Crash Modification Factors in the Highway Safety Manual: Resources for Evaluation (2023) / Chapter Skim
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Appendix E Development of Crash Modification Functions for Improving Curve Delineation
Appendix E Development of Crash Modification Functions for Improving Curve Delineation
Pages 113-125
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Appendix E Development of Crash Modification Functions for Improving Curve Delineation E-1
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E-5 4.1 Lane Departure Crashes ........................................................................................................... E-5 4.2 Dark Conditions Crashes ..........................................................................................................
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The original analysis found statistically significant reductions at the 95-percent confidence level in injury, dark condition and lane departure during dark conditions crashes using data from two States where installations were evaluated. Intersection-related crashes were not included in all crash types.
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Attempts to do so did not provide for reliable estimates of safety effectiveness for individual sites or for sites grouped by site characteristics. In the GLM regression approach, each site was considered as an observation.
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The model form for model 1 is shown below and the parameter estimates, standard errors and pvalues provided in Table 2. The estimated parameters are statistically significant and the overdispersion parameter of 0.3018 indicates a reasonable fit to the data.
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For curves with an AADT over approximately 3,000 the implied CMF is less than 1.0 and the CMF bottoms out at approximately 0.50. Pred LD CMF using AADT 1.80 1.60 1.40 1.20 1.00 Pred LD CMF using 0.80 AADT 0.60 0.40 0.20 0.00 0 5000 10000 15000 20000 Figure 1.
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For curves with an LDRATE over approximately 7 the implied CMF is less than 1.0. Pred LD CMF using EB rate 1.20 1.00 0.80 0.60 Pred LD CMF using EB rate 0.40 0.20 0.00 0.00 20.00 40.00 60.00 80.00 Figure 2.
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Table 4. Model Results for Dark Condition Crashes Parameter Estimate Standard Error p-value a 2.8489 2.3152 0.2185 b -0.3722 0.2755 0.1767 k 0.5478 0.3739 Figure 3 plots the CMFunction from Table 4 over the range of AADT values in the calibration dataset.
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4.3 Lane Departure During Dark Conditions Crashes For lane departure dark conditions crashes two models were estimated. The first model used the AADT prior to conversion as the predictor variable and the second used the EB expected number of crashes per mile-year prior to treatment.
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Pred LDDRK CMF using AADT 1.40 1.20 1.00 0.80 Pred LDDRK CMF using 0.60 AADT 0.40 0.20 0.00 0 5000 10000 15000 20000 Figure 4. CMFunction for Lane Departure Crashes in Dark Conditions (Model 1)
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Model Results for Lane Departure Crashes in Dark Conditions (Model 2) Parameter Estimate Standard Error p-value a -0.1136 0.2338 0.6272 b -0.0326 0.0264 0.2169 k 0.3442 0.3283 Figure 5 plots the CMFunction from Table 6 over the range of LDDRK values in the calibration dataset.
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This may in part explain why the AMFs … show decreases in safety benefits with decreased traffic volumes, which are in turn associated with roads with narrower pavement widths." The data available did not include lane or shoulder widths so the impact of those variables on the predicted CMFs could not be explored. Curve radius was available but no effects on the predicted CMFs could be determined for this dataset.
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Comparison of CMFs for Original and New Analysis CMF from Intercept Only Crash Type Original Study Model CMF Lane Departure 0.82 0.94 Dark Conditions 0.65 0.76 Lane Departure Dark 0.66 0.76 Conditions 6.
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