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Coordinating, Developing, and Delivering Highway Transportation Innovations

 
REPORT
This report is an archived publication and may contain dated technical, contact, and link information
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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

APPENDIX C. SUMMARY OF MODELS BY LAND USE AND CRASH TYPE

This appendix presents the final crash prediction models organized by land use (mixed-use, commercial, and residential) and crash type (total, injury, turning, rear-end, and right-angle). 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.

MIXED-USE MODELS

Total Crashes

Table 34 through table 36 present three alternate models for mixed-use total crashes. The model form for mixed-use total crashes is shown in figure 18.

Table 34. Alternate model 1 for mixed-use total crashes.

Variable Estimate Standard Error p-Value
Intercept –3.1845 1.9550 0.1033
Region 1.1410 0.2316 <0.0001
AADT 0.5187 0.1819 0.0043
ACCDENS 0.0053 0.0044 0.2279
SIGDENS 0.1095 0.0607 0.0710
PROPLANE1 –0.5185 0.3789 0.1711
k 0.5073 — —
Note: The p-values for ACCDENS and PROPLANE1 are larger than desirable. Region is included
for North Carolina or Minnesota; a value of 0 if in Northern or Southern California.
—Not applicable.

 

Table 35. Alternate model 2 for mixed-use total crashes.

Variable Estimate Standard Error p-Value
Intercept –3.2905 1.8743 0.0792
Region 1.0533 0.2086 <0.0001
AADT 0.5266 0.1738 0.0024
UNSIGDENS 0.0471 0.0224 0.0354
SIGDENS 0.0957 0.0594 0.1072
PROPLANE1 –0.6376 0.3796 0.0931
k 0.4897 — —
Note: Similar model to alternate model 1, but excluding driveways. Region is included for North
Carolina or Minnesota; a value of 0 if in Northern or Southern California.
—Not applicable.

 

Table 36. Alternate model 3 for mixed-use total crashes.

Variable Estimate Standard Error p-Value
Intercept –0.8926 0.5021 0.0755
Region 0.6166 0.1013 <0.0001
AADT 0.3766 0.0468 <0.0001
PROPNODEV –0.4252 0.2268 0.0608
k 0.5165 — —
Note: This model was developed from the dataset combining all land use types with factor
variables representing the land use. Region is included for North Carolina or Minnesota; a value of
0 if in Northern or Southern California.
—Not applicable.

 

Injury Crashes

Table 37 and table 38 present two alternate models for mixed-use injury crashes. The model form for mixed-use injury crashes is shown in figure 18.

Table 37. Alternate model 1 for mixed-use injury crashes.

Variable Estimate Standard Error p-Value
Intercept –3.5700 1.7816 0.0451
Region 0.5695 0.1980 0.0040
AADT 0.5010 0.1659 0.0025
SIGDENS 0.1239 0.0556 0.0258
PROPLANE1 –0.5814 0.3582 0.1046
k 0.4248 –– ––
Note: Region is included for North Carolina or Minnesota; a value of 0 if in Northern
or Southern California.

 

Table 38. Alternate model 2 for mixed-use injury crashes.

Variable Estimate Standard Error p-Value
Intercept –1.7775 0.5964 0.0029
Region 0.2465 0.0931 0.0081
AADT 0.3880 0.0558 <0.0001
PROPNODEV –0.3159 0.2201 0.1511
PROPLANE1 –0.6623 0.1404 <0.0001
k 0.4151 –– ––
Note: The p-value for PROPNODEV is larger than desirable. This model was developed from the
dataset combining all land use types with factor variables representing the land use. Region is
included for North Carolina or Minnesota; a value of 0 if in Northern or Southern California.
—Not applicable.

 

Turning Crashes

Table 39 through table 41 present three alternate models for mixed-use turning crashes. The model form for alternate model 1 and model 2 is shown in figure 19. The model form for alternate model 3 is shown in figure 18.

Table 39. Alternate model 1 for mixed-use turning crashes.

Variable Estimate Standard Error p-Value
Intercept –2.1083 0.4338 <0.0001
Region 0.9647 0.2843 0.0007
SIGDENS 0.1865 0.0754 0.0134
ACCDENS 0.0088 0.0061 0.1486
k 0.7920 — —
Note: The p-value for ACCDENS is larger than desirable. Region is included for North Carolina
or Minnesota; a value of 0 if in Northern or Southern California.
—Not applicable.

 

Table 40. Alternate model 2 for mixed-use turning crashes.

Variable Estimate Standard Error p-Value
Intercept –2.0792 0.3963 <0.0001
Region 0.8015 0.2354 0.0007
SIGDENS 0.1797 0.0742 0.0154
UNSIGDENS 0.0582 0.0323 0.0719
k 0.7780 — —
Note: Similar model to alternate model 1, but excluding driveways. The overall fit of the model
improves, and the p-value for unsignalized intersections is improved. Region is included for
North Carolina or Minnesota; a value of 0 if in Northern or Southern California.
—Not applicable.

 

Table 41. Alternate model 3 for mixed-use turning crashes.

Variable Estimate Standard Error p-Value
Intercept –0.4146 0.7632 0.5870
Region –0.3163 0.1301 0.0150
AADT 0.2179 0.0729 0.0028
PROPNODEV –0.5890 0.2827 0.0372
k 0.7791 –– ––
Note: This model was developed from the dataset combining all land use types with factor
variables representing the land use. Region is included for North Carolina or Minnesota; a value
of 0 if in Northern or Southern California.
—Not applicable.

 

Rear-End Crashes

Table 42 and table 43 present two alternate models for mixed-use rear-end crashes. The model form for mixed-use rear-end crashes is shown in figure 18.

Table 42. Alternate model 1 for mixed-use rear-end crashes.

Variable Estimate Standard Error p-Value
Intercept –6.6976 1.9985 0.0008
Region 1.2289 0.2479 <0.0001
AADT 0.7901 0.1876 <0.0001
SIGDENS 0.1122 0.0702 0.1099
k 0.7006 — —
Note: Region is included for North Carolina or Minnesota; a value of 0 if in Northern or Southern
California.
—Not applicable.

 

Table 43. Alternate model 2 for mixed-use rear-end crashes.

Variable Estimate Standard Error p-Value
Intercept –3.3091 0.6700 <0.0001
Region 0.8113 0.1136 <0.0001
AADT 0.5015 0.0618 <0.0001
SIGDENS 0.0621 0.0380 0.1021
PROPLANE1 –0.5548 0.1713 0.0012
k 0.6098 — —
Note: This model was developed from the dataset combining all land use types with factor
variables representing the land use. Region is included for North Carolina or Minnesota; a value
of 0 if in Northern or Southern California.
—Not applicable.

 

Right-Angle Crashes

Table 44 through table 46 present three alternate models for mixed-use right-angle crashes. The model form for mixed-use right-angle crashes is shown in figure 18.

Table 44. Alternate model 1 for mixed-use right-angle crashes.

Variable Estimate Standard Error p-Value
Intercept –5.8048 1.9472 0.0029
Region 1.8390 0.2616 <0.0001
AADT 0.4656 0.1856 0.0121
ACCDENS 0.0112 0.0051 0.0267
SIGDENS 0.2284 0.0637 0.0003
k 0.5585 — —
Note: Region is included for North Carolina or Minnesota; a value of 0 if in Northern or Southern
California.
—Not applicable.

 

Table 45. Alternate model 2 for mixed-use right-angle crashes.

Variable Estimate Standard Error p-Value
Intercept –5.2671 2.1768 0.0155
Region 1.2134 0.2457 <0.0001
AADT 0.5678 0.2103 0.0069
PROPDIV –0.4710 0.3461 0.1736
MEDOPDENS 0.1901 0.0884 0.0316
k 0.6796 — —
Note: The p-value for PROPDIV is higher than desirable, but the direction of effect for PROPDIV
and MEDOPDENS is logical. Region is included for North Carolina or Minnesota; a value of 0 if
in Northern or Southern California.
—Not applicable.

 

Table 46. Alternate model 3 for mixed-use right-angle crashes.

Variable Estimate Standard Error p-Value
Intercept –2.1485 0.6851 0.0017
Region 1.2344 0.1377 <0.0001
AADT 0.2433 0.0648 0.0002
PROPFULLDEV 0.6787 0.1846 0.0002
k 0.7674 — —
Note: This model was developed from the dataset combining all land use types with factor
variables representing the land use. Region is included for North Carolina or Minnesota; a value of
0 if in Northern or Southern California.
—Not applicable.

 

COMMERCIAL MODELS

Total Crashes

Table 47 and table 48 present two alternate models for commercial total crashes. The model form for commercial total crashes is shown in figure 18.

Table 47. Alternate model 1 for commercial total crashes.

Variable Estimate Standard Error p-Value
Intercept –0.7017 0.6873 0.3073
Region 0.8353 0.1883 <0.0001
AADT 0.3094 0.0660 <0.0001
ACCDENS 0.0069 0.0048 0.1507
SIGDENS 0.1002 0.0523 0.0556
k 0.4890 — —
Note: The p-value for ACCDENS is higher than desirable. Region is included for North Carolina
or Minnesota; a value of 0 if in Northern or Southern California.
—Not applicable.

 

Table 48. Alternate model 2 for commercial total crashes.

Variable Estimate Standard Error p-Value
Intercept –0.6854 0.5010 0.1713
Region 0.6166 0.1013 <0.0001
AADT 0.3766 0.0468 <0.0001
PROPNODEV –0.4252 0.2268 0.0608
k 0.5165 — —
Note: This model was developed from the dataset combining all land use types with factor
variables representing the land use. Region is included for North Carolina or Minnesota; a value
of 0 if in Northern or Southern California.
—Not applicable.

 

Injury Crashes

Table 49 through table 52 present four alternate models for commercial injury crashes. The model form for commercial injury crashes is shown in figure 18.

Table 49. Alternate model 1 for commercial injury crashes.

Variable Estimate Standard Error p-Value
Intercept –2.0602 0.7991 0.0099
Region 0.4672 0.1815 0.0100
AADT 0.3649 0.0766 <0.0001
ACCDENS 0.0085 0.0047 0.0679
SIGDENS 0.0566 0.0512 0.2696
k 0.4406 — —
Note: The p-value for SIGDENS is higher than desirable. Region is included for North Carolina
or Minnesota; a value of 0 if in Northern or Southern California.
—Not applicable.

 

Table 50. Alternate model 2 for commercial injury crashes.

Variable Estimate Standard Error p-Value
Intercept –0.9792 0.8386 0.2430
Region 0.2383 0.1497 0.1113
AADT 0.3225 0.0797 <0.0001
PROPNODEV –0.6472 0.3040 0.0333
PROPLANE1 –0.6047 0.2631 0.0216
k 0.4228 — —
Note: Region is included for North Carolina or Minnesota; a value of 0 if in Northern or Southern
California.
—Not applicable.

 

Table 51. Alternate model 3 for commercial injury crashes.

Variable Estimate Standard Error p-Value
Intercept 0.2127 0.7288 0.7704
Region 0.6769 0.1559 <0.0001
AADT 0.2705 0.0697 0.0001
PROPVC 0.5421 0.1990 0.0064
PROPLANE1 –0.6244 0.2566 0.0150
k 0.4739 — —
Note: Region is included for North Carolina or Minnesota; a value of 0 if in Northern or Southern
California.
—Not applicable.

 

Table 52. Alternate model 4 for commercial injury crashes.

Variable Estimate Standard Error p-Value
Intercept –1.9690 0.5862 0.0008
Region 0.3056 0.0923 0.0009
AADT 0.3751 0.0548 <0.0001
SIGDENS 0.1075 0.0300 0.0003
PROPLANE1 –0.5245 0.1430 0.0002
k 0.3951 — —
Note: This model was developed from the dataset combining all land use types with factor
variables representing the land use. Region is included for North Carolina or Minnesota; a value
of 0 if in Northern or Southern California.
—Not applicable.

 

Turning Crashes

Table 53 and table 54 present two alternate models for commercial turning crashes. The model form for commercial turning crashes is shown in figure 18.

Table 53. Alternate model 1 for commercial turning crashes.

Variable Estimate Standard Error p-Value
Intercept –0.9816 0.9366 0.2946
Region — — —
AADT 0.1650 0.0960 0.0855
ACCDENS 0.0110 0.0052 0.0359
SIGDENS 0.1995 0.0660 0.0025
k 0.7140 — —
Note: Region is not included; a value of 0 is assumed for all regions.
—Not applicable.

 

Table 54. Alternate model 2 for commercial turning crashes.

Variable Estimate Standard Error p-Value
Intercept 0.0085 1.1277 0.9940
Region –0.2548 0.2101 0.2251
AADT 0.1947 0.1068 0.0685
PROPNODEV –0.6967 0.4150 0.0932
PROPLANE1 –0.7328 0.3577 0.0405
k 0.7802 — —
Note: Region is included for North Carolina or Minnesota; a value of 0 if in Northern or Southern
California.
—Not applicable.

 

Rear-End Crashes

Table 55 and table 56 present two alternate models for commercial rear-end crashes. The model form for commercial rear-end crashes is shown in figure 18.

Table 55. Alternate model 1 for commercial rear-end crashes.

Variable Estimate Standard Error p-Value
Intercept –3.2746 0.8502 0.0001
Region 0.8114 0.1786 <0.0001
AADT 0.5050 0.0827 <0.0001
SIGDENS 0.0924 0.0552 0.0941
k 0.6055 — —
Note: Region is included for North Carolina or Minnesota; a value of 0 if in Northern or Southern
California.
—Not applicable.

 

Table 56. Alternate model 2 for commercial rear-end crashes.

Variable Estimate Standard Error p-Value
Intercept –3.0651 0.6691 <0.0001
Region 0.8113 0.1136 <0.0001
AADT 0.5015 0.0618 <0.0001
PROPLANE1 –0.5548 0.1713 0.0012
SIGDENS 0.0621 0.0380 0.1021
k 0.6098 — —
Note: This model was developed from the dataset combining all land use types with factor variables
representing the land use. Region is included for North Carolina or Minnesota; a value of 0 if in Northern
or Southern California.
—Not applicable.

 

Right-Angle Crashes

Table 57 and table 58 present two alternate models for commercial right-angle crashes. The model form for commercial right-angle crashes is shown in figure 18.

Table 57. Alternate model 1 for commercial right-angle crashes.

Variable Estimate Standard Error p-Value
Intercept –1.6746 0.9312 0.0721
Region 1.4756 0.2388 <0.001
AADT 0.1238 0.0912 0.1745
ACCDENS 0.0165 0.0064 0.0099
SIGDENS 0.1532 0.0658 0.0199
k 0.7288 — —
Note: The p-value for AADT is higher than desirable. Region is included for North Carolina or
Minnesota; a value of 0 if in Northern or Southern California.
—Not applicable.

 

Table 58. Alternate model 2 for commercial right-angle crashes.

Variable Estimate Standard Error p-Value
Intercept –1.9023 0.6838 0.0054
Region 1.2344 0.1377 <0.0001
AADT 0.2433 0.0648 0.0002
PROPFULLDEV 0.6787 0.1846 0.0002
k 0.7674 — —
Note: This model was developed from the dataset combining all land use types with factor
variables representing the land use. Region is included for North Carolina or Minnesota; a value of
0 if in Northern or Southern California.
—Not applicable.

 

RESIDENTIAL MODELS

Total Crashes

Table 59 through table 62 present four alternate models for residential total crashes. The model form for residential total crashes is shown in figure 18.

Table 59. Alternate model 1 for residential total crashes.

Variable Estimate Standard Error p-Value
Intercept –0.5615 0.7076 0.4275
Region 0.4443 0.1533 0.0038
AADT 0.3094 0.0673 <0.0001
PROPLANE1 –0.5479 0.1702 0.0013
SIGDENS 0.1262 0.0629 0.0449
PROPFULLDEV 0.3371 0.2317 0.1456
k 0.3277 — —
Note: The p-value for PROPFULLDEV is larger than desirable. Region is included for North
Carolina or Minnesota; a value of 0 if in Northern or Southern California.
—Not applicable.

 

Table 60. Alternate model 2 for residential total crashes.

Variable Estimate Standard Error p-Value
Intercept –0.4764 0.7211 0.5088
Region 0.3824 0.1499 0.0108
AADT 0.3025 0.0685 <0.0001
PROPLANE1 –0.5260 0.1722 0.0023
SIGDENS 0.1576 0.0622 0.0113
k 0.3384 — —
Note: This model is the same as alternate model 1 but without PROPFULLDEV. Region is
included for North Carolina or Minnesota; a value of 0 if in Northern or Southern California.
—Not applicable.

 

Table 61. Alternate model 3 for residential total crashes.

Variable Estimate Standard Error p-Value
Intercept –1.3644 0.4953 0.0059
Region 0.6850 0.1107 <0.0001
AADT 0.3883 0.0463 <0.0001
ACCDENS 0.0032 0.0022 0.1375
k 0.5181 — —
Note: This model was developed from the dataset combining all land use types with factor
variables representing the land use; region is included for North Carolina or Minnesota; a value of
0 if in Northern or Southern California.
—Not applicable.

 

Table 62. Alternate model 4 for residential total crashes.

Variable Estimate Standard Error p-Value
Intercept –1.1048 0.4876 0.0235
Region 0.6166 0.1013 <0.0001
AADT 0.3766 0.0468 <0.0001
PROPNODEV –0.4252 0.2268 0.0608
k 0.5165 — —
Note: This model was developed from the dataset combining all land use types with factor
variables representing the land use; region is included for North Carolina or Minnesota; a value of
0 if in Northern or Southern California.
—Not applicable.

 

Injury Crashes

Table 63 and table 64 present two alternate models for residential injury crashes. The model form for residential injury crashes is shown in figure 18.

Table 63. Alternate model 1 for residential injury crashes.

Variable Estimate Standard Error p-Value
Intercept –2.7357 0.8556 0.0014
Region 0.1656 0.1423 0.2447
AADT 0.4189 0.0820 <0.0001
PROPLANE1 –0.4040 0.1669 0.0155
SIGDENS 0.2081 0.0539 0.0001
k 0.2663 — —
Note: Region is included for North Carolina or Minnesota; a value of 0 if in Northern or Southern
California.
—Not applicable.

 

Table 64. Alternate model 2 for residential injury crashes.

Variable Estimate Standard Error p-Value
Intercept –2.7379 0.9147 0.0028
Region 0.2303 0.1603 0.1509
AADT 0.4615 0.0867 <0.0001
PROPLANE1 –0.6125 0.1715 0.0004
PROPFULLDEV 0.3720 0.2273 0.1017
k 0.3220 — —
Note: region is included for North Carolina or Minnesota; a value of 0 if in Northern or Southern
California.
—Not applicable.

 

Turning Crashes

Table 65 through table 68 present four alternate models for residential turning crashes. The model form for residential turning crashes is shown in figure 18.

Table 65. Alternate model 1 for residential turning crashes.

Variable Estimate Standard Error p-Value
Intercept –2.5087 1.0439 0.0163
Region — — —
AADT 0.2949 0.1008 0.0034
UNSIGDENS 0.0589 0.0289 0.0416
SIGDENS 0.2173 0.0845 0.0101
k 0.6710 — —
Note: Region is not included; a value of 0 is assumed for all regions.
—Not applicable.

 

Table 66. Alternate model 2 for residential turning crashes.

Variable Estimate Standard Error p-Value
Intercept –1.1275 1.1225 0.3152
Region –0.6520 0.2073 0.0017
AADT 0.1826 0.1059 0.0846
UNSIGDENS 0.0635 0.0283 0.0247
SIGDENS 0.2244 0.0818 0.0061
k 0.5792 — —
Note: This model is the same as alternate model 1 but with the Region variable included. Note
the large reduction in the AADT parameter; region is included for North Carolina or Minnesota; a
value of 0 if in Northern or Southern California.
—Not applicable.

 

Table 67. Alternate model 3 for residential turning crashes.

Variable Estimate Standard Error p-Value
Intercept –0.9528 0.7286 0.1910
Region –0.1651 0.1339 0.2174
AADT 0.1759 0.0708 0.0130
ACCDENS 0.0052 0.0028 0.0643
SIGDENS 0.1821 0.0426 <0.0001
k 0.7030 — —
Note: This model was developed from the dataset combining all land use types with factor
variables representing the land use; region is included for North Carolina or Minnesota; a
value of 0 if in Northern or Southern California.
—Not applicable.

 

Table 68. Alternate model 4 for residential turning crashes.

Variable Estimate Standard Error p-Value
Intercept –0.7154 0.7477 0.3387
Region –0.3163 0.1301 0.0150
AADT 0.2179 0.0729 0.0028
PROPNODEV –0.5890 0.2827 0.0372
k 0.7791 — —
Note: This model was developed from the dataset combining all land use types with factor
variables representing the land use; region is included for North Carolina or Minnesota; a value
of 0 if in Northern or Southern California.
—Not applicable.

 

Rear-End Crashes

Table 69 through table 71 present three alternate models for residential rear-end crashes. The model form for residential rear-end crashes is shown in figure 18.

Table 69. Alternate model 1 for residential rear-end crashes.

Variable Estimate Standard Error p-Value
Intercept –3.8941 0.9816 <0.0001
Region 0.5803 0.1984 0.0034
AADT 0.5392 0.0945 <0.0001
SIGDENS 0.1675 0.0864 0.0527
k 0.5541 — —
Note: Region is included for North Carolina or Minnesota; a value of 0 if in Northern or Southern
California.
—Not applicable.

 

Table 70. Alternate model 2 for residential rear-end crashes.

Variable Estimate Standard Error p-Value
Intercept –2.6180 1.0221 0.0104
Region 0.5406 0.1865 0.0037
AADT 0.4782 0.0967 <0.0001
PROPLANE1 –0.8174 0.2078 <0.0001
PROPTWLTL –0.5600 0.2439 0.0217
k 0.4803 — —
Note: Region is included for North Carolina or Minnesota; a value of 0 if in Northern or Southern
California.
—Not applicable.

 

Table 71. Alternate model 3 for residential rear-end crashes.

Variable Estimate Standard Error p-Value
Intercept –3.3056 0.6549 <0.0001
Region 0.8113 0.1136 <0.0001
AADT 0.5015 0.0618 <0.0001
PROPLANE1 –0.5548 0.1713 0.0012
SIGDENS 0.0621 0.0380 0.1021
k 0.6098
Note: This model was developed from the dataset combining all land use types with factor
variables representing the land use; region is included for North Carolina or Minnesota; a value
of 0 if in Northern or Southern California.
—Not applicable.

 

Right-Angle Crashes

Table 72 through table 74 present three alternate models for residential right-angle crashes. The model form for residential right-angle crashes is shown in figure 18.

Table 72. Alternate model 1 for residential right-angle crashes.

Variable Estimate Standard Error p-Value
Intercept –1.8958 1.1271 0.0926
Region 0.8655 0.2364 0.0003
AADT 0.2357 0.1098 0.0319
k 0.7812 — —
Note: Region is included for North Carolina or Minnesota; a value of 0 if in Northern or Southern
California.
—Not applicable.

 

Table 73. Alternate model 2 for residential right-angle crashes.

Variable Estimate Standard Error p-Value
Intercept –1.4079 1.0732 0.1896
Region 0.8858 0.2180 <0.0001
AADT 0.1332 0.1051 0.2049
SIGDENS 0.2267 0.0750 0.0025
PROPLANE1 –0.3633 0.2383 0.1274
PROPFULLDEV 0.4295 0.3125 0.1693
k 0.5555 — —
Note: The p-values for AADT, PROPLANE1, and PROPFULLDEV are higher than desirable;
region is included for North Carolina or Minnesota; a value of 0 if in Northern or Southern
California.
—Not applicable.

 

Table 74. Alternate model 3 for residential right-angle crashes.

Variable Estimate Standard Error p-Value
Intercept –2.1173 0.6540 0.0012
Region 1.1970 0.1314 <0.0001
AADT 0.1768 0.0639 0.0057
SIGDENS 0.2084 0.0390 <0.0001
ACCDENS 0.0044 0.0028 0.1078
k 0.6790 — —
Note: This model was developed from the dataset combining all land use types with factor
variables representing the land use; region is included for North Carolina or Minnesota; a value of
0 if in Northern or Southern California.
—Not applicable.

 

 

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