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Suggested Citation:"Chapter 7. Analysis Results." National Academies of Sciences, Engineering, and Medicine. 2021. Application of Crash Modification Factors for Access Management, Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/26162.
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Suggested Citation:"Chapter 7. Analysis Results." National Academies of Sciences, Engineering, and Medicine. 2021. Application of Crash Modification Factors for Access Management, Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/26162.
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Suggested Citation:"Chapter 7. Analysis Results." National Academies of Sciences, Engineering, and Medicine. 2021. Application of Crash Modification Factors for Access Management, Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/26162.
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Suggested Citation:"Chapter 7. Analysis Results." National Academies of Sciences, Engineering, and Medicine. 2021. Application of Crash Modification Factors for Access Management, Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/26162.
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Suggested Citation:"Chapter 7. Analysis Results." National Academies of Sciences, Engineering, and Medicine. 2021. Application of Crash Modification Factors for Access Management, Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/26162.
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Suggested Citation:"Chapter 7. Analysis Results." National Academies of Sciences, Engineering, and Medicine. 2021. Application of Crash Modification Factors for Access Management, Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/26162.
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Suggested Citation:"Chapter 7. Analysis Results." National Academies of Sciences, Engineering, and Medicine. 2021. Application of Crash Modification Factors for Access Management, Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/26162.
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Suggested Citation:"Chapter 7. Analysis Results." National Academies of Sciences, Engineering, and Medicine. 2021. Application of Crash Modification Factors for Access Management, Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/26162.
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Suggested Citation:"Chapter 7. Analysis Results." National Academies of Sciences, Engineering, and Medicine. 2021. Application of Crash Modification Factors for Access Management, Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/26162.
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Suggested Citation:"Chapter 7. Analysis Results." National Academies of Sciences, Engineering, and Medicine. 2021. Application of Crash Modification Factors for Access Management, Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/26162.
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Suggested Citation:"Chapter 7. Analysis Results." National Academies of Sciences, Engineering, and Medicine. 2021. Application of Crash Modification Factors for Access Management, Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/26162.
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Suggested Citation:"Chapter 7. Analysis Results." National Academies of Sciences, Engineering, and Medicine. 2021. Application of Crash Modification Factors for Access Management, Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/26162.
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Suggested Citation:"Chapter 7. Analysis Results." National Academies of Sciences, Engineering, and Medicine. 2021. Application of Crash Modification Factors for Access Management, Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/26162.
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Suggested Citation:"Chapter 7. Analysis Results." National Academies of Sciences, Engineering, and Medicine. 2021. Application of Crash Modification Factors for Access Management, Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/26162.
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Suggested Citation:"Chapter 7. Analysis Results." National Academies of Sciences, Engineering, and Medicine. 2021. Application of Crash Modification Factors for Access Management, Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/26162.
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Suggested Citation:"Chapter 7. Analysis Results." National Academies of Sciences, Engineering, and Medicine. 2021. Application of Crash Modification Factors for Access Management, Volume 2: Research Overview. Washington, DC: The National Academies Press. doi: 10.17226/26162.
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Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

97 C H A P T E R 7 Analysis Results Introduction This chapter presents the analysis results for intersections and segments with the respective access management strategies. Results are presented and discussed for each access management strategy, including goodness-of-fit measures, CURE plots, assessment tables, and CMFs to adjust the existing SPFs where applicable. Again, refer to Appendix C for guidance on interpreting goodness-of-fit measures, CURE plots, and assessment tables. Note: in all CURE plots, “Series1” is the cumulative residuals and “Series2” and “Series3” are the upper and lower 95th percentile limits, respectively. Intersections Distance to Ramp Terminal To consider distance to ramp terminal using the Ohio data, each crash prediction model (3ST, 4ST, 3SG, 4SG) was first calibrated using all available sites. Then all sites without a distance to ramp terminal measurement were removed. This left a small number of locations, so 3ST and 4ST were combined, as were 3SG and 4SG sites. Using these remaining sites, prediction biases were investigated for the calibrated prediction and distance to ramp terminal. Table 67 shows the CURE plot summary results for the Ohio sites and the CURE plots follow. The CURE plots, shown in Figure 27 and Figure 30, indicated that some bias exists across predicted values for the stop-controlled crash prediction models. For distance to ramp terminal, the CURE plots did not show substantial bias although the CURE plot does indicate that for distances under 1,500 ft., there may be an underprediction of crashes. That indicates that a CMF greater than 1.0 may be appropriate for sites where a ramp terminal is within 1,500 ft. For signalized intersections, the CURE plots indicated that the crash prediction models are predicting very well across the range of predicted values and distance to ramp terminal. Even so, CMFs were explored for signalized sites using the same 1,500 ft. criteria as for unsignalized sites. The results for the crash type models are not shown but agree with the results for total crashes in that no substantial bias was seen for either stop- or signal-controlled intersections for single-vehicle, same- direction, opposite-direction, or intersecting-direction crashes.

98 Table 67. Ohio distance to ramp terminal CURE plot measures. Site Types Number of Sites Number of Crashes Plotted Variable Factor MAX DEV % CURE >2 S.D. 3ST and 4ST 42 218 Fitted value 50.25 26 3ST and 4ST 42 218 Distance to ramp terminal 29.30 2 3SG and 4SG 26 529 Fitted value 47.02 4 3SG and 4SG 26 529 Distance to ramp terminal 50.15 4     Figure 27. CURE plot 3ST and 4ST fitted values. ‐60 ‐40 ‐20 0 20 40 60 0 2 4 6 8 10 12 14 16 18 Cu m ul at iv e  Re sid ua ls Highway Safety Manual (1st Edition) Calibrated Prediction Series1 Series2 Series3

99   Figure 28. CURE plot 3ST and 4ST distance to ramp terminal.   Figure 29. CURE plot 3SG and 4SG fitted values. ‐50 ‐40 ‐30 ‐20 ‐10 0 10 20 30 40 50 0 500 1000 1500 2000 2500 3000 Cu m ul at iv e  Re sid ua ls Distance to Ramp Terminal Series1 Series2 Series3 ‐80 ‐60 ‐40 ‐20 0 20 40 60 80 0 5 10 15 20 25 30 35 40 45 Cu m ul at iv e  Re sid ua ls Highway Safety Manual (1st Edition) Calibrated Prediction Series1 Series2 Series3

100 Figure 30. CURE plot 3SG and 4SG distance to ramp terminal. CMFs for distance to ramp terminal were developed using a categorical variable for less than or equal to 1,500 ft. The GLM approach was also applied using the same categorical variable and as a continuous variable but the latter was unsuccessful. The base condition is a ramp terminal that is greater than 1,500 ft. away from the intersection. Table 68 shows the estimated CMFs and Table 69 shows the details of the GLM models. For stop- controlled sites, the results logically indicate more crashes when ramp terminals are 1,500 ft. or less away. For signalized intersections the opposite is true which is counterintuitive. For both sets of results the magnitude of the standard errors is large making the results far from statistically significant at the 95 percent confidence level. Table 68. Ohio distance to ramp terminal CMFs. Site Type Calibration Factor <= 1,500’ Calibration Factor > 1,500’ CMF for Distance to Ramp Terminal <= 1,500’ Using Calibration Factors CMF for Distance to Ramp Terminal < 1,500’ Using GLM 3ST and 4ST 1.14 0.60 1.90 (1.14) 2.12 (0.91) 3SG and 4SG 0.97 1.04 0.93 (0.46) 0.82 (0.24) ‐100 ‐80 ‐60 ‐40 ‐20 0 20 40 60 80 100 0 500 1000 1500 2000 2500 3000 Cu m ul at iv e  Re sid ua ls Distance to Ramp Terminal Series1 Series2 Series3

101 Table 69. Ohio distance to ramp terminal GLM results. Site Type Crashes/year = (Predicted)*exp(intercept+3ST+a*ramp category) intercept (se) 3ST (se) a (se) overdispersion (se) 3ST and 4ST -0.3925 (0.4277) -0.2992 (0.3689) 0.7523 (0.4181) 0.9237 (0.2594) 3SG and 4SG 0.4987 (0.2200) n/a -0.1939 (0.2789) 0.4176 (0.1262) Notes: Ramp category = 1 if distance to ramp terminal is less than or equal to 1,500 feet; 0 otherwise. 3ST term used if site is a three-legged intersection; 0 otherwise. A validation of the results using the data collected from North Carolina could not be undertaken because only five sites, all 4SG, included a distance to ramp terminal measurement and all were under 1,500 feet. Channelized Right-Turn Lanes To consider channelized right-turn lanes, each crash prediction model (3ST, 4ST, 3SG, 4SG) was calibrated to sites with at least one right-turn lane. Since the Highway Safety Manual (1st Edition) already has a CMF for the presence of right-turn lanes, the interest here is if the right-turn lanes are channelized. In the analysis, it was assumed that if one right-turn lane was channelized, then all right-turn lanes present were channelized. This was the case for nearly all sites where there were 38 sites with a channelized right- turn lane and only five of these sites had another right-turn lane that was not channelized. Prediction biases were assessed for the calibrated prediction and presence or absence of channelization for right-turn lanes. For developing the CMF, the base condition is the presence of a right-turn lane with no channelization. Table 70 shows the calibration factors for sites with and without channelization of right-turn lanes. The derived CMFs from these calibration factors are provided as well as the CMFs derived from the GLM models presented in Table 71. The results indicate fewer total crashes with channelization with the exception of 4SG sites where the opposite is true. For the GLM models and derived CMFs, only the results for 3SG sites were statistically significant. CMFs by crash type were not explored because there are no Highway Safety Manual (1st Edition) CMFs for right-turn lane presence by crash type. Table 70. Ohio right-turn lane channelization CMFs. Site Type Number of Sites Number of Crashes Calibration Factor for No Right-Turn Channelization Calibration Factor for Right-Turn Channelization CMF for Channelized Right-Turn Using Calibration Factors CMF for Channelized Right-Turn from GLM 4ST 36 190 1.08 0.85 0.79 (0.28) 0.96 (0.36) 4SG 58 1,119 0.95 1.48 1.56 (1.26) 1.27 (0.51) 3ST 62 204 1.07 0.80 0.75 (0.35) 0.69 (0.28) 3SG 38 322 1.09 0.45 0.41 (0.24) 0.42 (0.19)

102 Table 71. Ohio right-turn lane channelization GLM results. Site Type Crashes/year = (Predicted)*exp(intercept+a*channelized right-turn lane) intercept (se) a (se) k (se) 4ST -0.0519 (0.2082) -0.0393 (0.3668) 0.8291 (0.2528) 4SG 0.4408 (0.1264) 0.2393 (0.3885) 0.7616 (0.1477) 3ST -0.1395 (0.1892) -0.3732 (0.3942) 1.3012 (0.3025) 3SG 0.2265 (0.1485) -0.8614 (0.4438) 0.5963 (0.1789) Note: Channelized right-turn lane = 1 if right-turn lane is channelized; 0 otherwise. A validation of the results was conducted by comparing the Ohio results to the North Carolina results, with the exception of 4ST sites for which none were channelized in North Carolina. Table 72 shows the estimated CMFs and Table 73 shows the details of the GLM models. As shown in Table 72, the North Carolina results for 4SG and 3SG disagree with the Ohio results. The results for 3ST agree between the two states and the CMFs are roughly the same magnitude. Table 72. North Carolina right-turn lane channelization CMFs. Site Type Number of Sites Number of Crashes Calibration Factor for No Right-Turn Channelization Calibration Factor for Right-Turn Channelization CMF for Channelized Right-Turn Using Calibration Factors CMF for Channelized Right-Turn from GLM 4ST 5 51 -- -- -- -- 4SG 77 5,195 1.01 0.97 0.96 (0.14) 0.89 (0.18) 3ST 9 55 1.05 0.86 0.82 (0.05) 0.75 (0.32) 3SG 18 529 1.00 1.03 1.03 (0.31) 1.27 (0.64) Note: -- indicates no channelized intersections in the sample. Table 73. North Carolina right-turn lane channelization GLM Results. Site Type Crashes/year = (Predicted)*exp(intercept+a*channelized right-turn lane) intercept (se) a (se) k (se) 4ST -- -- -- 4SG 1.3265 (0.1123) -0.1168 (0.2017) 0.6462 (0.1038) 3ST 0.1588 (0.2325) -0.2924 (0.4197) 0.1555 (0.1561) 3SG 0.7779 (0.1600) 0.2383 (0.4843) 0.3681 (0.1382) Notes: -- indicates no channelized intersections in the sample. Channelized right-turn lane = 1 if right-turn lane is channelized; 0 otherwise. Since the Ohio results for 3ST agree with the North Carolina results, the data were combined to estimate a CMF. Table 74 shows the details of the combined GLM model. The implied CMF from the GLM model is 0.72 (i.e., exp(-0.3340)) with a standard error of 0.25 (i.e., exp(0.1666)).

103 Table 74. Combined Ohio and North Carolina right-turn lane channelization GLM results. Site Type Crashes/year = (Predicted)*exp(intercept+a*channelized right-turn lane) intercept (se) a (se) k (se) 3ST -0.0989 (0.1666) -0.3340 (0.3377) 1.1280 (0.2504) Note: Channelized right-turn lane = 1 if right-turn lane is channelized; 0 otherwise. Segments Median Opening Density Median opening density, defined as median openings per mile, is only relevant for 4D segments. Table 75 presents the goodness-of-fit measures from the CURE plots. There is no evidence in the CURE plots of substantial prediction bias for median opening density. Table 76 shows the details of the GLM model. The estimated GLM model shows crashes decreasing with higher median opening density, the opposite to what would be expected, but the model parameters are not statistically significant at any reasonable level of significance. Table 75. Ohio median opening density goodness-of-fit measures. Site Types Number of Sites Number of Crashes Plotted Variable Factor MAX DEV % CURE >2 S.D. 4D 387 3,247 Fitted value 111.04 7 4D 387 3,247 Median Opening Density 183.68 5 Table 76. Ohio median opening density GLM results. Site Type Crashes/year = (Predicted)*exp(intercept+a*median opening density) intercept (se) a (se) k (se) 4D -0.1319 (0.0654) -0.0025 (0.0076) 0.8943 (0.0938) Note: Median opening density is defined as the number of median openings per mile. The results for the crash type models are not shown but agree with the results for total crashes in that no substantial bias was seen in the CURE plots for rear-end, single-vehicle, sideswipe-same-direction, multi- vehicle-non-driveway or nighttime crashes. GLM models for these crash types, shown in Figure 31 and Figure 32, showed no improvement in model fit with the addition of median opening density.

104 Figure 31. CURE plot Ohio 4D fitted values. Figure 32. CURE plot Ohio 4D median opening density. ‐250 ‐200 ‐150 ‐100 ‐50 0 50 100 150 200 250 0 20 40 60 80 100 120 140 160 Cu m ul at iv e  Re sid ua ls Highway Safety Manual (1st Edition) Calibrated Prediction Series1 Series2 Series3 ‐250 ‐200 ‐150 ‐100 ‐50 0 50 100 150 200 250 ‐10 0 10 20 30 40 50 60 70 80 Cu m ul at iv e  Re sid ua ls Median Opening Density Series1 Series2 Series3

105 A validation of the results was conducted by comparing the Ohio results to the Minnesota results. Table 77 presents the goodness-of-fit measures for the CURE plots in Figure 33 and Figure 34. Contrary to Ohio, there is some evidence of prediction bias against the median opening density variable with the % CURE >2 S.D. greater than 5 percent. Table 78 shows the details of the GLM model. The estimated GLM model indicates a statistically significant and positive parameter for median opening density, indicating that more crashes are expected with more median openings per mile, which is intuitive but contrary to the Ohio results. Table 77. Minnesota median opening density goodness-of-fit measures. Site Types Number of Sites Number of Crashes Plotted Variable Factor MAX DEV % CURE >2 S.D. 4D 133 600 Fitted value 62.64 1 4D 133 600 Median Opening Density 72.92 14 Table 78. Minnesota median opening density GLM results. Site Type Crashes/year = (Predicted)*exp(intercept+a*median opening density) intercept (se) a (se) k (se) 4D 0.0540 (0.1088) 0.0457 (0.0244) 0.7524 (0.1390) Note: Median opening density is defined as the number of median openings per mile. Figure 33. CURE plot Minnesota 4D fitted values. ‐80 ‐60 ‐40 ‐20 0 20 40 60 80 0 5 10 15 20 25 Cu m ul at iv e  Re sid ua ls Highway Safety Manual (1st Edition) Calibrated Prediction Series1 Series2 Series3

106 Figure 34. CURE plot Minnesota 4D median opening density. ‐100 ‐80 ‐60 ‐40 ‐20 0 20 40 60 80 ‐5 0 5 10 15 20 Cu m ul at iv e  Re sid ua ls Median Opening Density Series1 Series2 Series3

107 Median Opening Spacing Median opening spacing is only relevant for 4D segments. To consider median opening spacing, the analysis only included segments with at least one median opening spacing measurement. The two median opening spacing variables considered were the maximum and minimum spacing. Table 79 presents the goodness-of-fit measures for the CURE plots. There is no evidence in the CURE plots of bias for the fitted value or the maximum or minimum median spacing variables. Table 80 shows the details for the GLM models. The estimated GLM models indicate fewer crashes are expected as spacing increases, which is logical, but the standard errors are high and improvement in model fit was minimal compared to a model with only an intercept term. Table 79. Ohio median opening spacing goodness-of-fit measures. Site Types Number of Sites Number of Crashes Plotted Variable Factor MAX DEV % CURE >2 S.D. 4D 214 2,135 Fitted value 119.31 3 4D 214 2,135 Maximum MO Spacing 85.33 3 4D 214 2,135 Minimum MO Spacing 87.96 0 Table 80. Ohio median opening spacing GLM results. Site Type Crashes/year = (Predicted)*exp(intercept + a*maximum median opening spacing + b*minimum median opening spacing) intercept (se) a (se) b (se) k (se) 4D -0.1223 (0.0997) -- -0.1161 (0.1116) 0.7734 (0.1094) 4D -0.0932 (0.1164) -0.0677 (0.0612) -- 0.7719 (0.1093) Notes: Maximum median opening spacing is defined as the longest distance between two median-crossing points (i.e., median openings and intersections) within a segment. Minimum median opening spacing is defined as shortest distance between two median-crossing points (i.e., median openings and intersections) within a segment. -- indicates not applicable. The results for the crash type models are not shown but agree with the results for total crashes in that no substantial bias was seen in the CURE plots, shown in Figure 35 and Figure 36, for rear-end, single-vehicle, sideswipe-same-direction, multi-vehicle-non-driveway or nighttime crashes. GLM models for these crash types showed no substantial improvement in model fit with the addition of either median opening spacing variable. Validation with the Minnesota data was not possible because only 5 sites had a measurement for median opening spacing.

108   Figure 35. CURE plot Minnesota 4D maximum median opening spacing.   Figure 36. CURE plot Minnesota 4D minimum median opening spacing. ‐200 ‐150 ‐100 ‐50 0 50 100 150 200 0 2000 4000 6000 8000 10000 12000 Cu m ul at iv e  Re sid ua ls Maximum Median Opening Spacing Series1 Series2 Series3 ‐200 ‐150 ‐100 ‐50 0 50 100 150 200 0 2000 4000 6000 8000 10000 12000 Cu m ul at iv e  Re sid ua ls Minimum Median Opening Spacing Series1 Series2 Series3

109 Number of Median Openings by Type The number of median openings is only relevant for 4D segments. To consider the number of median openings by type, all 4D segments were included. Table 81 presents the goodness-of-fit measures for the CURE plots. The CURE plots, shown in Figure 37 through Figure 42, do not show significant bias for full median openings. Some bias is seen when looking at directional median openings or those with left-turn lanes. There is some indication that the crash prediction model overpredicts for larger numbers of median openings, indicating that the number of crashes is expected to be lower as the number of median openings increase. This is true for all openings and openings by type. That is contrary to expectations since median openings create more potential conflict points in the segment. Table 82 and Table 83 show the details for the GLM models based on two different model forms. For the GLM models, the parameter estimates also indicate the same trend with a few exceptions; however, the standard errors are typically high and improvement in model fit over a model with only an intercept term is marginal. In estimating the GLM models, only sites with zero or the median type of interest were included, thus the base condition would be no median openings. Two model forms were attempted. In model form 1, the impact of median openings is multiplicative. In model form 2, the impact of median openings is additive. Where no parameter estimates are provided, the models did not converge. Table 81. Ohio median openings by type goodness-of-fit measures. Site Types Number of Sites Number of Crashes Plotted Variable Factor MAX DEV % CURE >2 S.D. 4D 387 3,247 Fitted Value 111.04 7 4D 387 3,247 Number of Full Median Openings 169.52 3 4D 387 3,247 Number of 1-Direction Median Openings 169.52 7 4D 387 3,247 Number of 2-Direction Median Openings 183.68 9 4D 387 3,247 Number of Full Median Openings with Left-Turn Lane 183.68 6 4D 387 3,247 Number of 1-Direction Median Openings with Left-Turn Lane 184.69 13 4D 387 3,247 Number of 2-Direction Median Openings with Left-Turn Lane 184.69 9

110 Table 82. Ohio median openings by type GLM results - model form 1. Site Type Number of Median Openings by Type Crashes/year = (Predicted)*exp(intercept+a*number of median openings) intercept (se) a (se) k (se) 4D Full Median Openings -0.1116 (0.0682) -0.0009 (0.0177) 0.9663 (0.1103) 4D 1-Direction Median Openings -- -- -- 4D 2-Direction Median Openings (all have left-turn lanes) -0.0901 (0.0902) -1.5687 (1.1415) 1.0449 (0.1677) 4D Full Median Openings with Left-Turn Lane -0.1119 (0.0886) 0.0235 (0.0950) 1.0957 (0.1624) 4D 1-Direction Median Openings with Left-Turn Lane -- -- -- 4D 2-Direction Median Openings with Left-Turn Lane -0.0901 (0.0902) -1.5687 (1.1415) 1.0449 (0.1677) Notes: Number of median openings is the total number of median openings for each type within the segment. -- indicates model did not converge. Table 83. Ohio median openings by type GLM results - model form 2. Site Type Number of Median Openings by Type Crashes/year = (Predicted)*exp(intercept)+a*number of median openings intercept (se) a (se) k (se) 4D Full Median Openings -0.1200 (0.0715) 0.0254 (0.1269) 0.9663 (0.1103) 4D 1-Direction Median Openings -- -- -- 4D 2-Direction Median Openings (all have left-turn lanes) -0.0900 (0.0902) -15.2024 (4.8699) 1.0449 (0.1677) 4D Full Median Openings with Left-Turn Lane -0.1325 (0.0866) 0.4167 (0.3748) 1.0806 (0.1602) 4D 1-Direction Median Openings with Left-Turn Lane -- -- -- 4D 2-Direction Median Openings with Left-Turn Lane -0.0900 (0.0902) --15.2024 (4.8699) 1.0449 (0.1677) Notes: Number of median openings is the total number of median openings for each type within the segment. -- indicates model did not converge.

111   Figure 37. CURE plot Minnesota 4D number of full median openings.   Figure 38. CURE plot Minnesota 4D number of one-directional median openings. ‐250 ‐200 ‐150 ‐100 ‐50 0 50 100 150 200 250 ‐5 0 5 10 15 20 25 30 35 40 Cu m ul at iv e  Re sid ua ls Number of Full Median Openings Series1 Series2 Series3 ‐250 ‐200 ‐150 ‐100 ‐50 0 50 100 150 200 250 ‐2 0 2 4 6 8 10 12 Cu m ul at iv e  Re sid ua ls Number of One‐Directional Median Openings Series1 Series2 Series3

112 Figure 39. CURE plot Minnesota 4D number of two-directional median openings. Figure 40. CURE plot Minnesota 4D number full median openings with left-turn lane. ‐250 ‐200 ‐150 ‐100 ‐50 0 50 100 150 200 250 ‐2 0 2 4 6 8 10 Cu m ul at iv e  Re sid ua ls Number of Two‐Directional Median Openings Series1 Series2 Series3 ‐250 ‐200 ‐150 ‐100 ‐50 0 50 100 150 200 250 ‐2 0 2 4 6 8 10 Cu m ul at iv e  Re sid ua ls Number of Full Median Openings with Left‐Turn Lane Series1 Series2 Series3

113 Figure 41. CURE plot Minnesota 4D number one directional median openings with left-turn lane. Figure 42. CURE plot Minnesota 4D number two directional median openings with left-turn lanes. ‐250 ‐200 ‐150 ‐100 ‐50 0 50 100 150 200 250 ‐2 0 2 4 6 8 10 12 Cu m ul at iv e  Re sid ua ls Number of One‐Directional Median Openings with Left‐Turn Lane Series1 Series2 Series3 ‐250 ‐200 ‐150 ‐100 ‐50 0 50 100 150 200 250 ‐1 0 1 2 3 4 5 6 7 8 9 Cu m ul at iv e  Re sid ua ls Number of Two‐Directional Median Openings with Left‐Turn Lane Series1 Series2 Series3

114 The results for the crash type models are not shown but found no bias in the estimates for rear-end, single- vehicle, sideswipe-same-direction, multi-vehicle-non-driveway, or nighttime crashes. A validation of the results was done by comparing the Ohio results to the Minnesota results. In the Minnesota data, most segments have zero median openings and, of those that do, most have only one and the maximum number is two, so the CURE plots are not very useful and only the summary numbers are provided. Table 84 presents the goodness-of-fit measures for the CURE plots. There is some evidence of prediction bias against the number of median opening variables with the % CURE >2 S.D. greater than 5 percent. Table 85 and Table 86 show the details for the GLM models based on two different model forms. For the GLM models, the small sample size only allowed for the isolation of results for full median openings. The results indicate that more crashes are expected with an increase in median openings. However, it must be kept in mind that the data are limited to a maximum of two median openings per segment, so the results may not be applicable in all scenarios. The estimated parameters are approaching statistical significance for model form 1 but not model form 2. Table 84. Minnesota median openings by type goodness-of-fit measures. Site Types Number of Sites Number of Crashes Plotted Variable Factor MAX DEV % CURE >2 S.D. 4D 133 600 Fitted Value 62.64 1 4D 133 600 Number of Full Median Openings 67.42 11 4D 133 600 Number of 1-Direction Median Openings 67.42 11 4D 133 600 Number of 2-Direction Median Openings 66.45 10 4D 133 600 Number of Full Median Openings with Left-Turn Lane 71.60 14 4D 133 600 Number of 1-Direction Median Openings with Left-Turn Lane 73.84 12 4D 133 600 Number of 2-Direction Median Openings with Left-Turn Lane 76.55 17 Table 85. Minnesota median openings by type GLM results - model form 1. Site Type Number of Median Openings by Type Crashes/year = (Predicted)*exp(intercept + a*number of median openings) intercept (se) a (se) k (se) 4D Full Median Openings 0.0666 (0.1126) 0.2945 (0.1746) 0.7672 (0.1407) 4D 1-Direction Median Openings -- -- -- 4D 2-Direction Median Openings (all have left-turn lanes) -- -- -- 4D Full Median Openings with Left-Turn Lane 0.0688 (0.1135) 0.3147 (0.1787) 0.7829 (0.1444) 4D 1-Direction Median Openings with Left-Turn Lane -- -- -- 4D 2-Direction Median Openings with Left-Turn Lane -- -- -- Notes: Number of median openings is the total number of median openings for each type within the segment. -- indicates model did not converge.

115 Table 86. Minnesota median openings by type GLM results - model form 2. Site Type Number of Median Openings by Type Crashes/year = (Predicted)*exp(intercept) + a*number of median openings intercept (se) a (se) k (se) 4D Full Median Openings 0.1188 (0.1095) 0.6232 (0.6446) 0.7833 (0.1427) 4D 1-Direction Median Openings -- -- -- 4D 2-Direction Median Openings (all have left-turn lanes) -- -- -- 4D Full Median Openings with Left-Turn Lane 0.1247 (0.1113) 0.6430 (0.6651) 0.8026 (0.1468) 4D 1-Direction Median Openings with Left-Turn Lane -- -- -- 4D 2-Direction Median Openings with Left-Turn Lane -- -- -- Notes: Number of median openings is the total number of median openings for each type within the segment. -- indicates model did not converge. Given the limited number of median openings per segment, the CURE plots are not particularly useful as this does not represent a continuous variable. Instead, assessment tables are more useful to compare calibration factors among discrete categories. Table 87 shows the assessment table results from the Calibrator, which in essence provide a calibration factor for each level of number of median openings. Again, refer to Appendix C for guidance on interpreting assessment tables.

116 Table 87. Minnesota median openings by type assessment table results. Number of Full Median Openings Number of Openings 0 1 2 Observed 306 267 27 Calibration Bias Factor 0.8256 1.3275 0.9573 Number of sites 91 38 4 Number of 1-Direction Median Openings Number of Openings 0 1 2 Observed 594 6 -- Calibration Bias Factor 1.0058 0.6360 -- Number of sites 132 1 -- Number of 2-Direction Median Openings Number of Openings 0 1 2 Observed 600 0 -- Calibration Bias Factor 1.0016 0 -- Number of sites 132 1 -- Number of Jug Handle Median Openings Number of Openings 0 1 2 Observed 600 -- -- Calibration Bias Factor 1 -- -- Number of sites 133 -- -- Number of Median Openings with Left-Turn Lane Number of Openings 0 1 2 Observed 317 262 21 Calibration Bias Factor 0.8195 1.3476 1.1188 Number of sites 94 36 3 Number of 1-Direction Median Openings with Left-Turn Lane Number of Openings 0 1 2 Observed 543 57 -- Calibration Bias Factor 0.9832 1.1940 -- Number of sites 122 11 -- Number of 2-Direction Median Openings with Left-Turn Lane Number of Openings 0 1 2 Observed 366 221 13 Calibration Bias Factor 0.8476 1.4521 0.8119 Number of sites 104 27 2 Note: -- indicates no data.

117 Corner Clearance The analysis of corner clearance looked at all segment types individually. Table 88 presents the goodness- of-fit measures for the CURE plots. For Ohio, the CURE plots, shown in Figure 43 through Figure 57, for total crashes indicated that there is a general underprediction in crashes as the minimum, maximum, and average corner clearance distance increases and that the bias is significant. The results are contrary to expectation. Similar to the bias issues for the number of unsignalized access points and signalized intersections, larger corner clearance may not only be because there are fewer driveways but also that there could be fewer intersections, leading more crashes to be assigned to segments as opposed to intersections. Table 89, Table 90, and Table 91 show the details for the GLM models for minimum, maximum, and average corner clearance, respectively. GLM models were estimated and all of the statistically significant parameter estimates confirmed that more crashes are associated with segments with larger minimum, maximum, and average corner clearance measurements (i.e., crashes increase as corner clearance increases). A few site type/measurement combinations did have parameter estimates indicating fewer crashes with longer corner clearance measurements, but these were not statistically significant. Table 88. Ohio corner clearance goodness-of-fit measures. Site Types Number of Sites Number of Crashes Plotted Variable Factor MAX DEV % CURE >2 S.D. 2U 152 1,131 Fitted Value 95.47 3 2U 152 1,131 Minimum Corner Clearance 203.70 82 2U 152 1,131 Maximum Corner Clearance 144.31 42 2U 152 1,131 Average Corner Clearance 232.83 64 3T 72 499 Fitted Value 47.46 1 3T 72 499 Minimum Corner Clearance 61.04 25 3T 72 499 Maximum Corner Clearance 61.34 36 3T 72 499 Average Corner Clearance 96.43 56 4D 180 1,532 Fitted Value 112.25 9 4D 180 1,532 Minimum Corner Clearance 148.07 26 4D 180 1,532 Maximum Corner Clearance 96.45 1 4D 180 1,532 Average Corner Clearance 90.47 1 4U 137 1,169 Fitted Value 148.66 4 4U 137 1,169 Minimum Corner Clearance 138.02 1 4U 137 1,169 Maximum Corner Clearance 158.05 22 4U 137 1,169 Average Corner Clearance 96.96 1 5T 112 2,190 Fitted Value 294.41 15 5T 112 2,190 Minimum Corner Clearance 237.77 25 5T 112 2,190 Maximum Corner Clearance 270.04 32 5T 112 2,190 Average Corner Clearance 231.66 23

118 Table 89. Ohio minimum corner clearance GLM results. Site Type Crashes/year = (Predicted)*exp(intercept+a*minimum corner clearance/1000) intercept (se) a (se) k (se) 2U -0.6766 (0.1478) 1.9205 (0.9804) 1.3984 (0.2230) 3T -1.0037 (0.2480) 4.0794 (1.9871) 1.3623 (0.3116) 4D -0.0524 (0.1146) -0.1193 (0.4503) 0.8073 (0.1248) 4U -0.8615 (0.1653) 0.8193 (1.2780) 1.7510 (0.2512) 5T -1.2807 (0.1417) 3.4903 (0.9748) 1.0437 (0.1714) Note: Corner clearance is defined as the distance from the nearest driveway on the mainline to the corner of the intersection. Minimum corner clearance is the shortest corner clearance among all intersections within the segment. Table 90. Ohio maximum corner clearance GLM results. Site Type Crashes/year = (Predicted)*exp(intercept+a*maximum corner clearance/1000) intercept (se) a (se) k (se) 2U -0.4512 (0.1183) -0.0160 (0.0791) 1.4744 (0.2304) 3T -1.5329 (0.4149) 2.7546 (1.1491) 1.3428 (0.3055) 4D 0.0682 (0.1670) -0.3340 (0.3424) 0.8020 (0.1239) 4U -0.6548 (0.1947) -0.3899 (0.4390) 1.7468 (0.2509) 5T -1.2629 (0.2119) 0.9682 (0.4614) 1.1845 (0.1871) Note: Corner clearance is defined as the distance from the nearest driveway on the mainline to the corner of the intersection. Maximum corner clearance is the longest corner clearance among all intersections within the segment. Table 91. Ohio average corner clearance GLM results. Site Type Crashes/year = (Predicted)*exp(intercept+a*average corner clearance/1000) intercept (se) a (se) k (se) 2U -0.7913 (0.1897) 1.3655 (0.6825) 1.4080 (0.2228) 3T -1.7116 (0.4058) 5.422 (1.8941) 1.2635 (0.2927) 4D 0.0173 (0.1536) -0.3121 (0.4464) 0.8068 (0.1244) 4U -0.8355 (0.2081) 0.2554 (0.8965) 1.7576 (0.2518) 5T -1.5211 (0.2009) 2.8135 (0.8040) 1.0754 (0.1740) Note: Corner clearance is defined as the distance from the nearest driveway on the mainline to the corner of the intersection. Average corner clearance is the average value of all corner clearance measurements within the segment.

119   Figure 43. CURE plot Ohio 2U minimum corner clearance.   Figure 44. CURE plot Ohio 2U maximum corner clearance. ‐250 ‐200 ‐150 ‐100 ‐50 0 50 100 150 0 200 400 600 800 1000 1200 1400 Cu m ul at iv e  Re sid ua ls Minimum Corner Clearance Series1 Series2 Series3 ‐200 ‐150 ‐100 ‐50 0 50 100 150 0 5000 10000 15000 20000 25000 Cu m ul at iv e  Re sid ua ls Maximum Corner Clearance Series1 Series2 Series3

120   Figure 45. CURE plot Ohio 2U average corner clearance.   Figure 46. CURE plot Ohio 3T minimum corner clearance. ‐300 ‐250 ‐200 ‐150 ‐100 ‐50 0 50 100 150 0 500 1000 1500 2000 2500 Cu m ul at iv e  Re sid ua ls Average Corner Clearance Series1 Series2 Series3 ‐80 ‐60 ‐40 ‐20 0 20 40 60 80 0 50 100 150 200 250 300 350 400 Cu m ul at iv e  Re sid ua ls Minimum Corner Clearance Series1 Series2 Series3

121   Figure 47. CURE plot Ohio 3T maximum corner clearance.   Figure 48. CURE plot Ohio 3T average corner clearance. ‐80 ‐60 ‐40 ‐20 0 20 40 60 80 0 100 200 300 400 500 600 700 800 Cu m ul at iv e  Re sid ua ls Maximum Corner Clearance Series1 Series2 Series3 ‐120 ‐100 ‐80 ‐60 ‐40 ‐20 0 20 40 60 80 0 100 200 300 400 500 600 Cu m ul at iv e  Re sid ua ls Average Corner Clearance Series1 Series2 Series3

122   Figure 49. CURE plot Ohio 4D minimum corner clearance.   Figure 50. CURE plot Ohio 4D maximum corner clearance. ‐200 ‐150 ‐100 ‐50 0 50 100 150 0 200 400 600 800 1000 1200 Cu m ul at iv e  Re sid ua ls Minimum Corner Clearance Series1 Series2 Series3 ‐150 ‐100 ‐50 0 50 100 150 0 200 400 600 800 1000 1200 1400 1600 Cu m ul at iv e  Re sid ua ls Maximum Corner Clearance Series1 Series2 Series3

123   Figure 51. CURE plot Ohio 4D average corner clearance.   Figure 52. CURE plot Ohio 4U minimum corner clearance. ‐150 ‐100 ‐50 0 50 100 150 0 200 400 600 800 1000 1200 1400 Cu m ul at iv e  Re sid ua ls Average Corner Clearance Series1 Series2 Series3 ‐200 ‐150 ‐100 ‐50 0 50 100 150 200 0 200 400 600 800 1000 1200 1400 1600 Cu m ul at iv e  Re sid ua ls Minimum Corner Clearance Series1 Series2 Series3

124   Figure 53. CURE plot Ohio 4U maximum corner clearance.   Figure 54. CURE plot Ohio 4U average corner clearance. ‐200 ‐150 ‐100 ‐50 0 50 100 150 200 0 500 1000 1500 2000 2500 Cu m ul at iv e  Re sid ua ls Maximum Corner Clearance Series1 Series2 Series3 ‐200 ‐150 ‐100 ‐50 0 50 100 150 200 0 200 400 600 800 1000 1200 1400 1600 Cu m ul at iv e  Re sid ua ls Average Corner Clearance Series1 Series2 Series3

125   Figure 55. CURE plot Ohio 5T minimum corner clearance.   Figure 56. CURE plot Ohio 5T maximum corner clearance. ‐300 ‐200 ‐100 0 100 200 300 0 100 200 300 400 500 600 Cu m ul at iv e  Re sid ua ls Minimum Corner Clearance Series1 Series2 Series3 ‐300 ‐200 ‐100 0 100 200 300 0 200 400 600 800 1000 1200 1400 1600 1800 Cu m ul at iv e  Re sid ua ls Maximum Corner Clearance Series1 Series2 Series3

126   Figure 57. CURE plot Ohio 5T average corner clearance. For crash type models there was little evidence of bias seen and GLM models were not very strong in general. Those models that could be estimated showed little improvement in overall goodness of fit with the addition of corner clearance variables. A validation of the results was conducted by comparing the Ohio results to the Minnesota results. Table 92 presents the goodness-of-fit measures for the CURE plots, shown in Figure 58 through Figure 72. Some bias is seen in the predictions, particularly for 2U maximum and average spacing. Table 93, Table 94, and Table 95 show the details for the GLM models for minimum, maximum, and average corner clearance, respectively. For the GLM models, more often than not they were logical in predicting fewer crashes with increasing corner clearance, but the parameter estimates were not statistically significant at any reasonable level of significance. ‐300 ‐200 ‐100 0 100 200 300 0 200 400 600 800 1000 1200 1400 Cu m ul at iv e  Re sid ua ls Average Corner Clearance Series1 Series2 Series3

127 Table 92. Minnesota corner clearance goodness-of-fit measures. Site Types Number of Sites Number of Crashes Plotted Variable Factor MAX DEV % CURE >2 S.D. 2U 64 134 Fitted Value 23.55 64 2U 64 134 Minimum Corner Clearance 20.53 3 2U 64 134 Maximum Corner Clearance 22.77 38 2U 64 134 Average Corner Clearance 21.29 14 3T 24 49 Fitted Value 8.51 21 3T 24 49 Minimum Corner Clearance 6.77 0 3T 24 49 Maximum Corner Clearance 4.59 0 3T 24 49 Average Corner Clearance 5.37 0 4D 37 207 Fitted Value 33.47 11 4D 37 207 Minimum Corner Clearance 35.80 16 4D 37 207 Maximum Corner Clearance 16.86 3 4D 37 207 Average Corner Clearance 17.98 0 4U 45 103 Fitted Value 8.51 2 4U 45 103 Minimum Corner Clearance 9.25 2 4U 45 103 Maximum Corner Clearance 12.76 16 4U 45 103 Average Corner Clearance 12.53 11 5T 21 109 Fitted Value 7.92 10 5T 21 109 Minimum Corner Clearance 8.70 5 5T 21 109 Maximum Corner Clearance 9.95 10 5T 21 109 Average Corner Clearance 7.07 5     Figure 58. CURE plot Minnesota 2U minimum corner clearance. ‐25 ‐20 ‐15 ‐10 ‐5 0 5 10 15 20 25 0 200 400 600 800 1000 1200 1400 1600 Cu m ul at iv e  Re sid ua ls Minimum Corner Clearance Series1 Series2 Series3

128   Figure 59. CURE plot Minnesota 2U maximum corner clearance.   Figure 60. CURE plot Minnesota 2U average corner clearance. ‐30 ‐25 ‐20 ‐15 ‐10 ‐5 0 5 10 15 20 25 ‐20000 0 20000 40000 60000 80000 100000 120000 Cu m ul at iv e  Re sid ua ls Maximum Corner Clearance Series1 Series2 Series3 ‐25 ‐20 ‐15 ‐10 ‐5 0 5 10 15 20 25 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 Cu m ul at iv e  Re sid ua ls Average Corner Clearance Series1 Series2 Series3

129   Figure 61. CURE plot Minnesota 3T minimum corner clearance.   Figure 62. CURE plot Minnesota 3T maximum corner clearance. ‐15 ‐10 ‐5 0 5 10 15 0 200 400 600 800 1000 1200 1400 Cu m ul at iv e  Re sid ua ls Minimum Corner Clearance Series1 Series2 Series3 ‐15 ‐10 ‐5 0 5 10 15 0 200 400 600 800 1000 1200 1400 Cu m ul at iv e  Re sid ua ls Maximum Corner Clearance Series1 Series2 Series3

130   Figure 63. CURE plot Minnesota 3T average corner clearance.   Figure 64. CURE plot Minnesota 4D minimum corner clearance. ‐15 ‐10 ‐5 0 5 10 15 0 200 400 600 800 1000 1200 1400 Cu m ul at iv e  Re sid ua ls Average Corner Clearance Series1 Series2 Series3 ‐40 ‐30 ‐20 ‐10 0 10 20 30 40 0 100 200 300 400 500 600 700 800 900 Cu m ul at iv e  Re sid ua ls Minimum Corner Clearance Series1 Series2 Series3

131   Figure 65. CURE plot Minnesota 4D maximum corner clearance.   Figure 66. CURE plot Minnesota 4D average corner clearance. ‐30 ‐20 ‐10 0 10 20 30 0 100 200 300 400 500 600 700 800 900 Cu m ul at iv e  Re sid ua ls Maximum Corner Clearance Series1 Series2 Series3 ‐40 ‐30 ‐20 ‐10 0 10 20 30 40 0 100 200 300 400 500 600 700 800 900 Cu m ul at iv e  Re sid ua ls Average Corner Clearance Series1 Series2 Series3

132   Figure 67. CURE plot Minnesota 4U minimum corner clearance.   Figure 68. CURE plot Minnesota 4U maximum corner clearance. ‐20 ‐15 ‐10 ‐5 0 5 10 15 20 0 100 200 300 400 500 600 700 Cu m ul at iv e  Re sid ua ls Minimum Corner Clearance Series1 Series2 Series3 ‐20 ‐15 ‐10 ‐5 0 5 10 15 20 0 500 1000 1500 2000 2500 3000 Cu m ul at iv e  Re sid ua ls Maximum Corner Clearance Series1 Series2 Series3

133   Figure 69. CURE plot Minnesota 4U average corner clearance.   Figure 70. CURE plot Minnesota 5T minimum corner clearance. ‐15 ‐10 ‐5 0 5 10 15 0 100 200 300 400 500 600 700 800 900 Cu m ul at iv e  Re sid ua ls Average Corner Clearance Series1 Series2 Series3 ‐20 ‐15 ‐10 ‐5 0 5 10 15 20 0 100 200 300 400 500 Cu m ul at iv e  Re sid ua ls Minimum Corner Clearance Series1 Series2 Series3

134   Figure 71. CURE plot Minnesota 5T maximum corner clearance.   Figure 72. CURE plot Minnesota 5T average corner clearance. ‐20 ‐15 ‐10 ‐5 0 5 10 15 20 0 200 400 600 800 1000 1200 Cu m ul at iv e  Re sid ua ls Maximum Corner Clearance Series1 Series2 Series3 ‐20 ‐15 ‐10 ‐5 0 5 10 15 20 0 100 200 300 400 500 600 700 800 Cu m ul at iv e  Re sid ua ls Average Corner Clearance Series1 Series2 Series3

135 Table 93. Minnesota minimum corner clearance GLM results. Site Type Crashes/year = (Predicted)*exp(intercept+a*minimum corner clearance/1000) intercept (se) a (se) k (se) 2U -0.5912 (0.1523) 0.6958 (0.3378) 0.2508 (0.1441) 3T -0.4166 (0.2547) -0.4830 (0.8572) 0.4346 (0.3083) 4D 0.5651 (0.2346) -0.4540 (0.7781) 0.5204 (0.1948) 4U -0.2990 (0.2085) -0.1058 (1.1698) 0.3430 (0.1818) 5T -0.2469 (0.2806) -2.3187 (1.8628) 0.3078 (0.1712) Note: Corner clearance is defined as the distance from the nearest driveway on the mainline to the corner of the intersection. Minimum corner clearance is the shortest corner clearance among all intersections within the segment. Table 94. Minnesota maximum corner clearance GLM results. Site Type Crashes/year = (Predicted)*exp(intercept+a*maximum corner clearance/1000) intercept (se) a (se) k (se) 2U -0.3974 (0.1254) -0.0028 (0.0093) 0.3495 (0.1660) 3T -0.3588 (0.3186) -0.4818 (0.8263) 0.4257 (0.3071) 4D 0.1284 (0.3859) 0.6629 (0.7244) 0.5235 (0.1931) 4U -0.1016 (0.1830) -0.6307 (0.3938) 0.2824 (0.1671) 5T -0.6041 (0.3038) 0.2190 (0.7629) 0.3549 (0.1867) Note: Corner clearance is defined as the distance from the nearest driveway on the mainline to the corner of the intersection. Maximum corner clearance is the longest corner clearance among all intersections within the segment. Table 95. Minnesota average corner clearance GLM results. Site Type Crashes/year = (Predicted)*exp(intercept+a*average corner clearance/1000) intercept (se) a (se) k (se) 2U -0.4028 (0.1292) -0.0018 (0.0545) 0.3523 (0.1666) 3T -0.3832 (0.2862) -0.5067 (0.8578) 0.4286 (0.3072) 4D 04692 (0.3445) -0.0316 (0.8924) 0.5318 (0.1973) 4U -0.0876 (0.2238) -0.9831 (0.7938) 0.3034 (0.1740) 5T -0.4214 (0.3118) -0.5253 (1.2443) 0.3450 (0.1845) Note: Corner clearance is defined as the distance from the nearest driveway on the mainline to the corner of the intersection. Average corner clearance is the average value of all corner clearance measurements within the segment. An additional analysis was undertaken to see if the trends for corner clearance measurements hold for the corner clearance for vehicles arriving versus departing the intersection. GLM models were run using the Ohio data. Table 96, Table 97, and Table 98 show the details of the GLM models for minimum, maximum, and average corner clearance by approaching and departing leg. Similar to the previous Ohio models that did not differentiate between arriving or departing corner clearance, the new GLM models also indicated that in most cases more crashes are associated with segments with larger minimum, maximum, or average corner clearance measurements. A few site type/measurement combinations did have parameter estimates indicating fewer crashes with longer corner clearance measurements; however, most parameter estimates were not statistically significant.

136 Table 96. Ohio minimum corner clearance by upstream/downstream corner GLM results. Site Type Crashes/year = (Predicted)*exp(intercept+a*minimum corner clearance/1000) Upstream corners Downstream corners intercept (se) a (se) k (se) intercept (se) a (se) k (se) 2U -0.7490 (0.1630) 0.0019 (0.0010) 1.3996 (0.2233) -0.6047 (0.1437) 0.0007 (0.0007) 1.4422 (0.2320) 3T -1.1292 (0.2380) 0.0040 (0.0015) 1.2960 (0.3104) -0.9660 (0.2574) 0.0030 (0.0017) 1.3653 (0.3063) 4D -0.1444 (0.1292) 0.0002 (0.0004) 0.7886 (0.1258) 0.0004 (0.1196) -0.0003 (0.0004) 0.8191 (0.1285) 4U -1.0071 (0.1609) 0.0007 (0.0009) 1.5630 (0.2375) -0.8511 (0.1797) 0.0011 (0.0014) 1.6575 (0.2391) 5T -1.1620 (0.1470) 0.0012 (0.0008) 1.1020 (0.1749) -1.3212 (0.1423) 0.0031 (0.0008) 1.1015 (0.1733) Note: Corner clearance is defined as the distance from the nearest driveway on the mainline to the corner of the intersection. Upstream corners are the corners on which vehicles are moving toward the intersection. Downstream corners are the corners on which vehicles just passed the intersection and are moving away from it. Minimum corner clearance is the shortest corner clearance among upstream or downstream corners within the segment. Table 97. Ohio maximum corner clearance by upstream/downstream corner GLM results. Site Type Crashes/year = (Predicted)*exp(intercept+a*maximum corner clearance/1000) Upstream corners Downstream corners intercept (se) a (se) k (se) intercept (se) a (se) k (se) 2U -0.7692 (0.1760) 0.0008 (0.0004) 1.3858 (0.2206) -0.4984 (0.1177) -0.0000 (0.0001) 1.4600 (0.2333) 3T -0.9942 (0.3681) 0.0016 (0.0012) 1.5119 (0.3484) -1.0210 (0.3542) 0.0016 (0.0011) 1.3982 (0.3128) 4D -0.1192 (0.1619) 0.0001 (0.0004) 0.7918 (0.1260) 0.2292 (0.1453) -0.0008 (0.0003) 0.7923 (0.1244) 4U -0.8383 (0.1706) -0.0003 (0.0004) 1.5685 (0.2379) -0.6245 (0.1937) -0.0005 (0.0005) 1.6511 (0.2391) 5T -1.2907 (0.1820) 0.0008 (0.0005) 1.0898 (0.1734) -1.4064 (0.1915) 0.0015 (0.0005) 1.1746 (0.1812) Note: Corner clearance is defined as the distance from the nearest driveway on the mainline to the corner of the intersection. Upstream corners are the corners on which vehicles are moving toward the intersection. Downstream corners are the corners on which vehicles just passed the intersection and are moving away from it. Maximum corner clearance is the longest corner clearance among upstream or downstream corners within the segment.

137 Table 98. Ohio average corner clearance by upstream/downstream corner GLM results. Site Type Crashes/year = (Predicted)*exp(intercept+a*average corner clearance/1000) Upstream corners Downstream corners intercept (se) a (se) k (se) intercept (se) a (se) k (se) 2U -1.1015 (0.1943) 0.0027 (0.0008) 1.2702 (0.2090) -0.6005 (0.1631) 0.0004 (0.0005) 1.4551 (0.2326) 3T -1.2763 (0.3296) 0.0034 (0.0015) 1.3725 (0.3252) -1.3683 (0.3724) 0.0039 (0.0017) 1.3188 (0.2988) 4D -0.1684 (0.1582) 0.0003 (0.0004) 0.7885 (0.1258) 0.1290 (0.1398) -0.0007 (0.0004) 0.8109 (0.1268) 4U -0.9659 (0.1837) 0.0002 (0.0007) 1.5744 (0.2384) -0.8001 (0.2112) 0.0003 (0.0010) 1.6643 (0.2399) 5T -1.3044 (0.1859) 0.0013 (0.0007) 1.0875 (0.1729) -1.5552 (0.1834) 0.0030 (0.0008) 1.0882 (0.1716) Note: Corner clearance is defined as the distance from the nearest driveway on the mainline to the corner of the intersection. Upstream corners are the corners on which vehicles are moving toward the intersection. Downstream corners are the corners on which vehicles just passed the intersection and are moving away from it. Average corner clearance is the average value of all corner clearance measurements among upstream and downstream corners within the segment. Signalized Intersection Spacing The structure of the data for the Highway Safety Manual (1st Edition) Part C Predictive Method is such that roadways are segmented by intersection and midblock areas and crashes are assigned to one of the two. One of the important findings of this research found that as the number of intersections in a segment increased, the expected number of segment crashes often decreased. This is contrary to expectations since the presence of more intersections should lead to more conflicts, and certainly not fewer, even away from the intersections. However, the structure of the data may contribute to this counterintuitive finding. Specifically, if more intersections are present, then it is more likely that a crash will be assigned to an intersection and not a segment. Given this finding, the data are not conducive to segment-level analysis of some variables (e.g., intersection density, spacing, etc.). Instead, these variables are better assessed at the corridor level. Corridor-level analyses is discussed in a separate chapter of the Practitioner Guide. There were limited sites in the dataset with information on the minimum and maximum spacing. As such, signalized intersection spacing could not be analyzed. This is again a result of the structure of the data where signalized intersections often define the start and end points of segments. In measuring signalized intersection spacing, the signalized intersections at either end of the segment are not counted. Number of Signalized Intersections and Density The structure of the data for the Highway Safety Manual (1st Edition) Part C Predictive Method is such that roadways are segmented by intersection and midblock areas and crashes are assigned to one of the two. One of the important findings of this research found that as the number of intersections in a segment increased, the expected number of segment crashes often decreased. This is contrary to expectations since the presence of more intersections should lead to more conflicts, and certainly not fewer, even away from the intersections. However, the structure of the data may contribute to this counterintuitive finding. Specifically, if more intersections are present, then it is more likely that a crash will be assigned to an intersection and not a segment. Given this finding, the data are not conducive to segment-level analysis of some variables (e.g., intersection density, spacing, etc.). Instead, these variables are better assessed at the

138 corridor level. Corridor-level analyses is discussed in a separate chapter of the Practitioner Guide. The findings at the segment level are still presented next for completeness, but again, the results are counterintuitive. The analysis of number and density of signalized intersections looked at all segment types individually. Using the Ohio data, there is some evidence of bias from the CURE plot measures, including:  2U – Number of signals and signalized intersection density,  4D – Number of signals and signalized intersection density,  4U – Number of signals and signalized intersection density, and  5T – fitted value, number of signals, and signalized intersection density. Table 99 presents the goodness-of-fit measures for the CURE plots shown in Figure 73 through Figure 82. Table 100 and Table 101 show the details for the GLM models for signalized intersection density and the number of signalized intersections, respectively. The results are similar to unsignalized access (combined unsignalized intersections and driveways) in that for most site types, more signals and increased signal density are associated with fewer segment crashes; however, most parameter estimates are not statistically significant if added to a GLM model. The model fit of the GLM models also seems little improved when adding additional variables. Again, the reason for this could be that the Highway Safety Manual (1st Edition) approach measures the entire length of the segment, but intersection-related crashes are not included in the segment crash total. As the number of signalized intersections increases, the likelihood that a crash is not counted as a segment crash may increase as well. Table 99. Ohio signalized intersection goodness-of-fit measures. Site Types Number of Sites Number of Crashes Plotted Variable Factor MAX DEV % CURE >2 S.D. 2U 337 1,975 Fitted Value 71.68 1 2U 337 1,975 Number of Signalized Intersections 194.25 11 2U 337 1,975 Signalized Intersection Density 218.02 15 3T 113 624 Fitted Value 44.97 1 3T 113 624 Number of Signalized Intersections 41.98 1 3T 113 624 Signalized Intersection Density 57.34 1 4D 387 3,247 Fitted Value 111.04 7 4D 387 3,247 Number of Signalized Intersections 266.58 39 4D 387 3,247 Signalized Intersection Density 298.90 22 4U 216 1,408 Fitted Value 167.83 9 4U 216 1,408 Number of Signalized Intersections 187.06 18 4U 216 1,408 Signalized Intersection Density 141.87 6 5T 157 2,779 Fitted Value 399.26 27 5T 157 2,779 Number of Signalized Intersections 299.01 15 5T 157 2,779 Signalized Intersection Density 226.40 13  

139   Figure 73. CURE plot Ohio 2U number of signalized intersections.   Figure 74. CURE plot Ohio 2U signalized intersection density. ‐150 ‐100 ‐50 0 50 100 150 200 250 ‐1 0 1 2 3 4 5 6 7 8C um ul at iv e  Re sid ua ls Number of Signalized Intersections Series1 Series2 Series3 ‐150 ‐100 ‐50 0 50 100 150 200 250 ‐5 0 5 10 15 20 25 30C um ul at iv e  Re sid ua ls Signalized Intersection Density Series1 Series2 Series3

140   Figure 75. CURE plot Ohio 3T number of signalized intersections.   Figure 76. CURE plot Ohio 3T signalized intersection density. ‐80 ‐60 ‐40 ‐20 0 20 40 60 80 ‐1 0 1 2 3 4 5 6 Cu m ul at iv e  Re sid ua ls Number of Signalized Intersections Series1 Series2 Series3 ‐80 ‐60 ‐40 ‐20 0 20 40 60 80 ‐5 0 5 10 15 20 Cu m ul at iv e  Re sid ua ls Signalized Intersection Density Series1 Series2 Series3

141   Figure 77. CURE plot Ohio 4D number of signalized intersections.   Figure 78. CURE plot Ohio 4D signalized intersection density. ‐300 ‐200 ‐100 0 100 200 300 ‐5 0 5 10 15 20 25 30 Cu m ul at iv e  Re sid ua ls Number of Signalized Intersections Series1 Series2 Series3 ‐400 ‐300 ‐200 ‐100 0 100 200 300 ‐5 0 5 10 15 20 25 30 Cu m ul at iv e  Re sid ua ls Signalized Intersection Density Series1 Series2 Series3

142   Figure 79. CURE plot Ohio 4U number of signalized intersections.   Figure 80. CURE plot Ohio 4U signalized intersection density. ‐200 ‐150 ‐100 ‐50 0 50 100 150 200 250 ‐2 0 2 4 6 8 10 12 14 16 18 Cu m ul at iv e  Re sid ua ls Number of Signalized Intersections Series1 Series2 Series3 ‐200 ‐150 ‐100 ‐50 0 50 100 150 200 ‐5 0 5 10 15 20 25 30 Cu m ul at iv e  Re sid ua ls Signalized Intersection Density Series1 Series2 Series3

143   Figure 81. CURE plot Ohio 5T number of signalized intersections.   Figure 82. CURE plot Ohio 5T signalized intersection density. ‐400 ‐300 ‐200 ‐100 0 100 200 300 ‐2 0 2 4 6 8 10 12 Cu m ul at iv e  Re sid ua ls Number of Signalized Intersections Series1 Series2 Series3 ‐300 ‐200 ‐100 0 100 200 300 ‐5 0 5 10 15 20 25 Cu m ul at iv e  Re sid ua ls Signalized Intersection Density Series1 Series2 Series3

144 Table 100. Ohio signalized intersection density GLM results. Site Type Crashes/year = (Predicted)*exp(intercept)+a*signalized intersection density intercept (se) a (se) k (se) 2U -0.1505 (0.0792) -0.2870 (0.2540) 1.4634 (0.1653) 3T -0.4612 (0.1760) -0.2783 (0.5693) 1.4314 (0.2716) 4D -0.1941 (0.0731) 0.2724 (0.2645) 0.8916 (0.0935) 4U -0.7008 (0.0985) -0.4049 (0.2082) 1.3792 (0.1540) 5T -- -- -- Notes: Signalized intersection density is defined as the total number of signalized intersections within the segment, without counting either end of the segment, per mile. -- indicates the model did not converge. Table 101. Ohio signalized intersection count GLM results. Site Type Crashes/year = (Predicted)*exp(intercept+a*number of signalized intersections) intercept (se) a (se) k (se) 2U -0.1804 (0.0780) -0.0272 (0.0301) 1.3925 (0.1531) 3T -0.5649 (0.1605) 0.0175 (0.0366) 1.4249 (0.2707) 4D -0.1793 (0.0696) 0.0135 (0.0159) 0.8932 (0.0937) 4U -0.7287 (0.1275) -0.0246 (0.0304) 1.6783 (0.2061) 5T -0.8562 (0.1268) -0.0243 (0.0341) 1.1831 (0.1607) Note: Number of signalized intersections is the total number of signalized intersections within the segment, without counting either end of the segment. The results for crash type models similarly showed few signs of significant bias. GLM models consistently had statistically insignificant parameter estimates and did not improve model fit. A validation of the results was done by comparing the Ohio results to the Minnesota results. Table 102 presents the goodness-of-fit measures for the CURE plots shown in Figure 83 through Figure 92. In the Minnesota data, most sites did not have a signal within the segment and those that did mostly had only one and at most two, so the data are not very useful for developing CURE plots. As such, CURE plot results are only shown for signal density. There is some evidence of bias from the CURE plot measures for:  3T – signalized intersection density, and  5T – fitted value and signalized intersection density.   Table 103 and Table 104 show the details for the GLM models for signalized intersection density and the number of signalized intersections, respectively. The GLM models show the parameter estimates, with only one exception, indicate more crashes as the number of signals increase. The parameter estimates are not statistically significant, with one exception, and improvement in overall model fit is minimal.

145 Table 102. Minnesota signalized intersections goodness-of-fit measures. Site Types Number of Sites Number of Crashes Plotted Variable Factor MAX DEV % CURE >2 S.D. 2U 140 263 Fitted Value 23.14 1 2U 140 263 Signalized Intersection Density 28.05 1 3T 47 91 Fitted Value 6.95 9 3T 47 91 Signalized Intersection Density 7.99 9 4D 133 600 Fitted Value 62.64 1 4D 133 600 Signalized Intersection Density 46.70 3 4U 95 195 Fitted Value 10.07 1 4U 95 195 Signalized Intersection Density 19.14 6 5T 28 130 Fitted Value 10.33 7 5T 28 130 Signalized Intersection Density 12.42 11     Figure 83. CURE plot Minnesota 2U fitted values. ‐40 ‐30 ‐20 ‐10 0 10 20 30 40 0 2 4 6 8 10 12 Cu m ul at iv e  Re sid ua ls Highway Safety Manual (1st Edition) Calibrated Prediction Series1 Series2 Series3

146   Figure 84. CURE plot Minnesota 2U signalized intersection density.   Figure 85. CURE plot Minnesota 3T fitted values. ‐40 ‐30 ‐20 ‐10 0 10 20 30 40 ‐2 0 2 4 6 8 10 Cu m ul at iv e  Re sid ua ls Signalized Intersection Density Series1 Series2 Series3 ‐20 ‐15 ‐10 ‐5 0 5 10 15 20 0 1 2 3 4 5 6 Cu m ul at iv e  Re sid ua ls Highway Safety Manual (1st Edition) Calibrated Prediction Series1 Series2 Series3

147   Figure 86. CURE plot Minnesota 3T signalized intersection density.   Figure 87. CURE plot Minnesota 4D fitted values. ‐20 ‐15 ‐10 ‐5 0 5 10 15 20 ‐2 0 2 4 6 8 10 12 Cu m ul at iv e  Re sid ua ls Signalized Intersection Density Series1 Series2 Series3 ‐80 ‐60 ‐40 ‐20 0 20 40 60 80 0 5 10 15 20 25 Cu m ul at iv e  Re sid ua ls Highway Safety Manual (1st Edition) Calibrated Prediction Series1 Series2 Series3

148   Figure 88. CURE plot Minnesota 4D signalized intersection density.   Figure 89. CURE plot Minnesota 4U fitted values. ‐80 ‐60 ‐40 ‐20 0 20 40 60 80 ‐2 0 2 4 6 8 10 12 14 Cu m ul at iv e  Re sid ua ls Signalized Intersection Density Series1 Series2 Series3 ‐25 ‐20 ‐15 ‐10 ‐5 0 5 10 15 20 25 0 1 2 3 4 5 6 7 8 9 Cu m ul at iv e  Re sid ua ls Highway Safety Manual (1st Edition) Calibrated Prediction Series1 Series2 Series3

149   Figure 90. CURE plot Minnesota 4U signalized intersection density.   Figure 91. CURE plot Minnesota 5T fitted values. ‐25 ‐20 ‐15 ‐10 ‐5 0 5 10 15 20 25 ‐2 0 2 4 6 8 10 12 14 16 18 Cu m ul at iv e  Re sid ua ls Signalized Intersection Density Series1 Series2 Series3 ‐25 ‐20 ‐15 ‐10 ‐5 0 5 10 15 20 25 0 5 10 15 20 25 Cu m ul at iv e  Re sid ua ls Highway Safety Manual (1st Edition) Calibrated Prediction Series1 Series2 Series3

150   Figure 92. CURE plot Minnesota 5T signalized intersection density. Table 103. Minnesota signalized intersection density GLM results. Site Type Crashes/year = (Predicted)*exp(intercept+a*signalized intersection density) intercept (se) a (se) k (se) 2U -0.5341 (0.1074) 0.0885 (0.0475) 0.6614 (0.1682) 3T -0.5344 (0.1542) 0.0629 (0.0722) 0.4028 (0.2199) 4D 0.1123 (0.1040) 0.0328 (0.0275) 0.7807 (0.1413) 4U -0.4882 (0.1034) 0.0602 (0.0253) 0.2688 (0.1247) 5T -0.6343 (0.1847) -0.0005 (0.0480) 0.3129 (0.1532) Note: Signalized intersection density is defined as the total number of signalized intersections within the segment, without counting either end of the segment, per mile. Table 104. Minnesota number of signalized intersections GLM results. Site Type Crashes/year = (Predicted)*exp(intercept)+a*number of signalized intersections intercept (se) a (se) k (se) 2U -0.5187 (0.1100) 1.1228 (1.0121) 0.6864 (0.1711) 3T -0.5261 (0.1578) 0.5309 (1.0300) 0.4172 (0.2224) 4D 0.1511 (0.1011) 0.2800 (0.5519) 0.7938 (0.1426) 4U -0.4801 (0.1044) 1.0244 (0.5750) 0.2831 (0.1252) 5T -0.6646 (0.1774) 0.3359 (1.1900) 0.3130 (0.1529) Note: Number of signalized intersections is the total number of signalized intersections within the segment, without counting either end of the segment. ‐25 ‐20 ‐15 ‐10 ‐5 0 5 10 15 20 25 ‐2 0 2 4 6 8 10 Cu m ul at iv e  Re sid ua ls Signalized Intersection Density Series1 Series2 Series3

151 Unsignalized Intersection Spacing The structure of the data for the Highway Safety Manual (1st Edition) Part C Predictive Method is such that roadways are segmented by intersection and midblock areas and crashes are assigned to one of the two. One of the important findings of this research found that as the number of intersections in a segment increased, the expected number of segment crashes often decreased. This is contrary to expectations since the presence of more intersections should lead to more conflicts, and certainly not fewer, even away from the intersections. However, the structure of the data may contribute to this counterintuitive finding. Specifically, if more intersections are present, then it is more likely that a crash will be assigned to an intersection and not a segment. Given this finding, the data are not conducive to segment-level analysis of some variables (e.g., intersection density, spacing, etc.). Instead, these variables are better assessed at the corridor level. Corridor-level analyses is discussed in a separate chapter of the Practitioner Guide. The findings at the segment level are still presented next for completeness, but again, the results are counterintuitive. The Ohio data did not include minimum and maximum spacing for unsignalized intersections, so the analysis was restricted to the Minnesota data. In this dataset, only 48 sites with a total of 101 crashes included a spacing measurement for unsignalized intersections. Due to this small sample, all site types were combined for analysis. Table 105 presents the goodness-of-fit measures for the CURE plots. The CURE plots, shown in Figure 93 through Figure 95, do show bias for the fitted value and both minimum and maximum spacing when considering all types combined. There is some indication that the crash prediction models overpredict for low spacing and underpredict for large spacing, the opposite of what is expected. For the GLM models, models could only be developed for 2U and 4U sites. Table 106 and Table 107 show the details for the GLM models for minimum spacing and maximum spacing, respectively. For these models, the parameter estimates were highly insignificant and did not improve the model fit. Table 105. Minnesota unsignalized intersection spacing goodness-of-fit measures. Site Types Number of Sites Number of Crashes Plotted Variable Factor MAX DEV % CURE >2 S.D. All 48 101 Fitted Value 12.63 30 48 101 Minimum Unsignalized Intersection Spacing 21.66 21 48 101 Maximum Unsignalized Intersection Spacing 20.55 21  

152   Figure 93. CURE plot Minnesota unsignalized intersections fitted values.   Figure 94. CURE plot Minnesota unsignalized intersections maximum spacing. ‐20 ‐15 ‐10 ‐5 0 5 10 15 20 0 1 2 3 4 5 6 7 8 Cu m ul at iv e  Re sid ua ls Highway Safety Manual (1st Edition) Calibrated Prediction Series1 Series2 Series3 ‐25 ‐20 ‐15 ‐10 ‐5 0 5 10 15 20 0 200 400 600 800 1000 1200 Cu m ul at iv e  Re sid ua ls Unsignalized Intersections Maximum Spacing Series1 Series2 Series3

153   Figure 95. CURE plot Minnesota unsignalized intersections minimum spacing. Table 106. Minnesota minimum unsignalized intersection spacing GLM results. Site Type Crashes/year = (Predicted)*exp(intercept+a*minimum unsignalized intersection spacing) intercept (se) a (se) k (se) 2U -0.3835 (0.2967) 0.0000 (0.0006) 0.2247 (0.2302) 3T -- -- -- 4D -- -- -- 4U -1.1363 (0.6304) 0.0036 (0.0025) 0.4347 (0.3095) 5T -- -- -- Notes: Minimum unsignalized intersection spacing is defined as the shortest distance (in feet) between two unsignalized intersections within the segment. -- indicates model did not converge. Table 107. Minnesota maximum unsignalized intersection spacing GLM results. Site Type Crashes/year = (Predicted)*exp(intercept+a*maximum unsignalized intersection spacing) intercept (se) a (se) k (se) 2U -0.5498 (0.3635) 0.0004 (0.0006) 0.2039 (0.2261) 3T -- -- -- 4D -- -- -- 4U 0.0874 (0.5566) -0.0008 (0.0011) 0.5371 (0.3492) 5T -- -- -- Notes: Maximum unsignalized intersection spacing is defined as the longest distance (in feet) between two unsignalized intersections within the segment. -- indicates model did not converge. ‐25 ‐20 ‐15 ‐10 ‐5 0 5 10 15 20 0 200 400 600 800 1000 1200 Cu m ul at iv e  Re sid ua ls Unsignalized Intersections Minimum Spacing Series1 Series2 Series3

154 Number of Unsignalized Intersections and Density The structure of the data for the Highway Safety Manual (1st Edition) Part C Predictive Method is such that roadways are segmented by intersection and midblock areas and crashes are assigned to one of the two. One of the important findings of this research found that as the number of intersections in a segment increased, the expected number of segment crashes often decreased. This is contrary to expectations since the presence of more intersections should lead to more conflicts, and certainly not fewer, even away from the intersections. However, the structure of the data may contribute to this counterintuitive finding. Specifically, if more intersections are present, then it is more likely that a crash will be assigned to an intersection and not a segment. Given this finding, the data are not conducive to segment-level analysis of some variables (e.g., intersection density, spacing, etc.). Instead, these variables are better assessed at the corridor level. Corridor-level analyses is discussed in a separate chapter of the Practitioner Guide. The findings at the segment level are still presented next for completeness, but again, the results are counterintuitive. The analysis of number and density of unsignalized intersections looked at all segment types individually. Using the Ohio data, there is some evidence of bias from the CURE plot measures, including:  2U – Number of unsignalized intersections and unsignalized intersection density,  3T – Number of unsignalized intersections and unsignalized intersection density,  4U – Number of unsignalized intersections and unsignalized intersection density, and  5T – Number of unsignalized intersections and unsignalized intersection density. Table 108 presents the goodness-of-fit measures for the CURE plots shown in Figure 96 through Figure 105. Table 109 and Table 110 show the details for the GLM models for unsignalized intersection density and the number of unsignalized intersections, respectively. The results are similar to signalized intersections in that for most site types, more unsignalized intersections and increased unsignalized intersection density are associated with fewer segment crashes. In contrast to the results for signalized intersections, most parameter estimates are statistically significant at the 95 percent confidence level when added to the GLM model. Still, the overall model fit of the GLM models seems little improved when adding these variables. Again, the reason for this could be that the Highway Safety Manual (1st Edition) approach measures the entire length of the segment, but intersection-related crashes are not included in the segment crash total. As the number of signalized intersections increases, the likelihood that a crash is not counted as a segment crash may increase as well. The results for crash type models similarly showed consistent results.

155 Table 108. Ohio unsignalized intersection goodness-of-fit measures. Site Types Number of Sites Number of Crashes Plotted Variable Factor MAX DEV % CURE >2 S.D. 2U 338 1,975 Fitted Value 71.68 1 2U 338 1,975 Number of Unsignalized Intersections 157.98 26 2U 338 1,975 Unsignalized Intersection Density 157.98 55 3T 114 624 Fitted Value 44.97 1 3T 114 624 Number of Unsignalized Intersections 51.46 12 3T 114 624 Unsignalized Intersection Density 66.04 24 4D 388 3,247 Fitted Value 111.04 7 4D 388 3,247 Number of Unsignalized Intersections 155.09 10 4D 388 3,247 Unsignalized Intersection Density 155.09 11 4U 217 1,408 Fitted Value 167.83 9 4U 217 1,408 Number of Unsignalized Intersections 147.45 15 4U 217 1,408 Unsignalized Intersection Density 142.28 16 5T 158 2,779 Fitted Value 399.26 27 5T 158 2,779 Number of Unsignalized Intersections 267.97 17 5T 158 2,779 Unsignalized Intersection Density 267.97 31     Figure 96. CURE plot Ohio 2U number of unsignalized intersections. ‐150 ‐100 ‐50 0 50 100 150 200 ‐2 0 2 4 6 8 10 12 14 16 Cu m ul at iv e  Re sid ua ls Number of Unsignalized Intersections Series1 Series2 Series3

156   Figure 97. CURE plot Ohio 2U unsignalized intersection density.   Figure 98. CURE plot Ohio 3T number of unsignalized intersections. ‐150 ‐100 ‐50 0 50 100 150 200 ‐10 0 10 20 30 40 50 60 Cu m ul at iv e  Re sid ua ls Unsignalized Intersection Density Series1 Series2 Series3 ‐80 ‐60 ‐40 ‐20 0 20 40 60 80 ‐2 0 2 4 6 8 10 12 14 16 Cu m ul at iv e  Re sid ua ls Number of Unsignalized Intersections Series1 Series2 Series3

157   Figure 99. CURE plot Ohio 3T unsignalized intersection density.   Figure 100. CURE plot Ohio 4D number of unsignalized intersections. ‐80 ‐60 ‐40 ‐20 0 20 40 60 80 ‐5 0 5 10 15 20 25 Cu m ul at iv e  Re sid ua ls Unsignalized Intersection Density Series1 Series2 Series3 ‐250 ‐200 ‐150 ‐100 ‐50 0 50 100 150 200 250 ‐5 0 5 10 15 20 25 Cu m ul at iv e  Re sid ua ls Number of Unsignalized Intersections Series1 Series2 Series3

158   Figure 101. CURE plot Ohio 4D unsignalized intersection density.   Figure 102. CURE plot Ohio 4U number of unsignalized intersections. ‐250 ‐200 ‐150 ‐100 ‐50 0 50 100 150 200 250 ‐2 0 2 4 6 8 10 12 14 16 Cu m ul at iv e  Re sid ua ls Unsignalized Intersection Density Series1 Series2 Series3 ‐200 ‐150 ‐100 ‐50 0 50 100 150 200 ‐10 0 10 20 30 40 50 60 Cu m ul at iv e  Re sid ua ls Number of Unsignalized Intersections Series1 Series2 Series3

159   Figure 103. CURE plot Ohio 4U unsignalized intersection density.   Figure 104. CURE plot Ohio 5T number of unsignalized intersections. ‐200 ‐150 ‐100 ‐50 0 50 100 150 200 ‐5 0 5 10 15 20 25 Cu m ul at iv e  Re sid ua ls Unsignalized Intersection Density Series1 Series2 Series3 ‐400 ‐300 ‐200 ‐100 0 100 200 300 ‐5 0 5 10 15 20 25 30 Cu m ul at iv e  Re sid ua ls Number of Unsignalized Intersections Series1 Series2 Series3

160   Figure 105. CURE plot Ohio 5T unsignalized intersection density. Table 109. Ohio unsignalized intersection density GLM results. Site Type Crashes/year = (Predicted)*exp(intercept+a*unsignalized intersection density) intercept (se) a (se) k (se) 2U -0.0863 (0.0807) -0.0732 (0.0213) 1.3025 (0.1459) 3T -0.3197 (0.1381) -0.1308 (0.0363) 1.2420 (0.2426) 4D -0.1012 (0.0594) -0.0516 (0.0248) 0.8837 (0.0928) 4U -0.6643 (0.1082) -0.0746 (0.0262) 1.5973 (0.1988) 5T -0.7440 (0.1086) -0.0739 (0.0229) 1.1049 (0.1526) Note: Unsignalized intersection density is defined as the total number of unsignalized intersections within the segment, without counting driveways or either end of the segment, per mile. Table 110. Ohio unsignalized intersection count GLM results. Site Type Crashes/year = (Predicted)*exp(intercept)+a*number of unsignalized intersections intercept (se) a (se) k (se) 2U -0.1831 (0.0740) -0.0794 (0.0301) 1.3795 (0.1520) 3T -- -- -- 4D -0.1239 (0.0564) -0.2035 (0.0943) 0.8862 (0.0932) 4U -0.7890 (0.0976) -0.0379 (0.0904) 1.6768 (0.2067) 5T -- -- -- Notes: Number of unsignalized intersections is the total number of unsignalized intersections within the segment, without counting driveways or either end of the segment. -- indicates the model did not converge. ‐400 ‐300 ‐200 ‐100 0 100 200 300 ‐5 0 5 10 15 20 25 Cu m ul at iv e  Re sid ua ls Unsignalized Intersection Density Series1 Series2 Series3

161 A validation of the results was done by comparing the Ohio results to the Minnesota results. Table 111 presents the goodness-of-fit measures for the CURE plots shown in Figure 106 through Figure 115. There is some evidence of bias from the CURE plot measures for:  3T – unsignalized intersection density and number of unsignalized intersections,  4U – unsignalized intersection density and number of unsignalized intersections, and  5T – unsignalized intersection density and number of unsignalized intersections.   Table 112 and Table 113 show the details for the GLM models for unsignalized intersection density and the number of unsignalized intersections, respectively. Most of the GLM models show the parameter estimates indicate more crashes as the number and density of unsignalized intersections increase, but the parameter estimates are not statistically significant. For some site types the models did not converge. Table 111. Minnesota unsignalized intersections goodness-of-fit measures. Site Types Number of Sites Number of Crashes Plotted Variable Factor MAX DEV % CURE >2 S.D. 2U 140 263 Fitted Value 23.59 0 2U 140 263 Unsignalized Intersection Density 25.47 2 2U 140 263 Number of Unsignalized Intersections 25.47 1 3T 114 624 Fitted Value 44.97 1 3T 114 624 Unsignalized Intersection Density 66.04 24 3T 114 624 Number of Unsignalized Intersections 51.46 12 4D 388 3,247 Fitted Value 111.04 7 4D 388 3,247 Unsignalized Intersection Density 155.09 11 4D 388 3,247 Number of Unsignalized Intersections 155.09 10 4U 217 1,408 Fitted Value 167.83 9 4U 217 1,408 Unsignalized Intersection Density 142.28 16 4U 217 1,408 Number of Unsignalized Intersections 147.45 15 5T 158 2,779 Fitted Value 399.26 27 5T 158 2,779 Unsignalized Intersection Density 267.97 31 5T 158 2,779 Number of Unsignalized Intersections 267.97 17  

162   Figure 106. CURE plot Minnesota 2U number of unsignalized intersections.   Figure 107. CURE plot Minnesota 2U unsignalized intersection density. ‐150 ‐100 ‐50 0 50 100 150 200 ‐2 0 2 4 6 8 10 12 14 16 Cu m ul at iv e  Re sid ua ls Number of Unsignalized Intersections Series1 Series2 Series3 ‐150 ‐100 ‐50 0 50 100 150 200 ‐10 0 10 20 30 40 50 60 Cu m ul at iv e  Re sid ua ls Unsignalized Intersection Density Series1 Series2 Series3

163   Figure 108. CURE plot Minnesota 3T number of unsignalized intersections.   Figure 109. CURE plot Minnesota 3T unsignalized intersection density. ‐80 ‐60 ‐40 ‐20 0 20 40 60 80 ‐2 0 2 4 6 8 10 12 14 16 Cu m ul at iv e  Re sid ua ls Number of Unsignalized Intersections Series1 Series2 Series3 ‐80 ‐60 ‐40 ‐20 0 20 40 60 80 ‐5 0 5 10 15 20 25 Cu m ul at iv e  Re sid ua ls Unsignalized Intersection Density Series1 Series2 Series3

164   Figure 110. CURE plot Minnesota 4D number of unsignalized intersections.   Figure 111. CURE plot Minnesota 4D unsignalized intersection density. ‐250 ‐200 ‐150 ‐100 ‐50 0 50 100 150 200 250 ‐5 0 5 10 15 20 25 Cu m ul at iv e  Re sid ua ls Number of Unsignalized Intersections Series1 Series2 Series3 ‐250 ‐200 ‐150 ‐100 ‐50 0 50 100 150 200 250 ‐2 0 2 4 6 8 10 12 14 16 Cu m ul at iv e  Re sid ua ls Unsignalized Intersection Density Series1 Series2 Series3

165   Figure 112. CURE plot Minnesota 4U number of unsignalized intersections.   Figure 113. CURE plot Minnesota 4U unsignalized intersection density. ‐200 ‐150 ‐100 ‐50 0 50 100 150 200 ‐10 0 10 20 30 40 50 60 Cu m ul at iv e  Re sid ua ls Number of Unsignalized Intersections Series1 Series2 Series3 ‐200 ‐150 ‐100 ‐50 0 50 100 150 200 ‐5 0 5 10 15 20 25 Cu m ul at iv e  Re sid ua ls Unsignalized Intersection Density Series1 Series2 Series3

166   Figure 114. CURE plot Minnesota 5T number of unsignalized intersections.   Figure 115. CURE plot Minnesota 5T unsignalized intersection density. ‐400 ‐300 ‐200 ‐100 0 100 200 300 ‐5 0 5 10 15 20 25 30 Cu m ul at iv e  Re sid ua ls Number of Unsignalized Intersections Series1 Series2 Series3 ‐400 ‐300 ‐200 ‐100 0 100 200 300 ‐5 0 5 10 15 20 25 Cu m ul at iv e  Re sid ua ls Unsignalized Intersection Density Series1 Series2 Series3

167 Table 112. Minnesota unsignalized intersection density GLM results. Site Type Crashes/year = (Predicted)*exp(intercept+a*unsignalized intersection density) intercept (se) a (se) k (se) 2U -0.6244 (0.3398) 0.0197 (0.0215) 0.2173 (0.2208) 3T -0.5534 (0.3056) -0.0041 (0.0498) -0.1193 (0.1682) 4D 1.1460 (5.1302) -0.2261 (0.5326) -0.1667 (0.0000) 4U 0.2257 (0.6342) -0.0482 (0.0562) 0.5292 (0.3433) 5T -- -- -- Notes: Unsignalized intersection density is defined as the total number of unsignalized intersections within the segment, without counting driveways or either end of the segment, per mile. -- indicates the model did not converge. Table 113. Minnesota unsignalized intersection count GLM results. Site Type Crashes/year = (Predicted)*exp(intercept)+a*number of unsignalized intersections intercept (se) a (se) k (se) 2U -0.2603 (0.3668) -0.0996 (0.2912) 0.2498 (0.2488) 3T -- -- -- 4D -- -- -- 4U 0.2241 (0.4026) -0.7590 (0.5953) 0.5099 (0.3321) 5T -- -- -- Notes: Number of unsignalized intersections is the total number of unsignalized intersections within the segment, without counting driveways or either end of the segment. -- indicates the model did not converge. Number of Unsignalized Access Points and Density The structure of the data for the Highway Safety Manual (1st Edition) Part C Predictive Method is such that roadways are segmented by intersection and midblock areas and crashes are assigned to one of the two. One of the important findings of this research found that as the number of intersections in a segment increased, the expected number of segment crashes often decreased. This is contrary to expectations since the presence of more intersections should lead to more conflicts, and certainly not fewer, even away from the intersections. However, the structure of the data may contribute to this counterintuitive finding. Specifically, if more intersections are present, then it is more likely that a crash will be assigned to an intersection and not a segment. Given this finding, the data are not conducive to segment-level analysis of some variables (e.g., intersection density, spacing, etc.). Instead, these variables are better assessed at the corridor level. Corridor-level analyses is discussed in a separate chapter of the Practitioner Guide. The findings at the segment level are still presented next for completeness, but again, the results are counterintuitive. The analysis of unsignalized access points and density looked at all segment types individually. Using the Ohio data there is some evidence of bias from the CURE plot measures, including:  2U – number of unsignalized access points per mile (density),  4D – number of unsignalized access points,  4U – number of unsignalized access points, and  5T – fitted value, number of unsignalized access points, and number of unsignalized access points per mile (density).   Table 114 presents the goodness-of-fit measures for the CURE plots shown in Figure 116 through Figure 125. Contrary to what is expected, the results mostly indicate that the crash prediction models overpredict

168 for higher numbers of access point and access point density. The reason for this could be that the Highway Safety Manual (1st Edition) approach measures the entire length of the segment but crashes considered to be intersection-related are not included in the segment crash total. As the number of unsignalized intersections increases the likelihood that a crash is not counted as a segment crash may increase as well. The one exception is of 5T which shows an underprediction in total crashes as the number of access points and density increases. Table 115 and Table 116 show the details for the GLM models for unsignalized access density and the number of unsignalized access points, respectively. The estimated parameters of the GLM models were inconsistent in either indicating more or fewer crashes with more unsignalized access points and were not statistically significant. The one exception was for 4D segments and the number of unsignalized access points which was significant and indicates more crashes as the number of access points increases. Table 114. Ohio unsignalized access goodness-of-fit measures. Site Types Number of Sites Number of Crashes Plotted Variable Factor MAX DEV % CURE >2 S.D. 2U 337 1,975 Fitted Value 71.68 1 2U 337 1,975 Number of Unsignalized Access Points 82.56 2 2U 337 1,975 Unsignalized Access Density 231.58 66 3T 113 624 Fitted Value 44.97 1 3T 113 624 Number of Unsignalized Access Points 31.65 6 3T 113 624 Unsignalized Access Density 39.21 1 4D 387 3,247 Fitted Value 111.04 7 4D 387 3,247 Number of Unsignalized Access Points 175.49 19 4D 387 3,247 Unsignalized Access Density 141.34 8 4U 216 1,408 Fitted Value 167.83 9 4U 216 1,408 Number of Unsignalized Access Points 275.17 39 4U 216 1,408 Unsignalized Access Density 135.16 0 5T 157 2,779 Fitted Value 399.26 27 5T 157 2,779 Number of Unsignalized Access Points 346.33 29 5T 157 2,779 Unsignalized Access Density 260.46 20  

169   Figure 116. CURE plot Ohio 2U number of unsignalized access points.   Figure 117. CURE plot Ohio 2U unsignalized access density. ‐150 ‐100 ‐50 0 50 100 150 ‐50 0 50 100 150 200 250 300 Cu m ul at iv e  Re sid ua ls Number of Unsignalized Access Points Series1 Series2 Series3 ‐150 ‐100 ‐50 0 50 100 150 200 250 300 ‐50 0 50 100 150 200 250 300 350 Cu m ul at iv e  Re sid ua ls Unsignalized Access Density Series1 Series2 Series3

170   Figure 118. CURE plot Ohio 3T number of unsignalized access points.   Figure 119. CURE plot Ohio 3T unsignalized access density. ‐80 ‐60 ‐40 ‐20 0 20 40 60 80 ‐20 0 20 40 60 80 100 120 Cu m ul at iv e  Re sid ua ls Number of Unsignalized Access Points Series1 Series2 Series3 ‐80 ‐60 ‐40 ‐20 0 20 40 60 80 ‐50 0 50 100 150 200 250 Cu m ul at iv e  Re sid ua ls Unsignalized Access Density Series1 Series2 Series3

171   Figure 120. CURE plot Ohio 4D number of unsignalized access points.   Figure 121. CURE plot Ohio 4D unsignalized access density. ‐250 ‐200 ‐150 ‐100 ‐50 0 50 100 150 200 250 ‐20 0 20 40 60 80 100 120 140 160 180 Cu m ul at iv e  Re sid ua ls Number of Unsignalized Access Points Series1 Series2 Series3 ‐250 ‐200 ‐150 ‐100 ‐50 0 50 100 150 200 250 ‐50 0 50 100 150 200 250 300 350 Cu m ul at iv e  Re sid ua ls Unsignalized Access Density Series1 Series2 Series3

172   Figure 122. CURE plot Ohio 4U number of unsignalized access points.   Figure 123. CURE plot Ohio 4U unsignalized access density. ‐200 ‐150 ‐100 ‐50 0 50 100 150 200 250 300 ‐20 0 20 40 60 80 100 120 140 160 180Cu m ul at iv e  Re sid ua ls Number of Unsignalized Access Points Series1 Series2 Series3 ‐200 ‐150 ‐100 ‐50 0 50 100 150 200 ‐50 0 50 100 150 200 250 300 Cu m ul at iv e  Re sid ua ls Unsignalized Access Density Series1 Series2 Series3

173   Figure 124. CURE plot Ohio 5T number of unsignalized access points.   Figure 125. CURE plot Ohio 5T unsignalized access density. ‐400 ‐300 ‐200 ‐100 0 100 200 300 400 ‐50 0 50 100 150 200 250 Cu m ul at iv e  Re sid ua ls Number of Unsignalized Access Points Series1 Series2 Series3 ‐300 ‐200 ‐100 0 100 200 300 ‐50 0 50 100 150 200 Cu m ul at iv e  Re sid ua ls Unsignalized Access Density Series1 Series2 Series3

174 Table 115. Ohio unsignalized access density GLM results. Site Type Crashes/year = (Predicted)*exp(intercept+a*unsignalized access density) intercept (se) a (se) k (se) 2U 0.0525 (0.1163) -0.0065 (0.0022) 1.3416 (0.1492) 3T -0.8411 (0.2234) 0.0057 (0.0034) 1.3688 (0.2633) 4D -0.1746 (0.0658) 0.0016 (0.0018) 0.8942 (0.0937) 4U -0.7914 (0.1523) -0.0001 (0.0029) 1.6825 (0.2067) 5T -0.8961 (0.1793) -0.0004 (0.0033) 1.1878 (0.1612) Note: Unsignalized access density is defined as the number of unsignalized access points (i.e., both driveways and unsignalized intersections) per mile. Table 116. Ohio unsignalized access points GLM results. Site Type Crashes/year = (Predicted)*exp(intercept)+a*number of unsignalized access points intercept (se) a (se) k (se) 2U -0.2710 (0.1056) 0.0112 (0.0126) 1.3924 (0.1530) 3T -0.7839 (0.2875) 0.0527 (0.0488) 1.4122 (0.2683) 4D -0.1566 (0.0673) 0.0087 (0.0247) 0.8939 (0.0938) 4U -0.7824 (0.1086) -0.0036 (0.0145) 1.6810 (0.2067) 5T -0.8453 (0.1142) -0.0344 (0.0315) 1.1791 (0.1604) Note: Number of unsignalized access points is defined as the number of unsignalized access points (i.e., both driveways and unsignalized intersections) within the segment. The results for the crash type models indicated that for 2U segments there was little bias for rear-end crashes with some indication of underprediction for larger numbers of access points. The GLM models were not statistically significant and did not improve model fit. Some bias was also seen for multi-vehicle non-driveway crashes, but in the opposite direction to expectations. The GLM model was also not significant and did not improve the model fit. The remaining segment types followed a similar pattern with some indications of bias for some crash types but often counter to expectations, and the GLM models were not statistically significant, nor did the additional variables improve the overall model fit. A validation of the results was done by comparing the Ohio results to the Minnesota results. Table 117 presents the goodness-of-fit measures for the CURE plots shown in Figure 126 through Figure 140. The Minnesota data showed some evidence of bias for 2U and 4D segments for total crashes. The trends for over/under prediction appear similar to that for Ohio for 2U. For 4D the Minnesota results look more as expected with an overprediction for low access points (i.e., fewer expected crashes as the number of access points decreases) and underprediction at high numbers of access points (i.e., more expected crashes as the number of access points increases). Table 118 and Table 119 show the details for the GLM models for unsignalized access density and the number of unsignalized access points, respectively. The GLM models follow the Ohio trends in that the parameter estimates are not consistent in sign and are not statistically significant or resulting in an improved model fit.

175 Table 117. Minnesota unsignalized access goodness-of-fit measures. Site Types Number of Sites Number of Crashes Plotted Variable Factor MAX DEV % CURE >2 S.D. 2U 139 263 Fitted Value 23.59 0 2U 139 263 Number of Unsignalized Access Points 35.46 13 2U 139 263 Unsignalized Intersection Density 25.47 2 3T 47 91 Fitted Value 6.95 9 3T 47 91 Number of Unsignalized Access Points 14.32 13 3T 47 91 Unsignalized Intersection Density 9.20 2 4D 133 600 Fitted Value 62.64 1 4D 133 600 Number of Unsignalized Access Points 86.95 29 4D 133 600 Unsignalized Intersection Density 77.46 22 4U 95 195 Fitted Value 10.07 1 4U 95 195 Number of Unsignalized Access Points 11.90 1 4U 95 195 Unsignalized Intersection Density 8.59 1 5T 28 130 Fitted Value 10.33 7 5T 28 130 Number of Unsignalized Access Points 9.30 0 5T 28 130 Unsignalized Intersection Density 9.08 0     Figure 126. CURE plot Minnesota 2U fitted values. ‐40 ‐30 ‐20 ‐10 0 10 20 30 40 0 2 4 6 8 10 12 Cu m ul at iv e  Re sid ua ls Highway Safety Manual (1st Edition) Calibrated Prediction Series1 Series2 Series3

176   Figure 127. CURE plot Minnesota 2U unsignalized access points.   Figure 128. CURE plot Minnesota 2U unsignalized access density. ‐40 ‐30 ‐20 ‐10 0 10 20 30 40 ‐5 0 5 10 15 20 25 Cu m ul at iv e  Re sid ua ls Number of Unsignalized Access Points Series1 Series2 Series3 ‐40 ‐30 ‐20 ‐10 0 10 20 30 40 ‐20 0 20 40 60 80 100 120 140 160 Cu m ul at iv e  Re sid ua ls Unsignalized Access Density Series1 Series2 Series3

177   Figure 129. CURE plot Minnesota 3T fitted values.   Figure 130. CURE plot Minnesota 3T unsignalized access points. ‐20 ‐15 ‐10 ‐5 0 5 10 15 20 0 1 2 3 4 5 6 Cu m ul at iv e  Re sid ua ls Highway Safety Manual (1st Edition) Calibrated Prediction Series1 Series2 Series3 ‐20 ‐15 ‐10 ‐5 0 5 10 15 20 0 5 10 15 20 25 Cu m ul at iv e  Re sid ua ls Number of Unsignalized Access Points Series1 Series2 Series3

178 Figure 131. CURE plot Minnesota 3T unsignalized access density. Figure 132. CURE plot Minnesota 4D fitted values. ‐20 ‐15 ‐10 ‐5 0 5 10 15 20 0 20 40 60 80 100 120 Cu m ul at iv e  Re sid ua ls Unsignalized Access Density Series1 Series2 Series3 ‐80 ‐60 ‐40 ‐20 0 20 40 60 80 0 5 10 15 20 25 Cu m ul at iv e  Re sid ua ls Highway Safety Manual (1st Edition) Calibrated Prediction Series1 Series2 Series3

179   Figure 133. CURE plot Minnesota 4D unsignalized access points.   Figure 134. CURE plot Minnesota 4D unsignalized access density. ‐100 ‐80 ‐60 ‐40 ‐20 0 20 40 60 80 ‐2 0 2 4 6 8 10 12 14 Cu m ul at iv e  Re sid ua ls Number of Unsignalized Access Points Series1 Series2 Series3 ‐100 ‐80 ‐60 ‐40 ‐20 0 20 40 60 80 ‐10 0 10 20 30 40 50 60 70 Cu m ul at iv e  Re sid ua ls Unsignalized Access Density Series1 Series2 Series3

180   Figure 135. CURE plot Minnesota 4U fitted values.   Figure 136. CURE plot Minnesota 4U unsignalized access points. ‐25 ‐20 ‐15 ‐10 ‐5 0 5 10 15 20 25 0 1 2 3 4 5 6 7 8 9 Cu m ul at iv e  Re sid ua ls Highway Safety Manual (1st Edition) Calibrated Prediction Series1 Series2 Series3 ‐25 ‐20 ‐15 ‐10 ‐5 0 5 10 15 20 25 ‐5 0 5 10 15 20 25 30Nu m be r Number of Unsignalized Access Points Series1 Series2 Series3

181   Figure 137. CURE plot Minnesota 4U unsignalized access density.   Figure 138. CURE plot Minnesota 5T fitted values. ‐25 ‐20 ‐15 ‐10 ‐5 0 5 10 15 20 25 ‐20 0 20 40 60 80 100 120 140 Cu m ul at iv e  Re sid ua ls Unsignalized Access Density Series1 Series2 Series3 ‐25 ‐20 ‐15 ‐10 ‐5 0 5 10 15 20 25 0 5 10 15 20 25 Cu m ul at iv e  Re sid ua ls Highway Safety Manual (1st Edition) Calibrated Prediction Series1 Series2 Series3

182   Figure 139. CURE plot Minnesota 5T unsignalized access points.   Figure 140. CURE plot Minnesota 5T unsignalized access density. ‐25 ‐20 ‐15 ‐10 ‐5 0 5 10 15 20 25 ‐5 0 5 10 15 20 25 Cu m ul at iv e  Re sid ua ls Number of Unsignalized Access Points Series1 Series2 Series3 ‐25 ‐20 ‐15 ‐10 ‐5 0 5 10 15 20 25 ‐20 0 20 40 60 80 100 120 140 Cu m ul at iv e  Re sid ua ls Unsignalized Access Density Series1 Series2 Series3

183 Table 118. Minnesota unsignalized access density GLM results. Site Type Crashes/year = (Predicted)*exp(intercept + a*unsignalized access density) intercept (se) a (se) k (se) 2U -0.3094 (0.1487) -0.0059 (0.0042) 0.7153 (0.1727) 3T -0.3345 (0.2995) -0.0034 (0.0055) 0.4232 (0.2208) 4D 0.0667 (0.1072) 0.0115 (0.0066) 0.7499 (0.1397) 4U -0.3195 (0.1784) -0.0014 (0.0032) 0.3356 (0.1350) 5T -0.8237 (0.2595) 0.0043 (0.0050) 0.3019 (0.1487) Note: Unsignalized access density is defined as the number of unsignalized access points (i.e., both driveways and unsignalized intersections) per mile. Table 119. Minnesota unsignalized access points GLM results. Site Type Crashes/year = (Predicted)*exp(intercept) + a*number of unsignalized access points intercept (se) a (se) k (se) 2U -- -- -- 3T -0.3745 (0.2235) -0.0308 (0.0450) 0.4250 (0.2213) 4D 0.2004 (0.0984) -0.0616 (0.0687) 0.7998 (0.1427) 4U -0.4199 (0.1508) 0.0078 (0.0276) 0.3324 (0.1348) 5T -0.7000 (0.2626) 0.0381 (0.1277) 0.3125 (0.1525) Notes: Number of unsignalized access points is defined as the number of unsignalized access points (i.e., both driveways and unsignalized intersections) within the segment. -- indicates model did not converge

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Application of Crash Modification Factors for Access Management, Volume 2: Research Overview Get This Book
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The 1st Edition, in 2010, of the AASHTO Highway Safety Manual revolutionized the transportation engineering practice by providing crash modification factors and functions, along with methods that use safety performance functions for estimating the number of crashes within a corridor, subsequent to implementing safety countermeasures.

The TRB National Cooperative Highway Research Program's pre-publication draft ofNCHRP Research Report 974: Application of Crash Modification Factors for Access Management, Volume 2: Research Overview documents the research process related to access management features.

Supplementary to the report is the pre-publication draft of NCHRP Research Report 974: Application of Crash Modification Factors for Access Management, Volume 1: Practitioner’s Guide and a summary presentation for the two volumes.

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