Safety Performance of Part-Time Shoulder Use on Freeways, Volume 2: Conduct of Research Report (2021) / Chapter Skim
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148 C H A P T E R 7 -ADVANCED CRASH PREDICTION MODELS Advanced Crash Prediction Models This chapter describes the findings obtained during the development of random parameters (RP) , and latent class (LC)
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149 Fixed Parameters Model Regression analysis was used to estimate a cross-sectional model of expected FI and PDO crash frequencies with this project's dataset. The error distribution of the residual error was assumed to have a negative binomial distribution.
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150 The dependent and candidate independent variables used in the RP and LC CPMs are summarized in a subsequent section. The model specification in the RP and LC models is the same as the FP model, aside from the testing of independent variables as random parameters.
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151 where: j = 1, 2, âĶ, C are the latent classes, Îŧj,i = exp(x'iÎēj)
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152 4. When the model was developed using steps #1 through #3, the statistical significance of the variables was examined.
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153 Equation 181 ð min ð , 12 â 10 where fos is the factor for outside shoulder width; and Wos is the outside shoulder width in feet. The value of 10 is the baseline outside shoulder width.
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154 Table 61. Fixed parameters model for FI crashes.
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155 RP and LC models, so the research team erred on the side of retaining variables to enable their exploration in RP and LC models. Random Parameters Model The RP model for FI crashes is shown in Table 62.
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156 Table 62. Random parameters model for FI crashes.
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157 The CPM in Table 62 can be represented in equation form as follows: Equation 187 ð , ðĶ ð ð.ððð ðŋ ðīðīð·ð1000 .
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158 a. Proposed models.
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159 Figure 44. Estimated median width AF for FI crashes, RP model.
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160 Inside Shoulder with Rumble Strips. The variable representing the proportion of the inside shoulder with rumble strips is normally distributed with a mean of â0.323 and a standard deviation of 0.421.
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161 FI crash frequency, when compared to freeway segments. In contrast, 61.5 percent of the ramp entrance speed-change lane sites are associated with a decrease in FI crash frequency.
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162 Latent Class Model A LC CPM was also estimated using the same data as used to estimate the RP CPM. The independent variables used in the log-linear FP CPM and the RP CPM were included in the LC CPM.
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163 color represents Class 2. When the two bars overlap, they appear as darker green.
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164 The CPM in Table 64 for Class 2 can be represented in equation form as follows: Equation 189 ð , ðĶ ð .
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165 Figure 48. Estimated lane width AF for FI crashes.
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166 Figure 50. Estimated inside shoulder width AF for FI crashes.
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167 Figure 52. Estimated inside shoulder rumble strip AF for FI crashes.
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168 Property-Damage-Only Crash Frequency Prediction Models Fixed Parameters Model The variables initially explored for the FI FP model were explored for the development of a PDO FP model. The process described in the Statistical Modeling Principles section of this chapter was used to guide model development.
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169 RP and LC models, so the research team erred on the side of retaining variables to enable their exploration in RP and LC models. Random Parameters Model The RP model for PDO crashes is shown in Table 66.
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170 speed-change lanes and ramp exit speed-change lanes, indicator for presence of PTSU on the left-hand side, proportion of segment with a turnout, and the indicators for individual states. Among the fixed parameters shown in Table 66, the outside shoulder width factor; proportion of site with inside shoulder rumble strips; indicator for ramp entrance speed-change lane; indicator for PTSU facility on the left-hand side; proportion of segment with a turnout; and indicators for sites in Hawaii, Ohio, and Virginia are statistically significant.
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171 Figure 54. Scatterplot of predicted versus observed FI crash frequency.
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172 The results of the model comparisons indicate that RP models, when applied to the data used to estimate them, more accurately predict a site's average crash frequency than the FP model. However, when considering models for use in the HSM, there are other important considerations.
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173 Table 67. Fixed parameters model for FI crashes (no Georgia data)
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174 Table 68. Random parameters model for FI crashes (no Georgia data)
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175 Islam et al.
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176 Table 69. Model validation comparisons.
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177 Table 70. Predictive model re-estimation statistics for FI crashes based on empirical approach.
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178 Table 72. AFs included in models based on two modeling approaches.
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