2008. A 2010 in-depth study showed that the benefits of reduced congestion and crashes justified keeping the system in place but that minimal enforcement resulted in a negative driver reaction. The system was converted to nonenforceable, variable advisory speed limits in 2011.
A longer-term evaluation may also incorporate additional mobility and safety data and performance metrics to identify the impacts and benefits of the ATM system. While evaluations may be either qualitative or quantitative, the best evaluations employ a combination of both types of information. This effort may be used to identify larger operational changes that may improve the ATM system.
For example, the Missouri DOT (MoDOT) deployed enforceable VSLs on the I-270/I-255 corridor around St. Louis in 2008. After operating for a few years, the system was not delivering the expected benefits, so an in-depth study was conducted in 2010 (Bham et al. 2010). Because the objectives of this system were to improve traffic flow, reduce congestion and delay, and improve safety, this evaluation included a mobility and safety analysis as well as perceptions of the traveling public and law enforcement to gauge customer satisfaction. Mobility analysis included a before-and-after examination of traffic flow, critical occupancy, average speed, average travel time, travel-time reliability indices, delay, and speed compliance. Changes in the severity and number of crashes were examined to measure safety impacts. As a result of this study, MoDOT decided to convert the system to nonenforceable variable advisory speeds. The reason for this change was that the minimal enforcement of the VSLs either angered or confused motorists about the potential of enforcement, yet the benefits of reduced congestion and crashes justified keeping the system in place. This information was also captured as a lesson learned on the previously discussed ITS Deployment Evaluation website (ITS JPO 2023).
B/C Analysis
Quantifying and reporting benefits does not need to be overly complex and will likely employ similar data and techniques as those data and methods used for analyzing traditional ITS and operations. However, it is important for agencies to develop specific reports that are appropriate for various target audiences. At a minimum, periodic monitoring of data and analysis of the performance measures identified in Chapter 5 should be conducted, which still requires a plan to collect and archive relevant data to adequately assess system performance. However, the detail of any ongoing evaluation reports will likely vary at different intervals throughout the course of the project.
Decision-makers and elected officials should receive periodic feedback on the ATM deployment benefits to gain support, ongoing backing, and required funding for both existing ATM deployments and their expansion. Some agencies maintain public dashboards with various performance measures, which may require certain periodic (e.g., monthly) inputs from the deployment. Agency or TMC managers may request updates on a weekly basis, with more detailed reports on a quarterly basis.
Qualitative Assessments
Quantitative data alone will not be able to address all aspects of the ATM deployment that need to be monitored, and not all ATM benefits can be readily quantified. For instance, the MnDOT and WSDOT realized that some of the benefits of ATM signage, such as smoother flowing traffic and improved motorist understanding of the situation, could not easily be quantified even though drivers and operators observe improved conditions on the corridors with ATM signage (Finley et al. 2013). To document these perceived changes, surveys or interviews can be conducted with the public and operators, as well as law enforcement or transit drivers, for a qualitative assessment to supplement quantitative data.
Public feedback may be gathered in a variety of ways. Focus groups may be useful for gathering targeted feedback from specific user groups, such as law enforcement personnel, transit drivers, specific communities, or local businesses. Online or telephone surveys are another useful way to gather public feedback. Consideration should be made for how to distribute the survey to capture balanced feedback (both positive and negative, and across all user groups by income, demographics, etc.). As an example, the MnDOT maintains an online community that mirrors the demographics of the state’s population to periodically participate in online surveys about various transportation issues. A larger-scale panel survey, such as those surveys conducted in Seattle, Washington, and Atlanta, Georgia, can recruit a larger pool of applicants and examine longer-term impacts of a deployment. General comments and feedback from the public via agency websites, social media, and news media can also be a useful gauge of the value of a system in terms of customer satisfaction.
Agency staff can also make valuable observations using TMC resources, while law enforcement personnel can report observations made in the field. By monitoring cameras during incidents, operators can see how drivers respond to the signage and find ways to improve responses for a better, more desirable response to a given scenario. For example, it was apparent to operators in Minnesota that some symbols used on the ATM signage were not clear to drivers, so modifications were made. Operators determined that, in other instances, traffic calming upstream of an incident might be more effective if conducted over the course of 1 mi instead of 2 mi, requiring a change to the configuration of messages (Finley et al. 2013). Together, quantitative data and qualitative observations help ensure that the system is working correctly or identify any changes that may need to be made.
New Data Sources
For any evaluation, data needs vary according to the evaluation plan, but data should be collected before and throughout the course of the ATM deployment. Various data elements can be analyzed periodically to assess the system performance, determine the impacts on safety and mobility, and determine whether system modifications are needed.
It is expected that some aspects of performance will be monitored and analyzed more frequently than others (e.g., on a daily, weekly, monthly, quarterly, or annual basis). For example, primary performance measures may be analyzed more frequently than supporting performance measures. The performance measures analyzed most frequently should be those measures that most directly link to the goals and objectives of the ATM deployment; however, analysis may also depend on how easily accessible the data are. Also, some ATM strategies may focus on safety impacts, which are not easily assessed until several years of data have been collected for analysis.
Ideally, data collected and archived by the TMC will be robust and available for analysis. Depending on the deployment, the ATM system will likely rely on many data inputs that can also be used for analysis. ATM system data will need to be archived in a format and level of
completeness that allows analysts to investigate detailed impacts that can be quantified in context with other data being analyzed. Additionally, data collection may need to be done in coordination with the analysis, modeling, and simulation needs that are described in Chapter 6.
ATM has traditionally relied upon ITS field devices to provide data for planning and real-time operations, including, in some instances, dynamic and potentially automated strategies. These data sources are used by agencies to improve transportation systems; however, they require data maintenance to remain accurate and are often limited in their ability to provide real-time information or provide performance metrics without processing. Although the capability of equipment to store and archive data is continually improving, significant legacy equipment remains in place, which limits this capability and therefore impacts the ability to accurately assess operations. Archived data are essential for agencies to make informed decisions and design choices when planning which ATM strategies need to be implemented. Additionally, ATM evaluations are often limited to the extent or operating area of agency-owned data collection infrastructure.
Considerations and Impacts on ATM
Data sources such as private-sector data and connected vehicles (CVs) and infrastructure data can have a positive impact on ATM strategies. Traditional data sources such as loop detectors or CCTV surveillance images do not provide detailed insight or require operators to observe the data and perform an action. Data collected from these emerging data sources, including private-sector data, provide another alternative for transportation agencies when planning transportation systems at the local, state, or federal levels.
Types of new data sources include but are not limited to the following:
- Connected travelers: User-experienced and user-inputted data can provide road conditions, traffic, travel speed, and road hazards information. This data source includes private-sector big-data sources that may combine crowd-sourced data with purchased fleet data for a robust view into travel conditions.
- CVs and infrastructure: Vehicles with the capability to communicate with other vehicles, modes (e.g., bicyclists or pedestrians), and/or infrastructure (e.g., signals) can provide a unique set of data.
- New sensors types: Advanced sensor technologies can provide significantly more detailed data than existing systems.
Connected Travelers, Including Private-Sector Data
Data from connected travelers are typically collected directly from the traveler using cellular or Wi-Fi networks depending on a vehicle’s equipment base. Other short-range communication technologies such as Bluetooth may also be used. Data are most often collected through a traveler’s mobile device to provide information on travel speeds, travel times, routing, queues, etc. These data can be randomly collected from users using anonymous data collection measures or could include user-inputted data from mobile applications. Data are aggregated to provide segment or corridor conditions such as speeds, bottlenecks, and queue locations. Unlike agency sensors, these data are not currently able to provide lane-by-lane speeds and travel conditions. Although these data are subject to potential errors because they can be based on survey-like input from the public, it is one of the most cost-effective ways get near-real-time traffic data. The connected-traveler data collected by agencies allow them to utilize these data when making decisions on how to better direct traffic, especially in emergency situations, without investing in major infrastructure. The data could also improve how agencies communicate with users traveling on affected roads, such as transit vehicles.
CVs and Infrastructure
CVs are another emerging data source that could greatly benefit transportation agencies implementing ATM strategies because they provide more accurate information relating to driver and vehicle behaviors on the road as well as location-specific information. CVs use wireless communication and the vehicle’s onboard computer system to communicate directly with the infrastructure or other vehicles. CVs can send alerts to other drivers to warn of potential hazards on the road or delays due to collisions or congestion. This information can also be sent to roadside reception points to be broadcast to a larger audience, usually by a TMC. These alerts help drivers avoid certain roads and areas. CVs can also receive signal phase and timing information from signals to optimize signal operations.
Three types of CV applications exist: vehicle-to-vehicle (V2V), vehicle-to-other objects (V2X), and vehicle-to-infrastructure (V2I). V2V applications are mainly used for safety, V2X applications are used by vulnerable road users such as bicyclists and pedestrians, and V2I applications are used for traffic management. Commercial CVs are already utilized by private companies to collect data on real-time vehicle speeds on roadway links. Some private companies currently share these data with DOTs; however, it is possible for DOTs to collect these data themselves. If DOTs upgrade their technology and meet the communication requirements to use CV data sources, they will be able to collect these data without needing to go through and pay private companies to collect these data for them. A key challenge for agencies when considering how to collect, ingest, and use CV data is the impact of data overload, particularly for real-time operations. ATM systems and traffic operations staff could easily get overwhelmed with multiple data points coming in from multiple vehicles; these data points could vary from pavement/friction information to vehicle braking, speeds, queues, distances, and other information.
New Sensor Types
Other examples of emerging data sources to support ATM include infrastructure-based equipment such as mobile sensors and high-definition maps. Data collected from mobile sensors, such as light detection and ranging (LiDAR) and 3D camera analytics, provide additional data sources for ATM and are applicable to both freeway and arterial deployments. These data sources could be very useful for agencies when implementing ATM strategies relating to safely restoring traffic flow after traffic incidents. Data collected from high-definition maps, which are typically shared with public agencies by private companies, could potentially be useful for implementing V2I CV applications. Given the large quantity of intersections within a single map, big-data tools and technologies are necessary for traffic agencies to process and be able to use.
Impact on Sustainability
Emerging data sources can help improve a variety of ATM strategies, including adaptive traffic signal control, adaptive ramp metering, dynamic lane reversal, dynamic lane-use control, and dynamic speed limits. For example, they can help improve response to incidents, identification of hazard and warning on roads, issuance of speed warnings to drivers due to heavy traffic or dangerous weather conditions, accuracy of travel-time messages, and detection of traffic infrastructure equipment failures or malfunctions. Data can be available for a larger geographic area rather than relying on agency infrastructure, sensors, and communications networks. This expanded coverage area is particularly relevant for rural corridors that often have little to no agency monitoring infrastructure in place.
Emerging data sources and tools provide many benefits to both public and private agencies. Using emerging data sources instead of more traditional data sources, such as radar or video
detection, provides agencies with higher volumes of data at a faster rate and allows agencies to keep a larger amount of data stored due to increased storage capacities. Although the emerging data from CVs, for example, are not able to be stored in traditional storage locations used by existing traffic management systems due to its large size, it can be stored in cloud services online.
Incorporating emerging data sources can also be beneficial because they allow archived data to be accessible for on-demand analytics. Being able to access archived data could be beneficial to agencies when trying to predict travel behavior and would aid in actively managing traffic operations according to real-time traffic conditions. It is crucial for agencies to be able to have real-time data to better improve traffic operations and plan for any potential future infrastructure that addresses gaps in existing infrastructure or modes. Having access to real-time data as it occurs allows agencies to forgo the waiting period usually needed to process traffic data.
Overall, these benefits can improve reliability and sustainability as it allows deployments to be more reactive to conditions and to be operationally fine-tuned much more quickly for better performance. Better performance and efficiency are hallmarks of sustainability and have potential environmental effects because the vehicle stream is flowing more efficiently.
However, new data sources and the increased volume of data also mean that new data tools and technologies must be concurrently employed because many existing traffic management systems are not equipped to handle the large quantities of data. For example, given that the existing systems are not able to process the incoming large volume of data, a big-data solution is to use a streaming data software system that can store data in the short term, process and analyze it, and then erase it from its short-term memory and move it into long-term memory storage.
Another improvement to existing systems that these tools and technologies can provide relates to the rate at which incoming data get processed and analyzed. Big-data tools and technologies are capable of parallel processing, which is where large volumes of data are split into smaller portions, and the information from the different portions is processed at the same time. This process can be completed using either a single machine or several linked machines working simultaneously. These enhancements, along with others, would be very useful when processing big data from emerging data sources because data sometimes need to be processed in real time. These processes allow data to be output directly to transportation agencies in the form of usable performance metrics and performance dashboards.
New Technologies
Every year, roadway accidents and congestion cost billions of dollars in delays, productivity, injuries, and fuel consumption (Hooper 2018). Given the recent advancements in infrastructure and newer vehicles, the combination of technologies and ITS applications is expected to yield significant road safety and mobility improvements to mitigate these costs. For example, in-vehicle technologies, such as advanced driver-assistance systems and/or advanced rider assistance systems, are in-vehicle safety features that are meant to support operators with various driving and/or riding tasks, respectively. Many of these systems are considered Levels 1 and 2 based on SAE International’s Levels of Driving Automation J3016 standard (SAE International 2021) and have significantly helped reduce incident and injury rates by offering operator support (Teoh 2013; IIHS Highway Loss Data Institute 2023).
The next steps in advancing vehicle technologies include deploying autonomous vehicles (AVs) and CVs, which are recognized as ITS applications. Research has indicated that CV and AV technology can increase safety and mobility and provide positive environmental impacts once fully deployed (U.S. DOT, n.d.; NHTSA, n.d.).
AVs and/or CVs possess the technology to perform and/or assist a driver with driving tasks. CVs utilize information exchanged between other vehicles, infrastructure, or both to help optimize the system or driver decision-making. AVs rely on software and hardware to perform various driving tasks and can vary in ability depending on the level of automation. According to SAE International’s J3016 standard, the level of automation can range from manual (Level 0) to completely autonomous without the need for driver input (Level 5) (SAE International 2021).
Considerations and Impacts on ATM
ATM strategies have and are being deployed with existing technologies and information sources. As new ATM deployments take shape, the potential and need for new technologies to support them become a longer-term investment need, particularly as roadway infrastructure develops and self-driving vehicles emerge (Bayless et al. 2013). These requirements are particularly relevant for CVs and AVs, which are still developing; their full cost is not yet clear. The combination of ATM strategies and CV/AV technology may bring opportunities to reach more drivers and enhance safety, but it is unclear what expertise will be needed and what capability agencies will need to integrate these technologies to reach drivers and enhance ATM implementations. For example, in 2015, FHWA sponsored a demonstration that exhibited the ability for queue warning and speed harmonization messages to be delivered directly to vehicle drivers using cellular and dedicated short-range communication. The demonstration showed that the Intelligent Network Flow Optimization (INFLO) System possessed the bandwidth to support ATM functionality in a CV environment (Stephens et al. 2015). However, it is also theorized that technology advancements may complicate determining which ATM strategies to deploy and where (Kuhn et al. 2017).
Integrating ATM Needs into Agency Business Processes
Integrating ATM needs into agency business processes is essential for sustained success. By incorporating ATM considerations into decision-making frameworks, transportation agencies can prioritize the allocation of resources, ensure sufficient staffing, and foster a culture of sustainability. This integration helps establish a long-term commitment to ATM, enabling agencies to efficiently manage the complexities associated with implementation, operation, and maintenance of ATM systems.
One method of accomplishing this integration relies on agencies assessing their internal capability to deliver strategies that consider supporting technology, sound business processes, and an engaged workforce. To help evaluate organizational capacity, the FHWA utilized the capability maturity model (CMM) (initially applied to transportation by the AASHTO), which can assist agencies with self-assessments according to six topical categories, including performance management supported by effective metrics and data. The other categories relate to (1) business processes, (2) systems and technology, (3) culture, (4) organization and workforce, and (5) collaboration.
Within each topical category, the CMM also assess the level of capability. Level 1 includes agencies championing concepts and developing relationships. Level 2 includes managing staff training. Level 3 includes starting to build budgets and measuring progress. Level 4 includes an optimized formal program.
For agencies, various ATM strategies operate within a TSMO program. A TSMO program includes elements of operations, planning, design, construction, maintenance, and safety, whether led by a state DOT, regional entity, congestion management agency, or some other authority. A TSMO plan helps agencies manage ATM strategies throughout the various project development
and management processes. Effective integration of ATM strategies into a TMSO plan that is backed by a maturity model creates a systematic, stepwise method that enables key stakeholders and public partners to follow along to ensure buy-in for new projects. These projects will need to be backed by strong performance metrics to help to support the rationale for pursuing an ATM strategy.
Final Remarks
Evaluation of deployments is critical to their success in both the short term and long term. Additionally, the lessons learned from planning, installing, operating, evaluating, and enhancing ATM deployments can feed into the next project on a freeway or arterial. Implementers should consider that lessons learned can also come from elements beyond operational data and performance. Items such as operator staffing levels, leadership commitment, engaged and continued public support, operations and maintenance needs over time, software integration, and many more, can also provide significant lessons learned for operating agencies.
This information can be very valuable to expanding ATM strategy to additional corridors and locations along with integrating continually evolving technologies. Additionally, integrating ATM needs into agency business processes can help ensure the long-term commitment and success of these transportation solutions.
Chapter 10 References
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Bham, G.H., S. Long, H. Baik, T. Ryan, L. Gentry, K. Lall, M. Arezoumandi, D. Liu, T. Li, and B. Schaeffer. (2010). Evaluation of Variable Speed Limits on I-270/I-255 in St. Louis. Missouri University of Science and Technology. https://spexternal.modot.mo.gov/sites/cm/CORDT/or11014.pdf. Accessed July 2023.
Burt, M., G. Shao, C. Zimmerman, K. Turnbull, K. Balke, A. Cain, and E. Schreffler. (2009). Seattle- Lake Washington Corridor Urban Partnership Agreement National Evaluation Plan. Battelle.
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Hooper, A. (2018). Cost of Congestion to the Trucking Industry: 2018 Update. American Transportation Research Institute. https://truckingresearch.org/wp-content/uploads/2018/10/ATRI-Cost-of-Congestion-to-the-Trucking-Industry-2018-Update-10-2018.pdf. Accessed June 2022.
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IIHS (Insurance Institute for Highway Safety) Highway Loss Data Institute. (2023). “Real-World Benefits of Crash Avoidance Technologies.”
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Kuhn, B., K. Balke, and N. Wood. (2017). Active Traffic Management (ATM) Implementation and Operations Guide. Federal Highway Administration, U.S. Department of Transportation. Publication FHWA-HOP-17-056. https://ops.fhwa.dot.gov/publications/fhwahop17056/fhwahop17056.pdf. Accessed July 2023.
Mott MacDonald. (2008). ATM Monitoring and Evaluation: 4-Lane Variable Mandatory Speed Limits—12 Month Report (Primary and Secondary Indicators). Highways Agency, Transport Scotland.
Mott MacDonald. (2011). M42 Monitoring and Evaluation: Three Year Safety Review. Highways Agency, Transport Scotland.
NHTSA (National Highway Traffic Safety Administration). (n.d.). “Automated Vehicles for Safety.” https://www.nhtsa.gov/technology-innovation/automated-vehicles-safety. Accessed June 2022.
SAE International. (2021). Surface Vehicle Recommended Practice J3016—Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles. https://www.sae.org/standards/content/j3016_201806. Accessed June 2022.
Stephens, D., T. Timcho, J. Schroeder, J. Brown, P. Bacon, T. Smith, K. Balke, H. Charara, and S. Sunkari. (2015). Intelligent Network Flow Optimization (INFLO) Prototype, Seattle Small-Scale Demonstration Report. Battelle. Publication FHWA-JPO-15-223. https://rosap.ntl.bts.gov/view/dot/3571. Accessed June 2022.
Teoh, E. R. (2013). Effects of Antilock Braking Systems on Motorcycle Fatal Crash Rates: An Update. Insurance Institute for Highway Safety.
Turnbull, K., K. Balke, M. Burris, P. Songchitruksa, E. S. Park, B. Pessaro, J. Samus, E. Saunoi-Sandgren, D. Gopalakrishna, J. Schroeder, C. Zimmerman, E. Schreffler, and B. Joy. (2009). Minnesota Urban Partnership Agreement National Evaluation Plan. Texas A&M Transportation Institute and Battelle.
Turnbull, K., K. Balke, M. Burris, P. Songchitruksa, E. Park, B. Pessaro, J. Samus, E. Saunoi-Sandgren, D. Gopalakrishna, J. Schroeder, C. Zimmerman, E. Schreffler, and B. Joy. (2013). Minnesota Urban Partnership Agreement: National Evaluation Report. Texas A&M Transportation Institute and Battelle. Publication FHWA-JPO-13-048.
Ullman, G., J. Schroeder, and D. Gopalakrishna. (2014). Work Zone Intelligent Transportation Systems Implementation Guide: Use of Technology and Data for Effective Work Zone Management. Publication FHWA-HOP-14-008. Texas A&M Transportation Institute and Battelle. U.S. Department of Transportation, Federal Highway Administration.
U.S. DOT (U.S. Department of Transportation). (n.d.). “Connected Vehicle Benefits.” https://www.its.dot.gov/factsheets/connected_vehicle_benefits.htm. Accessed June 2022.
