6.1.3.2
Target Setting Approaches
The guide recognizes that transportation agencies take different approaches to target setting. Some may prioritize setting realistic targets based on fiscal constraints. Other agencies may set aspirational (or fiscally unconstrained) or conservative targets.
Regardless of the approach to target setting, the guide recognizes that both quantitative and qualitative approaches may be considered. In some instances, qualitative approaches that are heavily influenced by politics or agency leadership may be appropriate. An annual target to reduce fatalities to address a long-term Vision Zero goal is an example of a qualitative target. Other approaches may use statistics or probabilities to define a quantitative target. The use of travel demand forecasts to set a mobility target illustrates the use of a quantitative approach to target setting. A combination of qualitative and quantitative approaches may also be used to set effective targets.
The following five quantitative methods for setting targets are presented in detail within the guide (Grant, M., et.al. 2023A):
- Policy-based approaches, such as establishing a maximum rate of change (e.g., annual increase of at least 2 percent).
- Historical trends (e.g., set a value based on a 5-year trend).
- Probabilistic and risk-based approaches that consider performance variability (e.g., performance based on the likelihood of increased storm frequency and intensity).
- Statistical models (e.g., rates of deterioration based on regression models).
- Other tools and models (e.g., output from a bridge or pavement management system).
When selecting the appropriate method for target setting, the guide offers the following tips (Grant, M., et al. 2023A):
- Understand the complexity of the methods - some methods require sophisticated data that may not be readily available and may result in marginal improvements in the target's effectiveness.
- Consider combining methods - by using several approaches, an agency has the benefit of considering the results from multiple methods in setting the final target.
The Guide (NCHRP Report 1035) presents several target-setting methods for each of the Federal performance measure categories along with a summary of their ease of use, robustness, ease of communication, and support for agency policies. A high-level synopsis of the information presented in the Guide is presented in the following table.
Table 6.2 - Features associated with different target-setting approaches (based on Grant, M., et.al. 2023A)
| Federal Performance Measure Category | Target Setting Approach | Description | Ease of Application | Technical Robustness | Ease of Communication | Allows for Policy Preference |
|---|---|---|---|---|---|---|
| Safety | Targeted Reduction | Defined decrease from baseline regardless of past trends | H | L | H | H |
| Safety | Time-Series Trend | Based solely on historical performance data | H | M | H | L |
| Safety | Trend Plus Other Factors | Adjustments made to results from other approaches | H | L | M | H |
| Safety | Multivariable Statistical Model | Statistical analysis considering multiple variables | L | H | M | L |
| Infrastructure Condition | Target Based on Change in Condition | Consensus decision | H | None | L | H |
| Infrastructure Condition | Time-Series Trend | Based solely on historical performance data | H | L | M | M |
| Infrastructure Condition | Time-Series Trend Plus Future Funding | Historical trends extrapolated into the future | H | L | M | M |
| Infrastructure Condition | Asset Management System | Condition forecasts based on expected funds | L | H | M | M |
| Infrastructure Condition | Scenario Analysis | Management system analysis of multiple scenarios | L | H | M | M |
| Reliability | Building off the Baseline with Assumptions | Qualitative approach to adjusting baseline values | H | L | H | M |
| Reliability | Time-Series Trend Analysis | Based solely on historical performance data | H | M | M | M |
| Reliability | Trend Plus Other Factors | Adjustments made to results from other approaches | H | M | M | M |
| Reliability | Performance Risk Analysis | Statistical analysis of variations due to risks | M | H | M | M |
| Reliability | Segment Risk Analysis | Analysis of individual segments to determine those that shift between reliable and unreliable | L | H | M | M |
| Reliability | Multivariable Statistical Model | Statistical analysis considering multiple variables | L | H | L | L |
| Traffic Congestion | Building off the Baseline with Assumptions | Qualitative approach to adjusting baseline values | H | L | H | H |
| Traffic Congestion | Time-Series Trend Analysis | Based solely on historical performance data | M | M | M | M |
| Traffic Congestion | Trend Plus Other Factors | Adjustments made to results from other approaches | M | M | H | H |
| Traffic Congestion | Travel Forecasting Model | Model used to estimate excessive delay for the base year and forecasted year | H | M | M | L |
| Traffic Congestion | Policy Based | Model based on regional policy goals | H | L | H | H |
Features Summary (L=Low, M=Medium, H=High)