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39 The composite analyses for the two comparison groups Table 4-7. The AADT variable is the only significant vari- for Wisconsin showed conflicting results with respect to able. This variable was grouped for reasons explained earlier safety impact of PRPMs for all crash types. Therefore, the using the method outlined. research team concluded that these data did not provide the The model confirms the findings of the univariate analysis required integrity to continue into further disaggregate that the safety benefits of PRPMs on freeways increase with analyses. increasing traffic volumes. According to this model, PRPMs Key findings from the analysis included the following: may only be effective in reducing nighttime crashes where the AADT exceeds 20,000 veh/day. Since higher volumes Missouri shows significant reductions in fatal and injury are more likely to be found in urban areas, the underlying crashes (5.4 percent), daytime fatal and injury crashes reason for the increasing effect with increasing AADT may (6.2 percent), wet weather crashes (12.8 percent), and relate to factors other than or in addition to AADT that may guidance-related crashes (10.3 percent) after the non- be peculiar to urban areas. Data were not available to isolate selective implementation of PRPMs. the effects of such factors. Pennsylvania shows significant reductions in total crashes The research team studied the different design elements (5.7 percent), daytime crashes (6.5 percent), and wet for potential relationships with the safety effect of PRPMs. weather crashes (5.4 percent) after the nonselective Apparently because of little variation in the design attributes implementation of PRPMs. (e.g., lane widths and shoulder widths) of the freeway seg- ments, as shown in Tables 3-13 and 3-14, it was not statisti- 4.4.2 Univariate Disaggregate Analysis cally feasible to include these attributes as variables in the multivariate models. The same applied to PRPM installation As described previously, the univariate disaggregate analy- details, such as spacing. sis assists in the selection of variables to be considered in the subsequent multivariate analysis. Results from this analysis show that the safety benefit of PRPMs on nighttime crashes 4.5 RESULTS OF THE COMPOSITE ANALYSIS increases as traffic volumes increase and is greater on urban FOR FOUR-LANE DIVIDED EXPRESSWAYS than on rural freeways. The research team concluded that, because of the data con- straints and intrinsic difficulties encountered in Wisconsin 4.4.3 Multivariate Modeling of the Index of Effectiveness (site) and Pennsylvania for the data collected for the four-lane divided expressways, any further analysis would not result in The results of the modeling for freeways are shown in any reliable findings. Thus, four-lane divided expressways Table 4-6. The AMFs derived from this model are shown in could not be analyzed under this research project. TABLE 4-6 Index of effectiveness model for four-lane freeways (snowplowable PRPMs) Model Estimate Standard p-value Applicable Condition Parameters Error Constant AADT 20000 1.131 0.136 < 0.001 AADT 2 20,000 < AADT 60,000 -0.193 0.160 0.249 AADT 3 AADT > 60,000 -0.458 0.192 0.033 TABLE 4-7 AMFs (nighttime crashes) derived from Table 4-6 AADT (veh/day) AMF 20000 1.13 2000160000 0.94 > 60000 0.67