Approaches to Forecasting the Third Performance Management Rulemaking (PM3) Measures for Target Setting

Chapter 2. Using Historical Data for the Extrapolation of Past Trends

The extrapolation of past trends is a common target setting method used by State DOTs and local agencies because it is simple to implement. A trend line of recent years is developed for the performance measure in question and the trend is extended into the future by inspection or by curve fitting. It is useful to track the performance of key external factors as well, especially vehicle-miles traveled (VMT), as these can influence the observed trend. Economic conditions also can be tracked, but even though these are likely to be strongly correlated with VMT, the lag between economic variables and VMT should be noted. Extrapolation of past trends is best suited when the horizon year for the target is short term in nature and there is a history of performance available for such areas as safety, pavement, bridge, or mobility.

Data Requirements

The data requirements for calculating the performance metrics and measures are outlined in FHWA publications FHWA-HIF-18-040 and FHWA-HIF-18-024, respectively.^{9, 10} In general, the data required are continuously collected travel time data for relatively short one-way highway segments.

Analysis Tools

Detailed step-by-step guidelines on how to calculate the performance metrics and measures are provided by the FHWA in publications FHWA-HIF-18-040 and FHWA-HIF-18-024, respectively.^{11, 12}

For forecasting the performance measure/metrics and setting targets, this “Extrapolation of Past Trends” approach uses a spreadsheet-based analysis.

Forecasting Measures and Target Setting Process

Following detailed steps are developed for forecasting measures/metrics and identifying a target using the “Extrapolation of Past Trends” approach.

Step 1. Using the data sources outlined in FHWA publications FHWA-HIF-18-040 and FHWA-HIF-18-024, gather all the relevant information to calculate the travel time-based performance metrics and measures.^{13, 14}

Step 2. Using the detailed methodology provided in FHWA publications FHWA-HIF-18-040 and FHWA-HIF-18-024, calculate the travel time-based performance metrics and measures.

Step 3. Develop a trend line using monthly travel time-based performance metrics.

Step 4. Account for external factors that are typically outside of the control of State DOTs as well as internal factors that are under the control of State DOTs.

Step 5. Taking into account the impact of external and internal factors (step 4), as well as the forecasted performance (from step 3), a performance target could be selected based on the agency’s (either State DOT or MPO) level of comfort.

Step 1: Gather Data

Using the data sources outlined in FHWA publications FHWA-HIF-18-040 and FHWA-HIF-18-024, respectively, gather all the relevant information to calculate the PM3 performance metrics and measures. Since probe speed data from the National Performance Management Research Data Set (NPMRDS) is available in a consistent format since 2017, gather all the data from 2017 onwards.

Step 2: Calculate Performance Metrics

Using the detailed methodology outlined in FHWA publications FHWA-HIF-18-040 and FHWA-HIF-18-024, respectively, calculate the PM3 performance metrics and measures.

Step 3: Conduct a Trend Line Analysis

Conduct a trend line analysis using the monthly PM3 performance metrics. Depending on the data, a linear or a best-fit polynomial trend line could be developed. This analysis will provide a lower and higher end range for a future performance period.

Step 4: Account for Additional Factors

External Factors

External factors (also called exogenous factors) are those influences that affect transportation system performance but are typically outside the control (at least operationally) of transportation agencies. Common examples of external factors include fuel prices, traffic volumes, economic conditions, and employment levels. The list of possible influential external factors can be grouped into the following general categories:¹⁵

• Travel demand.
• Truck demand.
• Economic trends.
• Population.
• Other trends.

Internal Factors

Internal factors are those influences that affect transportation system performance, but which are under the control of transportation agencies. The Statewide Transportation Improvement Program (STIP) typically includes projects (both on Interstates and non-Interstates) which may potentially impact the system performance. The projects which may positively impact the system performance include capacity projects such as interchange improvements, intersection improvements and adding travel lanes, as well as transportation system management and operation (TSMO) strategies such as intelligent transportation systems (ITS), managed lanes, road weather management, and incident response. With major improvements, it is important to take into consideration that work zones will have short term impacts on system performance prior to realizing benefits of improvements.

Step 5: Set Targets

Analysts should assemble all the available information for the previous steps and use it, along with professional judgement, in setting the actual targets.

Example

The following hypothetical example shows how to apply the extrapolation method. Figure 1 shows the historical trends in the Interstate System Reliability measure. Historical traffic growth on Interstates is assumed to be two percent. Assuming that future traffic growth will be roughly the same, figure 2 shows a simple extrapolation of the general downward trend in unreliable person-miles. If future traffic growth is expected to be higher and lower than historical trends, the trend line can be adjusted up or down. As shown, the preliminary targets would be the following:

• Two-year Target—73.0 percent
• Four-year Target—67.0 percent

This information serves as input to the consensus-based target setting used by a transportation agency. That is, the final targets are set using a combination of professional judgement and the results of the target setting analysis.

Figure 1. Chart. Historical trends in Interstate system reliability performance.

(Source: FHWA.)

Figure 2. Chart. Extrapolated trends for the Interstate travel time reliability measure.

(Source: FHWA.)

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