Understanding and Communicating Reliability of Crash Prediction Models (2021) / Chapter Skim
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42 Chapter 4. Procedures for Quantifying the Reliability of Crash Prediction Model Estimates with a Focus on Error in Estimated Input Values Introduction The application of crash prediction models (CPMs)
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43 Factors Affecting the Potential Magnitude of Reliability Issues How significant erroneous input values affect the reliability of a CPM may depend on the context of its use and, in the case of estimating the safety effects of countermeasures or design decisions, potentially in the direction of the effect on the CPM, i.e., are more or fewer crashes predicted. For example, CPMs that are being used to evaluate a contemplated countermeasure may prove much more unreliable if incorrect values are used since a decision on implementing the countermeasure is based on cost-effectiveness, which is so directly tied to the empirical Bayes estimate of impacted crashes without the countermeasure, for which a CPM is used.
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44 ๐๐
๐ธ๐ท = predicted value from CPM with estimated value n = data sample size. Root Mean Squared Difference (RMSD)
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45 Spearman's Correlation Coefficient (Rho) The Spearman's Correlation Coefficient is used to compare Network Screening rankings using the CPMs with measurement error to the ranking using the CPM with the original estimated values.
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46 Case B Error for variable of interest is P% +/- Q% Xerror = Xest x (1 ยฑ P/100 + RandBetween(-Q,Q)
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47 The analyst should decide how many years of observed crash data will be used in their Network Screening program and whether sites are to be screened by the EB Expected or the EB Excess methods2. Step 6b: For each CPM applied, compute either the EB or EB Excess estimate for each site by combining the CPM predicted crash estimate with the observed crash data.
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48 Example For the example, the CPM that may be included in the HSM 2nd edition for total crashes on urban fourlane divided arterials is applied to each site. The dataset includes 5 years of observed crash data and 358 segments.
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49 Using the values of alpha and theta and tables for the gamma distribution, the 85th percentile value is determined to be 0.73. Extreme Value85th Percentile per year = 0.73 Step 7a: The root mean squared difference and extreme value were divided by the average predicted value per year using the CPM with known values, 1.33, and multiplied by 100 to express as a percentage.
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50 Comparing to the guidance in Table 17 the results indicate reliability rankings of medium for % false positives in the top 30 and 50 sites and rankings of Low for the Spearman's correlation coefficient and % false positives in the top 100 sites. Using the lowest ranking to characterize the CPM with errors results in a reliability of Low for Network Screening.
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