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From page 46... ... NCHRP Project 17-61 45 CHAPTER 4 Statistical Modeling of Work Zone Features Upon Crashes Overview Another focus effort under this research project was to gather the appropriate crash, roadway, and work zone feature data at a robust number of work zones nationally, and use state-of-the-art crash analysis techniques to produce CMFs that describe how certain high-priority features affect crashes. Many efforts have been undertaken in recent years to use statistical modeling techniques to develop predictive and explanatory models of work zone crashes (for example, see Khattak et al. Read the entire page →
From page 47... ... NCHRP Project 17-61 46  Work zone drawings, including maintenance of traffic plans (to show the locations of longitudinal barriers throughout the work zone) and typical cross sections (to obtain the key cross-sectional variables such as lane and shoulder width) Read the entire page →
From page 48... ... NCHRP Pr Figure 15 oject 17-61 . Work Zone Linear Refe rencing. Read the entire page →
From page 49... ... NCHRP Project 17-61 48 Table 8. Work Zone Database Elements Category Data Element Source Schedule Period (before, during phase number) Read the entire page →
From page 50... ... NCHRP Project 17-61 49 on the differences in average before and during work zone PDO crash rates, it appears that work zone crashes tended to be more severe in Ohio, Utah, and Virginia, but less severe in Texas and Washington. Table 9. Read the entire page →
From page 51... ... NCHRP Project 17-61 50 Table 10. Sample Sizes State Period Average AADT Length, mi Duration, months Number of mile-months Total crash count All All 47329 660 10807 8176 7652 Before 43977 134 6628 6019 4512 During 56687 526 4179 2157 3140 Ohio All 54382 35 214 429 617 Before 55112 7 130 320 332 During 52235 27 84 109 285 Texas All 59925 409 9284 4228 5191 Before 59354 44 5593 2547 2599 During 60788 365 3691 1681 2592 Utah All 17714 137 309 1886 410 Before 17790 49 268 1683 387 During 17086 88 42 204 23 Virginia All 79354 48 780 775 1156 Before 79087 18 432 648 932 During 80714 30 348 127 224 Washington All 17888 32 220 857 278 Before 17874 16 206 821 262 During 18206 16 14 36 16 Table 11. Read the entire page →
From page 52... ... NCHRP Project 17-61 51 Table 12. Crash Rate Comparison Between Pre- and During-Construction Periods State Period Crash rate (crashes per million vehicle-miles) Read the entire page →
From page 53... ... NCHRP Project 17-61 52 with, ܥܯܨ௟௪ = ݁௕೗ೢሺௐ೗ିଵଶሻ CMFlsw = eblswሺWls-6ሻ ܥܯܨ௥௦௪ = ݁௕ೝೞೢሺௐೝೞିଵ଴ሻ ܥܯܨ௟௦ = ݁௕೗ೞூ೗ೞ ܥܯܨ௠௪ = ሺ1.0 െ ௜ܲ௕ሻ݁௕೘ೢሺௐ೘ିଶௐ೗ೞିସ଼ሻ ൅ ௜ܲ௕݁௕೘ೢሺଶௐ೔೎್ିସ଼ሻ ܥܯܨ௜௕ = ݁௕೔್ூ೔್ ܥܯܨ௢௕ = ݁௕೚್ூ೚್ ܥܯܨ௟௖ = ݁௕೗೎ூ೗೎ where, ܥܯܨ௟௪ = lane width CMF ܥܯܨ௟௦௪ = left shoulder width CMF ܥܯܨ௥௦௪ = right shoulder width CMF ܥܯܨ௟௦ = lane shift CMF ܥܯܨ௠௪ = median width CMF ܥܯܨ௜௕ = inside (median) barrier CMF ܥܯܨ௢௕ = outside (roadside) Read the entire page →
From page 54... ... NCHRP Project 17-61 53 The research team also tried describing the segment length as a variable instead of an offset (i.e., instead of L, it is used as Lβ) Read the entire page →
From page 55... ... NCHRP Project 17-61 54 Table 13. Results of Analysis for Model 1 Variable Parameter Estimate Standard Error Pr > \|t\| Intercept (for 4-or 6-lane facilities) Read the entire page →
From page 56... ... NCHRP Project 17-61 55 Table 14. Results of Analysis for Model 2 Variable Parameter Estimate Standard Error Pr > \|t\| Intercept (for 4-or 6-lane facilities) Read the entire page →
From page 57... ... NCHRP Project 17-61 56 would not have a positive effect on productivity or efficiency, and in some cases could actually detract from that. Given that the lane width and shoulder widths were not statistically significant, the total pavement width in each direction was used as a variable instead, in models 2 and 3. Read the entire page →
From page 58... ... NCHRP Pr Figure 16 test secti Figure 17 test secti oject 17-61 . Estimated ons (based . Read the entire page →
From page 59... ... NCHRP Project 17-61 58 Because of the lack of statistical significance of the work zone variables computed in this analysis and the likely reasons for this as outlined above, the research team concluded that the best sources of estimates of the potential effect of these variables upon work zone crashes will continue to be the CMFs included in the HSM for permanent roadway conditions, as well as some of the CMFs included in the CMF Clearinghouse. Although this analysis did not yield specific CMFs for the individual variables, the results do provide general models that can be used by practitioners who do not have site-specific data or other means of estimating the number of crashes expected to occur in their interstate/freeway work zones. Read the entire page →
From page 60... ... NCHRP Pr Figure 18 Figure 19 oject 17-61 . Predicted . Read the entire page →
From page 61... ... NCHRP Pr R presence o roadway s models w nonlinear from preillustrated In abso work zone equations 4-lane f ܹܼܥܯܨସ 6-lane f ܹܼܥܯܨ Figure 2 segments oject 17-61 elative to pr f a work zon egment as co ere construct . Specifically work zone c graphically i lute terms, th presence sti are as shown acilities: ି௟௔௡௘௦ ൌ ݁݁ acilities: ଺ି௟௔௡௘௦ ൌ ݁݁ 0. Read the entire page →
From page 62... ... NCHRP Pr A pract where a w and apply Figure segme oject 17-61 itioner who a ork zone is it to the base 21. Averag nts. Read the entire page →

From page 46...

... NCHRP Project 17-61 45 CHAPTER 4 Statistical Modeling of Work Zone Features Upon Crashes Overview Another focus effort under this research project was to gather the appropriate crash, roadway, and work zone feature data at a robust number of work zones nationally, and use state-of-the-art crash analysis techniques to produce CMFs that describe how certain high-priority features affect crashes. Many efforts have been undertaken in recent years to use statistical modeling techniques to develop predictive and explanatory models of work zone crashes (for example, see Khattak et al.