Safety Evaluation of Access Management Policies and Techniques

CHAPTER 7. RESULTS

The models are presented in one of two forms. In most cases, the model form is represented by the equation in figure 18. In these cases, the result is expressed as crashes per mile per year. In other cases, the traffic volume variable is not statistically significant, indicating a linear relationship between traffic volume and crashes. In these limited cases, the model form is reduced to the equation in figure 19, and the result is expressed as crashes per MVMT. The result from the equation in figure 19 is multiplied by MVMT to express the result as crashes per mile per year.

Figure 18. Equation. Crash prediction model with regional calibration.

Figure 19. Equation. Normalized crash prediction model with regional calibration.

Where:

intercept = coefficient estimated for the model to account for unobserved variables.
region = coefficient estimated for the model when the applicable region is North Carolina or Minnesota; a value of 0 is used if the applicable region is Northern California or Southern California.
AADT = annual average daily two-way traffic for the corridor.
b = coefficient estimated for the AADT term in the model.
c_i = a vector of coefficients estimated for the other independent variables included in the model.
x_i = a vector of other independent variables included in the model (i.e., the specific roadway attributes such as access density).

An indicator variable is included in the equations in figure 18 and figure 19 to identify the region in which the corridor is located. In this study, the corridors were located in North Carolina, Minnesota, Northern California, or Southern California. The regional indicator variable accounts for differences between regions such as those related to crash reporting practices, driver demographics, weather, and other non-access-related factors affecting reported crashes. The regional indicators for Northern and Southern California were similar, and it was determined that the variables were sufficiently close to be considered as one region. Similarly, the regional indicators for Minnesota and North Carolina were sufficiently similar to consider them as one region. The aggregate regions helped to increase sample sizes within the models (i.e., two regions instead of four) and reflect the similarities in data between the aggregated regions. Summary statistics are provided by region in table 10 through table 20. An examination of the summary statistics revealed similarities among the corridors in the aggregated regions. When applying the models in appendix C, users should select an applicable region based on a comparison between the corridor of interest and the summary statistics in appendix D, not on geographic proximity.

The final models are presented in appendix C, organized by land use (mixed-use, commercial, and residential) and crash type (total, injury, turning, rear-end, and right-angle). Table 21 through table 23 provide an overview of the structure of appendix C, including a summary of the models and explanatory variables for each land use and crash type combination. The following specific notes should be considered when applying the models:

• Specific crash types cannot be summed to calculate total crashes because models were not developed for all possible crash types.
  
  
• North Carolina crashes coded as rear-end turn crashes are included in both rear-end and turning crashes. Because the specific crash types cannot be summed to calculate total crashes, it was determined that double-counting should not pose a problem for the crash type models.
  
  
• Models are not provided for property-damage-only crashes because of the inconsistent reporting of these crashes. The national focus is on fatal and injury crashes, and models are provided to assess the impacts on these severe crashes.
  
  
• All explanatory variables could not be accommodated in a single model; hence, there are the following alternate model forms with various combinations of variables:
  
  
  • In some cases, it is necessary to select from multiple available models because one or more models were successfully developed for each land use type/crash type combination. Further discussion of model selection and related examples are provided in chapter 8.
    
    
  • In some cases, explanatory variables are included for a specific crash type but not for the land use of interest. Further consideration of these variables is provided in chapter 8.
    
    
  • In some cases, explanatory variables could not be included in any models because of their lack of statistical significance or an illogical direction of effect. Further consideration of these variables is provided in chapter 8 and in appendix E.

Table 21. Overview of mixed-use models by crash type.

Table 22. Overview of commercial models by crash type.

Table 23. Overview of residential models by crash type.