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21st Century Operations Using 21st Century Technologies
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Approaches to Target Setting for PM3 Measures

Chapter 5. Resources for Target Setting

Several resources may be leveraged to effectively set targets. These include data on travel times and other aspects of the transportation system and surrounding environment, various models and analysis tools, insights from the experiences of peer agencies, and feedback from the public and stakeholders. This section of the report summarizes those resources and discusses their value to State DOTs and local agencies.

Data as a Resource

Perhaps the most important data resource needed for target setting in this context is travel time data. As discussed in the Federal requirements for target setting, most of the PM3 measures rely on the availability and quality of travel time data.26 The NPMRDS is the most common source of travel time data for target setting as it is made available to State DOTs and MPOs through FHWA. However, the NPMRDS coverage is only provided on the Interstate highway system and the NHS. As observed in the WSDOT case study in chapter 4, the extent of network coverage of a travel time database can significantly impact an agency’s ability to apply consistent target setting practices across its system.27

While travel time data resources are important, they are not the only data resources that an agency will find valuable. The travel time-based performance measures, and their associated targets, also rely on other data. These include vehicle occupancy rates, traffic volumes, classification counts, and network characteristics. These are all data that are regularly collected by State DOTs and MPOs. In the case of these data, internal resources (e.g., various units within an agency) may prove to be the most useful.

Models and Tools as Resources

Transportation models and statistical analysis tools represent another resource to State DOTs and MPOs engaged in target setting. There are numerous models and tools developed for the purpose of predicting future performance, some for transportation explicitly while others for general purposes that may be applied in a transportation context. While models that predict the demand for travel and congestion-related performance measures are commonplace, measuring and forecasting reliability is an emerging practice in transportation modeling. The ability of models and analysis tools to characterize reliability, assess the impact of reliability on the transportation system and its users, and to determine the effectiveness of investments and operational countermeasures to improve reliability is an important area of future research.

State DOTs are currently experimenting with a number of methodologies to forecast reliability performance and predict targets. However, time series analysis was a common theme of the various methods identified by the project team. This includes the use of statistical models such as regression, moving averages, and exponential smoothing for example. The following example procedure demonstrates the steps that may be taken as a part of a target setting process that relies on trend line analysis, though the procedure and the steps are not required:

  • Review the external (exogenous) factors. These factors are those that affect performance of the transportation system, but are typically outside the control (at least operationally) of transportation agencies. Common examples of external factors include fuel prices, economic conditions, and employment levels. FHWA has produced a report on this subject that is useful in examining external factors.28
  • Review the internal (endogenous) factors that affect transportation system performance and are under the control of or can be managed by transportation agencies. The latest STIP for fiscal year 2018 to 2021 includes projects (both on Interstates and non-Interstates), which may potentially impact the system performance measures. The projects which may positively impact the system performance measures include the following category of projects: interchange, intersection improvement, intelligent transportation systems, managed lanes, passing lanes, and roadway widening. Additionally, the management of traffic incidents, work zones, and weather will have a positive effect on travel times and reliability.
  • Conduct a trend line analysis for the performance measures using the baseline data. This analysis will provide a lower- and higher-end range for the projected future-year performance measures.
  • Set the targets. Factors such as increasing travel demand, improving economic conditions, and increasing population would indicate that system performance could worsen in the future and that a conservative target is recommended.

As an example, the Washington, DC area National Capital Region Transportation Planning Board (TPB) applied three general methodologies to determine travel time performance forecasting.29 TPB staff obtained data from the NPMRDS, and the utilization of RITIS with the MAP-21 widget. This enabled staff to review the travel time reliability and the truck travel time reliability (TTTR) for the TPB Planning Area from 2014 to 2017. With this collection of data, staff applied three general methodologies to determine performance forecasting: the extrapolation of measured performance, the use of travel demand model data, or the average of the two.

  • Extrapolation of Measured Performance: For this approach, measured data for the previous years of 2014 through 2017 would be selected either by month or year. This data would then be extrapolated, via polynomial regression, through the year 2021. This would cover both the two- and four-year targets. This approach would result in either a fitted line or a best fit curve as a means of forecasting.
  • Travel Demand Model: In 2016 TPB produced a travel demand model which produced congestion/related outputs for modeled years 2016, 2020, 2025, etc. Forecasting will be achieved by utilizing such outputs as percentage of congested AM peak-hour VMT estimates to project change in congestion, applying the percentage changes to measured performance.
  • Averaging: Taking the average of both the extrapolation of measured performance and the utilization of the Travel Demand Model as a means of forecasting the targets.

Similar to TPB, Connecticut DOT uses extrapolation method for their target setting.30

Public and Stakeholder Resources

The public and transportation system stakeholders can be a good resource to State DOTs and MPOs in setting targets. These groups can communicate their concerns and priorities in target setting. In some cases, stakeholders may provide information that is potentially important in setting targets at the State and regional levels. For example, a freight rail stakeholder may be planning an expansion of a rail intermodal terminal that could substantially increase truck traffic through an area and thus impact freight reliability targets; a local transit agency may be planning a major investment that they estimate will increase a region’s average vehicle occupancy rate, impacting reliability targets for the Interstate and Non-Interstate NHS. Involving other transportation system stakeholders in the target setting process can help agencies account for some of the exogenous factors discussed in the Models and Tools as Resources subsection.

The importance of public and transportation system stakeholders is reflected in the FAST Act’s encouragement of State DOTs to establish a State Freight Advisory Committee. For Freight Reliability, in particular, a State DOT’s ability to achieve a target is impacted by the actions of private-sector stakeholders that determine how, when, and where freight moves. Thus, these stakeholders are important resources for setting freight reliability targets, especially at the MPO level where a smaller geography coupled with freight system changes results in magnified performance impacts.

The traveling public is a resource to agencies for target setting because they can communicate those performance measures and targets that they most value and should be prioritized by a State DOT or MPO. In many cases, it may represent an opportunity for an agency to communicate to the public the resources needed to achieve a desired level of performance and the magnitude of the funding gap that exists.

Public engagement also can help to reconcile targets that are in conflict. For example, some stakeholders may desire higher speeds on certain roadways which may conflict with safety targets for reduced fatalities or serious injuries. By engaging the public on target setting, State DOTs and MPOs have an opportunity to communicate how the various aspects of performance management fit together and improve the overall system.

Peer Agencies as a Resource

Peer agencies are a resource that State DOTs and MPOs should use in target setting. This is a natural extension of collaboration, which is a key feature of performance-based planning and programming. Performance levels achieved by peer agencies can serve as benchmarks for a State DOT or MPO setting its own targets:

  • Compiling noteworthy practice case studies and lessons learned from peer agencies is a good starting point for target setting.
  • Staff from a select group of agencies could be interviewed to learn more about their experience.
  • Provides the opportunity to share data.

26 23 CFR 490.103(e). [Return to note 26]

27 Note that the NPMRDS has wider application beyond just supporting the PM3 measures. It can also be used to develop a wide variety of travel time and reliability performance measures for activities such as annual mobility reports, before/after project evaluations, bottleneck analysis, and providing data for model inputs and calibration. [Return to note 27]

28 Dadashova, Bahar, Lasley, Phil, Koeneman, Pete, and Turner, Shawn, Approaches to Presenting External Factors with Operations Performance Measures, FHWA-HOP-18-002, February 2018. [Return to note 28]

29 System Performance Targets Travel Time Reliability and Truck Travel Time Reliability – Draft, Performance-Based Planning and Programming, July 2018. [Return to note 29]

30 Great Falls Area, Long Range Transportation Plan - 2018 Update, Appendix I: Performance Measures and Targets, October 2019. [Return to note 30]