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Chapter 8. Guidance for Practical Applicationof The Models

Publication Number: FHWA-HRT-14-057 Date: February 2018

Publication Number: FHWA-HRT-14-057
Date: February 2018

Safety Evaluation of Access Management Policies and Techniques

CHAPTER 8. GUIDANCE FOR PRACTICAL APPLICATION OF THE MODELS

This chapter is intended to guide a user through the steps required to select and apply the most appropriate model(s) for estimating the safety impacts of a contemplated AM strategy or combination of strategies for a corridor. The model selection and application process involves the following four steps.

Select land use and region.
Select crash types and variables of interest.
Select analysis type of interest.
Select applicable model(s) and perform analysis.

The remainder of this chapter provides a detailed discussion of the four-step process. Chapter 9 provides numerical sample problems to illustrate the steps presented here.

STEP 1: SELECT LAND USE AND REGION

Select the applicable land use and region based on the application context. The land use categories include mixed-use, commercial, or residential as defined in chapter 2. Regions include North Carolina, Minnesota, Northern California, or Southern California. One consideration in selecting an applicable region is a comparison of local values with the mean values of the variables in each region (see appendix D). It is recommended that users select an applicable region based on the summary statistics that best match their study corridor rather than selecting the region based on geographic proximity. The result of this step is the identification of the most applicable land use type and region.

STEP 2: SELECT CRASH TYPES AND VARIABLES OF INTEREST

Select the crash types and variables of interest. The selection of crash types and variables determines which model(s) will be needed. The potential crash types include total, injury, turning, rear-end, and right-angle as defined in table 8. The potential variables of interest include AADT, corridor length, and the following access-related characteristics:

ACCDENS = number of driveways plus unsignalized intersections per mile.
MEDOPDENS = number of median openings per mile.
PROPDIV = proportion of corridor length with divided median.
PROPFULLDEV = proportion of corridor length with full roadside development.
PROPLANE1 = proportion of corridor length with two lanes.
PROPNODEV = proportion of length with no roadside development.
PROPVC = proportion of length with visual clutter.
PROPTWLTL = proportion of corridor length with TWLTL.
SIGDENS = number of signalized intersections per mile.
UNSIGDENS = number of unsignalized intersections per mile.

STEP 3: SELECT ANALYSIS TYPE OF INTEREST

Select the analysis type of interest from the following two choices:

Analysis option 1. Compare relative safety impact of strategies. This option applies to both existing corridors and new construction and provides an estimate of the change in predicted crashes or the percent change in crashes based on a proposed change in corridor characteristics (e.g., traffic volume, corridor length, and AM strategies). The results are presented as the change in predicted crash frequency or the percent change in crashes per year for alternative scenarios (e.g., scenario B is expected to result in 10 percent more injury crashes per year than scenario A). It is not appropriate to use this type of analysis in an economic evaluation because it does not account for the expected number of crashes (only the relative change). Note: Apply algorithm 1 in step 4.
Analysis option 2. Compare expected crashes between strategies. This option applies to existing corridors with an available crash history and provides an estimate of the expected crashes per year; however, it requires the observed crash history for the study corridor. The EB method is employed, combining the observed crash history and the predicted crashes from the model to obtain the expected number of crashes. The EB method corrects for several potential sources of bias, including variables that are not in the model. The results are presented as the expected crash frequency per year for each alternative. The results from this analysis may be used to compare the expected number of crashes by type among various scenarios and can be used in an economic evaluation (e.g., benefit–cost analysis). Note: Apply algorithm 2 in step 4.

STEP 4: SELECT APPLICABLE MODEL(S) AND PERFORM ANALYSIS

Select the applicable model(s) based on table 24 through table 26. Note the following factors, in priority order, were considered when populating table 24 through table 26 when more than one option was available in appendix C for the land use and crash type of interest:

Statistical significance of the coefficients for the variables of interest as indicated by the size of the p-value; a lower p-value indicates a higher level of significance.
A smaller value of k indicates a better fitting model.

Continue to algorithm 1 or algorithm 2 based on the analysis type selected in step 3 and the applicable models identified in table 24 through table 26. Recall that algorithm 1 applies to analysis option 1, and algorithm 2 applies to analysis option 2. Sample problems are presented in chapter 9 to illustrate various scenarios, and the following guiding principles are common to all scenarios:

If necessary, models may be extrapolated with caution across land use types for a given crash type. However, models for one crash type may not be extrapolated to another crash type.
In some situations, it may not be possible to estimate the impacts of a strategy for all or some crash types.
If several crash types are selected, the sum of differences between two alternatives for specific crash types cannot be greater than the number of all crash types combined. If this occurs, the estimate for all crash types combined should be equal to the sum of the specific crash types. Similarly, the estimate for injury crashes cannot be greater than total crashes.
The analyst should review the minimum and maximum values of each variable of interest for the given land use and region (see appendix D). If an entered value is outside the range of data on which the model is based, the analyst should note that the model may not provide a reliable estimate of the effect of that variable.
If analysis option 2 is selected in step 3 (i.e., compare expected crashes between strategies), the models should be calibrated for the local jurisdiction when possible. Model calibration is discussed in chapter 10.

Table 24. Relevant models by crash type of interest—mixed land use.

Crash Type	Variables Available for Specified Land Use	Applicable Model (Table No.)	Variables Available Through Extrapolation	Applicable Model for Extrapolation of Variables (Table No.)	Applicable Base Model for Extrapolation and EB Method (Table No.)
Total	ACCDENS	Table 34	—	—	Table 35
Total	PROPLANE1	Table 35	—	—	Table 35
Total	PROPNODEV	Table 36	—	—	Table 35
Total	SIGDENS	Table 35	—	—	Table 35
Total	UNSIGDENS	Table 35	—	—	Table 35
Total	—	—	PROPFULLDEV	Table 59	Table 35
Injury	PROPLANE1	Table 38	—	—	Table 38
Injury	PROPNODEV	Table 38	—	—	Table 38
Injury	SIGDENS	Table 37	—	—	Table 38
Injury	—	—	ACCDENS	Table 49	Table 38
Injury	—	—	PROPVC	Table 51	Table 38
Injury	—	—	PROPFULLDEV	Table 64	Table 38
Turning	ACCDENS	Table 39	—	—	Table 40
Turning	PROPNODEV	Table 41	—	—	Table 40
Turning	SIGDENS	Table 39	—	—	Table 40
Turning	UNSIGDENS	Table 40	—	—	Table 40
Turning	—	—	PROPLANE1	Table 54	Table 40
Rear-end	PROPLANE1	Table 43	—	—	Table 43
Rear-end	SIGDENS	Table 43	—	—	Table 43
Rear-end	—	—	PROPTWLTL	Table 70	Table 43
Right-angle	ACCDENS	Table 44	—	—	Table 44
Right-angle	MEDOPDENS	Table 45	—	—	Table 44
Right-angle	PROPDIV	Table 45	—	—	Table 44
Right-angle	PROPFULLDEV	Table 46	—	—	Table 44
Right-angle	SIGDENS	Table 44	—	—	Table 44
Right-angle	—	—	PROPLANE1	Table 73	Table 44
—Not applicable.

Table 25. Relevant models by crash type of interest—commercial land use.

Crash Type	Variables Available for Specified Land Use	Applicable Model (Table No.)	Variables Available Through Extrapolation	Applicable Model for Extrapolation of Variables (Table No.)	Applicable Base Model for Extrapolation and EB Method (Table No.)
Total	ACCDENS	Table 47	—	—	Table 47
Total	SIGDENS	Table 47	—	—	Table 47
Total	PROPNODEV	Table 48	—	—	Table 47
Total	—	—	UNSIGDENS	Table 35	Table 47
Total	—	—	PROPLANE1	Table 34	Table 47
Total	—	—	PROPFULLDEV	Table 59	Table 47
Injury	ACCDENS	Table 49	—	—	Table 52
Injury	SIGDENS	Table 52	—	—	Table 52
Injury	PROPNODEV	Table 50	—	—	Table 52
Injury	PROPLANE1	Table 52	—	—	Table 52
Injury	PROPVC	Table 51	—	—	Table 52
Injury	—	—	PROPFULLDEV	Table 64	Table 52
Turning	ACCDENS	Table 53	—	—	Table 53
Turning	SIGDENS	Table 53	—	—	Table 53
Turning	PROPNODEV	Table 54	—	—	Table 53
Turning	PROPLANE1	Table 54	—	—	Table 53
Turning	—	—	UNSIGDENS	Table 40	Table 53
Rear-end	SIGDENS	Table 56	—	—	Table 56
Rear-end	PROPLANE1	Table 56	—	—	Table 56
Rear-end	—	—	PROPTWLTL	Table 70	Table 56
Right-angle	ACCDENS	Table 57	—	—	Table 57
Right-angle	SIGDENS	Table 57	—	—	Table 57
Right-angle	PROPFULLDEV	Table 58	—	—	Table 57
Right-angle	—	—	MEDOPDENS	Table 45	Table 57
Right-angle	—	—	PROPDIV	Table 45	Table 57
Right- angle	—	—	PROPLANE1	Table 73	Table 57
—Not applicable.

Table 26. Relevant models by crash type of interest—residential land use.

Crash Type	Variables Available for Specified Land Use	Applicable Model (Table No.)	Variables Available Through Extrapolation	Applicable Model for Extrapolation of Variables (Table No.)	Applicable Base Model for Extrapolation and EB Method (Table No.)
Total	PROPLANE1	Table 59	—	—	Table 59
Total	SIGDENS	Table 59	—	—	Table 59
Total	PROPFULLDEV	Table 59	—	—	Table 59
Total	ACCDENS	Table 61	—	—	Table 59
Total	PROPNODEV	Table 62	—	—	Table 59
Injury	PROPLANE1	Table 64	—	—	Table 63
Injury	SIGDENS	Table 63	—	—	Table 63
Injury	PROPFULLDEV	Table 64	—	—	Table 63
Injury	—	—	ACCDENS	Table 49	Table 63
Injury	—	—	PROPNODEV	Table 38	Table 63
Injury	—	—	PROPVC	Table 51	Table 63
Turning	UNSIGDENS	Table 66	—	—	Table 66
Turning	SIGDENS	Table 67	—	—	Table 66
Turning	ACCDENS	Table 67	—	—	Table 66
Turning	PROPNODEV	Table 68	—	—	Table 66
Turning	—	—	PROPLANE1	Table 54	Table 66
Rear-end	SIGDENS	Table 69	—	—	Table 70
Rear-end	PROPLANE1	Table 70	—	—	Table 70
Rear-end	PROPTWLTL	Table 70	—	—	Table 70
Right-angle	SIGDENS	Table 73	—	—	Table 73
Right-angle	PROPLANE1	Table 73	—	—	Table 73
Right-angle	PROPFULLDEV	Table 73	—	—	Table 73
Right-angle	ACCDENS	Table 74	—	—	Table 73
—Not applicable.

ALGORITHM 1

Algorithm 1 pertains to analysis option 1, comparing the relative safety impact of two alternatives, alternative A and alternative B, one of which can be a do-nothing alternative.

Step 4.1.1: Data for Conditions of Interest

Step 4.1.2: Calculations for Nonextrapolated Variables

Using the model(s) from column 3 in table 24 through table 26, compute the predicted crashes for existing conditions based on the values identified for alternative A. The next calculation only changes the variable(s) of interest, and the following two cases may be distinguished:

Case 1. All variables of interest appear in one model for the crash type of interest. In this case, compute the predicted crashes for proposed conditions based on the values identified for alternative B.
Case 2. One or more variables of interest exist in multiple models for the crash type of interest. In this case, it is necessary to avoid double-counting the effect of variables. From a computational perspective, it is important to focus on one variable at a time. For each variable of interest, separately compute the predicted crashes for proposed conditions using the applicable model and the value identified for alternative B. The predicted crashes for each proposed condition are subtracted from the predicted crashes for the existing conditions (alternative A) to estimate the impact of each individual variable of interest. The impacts of the individual variables are then summed to estimate the aggregate impact of alternative B. Similarly, if either corridor length or AADT changes in alternative B, these changes are considered in isolation. The appropriate model for considering corridor length or AADT changes is identified in column 6 of table 24 through table 26. In this case, all variables, with the exception of corridor length and AADT, are kept constant, and the predicted crashes are computed for alternative B.

Step 4.1.3: Calculations for Extrapolated Variables

Variables available through extrapolation of another land use model are identified in column 4 of table 24 through table 26. The extrapolation method first requires the use of a base model from the land use and crash type of interest to predict crashes for existing conditions. Then, a model is selected from another land use to estimate the impacts of the variables of interest. For each variable to be considered through extrapolation, take following steps.

Step 4.1.3a Baseline Predicted Crashes for Existing Condition

Use the applicable base model from table 24 through table 26 with the values from the existing condition (alternative A) to estimate the baseline predicted crashes for the existing condition.

Step 4.1.3b Estimate the Impacts of the Variables of Interest for Existing Conditions

The effects of the variables of interest for the existing conditions are estimated using the equation in figure 20:

Figure 20. Equation. Formula to estimate effects of variables of interest for existing conditions.

The coefficient is obtained for the variable of interest from the extrapolation model identified in column 5 of table 24 through table 26. The Variable Actual Value is obtained from the existing condition (alternative A). The Variable Default Value is the mean value of the variable of interest for the region and land use type from which that model was developed. Default values can be found in appendix D.

Step 4.1.3c Adjusted Predicted Crashes for Existing Condition

The estimate from step 4.1.3b is then multiplied by the estimate from step 4.1.3a to compute the adjusted predicted crashes for existing conditions.

Step 4.1.3d Baseline Predicted Crashes for Proposed Condition

Use the applicable base model from table 24 through table 26 with the values from the proposed condition (alternative B) to estimate the baseline predicted crashes for the proposed condition.

Step 4.1.3e Estimate the Impacts of the Variables of Interest for Proposed Conditions

The effects of the variables of interest for the proposed conditions are estimated using the equation in figure 20.

The coefficient is obtained for the variable of interest from the extrapolation model identified in column 5 of table 24 through table 26. The Variable Proposed Value is obtained from the proposed condition (alternative B). The Variable Default Value is the mean value of the variable of interest for the region and land use type from which that model was developed. Default values can be found in appendix D.

Step 4.1.3f Adjusted Predicted Crashes for Proposed Condition

The estimate from step 4.1.3e is then multiplied by the estimate from step 4.1.3d to compute the adjusted predicted crashes for proposed conditions.

Step 4.1.4: Estimated Safety Impacts

The results from steps 4.1.2 and 4.1.3 can be used to compare the predicted crashes per year for alternative A and alternative B. The results may be presented as the difference or the percent change in predicted crashes per year.

ALGORITHM 2

Algorithm 2 pertains to analysis option 2, comparing expected crashes for existing and proposed conditions. Recall that one of the conditions is the existing condition because a crash history is required to apply algorithm 2. In this context, alternative A is the existing condition, and alternative B is the proposed condition.

Step 4.2.1: Data for Conditions of Interest

The user identifies values for alternative A and alternative B, including corridor length, AADT, and all variables of interest for all models to be used in the analysis. A value must be provided for corridor length and AADT. For all other variables, a default value may be used if a value cannot be entered (default values are given in appendix D). The default value is the mean value for the variable of interest and is determined by the land use and region selected. The observed crash history for the existing condition is also identified, including the number of years of crash data and crash totals for each crash type selected. Finally, the user must identify a calibration factor for all crash types selected. The default value is 1.0, but a user may compute a local calibration factor based on the procedure described in chapter 10.

Step 4.2.2: Prediction for Existing Condition

Steps 4.2.2a through 4.2.2d are completed for each crash type selected. The baseline predicted crashes for the existing condition are modified using the EB method, which uses the crash history of the corridor. The EB method is used to compute the expected crashes.⁽¹⁹⁾

Step 4.2.2a Baseline Predicted Crashes for Existing Conditions

Use the applicable base model from column 6 of table 24 through table 26 with the values from alternative A to estimate the baseline predicted crashes for the existing condition.

Step 4.2.2b Estimated EB Weight

The EB weight (w) is estimated using the formula in figure 21.

Figure 21. Equation. Formula to estimate w.

Note that k is given for each specific model in appendix C.

Step 4.2.2c Expected Crashes for Existing Condition

The annual expected crash frequency (EB estimate) for existing conditions is calculated using the formula in figure 22.

Figure 22. Equation. Formula to estimate the annual expected crash frequency (EB estimate).

Step 4.2.2d Estimated EB Correction Factor

The EB correction factor is calculated as the expected crashes for existing conditions (step4.2.2c) divided by the baseline predicted crashes for existing conditions (step 4.2.2a).

Step 4.2.3: Prediction for Proposed Condition

Step 4.2.3a Difference in Predicted Crashes for Existing and Proposed Condition

Apply steps 4.1.2 through 4.1.4 from algorithm 1 using the existing and proposed conditions as inputs. The result is an estimate of the difference in predicted crash frequency for the existing and proposed conditions.

Step 4.2.3b Adjusted Predicted Crashes for Existing Condition

Add the difference in predicted crashes from step 4.2.3a to the baseline predicted crashes for existing conditions from step 4.2.2a.

Step 4.2.3c Expected Crashes for Proposed Condition

Multiply the adjusted predicted crashes for the existing condition from step 4.2.3b by the EB correction factor from step 4.2.2d.

Step 4.2.4: Estimated Safety Impacts

The results from algorithm 2 can be used to compare the expected crashes per year for alternative A and alternative B. The expected crashes for the existing condition are estimated from step 4.2.2c. The expected crashes for the proposed condition are estimated from step 4.2.3c. The results may be presented as the difference or the percent change in expected crashes per year.

Page Owner: Office of Research, Development, and Technology, Office of Safety, RDT

Topics: research, safety
Keywords: research, safety, Access management, safety analysis, crash prediction models
TRT Terms: research, Safety and security, Safety, Transportation safety
Scheduled Update: Archive - No Update needed

This page last modified on 12/28/2018