Target Setting Approaches

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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):

  1. Policy-based approaches, such as establishing a maximum rate of change (e.g., annual increase of at least 2 percent).
  2. Historical trends (e.g., set a value based on a 5-year trend).
  3. Probabilistic and risk-based approaches that consider performance variability (e.g., performance based on the likelihood of increased storm frequency and intensity).
  4. Statistical models (e.g., rates of deterioration based on regression models).
  5. 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 CategoryTarget Setting ApproachDescriptionEase of ApplicationTechnical RobustnessEase of CommunicationAllows for Policy Preference
SafetyTargeted ReductionDefined decrease from baseline regardless of past trendsHLHH
SafetyTime-Series TrendBased solely on historical performance dataHMHL
SafetyTrend Plus Other FactorsAdjustments made to results from other approachesHLMH
SafetyMultivariable Statistical ModelStatistical analysis considering multiple variablesLHML
Infrastructure ConditionTarget Based on Change in ConditionConsensus decisionHNoneLH
Infrastructure ConditionTime-Series TrendBased solely on historical performance dataHLMM
Infrastructure ConditionTime-Series Trend Plus Future FundingHistorical trends extrapolated into the futureHLMM
Infrastructure ConditionAsset Management SystemCondition forecasts based on expected fundsLHMM
Infrastructure ConditionScenario AnalysisManagement system analysis of multiple scenariosLHMM
ReliabilityBuilding off the Baseline with AssumptionsQualitative approach to adjusting baseline valuesHLHM
ReliabilityTime-Series Trend AnalysisBased solely on historical performance dataHMMM
ReliabilityTrend Plus Other FactorsAdjustments made to results from other approachesHMMM
ReliabilityPerformance Risk AnalysisStatistical analysis of variations due to risksMHMM
ReliabilitySegment Risk AnalysisAnalysis of individual segments to determine those that shift between reliable and unreliableLHMM
ReliabilityMultivariable Statistical ModelStatistical analysis considering multiple variablesLHLL
Traffic CongestionBuilding off the Baseline with AssumptionsQualitative approach to adjusting baseline valuesHLHH
Traffic CongestionTime-Series Trend AnalysisBased solely on historical performance dataMMMM
Traffic CongestionTrend Plus Other FactorsAdjustments made to results from other approachesMMHH
Traffic CongestionTravel Forecasting ModelModel used to estimate excessive delay for the base year and forecasted yearHMML
Traffic CongestionPolicy BasedModel based on regional policy goalsHLHH

Features Summary (L=Low, M=Medium, H=High)