Safety Performance of Part-Time Shoulder Use on Freeways, Volume 2: Conduct of Research Report (2021) / Chapter Skim
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Pages 113-145
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115 C H A P T E R 6 - CRASH SEVERITYAND CRASH TYPE DISTRIBUTIONS Crash Severity and Crash Type Distributions This chapter describes the findings obtained during the development of crash severity and crash type distributions for freeways with part-time shoulder use (PTSU) operation.
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116 This chapter consists of two sections. The first section describes the development of the proposed severity distribution model.
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117 Georgia, Minnesota, and Virginia. Supplemental facilities are located in Ohio and Minnesota.
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118 Table 44 was previously presented as Table 11 in Chapter 4. However, one additional row is added to Table 44.
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119 Table 45. Crash count distribution by state, PTSU operation, and severity.
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120 Table 46. FI crash count distribution by state, PTSU operation, and severity.
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121 Table 47. FI crash count distribution by PTSU operation, site type, and severity.
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122 Table 48. FI crash proportions by PTSU operation, site type, and severity.
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123 Figure 40. Examination of proportion of AADT during hours with high volume.
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124 Figure 41. Examination of proportion of site adjacent to barrier.
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125 where Pl = proportion of FI crashes being described as severity l (l = K, A, B) ; PC = proportion of FI crashes being described as severity C; Sl = distribution score for severity l; Csdf = local calibration factor; Sl,b = base distribution score for severity l; fl,i = severity adjustment factor for the relationship between severity l of traffic characteristic, geometric element, or traffic control feature i (i = 1 to n)
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126 develop one SDF for each site type (i.e., one set of Equation 102 to Equation 106 for each site type) but with some adjustment factors being common to all SDFs and some unique to a site type.
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127 Table 49. Model development process and decision criteria.
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128 "null" model. The null model includes only an intercept term (i.e., no predictor variables)
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129 Equation 117 𝑓 , , exp 𝑏 , , 𝑃 Equation 118 𝑓 , , exp 𝑏 , , 𝑃 , Equation 119 𝑓 , , exp 𝑏 , , 𝑃 , Equation 120 𝑓 , , exp 𝑏 , , 𝑃 , Equation 121 𝐶 , , exp 𝑏 , , 𝐼 𝑏 , , 𝐼 𝑏 , , 𝐼 𝑏 , , 𝐼 𝑏 , , 𝐼 𝑏 , , 𝐼 𝑏 , , 𝐼 𝑏 , , 𝐼 B If the observation corresponds to a ramp entrance speed-change lane, the following model is used: Equation 122 𝑃 , 𝑆 ,1 𝑆 .
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130 Equation 135 𝑓 , , exp 𝑏 , , 𝑃 where bi = regression coefficient for condition i; Cstate,w,l = adjustment factor for crashes at a comparison site having severity l (l = K, A, B) at site type w (w = fs: freeway segment, en: ramp entrance speed-change lane, ex: ramp exit speed-change lane, ast: all site types)
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131 severity. It is possible that they have some influence, but it will likely require the use of a larger database with more sites having PTSU operation.
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132 The indicator variable for Ohio was removed, and an indicator variable was included in the regression model for the sites located in Minnesota. The coefficient for this variable was approximately the same as that for the Ohio indicator variable but opposite in sign.
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133 The coefficient in the adjustment factor for the "proportion of AADT during hours with high volume" (i.e., Equation 145) has a value of −0.9931.
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134 Equation 157 𝑓 , , exp 0.4597 𝑃 All variables are previously defined. The adjustment factors fphv and fptsu in the preceding equations are provided in Equation 145 to Equation 148.
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135 The procedure in Section B.1.4 of the HSM Supplement (AASHTO 2014) was used to compute one calibration factor for the three proposed SDFs.
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136 Based on this examination, the proposed SDFs have a sensitivity to the input variables (proportion of AADT in high volume hours and proportion of site adjacent to barrier) that is consistent with that of the HSM SDF.
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137 Table 51. Comparison of predicted severity distribution for freeway segments.
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138 Table 52. Comparison of predicted severity distribution for ramp entrance speed-change lanes.
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139 Table 53. Comparison of predicted severity distribution for ramp exit speed-change lanes.
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140 FI Crash Type Distribution The crash type distribution of reported FI crashes is shown in Table 54. The crashes are categorized by state, site type, and PTSU operation.
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141 The FI crash type distribution categorized by site type and PTSU operation is shown in Table 55. About 87 percent of the reported FI crashes are on freeway segments, with roughly an even split of the remaining 13 percent among the two speed-change lane types.
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142 Table 56. PDO crash type distribution by state, site type, and PTSU operation.
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143 Crash Type Distribution Table Development This section describes the activities undertaken to develop the proposed crash type distribution tables. The first subsection describes the technique used to minimize the bias in crash proportions due to unequal study period duration among sites.
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144 year period, and a total of 60 crashes occurred during the 3 years. The proportion of crash type X at this site is 0.5 (= 30/60)
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145 Standard Error of Proportions Equation 168 and Equation 169 were used to compute the proportions and standard error of the proportion, respectively. These equations incorporate the study period duration in the calculations to avoid possible bias when the study period duration varies among sites.
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146 crash frequency (i.e., crashes per year)
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147 The proportions shown in Table 59 indicate that PTSU operation is associated with an increase in the proportion of right-angle and rear-end FI crashes. Ramp entrance speed-change lanes tend to be associated with more right-angle and rear-end FI crashes that the other two site types.
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