Proposed Guidelines for Fixed Objects in the Roadside Design Guide (2022) / Chapter Skim
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53 Chapter 5. Empirical Model Development This chapter summarizes the development of empirical crash prediction models using the data assembled, as described in Chapter 4.
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54 β’ For roadside objects located on horizontal curves only, indicator variable for roadside object located on outside vs. inside of horizontal curve (equal to one for location on outside of horizontal curve and equal to zero for roadside object on the inside of a horizontal curve)
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55 Model Form #6 π = exp (π + (π Γ ππ(π΄π΄π·π)
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56 Model Form #8 π = 1 β ππΉ Γ exp (π + (π Γ π΄π΄π·π)
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57 The results of the crash prediction modeling are presented below separately by roadside object type and modeling approach. All statements concerning statistical significance in this report apply to the 5percent significance level (i.e., the 95-percent confidence level)
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58 The values for b0 through b2 in the parameter estimate column in Table 28 are the values that are intended for use in the model shown in Equation (10) to compute an estimate of the target crash frequency, NT.
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59 Table 30. Predicted Property-Damage-Only Primary Tree-Related Crash Frequency as a Function of Offset Distance to Roadside Objects for a Rural Two-Lane Undivided Highway Based on the Model Shown in Equation (19)
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60 5.6 Modeling Results for Tree Groups Using Logistic Regression Because of the limited results obtained with negative binomial regression, logistic regression models were also tried. Logistic regression estimates the probability that a nonzero crash frequency will occur for given roadway and roadside conditions.
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61 The models shown in Equations (20)
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62 distance from the traveled way to the utility pole increased. However, in all of these models, the AADT term was either not statistically significant or was statistically significant in the opposite direction to the expected direction; i.e., some models indicated that crashes decreased as AADT increased.
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