Safety Effects of Horizontal Curve and Grade Combinations on Rural Two-Lane Highways
CHAPTER 6â€”CONCLUSIONS AND RECOMMENDATIONS
This chapter presents the conclusions and recommendations based on the results of the study.
CONCLUSIONS
Conclusions for this study are as follows:
- For tangents and horizontal curves on straight grades, prediction models for crash frequency by severity level are presented in figure 10 and figure 11, with parameter estimates presented in table 8. These models include a main effect for AADT, a main effect for horizontal curve radius, a main effect for percent grade, and an interaction between horizontal curve radius and length of curve. The models indicate that crash frequency increases with decreasing horizontal curve radius, decreasing horizontal
curve length, and increasing percent grade. The interaction term shows that short sharp horizontal curves are associated with higher crash frequencies. CMFs corresponding to the crash prediction models are presented in figure 39, figure 40, and table 17.
- For tangents and horizontal curves at type 1 crest vertical curves, prediction models for crash frequency by severity level are presented in figure 16 and figure 17, with parameter estimates presented in table 10. Alternate prediction models are shown in figure 20 and figure 21. These models include a main effect for AADT and an interaction between horizontal curve radius and the difference between initial and final grade (alternatively, an interaction between horizontal curve radius, vertical curve length, and K). The models indicate that crash frequency increases with decreasing horizontal curve radius and increases with increasing grade difference. The interaction term shows that short horizontal curves at sharp crest vertical curves are associated with higher crash frequencies. CMFs corresponding to the crash prediction models are presented in
figure 43, figure 44, and table 18.
- For tangents and horizontal curves at type 1 sag vertical curves, prediction models for crash frequency by severity level are presented in figure 23 and figure 24, with parameter estimates presented in table 12. Alternate prediction models are shown in figure 27 and figure 28. These models include a main effect for AADT, a main effect for K, and an interaction between horizontal curve radius and the difference between initial and final grade (alternatively, an interaction between horizontal curve radius, vertical curve length, and K). The models indicate that crash frequency increases with decreasing K, decreasing horizontal curve radius, and increasing grade difference. The interaction term shows that short horizontal curves at sharp sag vertical curves are associated with higher crash frequencies. CMFs corresponding to the crash prediction models are presented in
figure 47, figure 48, and table 19.
- For tangents and horizontal curves at type 2 crest vertical curves, prediction models for crash frequency by severity level are presented in figure 30 and figure 31, with parameter estimates presented in table 14. These models include only two main effects: a main effect for AADT and a main effect for horizontal curve radius. The models indicate that crash frequency increases with decreasing horizontal curve radius. CMFs corresponding to the crash prediction models are presented in figure 51, figure 52, and table 20. Consideration will be given to using figure 10 and figure 11, with grade set equal to
the average of G_{1} and G_{2} in place of figure 30 and figure 31.
- For tangents and horizontal curves at type 2 sag vertical curves, prediction models
for crash frequency by severity level are presented in figure 35 and figure 36, with parameter estimates presented in table 16. The FI crash prediction model includes only two main effects: a main effect for AADT and a main effect for horizontal curve radius. This model indicates that FI crash frequency increases with decreasing horizontal curve radius. The PDO crash prediction model includes a main effect for AADT, an interaction between horizontal curve radius, and the difference between G_{1} and G_{2}. The PDO model indicates that crash frequency increases with decreasing horizontal curve radius and increases with increasing grade difference. The interaction term shows that short horizontal curves at sharp sag vertical curves are associated with higher crash frequencies. CMFs corresponding to the crash prediction models are presented in
figure 55, figure 56, table 21, and table 22. Consideration will be given to using
figure 10 and figure 11 with grade set equal to the average of G_{1} and G_{2} in place of
figure 30 and figure 17.
- For all five horizontal and vertical alignment combinations, the coefficient of the
AADT term in the crash prediction models is nearly equal to 1.0, indicating that crash frequencies are proportional to AADT.
- The AADT coefficients in the models range from 0.99 to 1.10. Consideration will be given as to whether the AADT coefficient should be set equal to 1.0 for consistency with the current AASHTO HSM.^{(1)}
RECOMMENDATIONS
Recommendations based on the study results are as follows:
- CMFs developed in this report should be considered for incorporation in the next edition of the AASHTO HSM.
- It would be desirable to validate the CMFs developed in this report using data from one or more additional States. The current emphasis being placed on roadway inventories for asset management may lead to additional suitable databases.
- Ultimately, it would be desirable to have safety prediction models for rural two-lane highways for use in the AASHTO HSM that consider the safety effects of a full range of variables of interest and all of their combinations and interactions. This will require more comprehensive databases than those currently available.