Research, Development, and Technology

Turner-Fairbank Highway Research Center

6300 Georgetown Pike

McLean, VA 22101-2296

PUBLICATION NO. FHWA-HRT-20-052 NOVEMBER 2020 

Contributing Factors for Focus

Crash and Facility Types
FOREWORD

The research documented in this report was conducted as part of the Federal Highway 

Administration’s (FHWA’s) Evaluation of Low-Cost Safety Improvements Pooled Fund Study 

(ELCSI-PFS). FHWA established this PFS in 2005 to conduct research on the effectiveness of 

safety improvements identified by the National Cooperative Highway Research Program Report 

500 Guides as part of the implementation of the American Association of State Highway and 

Transportation Officials’ Strategic Highway Safety Plan. ELCSI-PFS studies provide a crash 

modification factor and benefit–cost economic analysis for each targeted safety strategy 

identified as a priority by member States of the PFS. 

This project identified focus crash and facility types and associated contributing factors to better 

inform applications of systemic safety improvements. Three data sources were used to conduct 

contributing-factor analyses: crash and roadway inventory from the Highway Information 

System, climate data from the National Oceanic and Atmospheric Administration, and 

socioeconomic census data from the U.S. Census Bureau (FHWA 2018c; NOAA 2018; 

U.S. Census Bureau 2018). The contributing-factor analysis on road segments used data from 

Ohio and Washington State, and the analysis on intersections used data from California and 

Ohio. For the analysis, the research team used the random-forest method to identify the most 

predictive variables and then created plots of random forest–predicted crash frequencies as a 

function of the predictor variables to observe the general trends in the relationships. 

A Quick Reference Guide (FHWA-HRT-20-053) was also developed from this project and aims 

to assist safety practitioners in selecting countermeasures to address focus crash types (Porter 

et al. 2020). This report and the Quick Reference Guide will benefit safety engineers and safety 

planners by providing greater insight into increased highway safety. 

Brian P. Cronin, P.E. 

Director, Office of Safety and Operations 

Research and Development 

Notice

This document is disseminated under the sponsorship of the U.S. Department of Transportation 

(USDOT) in the interest of information exchange. The U.S. Government assumes no liability for 

the use of the information contained in this document. 

The U.S. Government does not endorse products or manufacturers. Trademarks or 

manufacturers’ names appear in this report only because they are considered essential to the 

objective of the document. 

Quality Assurance Statement

The Federal Highway Administration (FHWA) provides high-quality information to serve 

Government, industry, and the public in a manner that promotes public understanding. Standards 

and policies are used to ensure and maximize the quality, objectivity, utility, and integrity of its 

information. FHWA periodically reviews quality issues and adjusts its programs and processes to 

ensure continuous quality improvement.
TECHNICAL REPORT DOCUMENTATION PAGE

1. Report No.

FHWA-HRT-20-052

2. Government Accession No. 3. Recipient’s Catalog No.

4. Title and Subtitle

Contributing Factors for Focus Crash and Facility Types

5. Report Date

November 2020

6. Performing Organization Code

7. Author(s)

Taha Saleem (ORCID: 0000-0002-2878-0980), Richard Porter (ORCID:

0000-0001-8535-3451), Raghavan Srinivasan (ORCID: 0000-0002-

3097-5154), Daniel Carter (ORCID: 0000-0001-6572-6548),

Scott Himes (ORCID: 0000-0003-2932-7691), and Thanh Le

(ORCID: 0000-0001-5809-2751)

8. Performing Organization Report No.

9. Performing Organization Name and Address

VHB

940 Main Campus Drive, Suite 500

Raleigh, NC 27606

10. Work Unit No.

11. Contract or Grant No.

DTFH61-13-D-000001

12. Sponsoring Agency Name and Address

U.S. Department of Transportation

Federal Highway Administration

1200 New Jersey Avenue, SE

Washington, DC 20590

13. Type of Report and Period

Final Report; March 2017–February 2018

14. Sponsoring Agency Code

HRDS-20

15. Supplementary Notes

The Federal Highway Administration (FHWA) Office of Safety Research and Development managed this project

under the Development of Crash Modification Factors program. The FHWA Office of Safety Research and

Development Program Manager and Contracting Officer’s Representative was Roya Amjadi (HRDS-20; ORCID:

0000-0001-7672-8485), and the Technical Manager was Karen Scurry (HSSP; ORCID: 0000-0002-3499-5515).

16. Abstract

The main goal of this project was to identify focus crash and facility types (FCFTs) and associated contributing

factors to better inform applications of systemic safety improvements. The selection of FCFTs was guided by the

use of the Fatality Analysis Reporting System and the Highway Safety Information System (HSIS) databases

(NHTSA 2018a; FHWA 2018c). The method used to select potential FCFTs was based on the number of fatal

crashes and number of fatal-plus-incapacitating-injury crashes associated with various crash-related variables. A

total of 17 FCFTs (8 intersection FCFTs and 9 nonintersection FCFTs) were identified for analysis to identify

contributing factors. Three data sources were used to conduct the contributing-factor analysis: crash and roadway

inventory from HSIS, climate data from the National Oceanic and Atmospheric Administration, and socioeconomic

census data from the U.S. Census Bureau (FHWA 2018c; NOAA 2018; U.S. Census Bureau 2018). For the

analysis, the research team used the random-forest method to identify the most predictive variables and then created

plots of random forest–predicted crash frequencies as a function of the predictor variables to observe the general

trends in the relationships. The research laid out a process for identifying and selecting countermeasures for focus

crash types based on contributing factors. The countermeasure guidance focused on traffic and roadway findings.

Findings linked to socioeconomic- and weather-related factors showed promise, but there is not yet a significant

amount of theory to support or refute the socioeconomic- and weather-related results of this effort.

17. Key Words

Systemic safety, focus crash types, contributing factors,

random forests, crash prediction, proven countermeasures

18. Distribution Statement

No restrictions. This document is available to the public

through the National Technical Information Service,

Springfield, VA 22161.

http://www.ntis.gov

19. Security Classif. (of this report)

Unclassified

20. Security Classif. (of this page)

Unclassified

21. No. of Pages

308

22. Price

N/A

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized.
ii

SI* (MODERN METRIC) CONVERSION FACTORS

APPROXIMATE CONVERSIONS TO SI UNITS

Symbol When You Know Multiply By To Find Symbol

LENGTH

in inches 25.4 millimeters mm

ft feet 0.305 meters m

yd yards 0.914 meters m

mi miles 1.61 kilometers km

AREA

in2 square inches 645.2 square millimeters mm2

ft2 square feet 0.093 square meters m2

yd2 square yard 0.836 square meters m2

ac acres 0.405 hectares ha

mi2 square miles 2.59 square kilometers km2

VOLUME

fl oz fluid ounces 29.57 milliliters mL

gal gallons 3.785 liters L

ft3 cubic feet 0.028 cubic meters m3

yd3 cubic yards 0.765 cubic meters m3

NOTE: volumes greater than 1,000 L shall be shown in m3

MASS

oz ounces 28.35 grams g

lb pounds 0.454 kilograms kg

T short tons (2,000 lb) 0.907 megagrams (or “metric ton”) Mg (or “t”)

TEMPERATURE (exact degrees)

°F Fahrenheit 5 (F-32)/9 Celsius °C or (F-32)/1.8

ILLUMINATION

fc foot-candles 10.76 lux lx

fl foot-Lamberts 3.426 candela/m2 cd/m2

FORCE and PRESSURE or STRESS

lbf poundforce 4.45 newtons N

lbf/in2 poundforce per square inch 6.89 kilopascals kPa

APPROXIMATE CONVERSIONS FROM SI UNITS

Symbol When You Know Multiply By To Find Symbol

LENGTH

mm millimeters 0.039 inches in

m meters 3.28 feet ft

m meters 1.09 yards yd

km kilometers 0.621 miles mi

AREA

mm2 square millimeters 0.0016 square inches in2

m2 square meters 10.764 square feet ft2

m2 square meters 1.195 square yards yd2

ha hectares 2.47 acres ac

km2 square kilometers 0.386 square miles mi2

VOLUME

mL milliliters 0.034 fluid ounces fl oz

L liters 0.264 gallons gal

m3 cubic meters 35.314 cubic feet ft3

m3 cubic meters 1.307 cubic yards yd3

MASS

g grams 0.035 ounces oz

kg kilograms 2.202 pounds lb

Mg (or “t”) megagrams (or “metric ton”) 1.103 short tons (2,000 lb) T

TEMPERATURE (exact degrees)

°C Celsius 1.8C+32 Fahrenheit °F

ILLUMINATION

lx lux 0.0929 foot-candles fc

cd/m2 candela/m2 0.2919 foot-Lamberts fl

FORCE and PRESSURE or STRESS

N newtons 2.225 poundforce lbf

kPa kilopascals 0.145 poundforce per square inch lbf/in2

*SI is the symbol for International System of Units. Appropriate rounding should be made to comply with Section 4 of ASTM E380. 

(Revised March 2003)
iii

TABLE OF CONTENTS

EXECUTIVE SUMMARY ............................................................................................................ 1

FCFTs ........................................................................................................................................ 1

Analysis of Contributing Factors ............................................................................................ 2

Countermeasure-Selection Process ......................................................................................... 4

CHAPTER 1. INTRODUCTION ................................................................................................. 5

Background ............................................................................................................................... 5

Systemic Approach to Road-Safety Management ................................................................. 6

Objective ................................................................................................................................... 7

Organization of the Report ...................................................................................................... 8

CHAPTER 2. LITERATURE REVIEW ..................................................................................... 9

Overview of Contributing Factors and Methods .................................................................. 9

Contributing Factors ............................................................................................................. 9

Datasets .............................................................................................................................. 22

Methodologies .................................................................................................................... 22

Approaches to Systemic Safety Analysis .............................................................................. 28

Summary ................................................................................................................................. 40

CHAPTER 3. TERMINOLOGY ................................................................................................ 41

CHAPTER 4. FCFTS ................................................................................................................... 43

FARS Database and Elements .............................................................................................. 43

HSIS Database and Elements ................................................................................................ 44

Key Variables for Defining Potential FCFTs ...................................................................... 44

Crash Type ......................................................................................................................... 45

Area Type ........................................................................................................................... 45

Roadway Type .................................................................................................................... 46

Location Type .................................................................................................................... 46

Intersection Type ................................................................................................................ 46

Traffic Control Type .......................................................................................................... 46

Light Condition .................................................................................................................. 46

Road Alignment Type (Nonintersection Crashes) ............................................................. 46

Fatal and Incapacitating Injury Crash ................................................................................ 47

Potential FCFTs ...................................................................................................................... 47

Selecting FCFTs ...................................................................................................................... 48

Intersection FCFTs ............................................................................................................. 50

Nonintersection FCFTs ...................................................................................................... 50

CHAPTER 5. CONTRIBUTING FACTORS ........................................................................... 51

Data .......................................................................................................................................... 51

Potential Contributing Factors ............................................................................................. 52

Roadway ............................................................................................................................. 52

Climate ............................................................................................................................... 53

Census ................................................................................................................................ 53
iv

Summary of Data ................................................................................................................... 54

Lane Width ......................................................................................................................... 54

Shoulder Type .................................................................................................................... 54

Surface Type ...................................................................................................................... 54

Terrain Type ....................................................................................................................... 55

Curve Radius ...................................................................................................................... 55

Data Quality Checks ........................................................................................................... 55

Analysis Methodology ............................................................................................................ 66

Results ..................................................................................................................................... 68

ROR Crashes on Rural Two-Lane Highway Segments ...................................................... 70

Ohio .................................................................................................................................... 70

Washington ......................................................................................................................... 72

Discussion .......................................................................................................................... 74

LNDP Crashes on Rural Two-Lane Highway Segments .................................................... 75

Ohio .................................................................................................................................... 75

Washington ......................................................................................................................... 77

Discussion .......................................................................................................................... 80

HEO Crashes on Rural Two-Lane Highway Segments ...................................................... 81

Ohio .................................................................................................................................... 81

Washington ......................................................................................................................... 83

Discussion .......................................................................................................................... 85

ROLL Crashes on Rural Two-Lane Highway Segments ................................................... 86

Ohio .................................................................................................................................... 86

Washington ......................................................................................................................... 88

Discussion .......................................................................................................................... 91

ANG Crashes on Rural Two-Lane Highway Segments ...................................................... 92

Ohio .................................................................................................................................... 92

Washington ......................................................................................................................... 93

Discussion .......................................................................................................................... 94

ANG Crashes at Four-Leg Stop-Controlled Intersections on Rural Two-Lane 

Roads ....................................................................................................................................... 95

California ............................................................................................................................ 95

Ohio .................................................................................................................................... 96

Discussion .......................................................................................................................... 97

ANG Crashes at Four-Leg Stop-Controlled Intersections on Urban Two-Lane 

Roads ....................................................................................................................................... 99

California ............................................................................................................................ 99

Ohio .................................................................................................................................... 99

Discussion ........................................................................................................................ 100

ANG Crashes at Four-Leg Signalized Intersections on Urban Multilane Divided 

Roads ..................................................................................................................................... 101

California .......................................................................................................................... 101

Ohio .................................................................................................................................. 102

Discussion ........................................................................................................................ 103
v 

ANG Crashes at Four-Leg Signalized Intersections on Urban Multilane Undivided 

Roads ..................................................................................................................................... 104

California .......................................................................................................................... 104

Ohio .................................................................................................................................. 104

Discussion ........................................................................................................................ 105

ANG Crashes at Three-Leg Stop-Controlled Intersections on Rural Two-Lane 

Roads ..................................................................................................................................... 106

California .......................................................................................................................... 106

Ohio .................................................................................................................................. 107

Discussion ........................................................................................................................ 108

ANG Crashes at Four-Leg Stop-Controlled Intersections on Rural Multilane 

Divided Roads ....................................................................................................................... 109

California .......................................................................................................................... 109

Ohio .................................................................................................................................. 110

Discussion ........................................................................................................................ 111

Pedestrian Crashes ............................................................................................................... 111

CHAPTER 6. COUNTERMEASURE-SELECTION PROCESS ......................................... 115

Background Relevant to Countermeasure Selection ........................................................ 115

Roadway Variables .......................................................................................................... 116

Socioeconomic Variables ................................................................................................. 117

Climate Variables ............................................................................................................. 118

Step 1. Identify a Focus Crash Type ................................................................................... 118

Approach .......................................................................................................................... 118

Example ............................................................................................................................ 118

Step 2. Identify Contributing Factors for the Focus Crash Type .................................... 119

Approach .......................................................................................................................... 119

Example ............................................................................................................................ 119

Step 3. Assemble a List of Potential Countermeasures That Address the Crash 

Type ....................................................................................................................................... 119

Approach .......................................................................................................................... 119

Example ............................................................................................................................ 120

Step 4. Identify Countermeasures That Explicitly Address Contributing Factors 

Associated with the Focus Crash Type ............................................................................... 120

Approach .......................................................................................................................... 120

Example ............................................................................................................................ 120

Step 5. Identify Countermeasures with CMFs .................................................................. 121

Approach .......................................................................................................................... 121

Example ............................................................................................................................ 121

Step 6. Select a Countermeasure ......................................................................................... 122

Approach .......................................................................................................................... 122

Example ............................................................................................................................ 122

Additional Examples ............................................................................................................ 123

Example 1: HEO Crashes ................................................................................................. 123

Example 2: ANG Crashes ................................................................................................ 124
vi 

Targeted Countermeasures for Contributing Factors ...................................................... 127

Auxiliary Turn Lanes (FHWA 2017a) ............................................................................. 129

Yellow Change Intervals (FHWA 2017a) ........................................................................ 129

Backplates with Retroreflective Borders (FHWA 2017a) ............................................... 129

Application of Multiple Low-Cost Countermeasures (FHWA 2017a) ............................ 129

Advance Signs (ITE 2015) ............................................................................................... 130

SafetyEdge (FHWA 2017a) ............................................................................................. 130

Rumble Strips or Stripes (FHWA 2017a) ........................................................................ 130

Enhanced Friction for Horizontal Curves (FHWA 2017a) .............................................. 130

Enhanced Delineation for Horizontal Curves (FHWA 2017a) ........................................ 131

Roadside-Design Improvements at Curves (FHWA 2017a) ............................................ 131

Advance Markings for Curves (Albin et al. 2016) ........................................................... 131

Advance Signs (Albin et al. 2016) ................................................................................... 131

CHAPTER 7. SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS .................. 133

Identifying FCFTs ................................................................................................................ 133

Identifying Contributing Factors Associated with FCFTs ............................................... 134

Process for Selecting Countermeasures ............................................................................. 136

Conclusions and Recommendations ................................................................................... 137

Identifying FCFTs ............................................................................................................ 137

Identifying Contributing Factors ...................................................................................... 138

Implementing and Evaluating Countermeasures .............................................................. 140

APPENDIX A. LIST OF VARIABLES USED FROM FARS AND HSIS FOR FCFT 

SELECTION............................................................................................................................... 141

List of FARS Variables Used for FCFT Selection ............................................................. 141

Common Variables ........................................................................................................... 141

Accident Data File ............................................................................................................ 141

Vehicle Data File .............................................................................................................. 142

Person Data File ............................................................................................................... 142

List of HSIS Washington Variables Used for FCFT Selection ........................................ 142

Common Variables ........................................................................................................... 142

Accident Data Files .......................................................................................................... 142

Roadway and Curve Data Files ........................................................................................ 143

List of HSIS Ohio Variables Used for FCFT Selection..................................................... 143

Common Variables ........................................................................................................... 143

Accident Data Files .......................................................................................................... 144

Roadway Data Files ......................................................................................................... 144

List of HSIS California Variables Used for FCFT Selection ........................................... 144

Common Variables ........................................................................................................... 144

Accident Data Files .......................................................................................................... 145

Roadway and Intersection Data Files ............................................................................... 145

List of HSIS Minnesota Variables Used for FCFT Selection ........................................... 145

Common Variables ........................................................................................................... 145

Accident Data Files .......................................................................................................... 146

Roadway Data Files ......................................................................................................... 146
vii 

APPENDIX B. SYSTEMIC SAFETY-PLANNING PROCESS ............................................ 147

APPENDIX C. POTENTIAL INTERSECTION AND NONINTERSECTION FCFTS 

FROM FARS AND HSIS .......................................................................................................... 157

APPENDIX D. RANDOM-FOREST R CODE ....................................................................... 169

APPENDIX E. RANDOM-FOREST OUTPUTS .................................................................... 171

Random Forests of California Data .................................................................................... 171

Random Forests of Washington Data ................................................................................. 178

Random Forests of Ohio Data ............................................................................................. 195

APPENDIX F. SAMPLE PLOTS OF RANDOM FOREST–PREDICTED CRASH 

FREQUENCIES VERSUS PREDICTOR VARIABLES ....................................................... 227

ANG-D Crashes at Four-Leg Stop-Controlled Intersections on Rural Two-Lane 

Roads ..................................................................................................................................... 227

ROR-D Crashes at Horizontal Curves on Rural Two-Lane Highway Segments ........... 232

APPENDIX G. EXPLORATION OF FACTOR ANALYSIS ................................................ 237

Factor Analysis ..................................................................................................................... 237

Curves and Tangent Segments on Rural Two-Lane Highway Roads .............................. 237

Four-Leg Stop-Controlled Intersections on Urban Two-Lane Roads .............................. 238

Four-Leg Signalized Intersections on Urban Multilane Undivided Roads ...................... 239

Results from the Contributing-Factor Analysis ................................................................ 239

ROR Crashes on Rural Two-Lane Highway Roads in Ohio ............................................ 239

LNDP Crashes on Rural Two-Lane Highway Segments in Ohio .................................... 245

HEO Crashes on Rural Two-Lane Highway Segments in Ohio ...................................... 250

ROLL Crashes on Rural Two-Lane Highway Segments in Ohio .................................... 255

ANG Crashes on Rural Two-Lane Highway Segments in Ohio ...................................... 260

ANG Crashes at Four-Leg Stop-Controlled Intersections on Urban Two-Lane 

Roads in Ohio ................................................................................................................... 263

ANG Crashes at Four-Leg Stop-Controlled Intersections on Urban Two-Lane 

Roads in Ohio ................................................................................................................... 266

Summary of Results from the Contributing-Factor Analysis .......................................... 270

Intersection FCFTs ........................................................................................................... 270

Nonintersection FCFTs .................................................................................................... 270

APPENDIX H. OBSERVING PREDICTION MSES TO EXPLORE 

CONTRIBUTIONS OF SOCIOECONOMIC VARIABLES ................................................ 271

REFERENCES ........................................................................................................................... 277
viii

LIST OF FIGURES

Figure 1. Graph. Crash frequency per mile of ROR-D crashes on rural two-lane roads on 

horizontal curves: Washington. ............................................................................................ 67

Figure 2. Graph. ANG crash frequency versus mainline AADT for three-leg stop-controlled 

intersections on rural two-lane roads: California.................................................................. 68

Figure 3. Graph. ANG crash frequency versus percentage of households with income 

<$50,000 for four-leg stop-controlled intersections on urban two-lane roads: 

California. ............................................................................................................................. 68

Figure 4. Chart. Crash types defined by Manner of Collision. .................................................... 147

Figure 5. Chart. Crash types defined by First Harmful Event. ..................................................... 148

Figure 6. Chart. Crash tree for ANG crashes at nonintersection locations. ................................. 149

Figure 7. Chart. Crash tree for HEO crashes at nonintersection locations. .................................. 150

Figure 8. Chart. Crash tree for rear-end crashes at nonintersection locations. ............................ 151

Figure 9. Chart. Crash tree for fixed-object crashes in rural areas at nonintersection 

locations. ............................................................................................................................. 152

Figure 10. Chart. Crash tree for fixed-object crashes in urban areas at nonintersection 

locations. ............................................................................................................................. 153

Figure 11. Chart. Crash tree for pedestrian crashes in rural areas at nonintersection 

locations. ............................................................................................................................. 154

Figure 12. Chart. Crash tree for pedestrian crashes in urban areas at nonintersection 

locations. ............................................................................................................................. 155

Figure 13. Chart. Crash tree for ROLL crashes at nonintersection locations. ............................. 156

Figure 14. Screenshot. Sample random-forest code for R software. ............................................ 169

Figure 15. Graph. ANG-D crashes at four-leg stop-controlled intersections on rural 

two-lane roads. .................................................................................................................... 171

Figure 16. Graph. ANG-N crashes at four-leg stop-controlled intersections on rural 

two-lane roads. .................................................................................................................... 172

Figure 17. Graph. ANG-D crashes at four-leg stop-controlled intersection on urban 

two-lane roads. .................................................................................................................... 173

Figure 18. Graph. ANG-D crashes at four-leg signalized intersections on urban multilane 

divided roads. ...................................................................................................................... 174

Figure 19. Graph. ANG-D crashes at four-leg signalized intersections on urban multilane 

undivided roads. .................................................................................................................. 175

Figure 20. Graph. ANG-D crashes at three-leg stop-controlled intersections on rural 

two-lane roads. .................................................................................................................... 176

Figure 21. Graph. ANG-D crashes at four-leg stop-controlled intersections on rural 

multilane divided roads....................................................................................................... 177

Figure 22. Graph. ROR-D crashes at tangent segments on rural two-lane highway 

segments. ............................................................................................................................ 178

Figure 23. Graph. ROR-N crashes at tangent segments on rural two-lane highway 

segments. ............................................................................................................................ 179

Figure 24. Graph. ROR-D crashes at horizontal curves on rural two-lane highway 

segments. ............................................................................................................................ 180

Figure 25. Graph. ROR-N crashes at horizontal curves on rural two-lane highway 

segments. ............................................................................................................................ 181
ix

Figure 26. Graph. LNDP-D crashes at tangent segments on rural two-lane highway 

segments. ............................................................................................................................ 182

Figure 27. Graph. LNDP-N crashes at tangent segments on rural two-lane highway 

segments. ............................................................................................................................ 183

Figure 28. Graph. LNDP-D crashes at horizontal curves on rural two-lane highway 

segments. ............................................................................................................................ 184

Figure 29. Graph. LNDP-N crashes at horizontal curves on rural two-lane highway 

segments. ............................................................................................................................ 185

Figure 30. Graph. HEO-D crashes at tangent segments on rural two-lane highway 

segments. ............................................................................................................................ 186

Figure 31. Graph. HEO-N crashes at tangent segments on rural two-lane highway 

segments. ............................................................................................................................ 187

Figure 32. Graph. HEO-D crashes at horizontal curves on rural two-lane highway 

segments. ............................................................................................................................ 188

Figure 33. Graph. HEO-N crashes at horizontal curves on rural two-lane highway 

segments. ............................................................................................................................ 189

Figure 34. Graph. ROLL-D crashes at tangent segments on rural two-lane highway 

segments. ............................................................................................................................ 190

Figure 35. Graph. ROLL-N crashes at tangent segments on rural two-lane highway 

segments. ............................................................................................................................ 191

Figure 36. Graph. ROLL-D crashes at horizontal curves on rural two-lane highway 

segments. ............................................................................................................................ 192

Figure 37. Graph. ROLL-N crashes at horizontal curves on rural two-lane highway 

segments. ............................................................................................................................ 193

Figure 38. Graph. ANG-D crashes at tangent segments on rural two-lane highway 

segments. ............................................................................................................................ 194

Figure 39. Graph. ROR-KAB-D crashes on rural two-lane highway segments. ......................... 195

Figure 40. Graph. ROR-KAB-N crashes on rural two-lane highway segments. ......................... 196

Figure 41. Graph. ROR-KABCO-D crashes on rural two-lane highway segments. .................... 197

Figure 42. Graph. ROR-KABCO-N crashes on rural two-lane highway segments. .................... 198

Figure 43. Graph. LNDP-KAB-D crashes on rural two-lane highway segments. ....................... 199

Figure 44. Graph. LNDP-KAB-N crashes on rural two-lane highway segments. ....................... 200

Figure 45. Graph. LNDP-KABCO-D crashes on rural two-lane highway segments. ................. 201

Figure 46. Graph. LNDP-KABCO-N crashes on rural two-lane highway segments. ................. 202

Figure 47. Graph. HEO-KAB-D crashes on rural two-lane highway segments. ......................... 203

Figure 48. Graph. HEO-KAB-N crashes on rural two-lane highway segments. ......................... 204

Figure 49. Graph. HEO-KABCO-D crashes on rural two-lane highway segments. .................... 205

Figure 50. Graph. HEO-KABCO-N crashes on rural two-lane highway segments. .................... 206

Figure 51. Graph. ROLL-KAB-D crashes on rural two-lane highway segments. ....................... 207

Figure 52. Graph. ROLL-KAB-N crashes on rural two-lane highway segments. ....................... 208

Figure 53. Graph. ROLL-KABCO-D crashes on rural two-lane highway segments. ................. 209

Figure 54. Graph. ROLL-KABCO-N crashes on rural two-lane highway segments. ................. 210

Figure 55. Graph. ANG-KAB-D crashes on rural two-lane highway segments. ......................... 211

Figure 56. Graph. ANG-KABCO-D crashes on rural two-lane highway segments. ................... 212

Figure 57. Graph. ANG-KAB-D crashes at four-leg stop-controlled intersections on rural 

two-lane roads. .................................................................................................................... 213
x 

Figure 58. Graph. ANG-KAB-N crashes at four-leg stop-controlled intersections on rural 

two-lane roads. .................................................................................................................... 214

Figure 59. Graph. ANG-KABCO-D crashes at four-leg stop-controlled intersections on 

rural two-lane roads. ........................................................................................................... 215

Figure 60. Graph. ANG-KABCO-N crashes at four-leg stop-controlled intersections on 

rural two-lane roads. ........................................................................................................... 216

Figure 61. Graph. ANG-KAB-D crashes at four-leg stop-controlled intersections on urban 

two-lane roads. .................................................................................................................... 217

Figure 62. Graph. ANG-KABCO-D crashes at four-leg stop-controlled intersections on 

urban two-lane roads........................................................................................................... 218

Figure 63. Graph. ANG-KAB-D crashes at four-leg signalized intersections on urban 

multilane divided roads....................................................................................................... 219

Figure 64. Graph. ANG-KABCO-D crashes at four-leg signalized intersections on urban 

multilane divided roads....................................................................................................... 220

Figure 65. Graph. ANG-KAB-D crashes at four-leg signalized intersections on urban 

multilane undivided roads................................................................................................... 221

Figure 66. Graph. ANG-KABCO-D crashes at four-leg signalized intersections on urban 

multilane undivided roads................................................................................................... 222

Figure 67. Graph. ANG-KAB-D crashes at three-leg stop-controlled intersections on rural 

two-lane roads. .................................................................................................................... 223

Figure 68. Graph. ANG-KABCO-D crashes at three-leg stop-controlled intersections on 

rural two-lane roads. ........................................................................................................... 224

Figure 69. Graph. ANG-KAB-D crashes at four-leg stop-controlled intersections on rural 

multilane divided roads....................................................................................................... 225

Figure 70. Graph. ANG-KABCO-D crashes at four-leg stop-controlled intersections on 

rural multilane divided roads. ............................................................................................. 226

Figure 71. Graph. Predicted ANG crash frequency versus cross street AADT. .......................... 227

Figure 72. Graph. Predicted ANG crash frequency versus mainline AADT. .............................. 228

Figure 73. Graph. Predicted ANG crash frequency versus lane width. ....................................... 228

Figure 74. Graph. Predicted ANG crash frequency versus average annual maximum 

temperature. ........................................................................................................................ 229

Figure 75. Graph. Predicted ANG crash frequency versus proportion of people (ages 25+) 

without a high school diploma. ........................................................................................... 229

Figure 76. Graph. Predicted ANG crash frequency versus average annual rainfall. ................... 230

Figure 77. Graph. Predicted ANG crash frequency versus proportion of households with 

income >$100,000. ............................................................................................................. 230

Figure 78. Graph. Predicted ANG crash frequency versus average annual number of days 

with a minimum temperature of ≤32°F. .............................................................................. 231

Figure 79. Graph. Predicted ANG crash frequency versus proportion of population 

ages 45–64. ......................................................................................................................... 231

Figure 80. Graph. Predicted ROR crash frequency per mile versus curve radius. ....................... 232

Figure 81. Graph. Predicted ROR crash frequency per mile versus AADT. ............................... 232

Figure 82. Graph. Predicted ROR crash frequency per mile versus percentage of trucks on 

the roadway. ........................................................................................................................ 233

Figure 83. Graph. Predicted ROR crash frequency per mile versus percent grade. ..................... 233

Figure 84. Graph. Predicted ROR crash frequency per mile versus average shoulder width. ..... 234
xi 

Figure 85. Graph. Predicted ROR crash frequency per mile versus proportion of population 

ages 45–64. ......................................................................................................................... 234

Figure 86. Graph. Predicted ROR crash frequency per mile versus average annual 

minimum temperature. ........................................................................................................ 235

Figure 87. Graph. Predicted ROR crash frequency per mile versus average annual number 

of days with a minimum temperature of ≤32°F. ................................................................. 235

Figure 88. Graph. Predicted ROR crash frequency per mile versus proportion of people 

(ages 25+) without a high school diploma. ......................................................................... 236

Figure 89. Graph. Predicted ROR crash frequency per mile versus average annual rainfall. ...... 236

Figure 90. Graph. Output for ROR-KAB-D crashes on rural two-lane horizontal curves and 

highway tangent segments: Ohio. ....................................................................................... 240

Figure 91. Graph. Output for ROR-KABCO-D crashes on rural two-lane horizontal curves 

and highway tangent segments: Ohio. ................................................................................ 241

Figure 92. Graph. Output for ROR-KAB-N crashes on rural two-lane horizontal curves and 

highway tangent segments: Ohio. ....................................................................................... 242

Figure 93. Graph. Output for ROR-KABCO-N crashes on rural two-lane horizontal curves 

and highway tangent segments: Ohio. ................................................................................ 243

Figure 94. Graph. Output for LNDP-KAB-D crashes on rural two-lane horizontal curves 

and highway tangent segments: Ohio. ................................................................................ 245

Figure 95. Graph. Output for LNDP-KABCO-D crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio...................................................................... 246

Figure 96. Graph. Output for LNDP-KAB-N crashes on rural two-lane horizontal curves 

and highway tangent segments: Ohio. ................................................................................ 247

Figure 97. Graph. Output for LNDP-KABCO-N crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio...................................................................... 248

Figure 98. Graph. Output for HEO-KAB-D crashes on rural two-lane horizontal curves and 

highway tangent segments: Ohio. ....................................................................................... 250

Figure 99. Graph. Output for HEO-KABCO-D crashes on rural two-lane horizontal curves 

and highway tangent segments: Ohio. ................................................................................ 251

Figure 100. Graph. Output for HEO-KAB-N crashes on rural two-lane horizontal curves 

and highway tangent segments: Ohio. ................................................................................ 252

Figure 101. Graph. Output for HEO-KABCO-N crashes on rural two-lane horizontal curves 

and highway tangent segments: Ohio. ................................................................................ 253

Figure 102. Graph. Output for ROLL-KAB-D crashes on rural two-lane horizontal curves 

and highway tangent segments: Ohio. ................................................................................ 255

Figure 103. Graph. Output for ROLL-KABCO-D crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio...................................................................... 256

Figure 104. Graph. Output for ROLL-KAB-N crashes on rural two-lane horizontal curves 

and highway tangent segments: Ohio. ................................................................................ 257

Figure 105. Graph. Output for ROLL-KABCO-N crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio...................................................................... 258

Figure 106. Graph. Output for ANG-KAB-D crashes on rural two-lane horizontal curves 

and highway tangent segments: Ohio. ................................................................................ 261

Figure 107. Graph. Output for ANG-KABCO-D crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio...................................................................... 262

Figure 108. Graph. Output for ANG-KAB-D crashes at four-leg stop-controlled 

intersections on urban two-lane roads: Ohio. ..................................................................... 264
xii 

Figure 109. Graph. Output for ANG-KABCO-D crashes at four-leg stop-controlled 

intersections on urban two-lane roads: Ohio. ..................................................................... 265

Figure 110. Graph. Output for ANG-KAB-D crashes at four-leg signalized intersections on 

urban multilane undivided roads: Ohio. ............................................................................. 267

Figure 111. Graph. Output for ANG-KABCO-D crashes at four-leg signalized intersections 

on urban multilane undivided roads: Ohio. ........................................................................ 268

Figure 112. Graph. Output for ROR-KAB-D crashes on rural two-lane horizontal curves 

and tangent segments: Ohio. ............................................................................................... 272

Figure 113. Graph. Output for ROR-KABCO-D crashes on rural two-lane horizontal curves 

and tangent segments: Ohio. ............................................................................................... 272

Figure 114. Graph. Output for LNDP-KAB-D crashes on rural two-lane horizontal curves 

and tangent segments: Ohio. ............................................................................................... 273

Figure 115. Graph. Output for LNDP-KABCO-D crashes on rural two-lane horizontal 

curves and tangent segments: Ohio. ................................................................................... 273

Figure 116. Graph. Output for ROLL-KAB-D crashes on rural two-lane horizontal curves 

and tangent segments: Ohio. ............................................................................................... 274

Figure 117. Graph. Output for ROLL-KABCO-D crashes on rural two-lane horizontal 

curves and tangent segments: Ohio. ................................................................................... 274

Figure 118. Graph. Output for HEO-KAB-D crashes on rural two-lane horizontal curves 

and tangent segments: Ohio. ............................................................................................... 275

Figure 119. Graph. Output for HEO-KABCO-D crashes on rural two-lane horizontal curves 

and tangent segments: Ohio. ............................................................................................... 275

Figure 120. Graph. Output for ANG-KAB-D crashes on rural two-lane horizontal curves 

and tangent segments: Ohio. ............................................................................................... 276

Figure 121. Graph. Output for ANG-KABCO-D crashes on rural two-lane horizontal 

curves and tangent segments: Ohio. ................................................................................... 276
xiii

LIST OF TABLES

Table 1. Example publications addressing subsets of total crashes. .............................................. 11

Table 2. Example publications addressing subsets of single-vehicle crashes. ............................... 12

Table 3. Example publications addressing subsets of multiple-vehicle crashes. ........................... 13

Table 4. Prevalence of roadway factors by facility type for segments. ......................................... 16

Table 5. Prevalence of countermeasures by facility type for segments. ........................................ 18

Table 6. Prevalence of roadway factors by facility type for intersections. .................................... 19

Table 7. Prevalence of countermeasures by facility type for intersections. ................................... 20

Table 8. Summary of selected literature on systemic safety analysis. ........................................... 28

Table 9. Systemic approach summary. ........................................................................................... 35

Table 10. Summary statistics for intersections (all types) in California: roadway 

inventory data. ...................................................................................................................... 57

Table 11. Summary statistics for intersections (all types) in California: climate data. .................. 57

Table 12. Summary statistics for intersections (all types) in California: census data. ................... 58

Table 13. Summary statistics for curved segments in Washington: roadway inventory data. ....... 59

Table 14. Summary statistics for curved segments in Washington: climate data. ......................... 59

Table 15. Summary statistics for curved segments in Washington: census data. .......................... 60

Table 16. Summary statistics for tangent segments in Washington: roadway inventory data. ...... 60

Table 17. Summary statistics for tangent segments in Washington: climate data. ........................ 61

Table 18. Summary statistics for tangent segments in Washington: census data. ......................... 61

Table 19. Summary statistics for curved segments in Ohio: roadway inventory data. .................. 62

Table 20. Summary statistics for curved segments in Ohio: climate data. .................................... 62

Table 21. Summary statistics for curved segments in Ohio: census data. ..................................... 63

Table 22. Summary statistics for tangent segments in Ohio: roadway inventory data. ................. 63

Table 23. Summary statistics for segments in Ohio: climate data. ................................................ 63

Table 24. Summary statistics for segments in Ohio: census data. ................................................. 64

Table 25. Summary statistics for intersection (all types) in Ohio: roadway inventory data. ......... 64

Table 26. Summary statistics for intersection (all types) in Ohio: climate data. ........................... 65

Table 27. Summary statistics for intersections (all types) in Ohio: Census data. .......................... 65

Table 28. Contributing factors for ROR-KAB-D crashes on rural two-lane horizontal 

curves and tangent segments: Ohio. ..................................................................................... 70

Table 29. Contributing factors for ROR-KABCO-D crashes on rural two-lane horizontal 

curves and tangent segments: Ohio. ..................................................................................... 70

Table 30. Contributing factors for ROR-KAB-N crashes on rural two-lane horizontal 

curves and tangent segments: Ohio. ..................................................................................... 71

Table 31. Contributing factors for ROR-KABCO-N crashes on rural two-lane horizontal 

curves and tangent segments: Ohio. ..................................................................................... 71

Table 32. Contributing factors for ROR-KAB-D crashes on rural two-lane highway tangent 

segments: Washington. ......................................................................................................... 72

Table 33. Contributing factors for ROR-KAB-N crashes on rural two-lane highway tangent 

segments: Washington. ......................................................................................................... 73

Table 34. Contributing factors for ROR-KAB-D crashes on rural two-lane horizontal 

curves: Washington. ............................................................................................................. 73

Table 35. Contributing factors for ROR-KAB-N crashes on rural two-lane horizontal 

curves: Washington. ............................................................................................................. 74
xiv 

Table 36. Contributing factors for LNDP-KAB-D crashes on rural two-lane horizontal 

curves and tangent segments: Ohio. ..................................................................................... 76

Table 37. Contributing factors for LNDP-KABCO-D crashes on rural two-lane horizontal 

curves and tangent segments: Ohio. ..................................................................................... 76

Table 38. Contributing factors for LNDP-KAB-N crashes on rural two-lane horizontal 

curves and tangent segments: Ohio. ..................................................................................... 77

Table 39. Contributing factors for LNDP-KABCO-N crashes on rural two-lane horizontal 

curves and tangent segments: Ohio. ..................................................................................... 77

Table 40. Contributing factors for LNDP-KAB-D crashes on rural two-lane highway 

tangent segments: Washington. ............................................................................................ 78

Table 41. Contributing factors for LNDP-KAB-N crashes on rural two-lane highway 

tangent segments: Washington. ............................................................................................ 78

Table 42. Contributing factors for LNDP-KAB-D crashes on rural two-lane horizontal 

curves: Washington. ............................................................................................................. 79

Table 43. Contributing factors for LNDP-KAB-N crashes on rural two-lane horizontal 

curves: Washington. ............................................................................................................. 79

Table 44. Contributing factors for HEO-KAB-D crashes on rural two-lane horizontal 

curves and tangent segments: Ohio. ..................................................................................... 81

Table 45. Contributing factors for HEO-KABCO-D crashes on rural two-lane horizontal 

curves and tangent segments: Ohio. ..................................................................................... 82

Table 46. Contributing factors for HEO-KAB-N crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio........................................................................ 82

Table 47. Contributing factors for HEO-KABCO-N crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio........................................................................ 83

Table 44. Contributing factors for HEO-KAB-D crashes on rural two-lane highway tangent 

segments: Washington. ......................................................................................................... 83

Table 49. Contributing factors for HEO-KAB-N crashes on rural two-lane highway tangent 

segments: Washington. ......................................................................................................... 84

Table 50. Contributing factors for HEO-KAB-D crashes on rural two-lane horizontal 

curves: Washington. ............................................................................................................. 84

Table 51. Contributing factors for HEO-KAB-N crashes on rural two-lane horizontal 

curves: Washington. ............................................................................................................. 85

Table 52. Contributing factors for ROLL-KAB-D crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio........................................................................ 87

Table 53. Contributing factors for ROLL-KABCO-D crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio........................................................................ 87

Table 54. Contributing factors for ROLL-KAB-N crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio........................................................................ 88

Table 55. Contributing factors for ROLL-KABCO-N crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio........................................................................ 88

Table 56. Contributing factors for ROLL-KAB-D crashes on rural two-lane highway 

tangent segments: Washington. ............................................................................................ 89

Table 57. Contributing factors for ROLL-KAB-N crashes on rural two-lane highway 

tangent segments: Washington. ............................................................................................ 89

Table 58. Contributing factors for ROLL-KAB-D crashes on rural two-lane horizontal 

curves: Washington. ............................................................................................................. 90
xv 

Table 59. Contributing factors for ROLL-KAB-N crashes on rural two-lane horizontal 

curves: Washington. ............................................................................................................. 90

Table 60. Contributing factors for ANG-KAB-D crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio........................................................................ 92

Table 61. Contributing factors for ANG-KABCO-D crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio........................................................................ 93

Table 62. Contributing factors for ANG-KAB-D crashes on rural two-lane highway tangent 

segments: Washington. ......................................................................................................... 93

Table 63. Contributing factors for ANG-KAB-D crashes at four-leg stop-controlled 

intersections on rural two-lane roads: California.................................................................. 95

Table 64. Contributing factors for ANG-KAB-N crashes at four-leg stop-controlled 

intersections on rural two-lane roads: California.................................................................. 95

Table 65. Contributing factors for ANG-KAB-D crashes at four-leg stop-controlled 

intersections on rural two-lane roads: Ohio. ......................................................................... 96

Table 66. Contributing factors for ANG-KABCO-D crashes at four-leg stop-controlled 

intersections on rural two-lane roads: Ohio. ......................................................................... 96

Table 67. Contributing factors for ANG-KAB-N crashes at four-leg stop-controlled 

intersections on rural two-lane roads: Ohio. ......................................................................... 97

Table 68. Contributing factors for ANG-KABCO-N crashes at four-leg stop-controlled 

intersections on rural two-lane roads: Ohio. ......................................................................... 97

Table 69. Contributing factors for ANG-KAB-D crashes at four-leg stop-controlled 

intersections on urban two-lane roads: California. ............................................................... 99

Table 70. Contributing factors for ANG-KAB-D crashes at four-leg stop-controlled 

intersections on urban two-lane roads: Ohio. ..................................................................... 100

Table 71. Contributing factors for ANG-KABCO-D crashes at four-leg stop-controlled 

intersections on urban two-lane roads: Ohio. ..................................................................... 100

Table 72. Contributing factors for ANG-KAB-D crashes at four-leg signalized intersections 

on urban multilane divided roads: California. .................................................................... 102

Table 73. Contributing factors for ANG-KAB-D crashes at four-leg signalized intersections 

on urban multilane divided roads: Ohio. ............................................................................ 102

Table 74. Contributing factors for ANG-KABCO-D crashes at four-leg signalized 

intersections on urban multilane divided roads: Ohio. ....................................................... 103

Table 75. Contributing factors for ANG-KAB-D crashes at four-leg signalized intersections 

on urban multilane undivided roads: California. ................................................................ 104

Table 76. Contributing factors for ANG-KAB-D crashes at four-leg signalized intersections 

on urban multilane undivided roads: Ohio. ........................................................................ 105

Table 77. Contributing factors for ANG-KABCO-D crashes at four-leg signalized 

intersections on urban multilane undivided roads: Ohio. ................................................... 105

Table 78. Contributing factors for ANG-KAB-D crashes at three-leg stop-controlled 

intersections on rural two-lane roads: California................................................................ 107

Table 79. Contributing factors for ANG-KAB-D crashes at three-leg stop-controlled 

intersections on rural two-lane roads: Ohio. ....................................................................... 107

Table 80. Contributing factors for ANG-KABCO-D crashes at three-leg signalized 

intersections on rural two-lane roads: Ohio. ....................................................................... 108

Table 81. Contributing factors for ANG-KAB-D crashes at four-leg stop-controlled 

intersections on rural multilane divided roads: California. ................................................ 109
xvi 

Table 82. Contributing factors for ANG-KAB-D crashes at four-leg stop-controlled 

intersections on rural multilane divided roads: Ohio. ......................................................... 110

Table 83. Contributing factors for ANG-KABCO-D crashes at four-leg stop-controlled 

intersections on rural multilane divided roads: Ohio. ......................................................... 110

Table 84. Contributing factors for all pedestrian crashes at intersections. .................................. 113

Table 85. Contributing factors for pedestrians crossing at intersections and struck by 

motorists going straight. ..................................................................................................... 113

Table 86. Example selection process: countermeasure summary with contributing factors. ...... 121

Table 87. Example selection process: countermeasure summary with contributing factors 

and CMF indicators. ........................................................................................................... 122

Table 88. Countermeasure summary for example 1: HEO crashes. ............................................ 124

Table 89. Countermeasure summary for example 2: ANG crashes. ............................................ 125

Table 90. Systemic countermeasures for intersection contributing factors. ................................ 128

Table 91. Systemic countermeasures for nonintersection contributing factors. .......................... 128

Table 92. Most influential factors found to influence expected crash frequencies. ..................... 135

Table 93. Potential nonintersection FCFTs from FARS. ............................................................. 157

Table 94. Potential intersection FCFTs from FARS. ................................................................... 159

Table 95. Potential nonintersection FCFTs from HSIS: Minnesota (by K crashes). ................... 159

Table 96. Potential nonintersection FCFTs from HSIS: Minnesota (by KA crashes). ................ 160

Table 97. Potential intersection FCFTs from HSIS: Minnesota (by K crashes). ......................... 160

Table 98. Potential intersection FCFTs from HSIS: Minnesota (by KA crashes). ...................... 160

Table 99. Potential nonintersection FCFTs from HSIS: Ohio (by K crashes). ............................ 161

Table 100. Potential nonintersection FCFTs from HSIS: Ohio (by KA crashes). ....................... 162

Table 101. Potential intersection FCFTs from HSIS: Ohio (by K crashes). ................................ 163

Table 102. Potential intersection FCFTs from HSIS: Ohio (by KA crashes). ............................. 164

Table 103. Potential nonintersection FCFTs from HSIS: California (by K crashes). .................. 165

Table 104. Potential nonintersection FCFTs from HSIS: California (by KA crashes). ............... 165

Table 105. Potential intersection FCFTs from HSIS: California (by K crashes). ........................ 166

Table 106. Potential intersection FCFTs from HSIS: California (by KA crashes). ..................... 166

Table 107. Potential nonintersection FCFTs from HSIS: Washington (by K crashes). ............... 166

Table 108. Potential nonintersection FCFTs from HSIS: Washington (by KA crashes). ............ 167

Table 109. Potential intersection FCFTs from HSIS: Washington (by K crashes). ..................... 167

Table 110. Potential intersection FCFTs from HSIS: Washington (by KA crashes). .................. 168

Table 111. Factor analysis for curves and tangent segments. ...................................................... 238

Table 112. Factor analysis for four-leg stop-controlled intersections. ......................................... 238

Table 113. Factor analysis for four-leg signalized intersections. ................................................. 239

Table 114. Contributing factors for ROR-KAB-D crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio...................................................................... 240

Table 115. Contributing factors for ROR-KABCO-D crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio...................................................................... 241

Table 116. Contributing factors for ROR-KAB-N crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio...................................................................... 242

Table 117. Contributing factors for ROR-KABCO-N crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio...................................................................... 243

Table 118. Contributing factors for LNDP-KAB-D crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio...................................................................... 245
xvii 

Table 119. Contributing factors for LNDP-KABCO-D crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio...................................................................... 246

Table 120. Contributing factors for LNDP-KAB-N crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio...................................................................... 247

Table 121. Contributing factors for LNDP-KABCO-N crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio...................................................................... 248

Table 122. Contributing factors for HEO-KAB-D crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio...................................................................... 250

Table 123. Contributing factors for HEO-KABCO-D crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio...................................................................... 251

Table 124. Contributing factors for HEO-KAB-N crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio...................................................................... 252

Table 125. Contributing factors for HEO-KABCO-N crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio...................................................................... 253

Table 126. Contributing factors for ROLL-KAB-D crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio...................................................................... 255

Table 127. Contributing factors for ROLL-KABCO-D crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio...................................................................... 256

Table 128. Contributing factors for ROLL-KAB-N crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio...................................................................... 257

Table 129. Contributing factors for ROLL-KABCO-N crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio...................................................................... 258

Table 130. Contributing factors for ANG-KAB-D crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio...................................................................... 260

Table 131. Contributing factors for ANG-KABCO-D crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio...................................................................... 261

Table 132. Contributing factors for ANG-KAB-D crashes at four-leg stop-controlled 

intersections on urban two-lane roads: Ohio. ..................................................................... 263

Table 133. Contributing factors for ANG-KABCO-D crashes at four-leg stop-controlled 

intersections on urban two-lane roads: Ohio. ..................................................................... 264

Table 134. Contributing factors for ANG-KAB-D crashes at four-leg signalized 

intersections on urban multilane undivided roads: Ohio. ................................................... 267

Table 135. Contributing factors for ANG-KABCO-D crashes at four-leg signalized 

intersections on urban multilane undivided roads: Ohio. ................................................... 268

Table 136. Comparison of MSEs for analyses using data with and without socioeconomic 

variables. ............................................................................................................................. 271
xviii 

LIST OF ACRONYMS AND ABBREVIATIONS

A incapacitating injury

AADP annual average daily pedestrian

AADT annual average daily traffic

AASHTO American Association of State Highway and Transportation Officials 

ADT average daily traffic

ANG angle 

ANG-D angle daytime 

ANG-KABCO-D angle fatality, incapacitating injury, nonincapacitating injury, possible 

injury, or property-damage-only daytime

ANG-KABCO-N angle fatality, incapacitating injury, nonincapacitating injury, possible 

injury, or property-damage-only nighttime

ANG-KAB-D angle fatality, incapacitating injury, or nonincapacitating injury daytime 

ANG-KAB-N angle fatality, incapacitating injury, or nonincapacitating injury nighttime 

ANG-N angle nighttime crashes

B nonincapacitating injury 

B/C benefit–cost

C possible injury 

CART classification and regression trees

CMF crash modification factor

CRSP Country Road Safety Plan 

DCMF Development of Crash Modification Factors 

DOT Department of Transportation 

FARS Fatality Analysis Reporting System 

FCFT focus crash and facility type

FHWA Federal Highway Administration

GLM generalized linear model

HEO head on 

HEO-D head-on daytime 

HEO-KABCO-D head-on fatality, incapacitating injury, nonincapacitating injury, possible 

injury, or property-damage-only daytime

HEO-KABCO-N head on fatality, incapacitating injury, nonincapacitating injury, possible 

injury, or property-damage-only nighttime

HEO-KAB-D head on fatality, incapacitating injury, or nonincapacitating injury daytime 

HEO-KAB-N head on fatality, incapacitating injury, or nonincapacitating injury 

nighttime

HEO-N head-on nighttime 

HSIS Highway Safety Information System 

HSM Highway Safety Manual

K fatal injury 

LNDP lane departure 

LNDP-D lane departure daytime

LNDP-KABCO-D lane-departure fatality, incapacitating injury, nonincapacitating injury, 

possible injury, or property-damage-only daytime
xix 

LNDP-KABCO-N lane-departure fatality, incapacitating injury, nonincapacitating injury, 

possible injury, or property-damage-only nighttime

LNDP-KAB-D lane-departure fatality, incapacitating injury, or nonincapacitating injury 

daytime

LNDP-KAB-N lane-departure fatality, incapacitating injury, or nonincapacitating injury 

nighttime

LNDP-N lane departure nighttime 

MnDOT Minnesota Department of Transportation 

MSE mean squared error

NB negative binomial 

NCHRP National Cooperative Highway Research Program 

NOAA National Oceanic and Atmospheric Administration 

O no injury 

ODOT Ohio Department of Transportation 

PDO property damage only 

ROLL rollover/overturn

ROLL-D rollover/overturn daytime 

ROLL-KABCO-D rollover/overturn fatality, incapacitating injury, nonincapacitating injury, 

possible injury, or property-damage-only daytime

ROLL-KABCO-N rollover/overturn fatality, incapacitating injury, nonincapacitating injury, 

possible injury, or property-damage-only nighttime

ROLL-KAB-D rollover/overturn fatality, incapacitating injury, or nonincapacitating injury 

daytime

ROLL-KAB-N rollover/overturn fatality, incapacitating injury, or nonincapacitating injury 

nighttime

ROLL-N rollover/overturn nighttime 

ROR run off road

ROR-D run off road daytime 

ROR-KABCO-D run-off-road fatality, incapacitating injury, nonincapacitating injury, 

possible injury, or property-damage-only daytime

ROR-KABCO-N run-off-road fatality, incapacitating injury, nonincapacitating injury, 

possible injury, or property-damage-only nighttime

ROR-KAB-D run-off-road fatality, incapacitating injury, or nonincapacitating injury 

daytime

ROR-KAB-N run-off-road fatality, incapacitating injury, or nonincapacitating injury 

nighttime

ROR-N run off road nighttime 

RRSI rural road safety index 

SAS statistical analysis software

SHRP2 second Strategic Highway Research Program

SHSP strategic highway safety plan 

TTE time-to-event

usRAP United States Road Assessment Program

1 

EXECUTIVE SUMMARY

This report describes the efforts to identify focus crash and facility types (FCFTs) and associated 

crash-contributing factors (herein referred to as “contributing factors”) to better inform 

applications of systemic safety improvements. Systemic safety improvements—when selected 

and targeted appropriately—are a tremendous opportunity to proactively reduce crashes and their 

resulting harm. These improvements are particularly useful for crash types that are prevalent but 

somewhat disperse in their occurrence, or in other words, high numbers of certain crash types 

scattered across the road system at low densities of resultant fatalities. An FHWA publication, 

Using Risk to Drive Safety Investments, notes that “fatal and other life-threatening crashes often 

are distributed widely across State and local highway systems, in both urban and rural 

environments, with few individual locations experiencing a high number or sustained occurrence 

of severe crashes” (Preston et al. 2013b). These types of prevalent but scattered systemic safety 

issues do not lend themselves to the site-specific detection and diagnosis that characterize a more 

traditional approach to road-safety management.

Within the context of systemic safety analysis and management, the main objectives of this 

project were to select reliable and applicable data resources, statistical methodologies, analysis 

procedures, and tools; conduct data analysis to identify and validate FCFTs and associated 

contributing factors; and identify potential low-cost safety strategies that may effectively be used 

as systemic safety improvements.

FCFTS

The research team analyzed the Fatality Analysis Reporting System (FARS) and the Highway 

Safety Information System (HSIS) databases to select FCFTs (NHTSA 2018a; FHWA 2018c). 

The FARS database contains information on all fatal crashes involving a motor vehicle traveling 

on a public trafficway in all 50 States, the District of Columbia, and Puerto Rico. The HSIS 

database contains information on crashes of all severities (i.e., fatal crashes, injury crashes, and 

property-damage-only crashes) occurring on State-operated and -maintained roads for the 

participating States. The analysis incorporated data from four States that are part of HSIS: 

California, Minnesota, Ohio, and Washington State. The research team defined potential FCFTs 

by using combinations of variables in FARS and HSIS. To rank the list of FCFTs, the research 

team used the number of fatal crashes and, for the HSIS States, the number of fatal-plusincapacitating-injury crashes during the observation period that corresponded to each potential 

FCFT. Based on the rankings, the research team selected 15 FCFTs for the first contributingfactors analysis:

• Intersection FCFTs:

1. Angle (ANG) crashes on rural two-lane roads at four-leg minor-road stop-controlled 

intersections (daytime and nighttime).

2. ANG crashes on urban two-lane roads at four-leg minor-road stop-controlled 

intersections (daytime).

3. ANG crashes on rural two-lane roads at three-leg minor-road stop-controlled 

intersections (daytime).
2 

4. ANG crashes on urban multilane divided roads at four-leg signalized intersections 

(daytime).

5. ANG crashes on urban multilane undivided roads at four-leg signalized intersections 

(daytime).

6. ANG crashes on rural multilane divided roads at four-leg minor-road stop-controlled 

intersections (daytime).

• Nonintersection FCFTs: 

1. Run-off-road (ROR) crashes on rural two-lane roads on horizontal curves (daytime 

and nighttime). 

2. ROR crashes on rural two-lane roads on tangent segments (daytime and nighttime). 

3. Lane-departure (LNDP) crashes on rural two-lane roads on horizontal curves (daytime

and nighttime).

4. LNDP crashes on rural two-lane roads on tangent segments (daytime and nighttime).

5. Head-on (HEO) crashes on rural two-lane roads on horizontal curves (daytime and 

nighttime).

6. HEO crashes on rural two-lane roads on tangent segments (daytime and nighttime).

7. ANG crashes on rural two-lane roads on tangent segments (daytime).

8. Rollover/overturn (ROLL) crashes on rural two-lane roads on horizontal curves 

(daytime and nighttime).

9. ROLL crashes on rural two-lane roads on tangent segments (daytime and nighttime).

Pedestrian crashes represented a significant number of fatal crashes; however, identifying 

contributing factors for pedestrian crashes is a challenging task without quality exposure data 

(i.e., the total number of pedestrians crossing at segments and intersections or walking parallel to 

segments). Given the significant number of pedestrian crashes, this report incorporates the results 

of Thomas et al. (2017) to identify contributing factors for two types of pedestrian crashes: 

• All types of pedestrian crashes at intersections.

• Pedestrian crashes at intersections involving a crossing pedestrian and a vehicle going 

straight.

ANALYSIS OF CONTRIBUTING FACTORS

The research team analyzed contributing factors using data from three different sources: crash 

and roadway inventory from HSIS, climate data from the National Oceanic and Atmospheric 

Administration, and socioeconomic census data from the U.S. Census Bureau (FHWA 2018c; 

NOAA 2018; U.S. Census Bureau 2018). The research team completed all linkages of road 

segments to climate and census data in the spatial environment. To simplify the linking process, 

roadway segments were represented as point features according to the midpoint of the segment. 

For climate data, the source data were in a point file with each weather station shown as a point 

on the map. The research team linked each roadway segment to the closest weather station by a 

simple straight-line distance measurement. For census data, the source data were in a polygon file 

with each census-block group shown as a shape on the map. Each roadway segment was linked to 

the census-block group that contained the midpoint of the segment. 
3 

The research team used the random-forest method to identify contributing factors that 

corresponded to the FCFTs. Random forests help identify predictors that may not appear in the 

output of a single classification or regression tree but are nevertheless highly related to the target 

variable. Researchers commonly use random forests to display the percentage increase in mean 

squared error with the removal of a variable from the random-forest model. Random forests do 

not directly indicate if variables correspond to contributing factors that increase or decrease 

predicted crash frequencies. However, plots of random forest–predicted crash frequencies as a 

function of the variables of interest provided the information needed to identify contributing 

factors, estimate the direction of the relationship, and inform countermeasure identification to 

mitigate the presence of factors that increase predicted crash frequencies. The research team used 

data from California and Ohio to analyze contributing factors for intersection FCFTs and data 

from Ohio and Washington to analyze contributing factors for nonintersection (i.e., segment) 

FCFTs. 

Roadway factors uncovered by the analyses as influencing the frequencies of the different crash 

types were generally consistent with what was expected based on previous research and existing 

practice. Factors associated with increasing crash frequencies include the following: 

• Larger average daily traffic volumes.

• Steeper vertical grades.

• Sharper horizontal curve radii.

• Narrower lane and shoulder widths. 

• Unpaved shoulders or no shoulders. 

• Mountainous terrains. 

• Higher speed limits.

• Wider crossing distances at intersections (captured by lane and median widths on 

approaches).

• Absence of left- and right-turn channelization at intersections. 

Agencies with sufficient data and analysis capabilities can refer to the FHWA Systemic Safety 

Project Selection Tool for discussion of how to analyze data to identify contributing factors given 

a specific FCFT (Preston et al. 2013a). Agencies without sufficient data and analysis capabilities 

can reference the factors developed in this research to help identify countermeasures and 

prioritize sites for systemic safety improvements.

Findings connected to socioeconomic- and weather-related factors showed promise, but there 

is not yet a significant amount of theory to support or refute the socioeconomic- and 

weather-related results of this effort. Findings related to socioeconomic variables are likely 

representing differences in travel behavior, driving behavior, and driving capabilities that seem 

key for safety analyses but are generally not incorporated into segment- and intersection-specific 

analyses that also include traffic and roadway factors. Weather-related findings are likely 

representing differences in visibility, road conditions, and driver experience and behavior. Both 

sets of factors bring significant potential to the process of making more informed decisions about 

sites that have higher levels of crash potential. (Appendix H includes an example analysis of 

model performance with and without consideration of socioeconomic variables.) A multiyear 

study focused on testing various alternatives and developing safety-analysis guidance on 

collecting, merging, and analyzing crash, traffic, roadway, census, and weather data is needed. 
4 

The most challenging aspect of implementing the random-forest method within the context of 

this project was the limited ability to interpret the direction and form of the relationship between 

a factor of interest and expected crash frequency. This research implemented a relatively simple 

approach, searching for primarily linear trends between each factor and the corresponding 

random forest–predicted crash frequency. Future efforts should explore more effective ways to 

uncover and interpret the relationships contained in the random forests. 

While the research team was able to compare the random-forests results to the findings of 

previous research on related crash types, it was not able to incorporate prior information or 

knowledge into the analyses. Bayesian approaches hold significant promise, and future efforts 

should explore these approaches to identify contributing factors for FCFTs. Causal Bayesian 

networks, such as those applied by Karwa et al. (2011), have significant potential but will require 

a considerable amount of time to fully explore within the context of this project. 

COUNTERMEASURE-SELECTION PROCESS

Following the selection of FCFTs and analysis of contributing factors, this report presents a 

six-step countermeasure-selection process, as follows: 

1. Identify a focus crash type.

2. Identify contributing factors for the focus crash type.

3. Assemble a list of potential countermeasures that address the focus crash type.

4. Identify countermeasures that address roadway factors associated with the focus crash 

type. 

5. Identify countermeasures with crash modification factors (CMFs). 

6. Select a countermeasure.

To assist practitioners with implementing this process, this report also provides various examples 

demonstrating the process for selecting countermeasures to address focus crash types. 

Additionally, this report provides proven safety countermeasures to mitigate the presence of 

common contributing factors. In developing practitioner guidance, the research team observed 

that most CMFs have not been developed for crash types at the level of disaggregation that may 

be needed for systemic safety applications. Similarly, expected changes in crash frequencies 

reflected by CMFs typically represent evaluation results of site-specific applications of a 

treatment. Additional work on estimating network-wide safety impacts resulting from systemic 

applications of countermeasures is needed. 
5 

CHAPTER 1. INTRODUCTION

BACKGROUND

The Federal Highway Administration (FHWA) established the Development of Crash 

Modification Factors (DCMF) program in 2012 to address highway safety research needs for 

evaluating new and innovative safety-improvement strategies and developing reliable quantitative 

estimates of their effectiveness in reducing crash frequency and severity. The ultimate goal of the 

DCMF program is to save lives by identifying new strategies that effectively reduce crash 

frequency and severity and promoting those strategies for nationwide implementation by 

providing measures of their safety effectiveness and benefit–cost (B/C) ratios gleaned through 

research. State transportation departments and other transportation agencies should have 

objective measures of safety effectiveness before investing in broad applications of safety 

countermeasures. Forty State transportation departments provide technical input to the DCMF 

program and implement new safety improvements to facilitate evaluations. These States are 

members of the Evaluation of Low-Cost Safety Improvements Pooled Fund Study, which 

functions under the DCMF program. 

Another goal of the DCMF program is to advance highway safety and related research by 

establishing a sound foundation for developing highway transportation-specific statistical 

methodologies in cooperation with the American Statistical Association and other statistician 

communities. Several efforts have been conducted or are underway in pursuit of that goal. One 

such effort, Highway Safety Statistical Paper Synthesis, included a review and critical synthesis 

of recent papers that explored refinements to current research methods (including study design 

and statistical analysis) and proposed new methods to assess the safety performance of highways 

and streets (Persaud et al. 2001). This effort included methods to predict expected crash 

frequencies and severities, assess underreporting in crash frequency models, use nontraditional 

datasets to analyze safety, and estimate crash modification factors (CMFs) using alternative 

approaches to study design and analysis. The intention of the critical synthesis was to serve as a 

resource to researchers and others looking to advance the science of highway safety. 

As part of the Highway Safety Statistical Paper Synthesis, researchers demonstrated classification 

and regression trees (CART) and random forests within the context of conducting statistical 

road-safety analyses. They concluded that tree-based models hold strong potential for road safety 

analyses. These models are particularly effective in making predictions of expected crash 

frequency, which has applications in multiple contexts (e.g., network screening, alternatives 

assessment, predicting “what would have been” in before–after studies). Tree-based methods also 

have the potential to inform specifications that are part of more traditional modeling approaches 

through identifying the “most predictive” right-hand-side variables and uncovering informative 

relationships between left-hand-side and right-hand-side variables.

The DCMF effort applied CART and random forests to make predictions of expected crash 

frequency as a function of traffic, geometric design, and operational features along directional 

freeway segments that have a right-hand-side entrance ramp followed by a right-hand-side exit 

ramp. This current project situates the exploration of potential applications of tree-based methods 

within the context of quantitative approaches to systemic safety analysis. 
6 

SYSTEMIC APPROACH TO ROAD-SAFETY MANAGEMENT

Common approaches to road-safety management focus on identifying and diagnosing high-crash 

locations and implementing projects to address the predominant safety concerns at those 

locations. Such approaches to road-safety management generally consist of six steps:

1. Network screening. 

2. Site diagnoses. 

3. Countermeasure selections. 

4. Economic appraisals. 

5. Project prioritization. 

6. Safety-effectiveness evaluations.

Researchers use network screening to identify “sites with promise” (i.e., sites that show potential 

for safety improvements), usually characterized as sites with substantially higher than expected 

crash frequencies given traffic volumes and other site characteristics. Network screening methods 

use “total” crashes (i.e., all crash types and severities) or crashes of a specific type and/or severity 

to inform site ranking. Once researchers select specific sites for investigation, the subsequent 

steps occur at the individual site level. Safety practitioners attempt to diagnose underlying 

crash-contributing factors (herein referred to as “contributing factors”) occurring at a specific 

roadway segment or intersection and develop targeted countermeasures. Researchers estimate 

economic and safety benefits to ensure a project is cost effective and to prioritize investments. 

Prioritization can occur at two levels: the project level to prioritize proposed countermeasures 

within a project and the program level to determine an optimal combination of projects within a 

fixed budget to maximize the benefits of the entire program of projects. The final step is to 

evaluate the impacts of completed projects, including safety, for individual projects and for 

overall program effectiveness.

Significant progress has been made in the development of methodologies (e.g., empirical Bayes 

estimation) and tools (e.g., safety-performance functions, CMFs) to support data-driven, 

quantitative approaches to the safety-management process. The current states of related 

knowledge, tools, and practices are captured in the Highway Safety Manual (HSM), 

AASHTOWare® Safety AnalystTM, CMF Clearinghouse, Interactive Highway Safety Design 

Model, and a number of other tools and resources listed in the Roadway Safety Data and Analysis 

Toolbox (AASHTO 2010; AASHTO n.d.; FHWA 2018a; FHWA 2018d; FHWA 2018e). 

While identifying and treating sites with promise can result in significant safety benefits, some 

focus crash and facility types (FCFTs) cannot be fully addressed by this approach to road-safety 

management. Rather than a high number of crashes at specific locations, these FCFTs are 

characterized by higher numbers of crashes scattered across the road system at low densities. 

Examples of crash types include those involving nonmotorized-vehicle users (typically motor 

vehicles hitting pedestrians or bicycles), roadway-departure crashes on low-volume roads, and 

fatal and incapacitating-injury crashes (i.e., KA crashes) on almost any facility type. An FHWA 

publication, Using Risk to Drive Safety Investments, notes that “fatal and other life-threatening 

crashes often are distributed widely across State and local highway systems, in both urban and 

rural environments, with few individual locations experiencing a high number or sustained 

occurrence of severe crashes” (Preston et al. 2013b). These types of systemic safety issues do not 
7 

lend themselves to site-specific detection and diagnosis. Alternative approaches that identify and 

diagnose potential for safety improvements at a system level are needed.

The systemic approach to road-safety management is a method of safety management that 

typically involves widely implementing lower (unit) cost safety improvements based on 

characteristics that indicate greater potential for future crashes. While the outcome of a site-based

approach to identifying safety projects is a range of appropriate countermeasures for varying 

safety issues and crash types at individual locations, the outcome of a systemic approach is 

projects that implement one or more proven, cost-effective countermeasures across a number of 

sites to effectively manage the potential for future crashes of a particular focus crash type. This 

key distinction results in sites being treated not necessarily because of crash history but because 

of estimated crash potential, a strategy identified in FHWA’s Highway Safety Improvement 

Program for reducing fatalities and serious injuries in the United States. The systemic approach is 

also different than a systematic approach, in which practitioners implement countermeasures at 

all sites regardless of the potential for future crashes. 

Agency experiences with systemic approaches to safety management continue to increase, but a 

review of practices shows that, to date, systemic safety analysis often includes subjective 

approaches to identifying contributing factors and characterizing crash potentials. Agencies need 

additional guidance to help select and target their systemic safety improvements to make the most 

efficient investment decisions, including detailed and data-driven information on crash types that 

can be effectively addressed with systemic approaches to safety management as well as the 

situations (characterized by contributing factors and facility types) where these crashes are more 

likely to occur. 

OBJECTIVE

The goal of this project was to identify FCFTs and associated contributing factors to inform 

applications of systemic safety improvements. Within the context of conducting systemic safety 

analysis and management, the objectives of this project were as follows: 

• Select reliable and applicable data resources, statistical methodologies, analysis 

procedures, and tools. 

• Conduct data analysis to identify and validate FCFTs and associated contributing factors. 

• Identify potential low-cost safety strategies that may effectively be used as systemic 

safety improvements. 

• Develop a technical report and Quick Reference Guide for identifying focus crash types 

and contributing factors (Porter et al. 2020). 

• Develop a solicitation and evaluation criteria to identify volunteer agencies to implement 

systemic safety improvements.

The Quick Reference Guide is a stand-alone document separate from this technical report (Porter 

et al. 2020). The solicitation and evaluation-criteria documents are not available for distribution. 
8 

ORGANIZATION OF THE REPORT

This technical report is organized into seven chapters:

• Chapter 1 provides background information and project objectives.

• Chapter 2 includes a literature and database review, with a focus on promising data 

sources that support the objectives of this project. 

• Chapter 3 outlines the framework for defining crash types, facility types, potential for 

future crashes, and contributing factors. 

• Chapter 4 focuses on identifying FCFTs. It contains a discussion of databases used and 

implemented methodology for FCFT identification. A list of the 15 FCFTs identified for 

further analysis is also contained in this chapter. 

• Chapter 5 describes the analysis of contributing factors for the 15 FCFTs. An overview of 

databases used for the analysis is followed by identifying the most influential predictor 

variables for each FCFT and determining whether the influential predictor variables are

associated with increases or decreases in expected crash frequencies. 

• Chapter 6 provides a process for identifying and selecting countermeasures for focus 

crash types, multiple examples of applying the countermeasure-selection process, and a 

concise overview of countermeasures that address the FCFTs and contributing factors. 

• Chapter 7 concludes this report by providing a summary of accomplishments, 

conclusions, and recommendations for future work.
9 

CHAPTER 2. LITERATURE REVIEW

The objectives of this literature review were to draw key information from past studies to identify 

common contributing factors for various FCFTs, including methodologies used to select these 

factors, and identify and review emerging research and practice on systemic safety analysis.

OVERVIEW OF CONTRIBUTING FACTORS AND METHODS

This section provides a review of relevant past studies, identifying common contributing factors 

for various FCFTs. Additionally, this section provides a review of methodologies commonly used 

to identify these contributing factors. Together, this information provided the basis for selecting 

user types, crash types, facility types, contributing factors, and methodologies for this research. 

This section also defines each of the aforementioned items and presents a discussion of literature 

findings for each. Last, contributing factors by applicable facility types, which the research team 

determined by the presence of statistical associations in cross-sectional or before–after models in 

the literature findings, are summarized. 

The literature review is concentrated on studies, guides, and reports with a focus on the broad 

consideration of contributing factors. Since the motivation was to identify potential crash types, 

facility types, contributing factors, datasets, and statistical methodologies, and there are 

innumerable studies that have examined the impact of an isolated contributing factor, it was not 

cost- or time-effective to focus on studies looking only at individual contributing factors.

The following sections identify further considerations for this research with respect to user types, 

crash types, facility types, contributing roadway factors, datasets, and methodologies. 

Contributing Factors

User Types

User types refer to the users of the roadway. The American Association of State Highway and 

Transportation Officials’ (AASHTO’s) A Policy on Geometric Design of Highways and Streets

(2011) considers the following roadway users: 

• Pedestrians. 

• Vehicles:

o Passenger cars.

o Buses.

o Trucks. 

o Recreational vehicles.

o Bicycles.

Pedestrian crashes and bicycle crashes almost universally involve a different vehicle type 

(e.g., passenger car) and are typically considered as a separate crash type (e.g., bicycle–vehicle 

collisions). In crash analyses, buses and recreational vehicles are often not considered 

independently as vehicle types but instead are lumped into “all vehicles” or “large vehicles”.

Noting how these vehicle types are treated when identifying crash contributing factors is a key 
10 

detail of the analysis; if considered separately, crashes involving these vehicle types may have 

differing crash contributing factors than crashes that do not distinguish between these vehicle 

types. 

Crash Severities and Types

Crashes are generally defined by two attributes: type and level of severity. Type refers to the 

number of vehicles, crash location, and manner of collision (e.g., single-vehicle crash, 

run-off-road [ROR] crash), while severity refers to the level of injury resulting from a crash. 

Police officers code type and severity on crash-reporting forms, and codes for both attributes are 

determined subjectively based on physical evidence and statements made by those involved in the 

crash.

Most agencies use the KABCO crash-severity scale—which consists of five levels of injury—for 

reporting the most severe injury in a given crash as well as injuries of drivers and occupants 

involved in a crash. KABCO severities, as defined in the 5th edition of Model Minimum Uniform 

Crash Criteria, include the following (NHTSA 2017): 

• Fatal Injury (K)—A fatal injury is any injury that results in death within 30 days after the 

motor vehicle crash in which the injury occurred. If the person did not die at the scene but 

died within 30 days of the motor vehicle crash in which the injury occurred, the injury 

classification is changed from the attribute previously assigned to the attribute “Fatal 

Injury.” 

• Suspected Serious Injury (A)—A suspected serious injury is any injury other than fatal 

that results in one or more of the following: 

o Severe laceration resulting in exposure of underlying tissues/muscles/organs or 

significant loss of blood. 

o Broken or distorted extremity (arm or leg). 

o Crush injury. 

o Suspected skull, chest, or abdominal injury other than a bruise or minor laceration. 

o Significant burn (second- and third-degree burn over 10 percent or more of the body). 

o Unconsciousness when taken from the crash scene. 

o Paralysis.

• Suspected Minor Injury (B)—A minor injury is any injury that is evident at the scene of 

the crash other than fatal or serious injuries. Examples of minor injuries include lumps on 

the head, abrasions, bruises, and minor lacerations (i.e., cuts on the skin surface with 

minimal bleeding and no exposure of deeper tissue/muscle).

• Possible Injury (C)—A possible injury is any injury reported or claimed that is not a fatal, 

suspected serious, or suspected minor injury. Examples of possible injuries include a 

momentary loss of consciousness, claim of injury, limping, or complaint of pain or 

nausea. There are two ways to identify possible injuries: self-reported by the person

involved in the crash or indicated by the person involved in the crash but no wounds or 

injuries are readily evident. 
11 

• No Apparent Injury (O)—No apparent injury is a situation where there is no reason to 

believe that the person involved in the crash received any bodily harm. There is no 

physical evidence of injury and the person does not report any change in normal function. 

Crash types often fall into one of the broader categories of crash types characterized by the 

number of vehicles involved: single-vehicle crashes, multiple-vehicle crashes, or total crashes

(i.e., single- and multiple-vehicle crashes combined). Since systemic safety focuses on crash 

types scattered across the roadway network at low densities, attention is most typically paid to 

specific crash types that result in fatalities or severe injuries. The literature review found that 

models use many variations of crash-type definitions to identify contributing factors. Research 

also often distinguishes crash types by severity (e.g., all angle [ANG] crashes versus all KABC

ANG crashes); therefore, crash-severity levels can be considered part of crash-type definitions. 

Example publications using different crash-type definitions under the three broadly defined crash 

types (i.e., single-vehicle crashes, multiple-vehicle crashes, and total crashes) are shown in 

table 1 through table 3. 

Table 1. Example publications addressing subsets of total crashes. 

Crash Type Reviewed Topical Literature

All crashes AASHTO 2010; National Academy of Sciences 2003e, 2004a, 2004c, 2004f, 

2008b, 2009; FHWA 2004, 2008a, 2008b, 2009; Fink and Krammes 1995; 

Fitzpatrick et al. 2005; Harkey et al. 2008; Karlaftis and Golias 2002; Lord 

et al. 2011; Milton and Mannering 1996, 1998; Orner and Drakopoulos 2007; 

Shankar et al. 1995; Wang et al. 1998

Severe crashes (KAB) AASHTO 2010; Preston et al. 2013a; National Academy of Sciences 2004d

KABC crashes AASHTO 2010; AAA Foundation for Traffic Safety 2012; FHWA 2008b; 

Harkey et al. 2008; MnDOT 2011

Nonfatal-injury crashes (ABC) FHWA 2008b; Harkey et al. 2008

Young-driver crashes FHWA 2009; Goodwin et al. 2013; Preston et al. 2013a

Older-driver crashes FHWA 2004, 2009; Goodwin et al. 2013; National Academy of Sciences 

2004c; Preston et al. 2013a

Weather condition–related crashes Lord et al. 2011

Weather condition–related crashes: 

wet-road crashes

FHWA 2008a, 2009; Harkey et al. 2008; National Academy of Sciences 

2008b 

Weather condition–related crashes: 

dry-road crashes

Harkey et al. 2008

Weather condition–related crashes: 

snow-related crashes

FHWA 2008a

Light condition–related crashes Lord et al. 2011

Light condition–related crashes: 

nighttime crashes

FHWA 2008a, 2009; Harkey et al. 2008; National Academy of Sciences 

2004a

Light condition–related crashes: 

nighttime-wet crashes

FHWA 2009

Light condition–related crashes: 

daytime crashes

FHWA 2009

Light condition–related crashes: 

daytime/nighttime ratio

National Academy of Sciences 2004a

Aggressive driving–related crashes Goodwin et al. 2013; Preston et al. 2013a

Drug- and alcohol-related crashes Goodwin et al. 2013; Preston et al. 2013a

Inattentive, distracted, asleep 

crashes

Goodwin et al. 2013; National Academy of Sciences 2004g; Preston et al. 

2013a
12 

Crash Type Reviewed Topical Literature

Motorcycle crashes Goodwin et al. 2013; National Academy of Sciences 2008d; Preston et al. 

2013a

Heavy-vehicle crashes FHWA 2008a; National Academy of Sciences 2004g; Miaou 1994; Preston 

et al. 2013a

Intersection-related crashes AAA Foundation for Traffic Safety 2012; Fitzpatrick et al. 2005; Poch and 

Mannering 1996; Preston et al. 2013a

Nonintersection-related crashes Fitzpatrick et al. 2005, 2008

Work zone–related crashes Preston et al. 2013a

Surface width–influence crashes Fitzpatrick et al. 2005

Cross-median crashes Harkey et al. 2008

Cross-median crashes: cross-median 

HEO crashes

National Academy of Sciences 2008b 

Speed-related crashes FHWA 2008a, 2009; National Academy of Sciences 2009

Speed-related crashes: speed-related 

daytime crashes

FHWA 2009

Speed-related crashes: speed-related 

nighttime crashes

FHWA 2009 

Speed-related crashes: speed-related 

dry crashes

FHWA 2009

Speed-related crashes: speed-related 

wet crashes

FHWA 2009

Emergency-vehicle crashes National Academy of Sciences 2004f; FHWA 2009

Red-light-running crashes National Academy of Sciences 2004f; FHWA 2004

Lane-change crashes National Academy of Sciences 2004f

Wrong-way crashes National Academy of Sciences 2008b 

HEO = head on.

Table 2. Example publications addressing subsets of single-vehicle crashes. 

Crash Type Reviewed Topical Literature

All single-vehicle crashes AASHTO 2010; Harkey et al. 2008; Zegeer et al. 1987

Roadway-departure/ROR crashes AASHTO 2010; AAA Foundation for Traffic Safety 2012; Cato et al. 2013; 

FHWA 2008a, 2009; Hallmark et al. 2006; Knapp et al. 2014; National 

Academy of Sciences 2003e, 2003f, 2004b, 2008b, 2009; Lee and Nam 

2003; Liu and Ye 2011; Lord et al. 2011; MnDOT 2011; Orner and 

Drakopoulos 2007; Patel et al. 2007; Preston et al. 2013a

Fatal ROR crashes AASHTO 2010; Liu and Subramanian 2009; Patel et al. 2007

Collision-with-animal crashes AASHTO 2010; FHWA 2008a

Overturn crashes AASHTO 2010; FHWA 2008a, 2009; Shankar et al. 1995

Collision-with-parked-vehicle 

crashes

AASHTO 2010; National Academy of Sciences 2004f; FHWA 2008a; 

Shankar et al. 1995

Collision-with-fixed-object crashes AASHTO 2010; FHWA 2008a, 2009; Shankar et al. 1995

Collision-with-fixed-object crashes: 

collision-with-tree crashes

National Academy of Sciences 2003c 

Collision-with-other-object crashes AASHTO 2010

Noncollision AASHTO 2010

Wet-road crashes Harkey et al. 2008
13 

Table 3. Example publications addressing subsets of multiple-vehicle crashes. 

Total Multiple-Vehicle Crashes Reviewed Topical Literature

All multivehicle crashes AASHTO 2010; National Academy of Sciences 2004f; FHWA 2009

ANG crashes AASHTO 2010; National Academy of Sciences 2003e, 2004f, 2009; Poch 

and Mannering 1996

ANG crashes: ANG-N crashes Harkey et al. 2008

Right-ANG crashes National Academy of Sciences 2004f; FHWA 2004, 2008a, 2009; Harkey 

et al. 2008; Preston et al. 2013a

Right-ANG crashes: wet-road rightANG crashesHarkey et al. 2008

HEO crashes AASHTO 2010; AAA Foundation for Traffic Safety 2012; FHWA 2008a, 

2009; Preston et al. 2013a

Rear-end crashes AASHTO 2010; National Academy of Sciences 2003e, 2004f; FHWA 2004, 

2008a; Harkey et al. 2008; Poch and Mannering 1996; Preston et al. 2013a; 

Shankar et al. 1995

Rear-end crashes: wet-road rear-end 

crashes

Harkey et al. 2008

Turning crashes FHWA 2008a, 2009; National Academy of Sciences 2003e, 2009; Poch and 

Mannering 1996; Preston et al. 2013a

Turning crashes: left-turn crashes National Academy of Sciences 2004f; FHWA 2004, 2008a, 2009; Harkey 

et al. 2008

Turning crashes: right-turn crashes FHWA 2008a, 2009

Sideswipe crashes AASHTO 2010; National Academy of Sciences 2004f; FHWA 2004, 2008a, 

2009; National Acadmey of Science 2003e; Preston et al. 2013a; Shankar 

et al. 1995

Sideswipe crashes: same-direction 

sideswipe crashes

Zegeer et al. 1987

Sideswipe crashes: oppositedirection sideswipe crashesZegeer et al. 1987

Nondriveway Crashes AASHTO 2010

Driveway crashes AASHTO 2010; Fitzpatrick et al. 2005

Vehicle–pedestrian crashes AASHTO 2010; AAA Foundation for Traffic Safety 2012; National 

Academy of Sciences 2004d, 2004f; FHWA 2004, 2008a, 2008b, 2009; 

Goodwin et al. 2013; Preston et al. 2013a

Vehicle–pedestrian crashes: 

crossing-roadway crashes

AAA Foundation for Traffic Safety 2014; National Academy of Sciences 

2004d

Vehicle–pedestrian crashes: 

walking-along-roadway crashes

AAA Foundation for Traffic Safety 2012; FHWA 2008a, 2008b; National 

Academy of Sciences 2004d

Vehicle–bicycle crashes AASHTO 2010; AAA Foundation for Traffic Safety 2012; National 

Academy of Sciences 2004f, 2008a; FHWA 2009; Goodwin et al. 2013; 

Preston et al. 2013a

Vehicle–bicycle crashes: crossingroadway crashesAAA Foundation for Traffic Safety 2012

Vehicle–bicycle crashes: bikingalong-roadway crashesAAA Foundation for Traffic Safety 2012

Vehicle–train crashes Preston et al. 2013a

HEO = head on.

Researchers more commonly analyze KA crashes separately from minor injuries or 

property-damage-only (PDO) crashes because the reliability of the data is greatest for 

higher-severity crashes (KA or KAB). As crash severity decreases, the reliability of reporting 

decreases.
14 

Underreporting increases as crash severity decreases, and it is generally accepted that fewer than 

5 percent of K crashes are unreported while more than 50 percent of PDO crashes are unreported. 

Underreported data will produce biased estimates in crash-frequency and crash-severity models. 

Additionally, this distribution of underreporting leads to overrepresentation of crashes with 

higher severity and underrepresentation of crashes with lower severity. This fact, along with other 

factors like the higher economic and social costs of KA crashes and the purpose of the Highway 

Safety Improvement Program (i.e., to reduce K and A crashes), leads researchers and 

practitioners to focus on reducing KA crashes.

Conversely, considering only K, KA, and KB crashes for specific crash types leads to very small 

sample sizes. Disaggregation of analysis to this level may lead to analyses that do not yield 

contributing factors associated with specific crash types and severities. Inclusion of all severities 

or combining crash types may affect the underlying relationships between contributing factors 

and severe crash outcomes but help increase the confidence in statistical associations between 

contributing factors and crash outcomes. Based on the strengths and limitations associated with 

different levels of disaggregation, this research focuses on crash types well represented within the 

three databases. 

Facility Types

Researchers present facility types in several different formats. Most commonly, researchers use 

the HSM definitions for facility types, which include the following (AASHTO 2010; AAA 

Foundation for Traffic Safety 2012; FHWA 2004, 2008a, 2008b, 2009, Fink and Krammes 1995; 

Fitzpatrick et al. 2005, 2008; Goodwin et al. 2013; Hallmark et al. 2006; Harkey et al. 2008; 

Karlaftis and Golias 2002; Lord et al. 2011; Miaou 1994; National Academy of Sciences 2003c, 

2003e, 2003f, 2004a, 2004b, 2004c, 2004d, 2004f, 2004g, 2008a, 2008b, 2008d, 2009; Patel et al. 

2007; Shankar et al. 1995; Wang et al. 1998; Zegeer et al. 1987): 

• Rural two-lane two-way roads: 

o Segments.

o Unsignalized intersections. 

o Signalized intersections.

• Rural multilane highways: 

o Segments.

o Unsignalized intersections. 

o Signalized intersections.

• Urban and suburban arterials: 

o Segments.

o Unsignalized intersections. 

o Signalized intersections.

• Rural and urban freeways: 

o Segments.

o Ramp/collector–distributor segments. 

o Crossroad ramp terminals. 
15 

Basic facility types can be further subdivided by number of lanes and median type for all but 

rural two-lane two-way roads and by number of approach legs for intersections. Often, literature 

notes that contributing factors are applicable to specific facility types, such as rural and urban 

unsignalized intersections, but does not differentiate for which specific facility types each 

contributing factor is appropriate. If researchers consider the contributing factors of specific 

facility types but report them in a more generalized manner, then this creates potential for users to 

consider inappropriate contributing factors or apply incorrect countermeasures.

Several resources do not discuss when the authors use cross-sectional data for segments and 

intersections across different facility types (Lee and Nam 2003; Liu and Subramanian 2009; 

Liu and Ye 2011; Orner and Drakopoulos 2007; Poch and Mannering 1996; Preston et al. 2013a). 

These results were generalized for all facility types, excluding ramps and freeways unless 

specifically identified as being included. In these cases, contributing factors were reported for all 

facility types included in the data, with facility type–level indicators included in the model 

(i.e., number of lanes, area type, median presence and width, intersection approaches, and traffic 

control type). When larger amounts of data are available, identifying contributing factors at this 

level can be effectively accomplished by considering interactions between contributing factors 

and facility type–level information (e.g., an interaction between shoulder width and number of 

lanes will identify facilities where shoulder width has the greatest impact on safety by number of 

lanes).

Two resources identified facility types as paved or unpaved roads (Calvert et al. 1999; Knapp 

et al. 2014). These resources identified contributing factors, with specific factors identified for 

unpaved roadways versus paved roadways. In all cases, the roadways were rural two-lane 

two-way roads. Researchers can consider unpaved roadways as an additional facility type or a 

subset of rural two-lane roads.

Alternatively, two resources disaggregated the appropriate facilities at levels further than the 

HSM (Cato et al. 2013; MnDOT 2011). These authors considered contributing factors for 

horizontal curves and tangent segments separately and identified contributing factors in relation 

to bridge location. At this level, contributing factors can be considered more fully; however, 

crash data become sparser, resulting in more zero-crash sites in the database.

Milton and Mannering (1996, 1998) considered facility types by functional classification in 

cross-sectional regression models. In their research, roadway-segment models considered 

contributing factors for principal arterials, minor arterials, and all collectors. Other research by 

Shankar et al. (1995) considered only interstate segments, which are also a level of functional 

classification. Shankar et al.’s definition of facility type only considered segments and excluded 

intersections and interchanges. Since this research used cross-sectional models, HSM-defining 

characteristics were included as predictors in the models (e.g., number of lanes). However, this 

definition could become more cumbersome when considering intersections. In many cases, the 

intersecting roadways would consist of roadways with differing functional classifications, 

requiring contributing factors to be considered for several more levels of intersection types. 

While differing facility-type definitions were identified in the literature, it is clear that the HSM 

definitions are most commonly used. As agencies are already aligning their data by these 

definitions for network screening, the HSM definitions are the most practical for use in this 
16 

current project, possibly with an additional level of disaggregation of segments into horizontal 

curves and tangent segments, as outlined in Cato et al. (2013) and MnDOT (2011). The HSM 

definitions provide the most practical level of disaggregation for identifying appropriate 

contributing factors.

Contributing Roadway Factors 

This section summarizes contributing factors and safety countermeasures identified in the 

literature. Contributing factors are characteristics of roadway segments and intersections that are 

associated with an increase or decrease in crash frequency or severity. Safety countermeasures or 

treatments are applied by agencies to roadway segments or intersections with the specific 

intention of reducing crash frequency or severity. 

Table 4 summarizes roadway factors and table 5 summarizes safety countermeasures that 

published studies have identified as influencing crash frequencies and severities on the noted 

facility types for segments. With the intent of providing a high-level summary of possible 

influential factors, the tables do not distinguish findings by crash type or direction of safety 

effects (i.e., increases or decreases in crashes). The “Prevalence” column in each table indicates 

the prevalence of the roadway factors or countermeasures in the literature as being low, medium, 

or high. Based on the resources examined, these categories are defined as follows:

• Low = appeared in one to three resources. 

• Medium = appeared in four to six resources. 

• High = appeared in seven or more resources.

Roadway factors or countermeasures identified as having a higher prevalence are those that are 

known to be related to crash frequency or severity. These factors should be prioritized for 

inclusion in crash-frequency or -severity models. For instance, annual average daily traffic 

(AADT) was found to be the most commonly considered roadway factor across all resources, 

leading AADT to be labeled as high prevalence. Other factors were only found in one resource 

(e.g., sign-support density) due to the lack of readily available data and difficulty collecting data 

for a large number of sites, leading these factors to be labeled as low prevalence. 

Table 4. Prevalence of roadway factors by facility type for segments. 

Factor Prevalence

Rural 

2-Lane

Rural 

Multilane

Urban 

Street Freeway Ramp

AADT High ● ● ● ● ●

Peak hour Low ● ● ● ● — 

Area type High — — — ● ●

Number of lanes High — ● ● ● ●

Lane width High ● ● ● ● ●

Shoulder width High ● ● ● ● ●

Shoulder type High ● ● ● ● — 

Shoulder location (e.g., inside) Medium — — — ● ●

Presence of surfaced shoulder Medium ● — — — — 

Presence of combinationsurface/stabilized shoulderLow ● — — — — 

Horizontal curve density High ● ● ● ● ●

Driveway type Low ● ● ● ● — 
17 

Factor Prevalence

Rural 

2-Lane

Rural 

Multilane

Urban 

Street Freeway Ramp

Driveway density High ● ● ● ● — 

Presence of horizontal curve High ● ● ● ● ●

Degree of curvature/radius High ● ● ● ● ●

Length of horizontal curve High ● ● ● ● ●

Horizontal curve-approach 

tangent length

Low ● ● ● — — 

Superelevation rate Medium ● ● ● ● — 

Presence of spiral transition Low ● — — — — 

Posted speed limit High ● ● ● ● — 

Lateral-clearance distance High ● ● ● — 

Side-slope rating High ● ● ● — 

Pavement-surface condition 

(friction)

High ● ● ● ● — 

Combination horizontal and 

vertical alignment (visual trap)

Low ● ● ● ● — 

Pavement-surface condition 

(weather)

Low ● ● ● ● — 

Presence of intersection Low ● ● ● — 

Proportion of commercial 

vehicles in the traffic stream

Medium ● ● ● ● — 

Topography Low ● ● ● ● — 

Roadway-edge quality Medium ● ● ● ● — 

Speed differential between 

horizontal curve and tangent

Low ● ● ● ● — 

Roadway gradient High ● ● ● ● — 

Median width High — ● ● ● — 

Median type High — ● ● ● — 

Hazard rating of roadsides Medium ● ● ● ● — 

Adjacent land use Low ● ● ● ● — 

Location and presence of bus 

stops

Low ● ● ● ● — 

Presence of on-street parking Low — — ● — — 

Type of on-street parking Low — — ● — — 

Roadside fixed-object density Low — — ● — — 

Distance to shoulder barrier Low ● ● ● ● ●

Speed-change lane presence Low — — — ● ●

Speed-change lane type Low — — — ● — 

Distance to inside barrier Low — — — ● ●

Length of ramp entrance Low — — — ● — 

Length of ramp exit Low — — — ● — 

Hours exceeding a threshold 

(1,000 hr for freeways; 2,500 hr

for others)

Low ● ● ● ● — 

Ramp side (e.g., right side) Low — — — ● — 

Presence of type B weaving 

section 

Low — — — ● ●

Length of type B weaving section Low — — — ● ●

Ramp type Low — — — — ●

Mean travel speed Medium ● ● ● ● — 

Bridge width Low ● ● ● ● — 

Presence of vertical curve Medium ● ● ● ● ●

Vertical-curve rate Low ● ● ● ● ●
18 

Factor Prevalence

Rural 

2-Lane

Rural 

Multilane

Urban 

Street Freeway Ramp

Length of vertical curve Low ● ● ● — — 

Median-barrier type/condition Low — ● ● ● ●

Vertical-curve density Low ● ● ● — — 

Available sight distance Medium ● ● ● ● ●

Edgeline-marking width Low ● ● ● ● — 

Ditch design Low ● — — — — 

Outside-barrier type/condition Medium ● ● ● ● ●

Intersection density Medium ● ● — — — 

Functional classification Low ● ● — — — 

Pavement width Low ● ● — — — 

Season Low ● ● ● — — 

Year Low ● ● ● ● — 

Catch-basin density Low ● ● ● — — 

Culvert density Low ● ● ● — — 

Presence of fence/wall Low ● ● ● — — 

Sign-support density Low ● ● ● — — 

Utility-pole density Low ● ● ● — — 

Pavement condition High ● ● ● ● ●

Pavement type Low ● — — — — 

Monthly precipitation 

(i.e., rain/snow)

Low — — — ● — 

Letter height of roadway signs Low ● ● ● — — 

Presence of variable message 

signs

Low — — — ● — 

●Factor that influenced crash frequency and severity on the segment (as identified in a published study). 

—No data available.

Table 5. Prevalence of countermeasures by facility type for segments. 

Factor Prevalence

Rural 

2-Lane

Rural 

Multilane

Urban 

Street Freeway Ramp

Centerline markings Low ● ● ● ● — 

Edge-line markings Medium ● ● ● ● — 

Advisory signs Medium ● ● ● ● — 

Chevrons Medium ● ● ● ● — 

Post-mounted delineators High ● ● ● ● — 

Flashing beacon Low ● — — — — 

Reflective-barrier delineation Low ● — — — — 

Profile thermoplastic markings Low ● — — — — 

Dynamic-curve warning system Low ● — ● — — 

Speed-limit-advisory marking 

lane

Low ● — — — 

Lighting High ● ● ● ● — 

SafetyEdgeSM Low ● — — — — 

Curve-advance marking Low ● — — — — 

Optical speed bars Low ● — — — — 

Centerline rumble strips High ● ● ● ● — 

Shoulder rumble strips High ● ● ● ● — 

Raised pavement markings Low ● ● ● ● — 

Outside barrier High ● — — ● — 

Presence of median High — ● ● ● — 

Automated speed enforcement Medium ● ● ● ● — 
19 

Factor Prevalence

Rural 

2-Lane

Rural 

Multilane

Urban 

Street Freeway Ramp

Passing lane Medium ● — — — — 

Short four-lane section Low ● — — — — 

Two-way left-turn lane Low ● — ● — — 

Median barrier Low — — — ● — 

Lane drop/add Low — — — — ●

Raised median at crosswalk Low — — ● — — 

Presence of sidewalk Low ● ● — — — 

Side friction Low ● ● — — — 

Crosswalk Low ● ● — — — 

Bicycle lane Low ● ● — — — 

Impact attenuators Low ● ● ● ● — 

Presence of bridge Low ● ● ● ● — 

Truck escape ramp Low ● ● ● ● — 

Access control Low — ● — — — 

Object delineation in clear zone Medium ● ● ● — — 

Truck prohibition Low ● — ● — — 

Anti-icing system Low ● — ● — — 

Break-away devices Low ● — ● — — 

Advance guide signs and street 

names

Low ● ● ● — — 

Pedestrian-crossing warning Low ● ● ● — — 

Traffic calming Low ● ● ● — — 

Interactive truck-rollover signing Low ● ● ● ● ●

Truck-related posted speed limit Low — — — ● — 

Variable speed limit Low ● ● ● ● — 

Active speed warning Low ● ● ● ● — 

Reduced-traction warning sign Low ● ● ● — — 

●Factor that influenced crash frequency and severity on the segment (as identified in a published study). 

—No data available.

Table 6 summarizes roadway factors and table 7 summarizes countermeasures that published 

studies have identified as influencing crash frequencies and severities on the noted facility types 

at signalized and unsignalized intersections. With the intent of providing a high-level summary of 

possible influential factors, the tables do not distinguish findings by crash type or direction of 

safety effects. The prevalence of the roadway factors or countermeasures in the literature is used 

in the same way as the segment tables.

Table 6. Prevalence of roadway factors by facility type for intersections. 

Factor Prevalence R2U R2S RMU RMS UU US

Major AADT High ● ● ● ● ● ●

Minor AADT High ● ● ● ● ● ●

Left-turn volume Low — — — — ● ●

Right-turn volume Low — — — — ● ●

Total opposing approach volume Low — — — — ● ●

AADT ratio Low ● ● — — — — 

Number of approaches High ● ● ● ● ● ●

Number of approach lanes Medium — — ● ● ● ●

Number of signal heads Low — ● — ● — ●

Signal head–mount location Low — ● — ● — ●

Signal timing/phases Medium — ● — ● — ●
20 

Factor Prevalence R2U R2S RMU RMS UU US

Signal-head size Low — — — — — ●

Yellow interval length Low — ● — ● — ●

Intersection sight distance High ● ● ● ● ● ●

Pedestrian-crossing distance Low ● ● ● ● ● ●

Pedestrian signal–head type Low — ● — ● — ●

Skew ANG High ● ● ● ● ● ●

Proximity to horizontal and vertical curves Low ● ● ● ● ● ●

Proximity to at-grade railroad crossing Low ● ● ● ● ● ●

Traffic control type (e.g., all-way stop) High ● — ● — ● — 

Presence of commercial development Low ● ● ● ● ● ●

Maximum number of lanes crossed by 

pedestrian in maneuver

Low — — — — ● ●

Number of bus stops within 1,000 ft Low — — — — — ●

Number of schools within 1,000 ft Low — — — — — ●

Number of alcohol-sales establishments 

with 1,000 ft

Low — — — — — ●

Left turn–lane length Low ● ● ● ● ● ●

Right turn–lane length Low ● ● ● ● ● ●

Shoulder width Low ● ● ● ● — — 

Pavement-surface condition (friction) Medium ● ● ● ● ● ●

Median type Low — — ● ● ● ●

Presence of parking near intersection Low ● ● ● ● ● ●

Minor route access density Low ● ● ● ● — — 

Functional classification Low — — — — ● ●

Approach posted speed limit Low — — — — ● ●

Opposing approach posted speed limit Low — — — — ● ●

Intersection approach gradient Low — — — — ● ●

Roadway drainage Low — ● — ● — ●

Signal hardware located in clear zone Low — ● — ● — ●

●Factor that influenced crash frequency and severity on the segment (as identified in a published study). 

—No data available.

R2U = rural two-lane unsignalized intersection; R2S = rural two-lane signalized intersection; RMU = rural multilane 

unsignalized intersection; RMS = rural multilane signalized intersection; UU = urban unsignalized intersection; 

US = urban signalized intersection.

Table 7. Prevalence of countermeasures by facility type for intersections. 

Factor Prevalence R2U R2S RMU RMS UU US

Advance intersection warning Low ● ● ● ● ● ●

Back plates Low — ● — ● — ●

Retroreflective sheeting on back plate Low — ● — ● — ●

Left-turn lane High ● ● ● ● ● ●

Right-turn lane High ● ● ● ● ● ●

Presence of crosswalk Low ● ● ● ● ● ●

Intersection lighting High ● ● ● ● ● ●

Right turn on red prohibition Medium — — — — — ●

Red-light camera Medium — — — — — ●

Automated speed enforcement Low — — — — — ●

Protected left-turn phase High — ● — ● — ●

Protected/permissive left-turn phase Low — ● — ● — ●

Right-turn channelization Low — — ● ● ● ●

Left-turn channelization Low — — ● ● ● ●

Exclusive pedestrian phasing Low — ● — ● — ●
21 

Factor Prevalence R2U R2S RMU RMS UU US

Emergency pre-emption Low — ● — ● — ●

Lead-pedestrian interval Low — ● — ● — ●

Lead-bicycle interval Low — ● — ● — ●

Actuated signal Low — ● — ● — ●

Signal coordination Low — ● — ● — ●

Advanced dilemma-zone detection Low — ● — ● — — 

Flash mode Low — ● — ● — ●

Stop-sign size Low ● — ● — ● — 

Pedestrian signing Low ● ● ● ● ● ●

Overhead lane-use signs Low ● ● ● ● ● ●

Raised pavement marker Low ● ● ● ● ● ●

Stop bars Low ● — ● — ● — 

Directional median opening Low — ● — ● — ●

Double left-turn lane Low — ● — ● — ●

Left turn–painted separation Low — ● — ● — ●

Presence of median Low — — ● ● ● ●

Raised median crosswalk Low ● — ● — ● — 

Presence of raised island Low ● — ● — ● — 

Splitter island on minor approach Low ● — ● — ● — 

Bypass lane Low ● ● ● ● ● ●

Double stop sign Low ● — ● — ● — 

Flashing beacons Low ● — ● — ● — 

Stop-ahead pavement markings Low ● — ● — ● — 

Transverse rumble strips Low ● ● ● ● ● ●

Bicycle lane Low ● ● ● ● ● ●

Contraflow bicycle lane Low ● ● ● ● ● ●

No-left-turn sign Medium ● ● ● ● ● ●

No-U-turn sign Low ● ● ● ● ● ●

Pedestrian overpass/underpass Low — — — — ● ●

Presence of sidewalk Medium ● ● ● ● ● ●

Offset left-turn lanes Low — ● — ● ● ●

Extended edgelines Low ● — ● — ● — 

Driveway closure Low ● — ● — ● — 

Acceleration lane Low ● — ● — ● — 

Offset T versus four-leg intersection Low ● — ● — ● — 

ICWS Low ● — ● — ● — 

Roadside markers Low ● — ● — ● — 

Clearance interval Medium — ● — ● — ●

Pedestrian signals Low ● ● ● ● ● ●

Pedestrian crossing warning Low ● ● ● ● ● ●

Restricted turning maneuvers Low — ● — ● — ●

Access management near intersection Low — ● — ● — ●

Bicycle-detecting sensor Low — ● — ● — ●

●Factor that influenced crash frequency and severity on the segment (as identified in a published study). 

—No data available.

R2U = rural two-lane unsignalized intersection; R2S = rural two-lane signalized intersection; RMU = rural multilane 

unsignalized intersection; RMS = rural multilane signalized intersection; UU = urban unsignalized intersection; 

US = urban signalized intersection; ICWS = intersection curve warning sign. 
22 

Datasets

Nearly all resources identified in this literature review used databases that were developed 

specifically for their research, including field data collections or State crash, traffic, and roadway 

inventory data. The databases were developed to suit the needs of the research at hand, and a 

limited set of predictors was considered in the developed models. As such, these databases were 

not usable for this study. However, as part of the DCMF program, States supplied information on 

their data sources and availability via a DCMF Needs Assessment. In addition to these data, the 

research team identified the following databases for further consideration for identifying focus 

crash types, facility types, and contributing roadway factors:

• Crash Injury Research and Engineering Network (NHTSA 2018d). 

• Crashworthiness Data System (NHTSA 2018c). 

• Fatality Analysis Reporting System (FARS) (NHTSA 2018a). 

• Federal Transit Administration National Transit Database (FTA 2018). 

• General Estimates System (NHTSA 2018b). 

• Highway Safety Information System (HSIS) (FHWA 2018c). 

• Motor Carriers Management Information System (FMCSA 2016). 

• National EMS Information System (NEMSIS Technical Assistance Center 2018). 

• National Motor Vehicle Crash Causation Study (NHTSA 2008). 

• National Park Service Service‐Wide Traffic Accident Reporting System.1

• Second Strategic Highway Research Program (SHRP2) Naturalistic Driving and 

Roadway Databases (Virginia Tech Transportation Institute 2018). 

Chapter 4 and chapter 5 describe the ultimate selection of data sources for this project. 

Methodologies 

The previous sections of this chapter summarized roadway factors and countermeasures expected 

to be associated with crash frequency and severity for various facility types. The previous 

sections also identified commonly used data resources upon which these findings were based. 

This section summarizes relevant statistical methods that could be employed to the 

aforementioned data resources (or others) to identify contributing roadway factors for FCFTs. 

That is, this section provides an overview of statistical methods that can be used to estimate and 

quantify the uncertainty about factors that either increase or decrease the frequency and/or 

severity of various crash types. 

In determining which statistical method to use to identify contributing roadway factors for 

FCFTs, it is imperative that the selected method be appropriate for the type of data under 

consideration. For example, researchers may use generalized linear modeling to identify roadway 

factors for data on the number of crashes that occur in a certain section of a highway (e.g., count 

data), but this method may not be applicable when trying to analyze the time between crashes on 

the same section of the highway. The chosen statistical method must match the selected data to 

identify the contributing roadway factor.

1

This unpublished dataset is available through the National Park Service.
23 

The following sections detail the selected methods many researchers have used to identify factors 

that increase or decrease the potential for future crashes of various crash types. Each section 

includes a high-level overview of each method, along with the type of data to which the method 

would apply, and a list of the most popular textbooks for using the proposed method. 

Normal Linear Regression Models 

Normal linear regression models (commonly referred to as “linear models”) seek to quantify the 

linear relationship between or among an outcome (often called a response, dependent variable, or 

output) and one or more other measurements (often called predictors, independent variables, or 

inputs). In other words, the goal of a normal regression model is to estimate the change in an 

output that results from changing one or more of the input variables. By way of example, a 

normal regression model would estimate the change in friction on a road surface due to a change 

in surface temperature. Relevant results from a normal regression analysis include but are not 

limited to a measure, which represents the probability of a Type I error (e.g., rejecting a null 

hypothesis when it is actually true, also known as a false positive), of whether each input variable 

has a statistically significant effect on the output variable in the form of p-values and a summary 

of the estimated effect of each input variable on the output variable.

Certain assumptions that are used to estimate the effect of each input variable on the output 

variable restrict the applicability of linear models. As the name implies, linear models are only 

able to estimate linear relationships between inputs (predictors) and the output (the response) and 

any transformations of the input and output variables. Admittedly, many relationships are more 

complicated (i.e., nonlinear), and linear models could fail to recognize such relationships. 

Normal linear models are only applicable to continuous outputs. A continuous variable is a 

variable that can take any value (e.g., rational and irrational numbers) on the real number line. 

Linear regression would not apply, for example, to count data because a count has to be an 

integer (e.g., 0, 1, 2...). 

Linear regression is a well-established method and taught as a core course in many statistics 

department. The most popular introductory texts to linear regression include the following: 

• Applied Linear Regression by S. Weisberg (2014). 

• Regression Analysis by Example by S. Chatterjee and A. S. Hadi (2012). 

• Linear Models with R by J. J. Faraway (2015). 

• Introduction to Linear Regression Analysis by D. C. Montgomery, E. A. Peck, and 

G. G. Vining (2012). 

The Faraway (2015) and Montgomery et al. (2012) books are particularly popular due to their use 

of R programming language and statistical analysis software (SAS). 

Generalized Linear Models

As previously mentioned in the discussion about normal linear regression models, one of the 

drawbacks of such models is their reliance on continuous outputs. For many applications, 

particularly for traffic-safety data, the outcome of interest is not continuous. Generalized linear 

models (GLMs) are an extension of linear models by use of noncontinuous data. GLMs are 
24 

similar to normal linear models in that GLM results include a measure of whether each input 

variable has a significant effect on the output variable in the form of p-values and a summary of 

the estimated effect of each input variable on the output variable. However, GLMs explicitly take 

into account the noncontinuous nature of the outcome variable to estimate these effects.

A few specific GLMs that are of particular applicability for traffic-safety data are Poisson,

negative binomial (NB) regression, and logistic models. Poisson and NB regression models are

specifically used to estimate effects of a particular input variable on a count output variable. For 

example, Poisson or NB regression models can be used to estimate effects of including a high 

occupancy–vehicle lane on the number of crashes along a particular stretch of highway. 

Estimated effects from Poisson or NB regression models are interpreted as the effect of a change 

in the input variable on the expected number of outcomes.

Binary logistic regression models are used to estimate the effect of input variables on a 

0/1 outcome variable. A 0/1 outcome variable is any variable that either occurred (typically 

denoted as “1”) or did not occur (typically denoted as “0”). For example, a binary logistic 

regression model could be used to estimate the effect of a 5-ft-wide shoulder on the occurrence of 

one or more ROR crashes. Estimated effects from a binary logistic regression model are 

interpreted as the effect of an input variable on the probability of the outcome variable occurring 

(e.g., the probability of one or more ROR crashes). Multinomial logistic regression models 

extend this concept to situations where the response variable has more than two outcomes. 

Similar to normal linear regression models, GLMs assume that the relationship between each 

input variable and the output variable is linear. While the assumption of linearity is often a good 

approximation, nonlinear relationships are often more realistic (but also more difficult to model 

and interpret). 

GLMs inherently respect the noncontinuous nature of an outcome variable, but interpreting the 

estimated effects from a GLM can be more complicated than estimating those from a normal

linear regression model. For example, in a logistic regression model, the effects displayed by 

SAS are typically on a log–odds ratio scale; that is, the displayed effects can be interpreted as the 

effect of an input variable on the log–odds ratio of an outcome variable. Therefore, special care 

must be given when considering the estimated effects in a GLM framework. If necessary, the 

scale of the estimated effects can be transformed back to the original scale of the data, but this 

back-transformation is typically not the default for most SAS packages.

Approachable, high-level textbooks for GLMs include the following: 

• Extending the Linear Model with R by J. J. Faraway (2006). 

• An Introduction to Generalized Linear Models by A. J. Dobson and A. G. Barnett (2008). 

• Chapter 12 of Applied Linear Regression by S. Weisberg (2014). 

• Chapter 4 of Introduction to Statistical Learning and Data Mining by G. James, 

D. Witten, T. Hastie, and R. Tibshirani (2013). 

Chapter 4 of The Elements of Statistical Learning and Data Mining by T. Hastie, R. Tibshirani, 

and J. Friedman (2009) and Generalized Linear Models by P. McCullagh and J. A. Nelder (1994) 

are more theoretically based texts. 
25 

Longitudinal Analyses 

Longitudinal data are repeated measurements collected on a single entity over time. For example, 

data on the number of crashes along a given road over time would constitute longitudinal data for 

that specific road. The goal of a longitudinal analysis is to relate input variables to changes in the 

output over time. For example, a longitudinal analysis could infer the effect of increasing the 

width of a rural road lane from 9 to 12 ft on the number of head-on (HEO) crashes along the road. 

The primary advantage of a longitudinal analysis is that it allows unit-specific inference. For 

instance, increasing the lane width from 9 to 12 ft might have no effect on the number of HEO

crashes for some rural roads but might significantly decrease the number of HEO crashes for 

others. Hence, measuring the same unit repeatedly over time allows for inference specific to the 

unit. 

Standard textbooks for longitudinal analysis include the following: 

• Applied Longitudinal Analysis by G. M. Fitzmaurice, N. M. Laird, and J. H. Ware (2011). 

This text includes examples of how to use SAS for longitudinal analyses.

• Longitudinal Data Analysis by D. Hedeker and R. D. Gibbons (2006). 

Additionally, the following books have discussion on the subject:

• Extending the Linear Model with R by J. J. Faraway (2006). 

• An Introduction to Generalized Linear Models by A. J. Dobson and A. G. Barnett (2008). 

Survival Analysis

Survival analysis, also referred to as hazard analysis, is a branch of statistics primarily concerned 

with time-to-event (TTE) data. TTE data are collected on the amount of time passed until a 

certain event occurs (e.g., the time between consecutive crashes on the same section of highway). 

The focus of many survival analyses is the relation of input variables to the probability of an 

event occurring given that the event has not yet occurred. For example, a survival analysis would 

seek to relate the presence of a median to the probability of a crash occurring between 5 and 

6 p.m. given that a crash has not previously occurred that day. 

Reasons why TTE data are not amenable to traditional statistical analysis (e.g., normal or GLMs) 

include the following: 

• TTE data are strictly positive.

• TTE data are typically described by nonsymmetric and highly skewed distributions. 

• TTE data can often be censored. 

Censored data occur when an event is not observed in the timeframe of the study and, hence, the 

TTE is unknown. Much of survival analysis is concerned with appropriately accounting for such 

data characteristics in a statistical analysis.
26 

Common textbooks for introductory courses in survival analysis include the following: 

• Modelling Survival Data in Medical Research by D. Collett (2003). 

• Survival Analysis Using SAS: A Practice Guide by P. D. Allison (2010). 

The text by P. D. Allison (2010) is more applied in nature, but both texts detail how to use SAS 

to perform analyses. Another approachable text is Survival Analysis: A Self-Learning Text by 

D. G. Kleinbaum and M. Klein (2012). Additionally, other books (e.g., An Introduction to 

Generalized Linear Models by A. J. Dobson and A. G. Barnett [2008]) have individual chapters 

dedicated to survival analysis. 

Spatial Modeling 

Spatial modeling, or spatial statistics, is a branch of statistics that analyzes data collected 

regarding a specific space and produces information about the spatial dimensions of the data 

(known as spatial data). Some examples of spatial data include the following: 

• Weather measurements taken at different stations across the United States. 

• Counts of flu cases observed in each county within a State. 

• The number of crashes observed on adjacent sections of a highway. 

Researchers collect each of these example datasets at various spatial locations (e.g., a weather 

station or section of highway) and use spatial methods for analysis. 

The primary concern of spatial statistics is accounting for and exploiting correlations with data 

collections at nearby locations. For example, the weather in Raleigh, NC, is expected to be 

similar to (or correlated with) weather in Durham, NC, due to their proximity of approximately 

25 mi. Similarly, the effect of a random, unmeasured event that occurs in Raleigh could 

“spillover” to nearby Durham. Accounting for and exploiting the spatial correlation in such data 

leads to more appropriate and correct statistical inferences and the ability to predict at unobserved 

locations. 

Common tools to analyze spatial data include Gaussian process regression, autoregressive 

models, and point processes. Because these tools are most commonly used within the previously 

discussed methods (e.g., as part of a normal regression model or GLM), they are considered an 

extension of these methods when used with data collected over space.

Approachable textbooks in spatial statistics include the following: 

• Applied Spatial Statistics for Public Health Data by L. A. Waller and C. A. Gotway 

(2004). 

• Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology by A. B. 

Lawson (2013). 
27 

More theoretical, low-level texts include the following:

• Statistics for Spatial Data by N. A. C. Cressie (1993). 

• Statistics for Spatio-temporal Data by N. A. C. Cressie and C. K. Wikle (2011). 

• Hierarchical Modeling and Analysis of Spatial Data by S. Banerjee, B. P. Carlin, and 

A. E. Gelfand (2015). 

Bayesian Modeling 

Bayesian modeling is not a single method, per se, but more of a general philosophy toward any 

statistical analysis. Unlike traditional statistics, Bayesian analysis uses laws of probability to 

quantify the uncertainty associated with statistical inferences. That is, the Bayesian framework 

(or philosophy) performs statistical estimation using laws of probability to combine a priori 

information (prior distribution) with data (data distribution) into a composite summary of 

unknown parameters (posterior distribution). 

Since the mid-1990s, researchers have widely adopted Bayesian philosophy to perform complex 

analyses of datasets. Advantages of Bayesian philosophy include the following: 

• A unified, single approach to quantifying uncertainty regarding statistical conclusions. 

• Grounding inference only in observed data (rather than relying on complex asymptotic 

mathematics).

• The ability to combine prior knowledge into the analysis.

Despite its popularity and advantages, the Bayesian paradigm is more complex to implement. 

Employing Bayesian estimation requires advanced computation skills and often requires writing 

problem-specific software (code). The analyst must also assign appropriate prior distributions to 

parameters they are estimating and be able to interpret the posterior distributions that are a result 

of the modeling. A single set of results may take hours of computing time to obtain. For these 

reasons, researchers who lack significant experience with or training in advanced statistical 

analyses often do not adopt the Bayesian paradigm for statistical inference. 

Standard texts for Bayesian statistics include the following: 

• Bayesian Data Analysis by A. Gelman, J. B. Carlin, H. S. Stern, D. B. Dunson, 

A. Vehtari, and D. B. Rubin (2013). 

• Bayesian Methods for Data Analysis by B. P. Carlin and T. A. Louis (2008). 

• A First Course in Bayesian Statistical Methods by P. D. Hoff (2009). 

• Doing Bayesian Data Analysis by J. K. Kruschke (2015). 

The texts by Hoff (2009) and Kruschke (2015) are perhaps the most approachable but still require 

a significant understanding of mathematical statistics.

Other Methods 

While the previously discussed methods cover a breadth of statistical methodology that can be 

used to identify contributing factors from various types of data, the list is by no means 
28 

comprehensive. Other methods worth mentioning are data-mining techniques, such as treed 

regression or nonlinear regression, and multivariate statistical methods, such as clustering, 

multivariate regression, and factor analysis. The Elements of Statistical Learning and Data 

Mining by T. Hastie, R. Tibshirani, and J. Friedman (2009) and Introduction to Statistical 

Learning and Data Mining by G. James, D. Witten, T. Hastie, and R. Tibshirani (2013) discuss 

data-mining techniques, while Methods of Multivariate Analysis by A. C. Rencher and 

W. F. Christensen (2012) discusses multivariate methods.

APPROACHES TO SYSTEMIC SAFETY ANALYSIS

This section of the literature review summarizes additional resources that address crash types, 

facility types, and contributing roadway factors, primarily within the context of systemic 

approaches to road-safety management. Table 8 provides brief summaries of the resources the 

research team identified and reviewed. The table also provides a more detailed summary of a 

selected number of these resources. 

Table 8. Summary of selected literature on systemic safety analysis. 

Publication Summary of Publication

NCHRP Synthesis 128: “Methods for 

Identifying Hazardous Highway Elements” 

(Zegeer 1986)

Synthesizes methods used by 39 State agencies and 17 local 

agencies to identify and treat hazardous highway elements and 

determine which methods have been most successful.

“Variable Safety Improvements for Unpaved 

Roads” (Caldwell and Wilson 1996)

Provides a list of 12 roadway-design elements that should be 

considered in unpaved-road safety audits based on a survey of a 

steering committee.

NCHRP 500 Series (National Academy of 

Sciences 2003a, 2003b, 2003c, 2003d, 2003e, 

2003f, 2004a, 2004b, 2004c, 2004d, 2004e, 

2004f, 2004g, 2005a, 2005b, 2005c, 2006, 

2007, 2008a, 2008b, 2008c, 2008d, 2009)

Presents 22 emphasis areas that affect overall highway safety, as 

well as strategies for reducing crashes corresponding to these 

emphasis areas and an outline of what is needed to implement each 

strategy.

HSM (AASHTO 2010) Includes a framework for identifying issues, developing 

countermeasures, and evaluating treatment effectiveness. Provides 

safety performance functions and CMFs for various 

facility/treatment types. Details quantitative approaches that can 

help to understand relative safety impacts of various elements.

NCHRP Research Results Digest 345: 

Alternate Strategies for Safety Improvement 

Investments (National Academy of Sciences 

2010)

Synthesizes practices and compares two approaches used by States 

to allocate safety funds: “black spot” analysis and “systematic” 

methods.

“Mn/DOT County Road Safety Plans” 

(Preston and Gute 2010)

Provides safety plans for 20 counties based on analysis of Statespecific crash data. Identifies contributing factors that increase crash 

potential and focus crash types for various facility types, including 

roadway segments, horizontal curves, and rural stop-controlled 

intersections.

Minnesota Department of Transportation 

Traffic Safety Analysis Software State of the 

Art (Brown et al. 2011)

Identifies and assesses safety-analysis practices and existing 

supporting software tools currently used by State transportation 

agencies, including systemic approaches to safety analysis.

Quantitative Assessment of Local Rural Road 

Safety – Case Study

(Mahgoub et al. 2011)

Proposes a rural road safety index for local rural roads to rank the 

road network according to safety features and identify deficiencies 

in road sections.
29 

Publication Summary of Publication

Benefit/Cost Evaluation of MODOT’s Total 

Striping and Delineation Program (Potts 

et al. 2011)

Documents the methodology and results of a before–after safety 

evaluation and B/C analysis of a major Missouri Department of 

Transportation program, known as the Smooth Roads Initiative, to 

improve both rideability and visibility of over 2,300 mi of major 

roadways in Missouri.

Centerline Rumble Strips on Secondary 

Highways – A Systemic Crash Analysis

(Wilder 2011)

Summarizes a systemic approach to evaluate potential locations for

and recommend the best way to implement centerline rumble strips 

in New York State on nonfreeway highway segments.

“A Systemic Safety Project Identification 

Process – Minnesota’s County Road Safety 

Plans” (Preston 2012)

Identifies contributing factors that increase crash potential on rural 

paved roadway segments and rural unsignalized intersections.

Development of a Systemic Road Safety 

Analysis Tool – Roadway Departure Crashes 

at Bridges in Salem County, New Jersey

(Cato et al. 2013)

Presents a systemic road-safety analysis tool to examine roadwaydeparture crashes at bridges. Identifies numerous characteristics 

related to a high potential of roadway-departure crashes at bridges.

Comparison of Countermeasure Selection 

Methods for Use in Road Safety Management

(Harwood et al. 2013)

Compares three methods for selecting highway-infrastructure 

countermeasures to reduce crash frequency and severity: the usRAP 

Tools software (usRAP 2020), the FHWA Systemic Safety Project 

Selection tool, and road-safety audits (Preston et al. 2013a). 

Systemic Safety Project Selection Tool

(Preston et al. 2013a)

Describes a systemic safety-planning process, which includes 

identifying focus crash types and contributing factors, screening and 

prioritizing candidate locations, selecting countermeasures, and 

prioritizing projects.

Using Risk to Drive Safety Investments

(Preston et al. 2013b)

Characterizes the systemic approach to safety management, 

identifies context (e.g., crashes, road types) suited for systemic 

safety approaches, and summarizes the cyclical three-element 

process in the Systemic Safety Project Selection Tool (Preston et al. 

2013a). 

Local and Rural Road Safety Briefing Sheets: 

Applying the Systemic Safety Approach on 

Local Roads (FHWA 2014)

Identifies contributing factors that increase crash potential for 

roadway departures along horizontal curves in Thurston County, 

WA, for screening and prioritizing candidate locations for systemic 

improvements.

Systemic Safety Improvement Risk Factor 

Evaluation and Countermeasure Summary: 

Final Report (Knapp et al. 2014)

Investigates and compares two systemic safety tools/methodologies, 

an approach used to produce Minnesota county road-safety plans 

and usRAP, when applied to two counties in Iowa (usRAP 2020). 

A Systematic Approach to Identifying Traffic 

Safety Needs and Intervention Programs for 

Indiana: Volume I—Research Report (Tarko 

et al. 2014)

Presents a method and example application for systemically 

identifying thresholds of specific characteristics that note horizontal 

curves with a high potential for roadway-departure crashes. 

Risk Factors Associated with High Potential 

for Serious Crashes (Al-Kaisy et al. 2015)

Presents an approach that uses extensive data collection and 

analysis for a large sample of Oregon’s low-volume roads to 

develop an index that expresses the crash potential for different road 

geometries and roadside features, as well as crash history and traffic 

exposure.

Reliability of Safety Management Methods: 

Systemic Safety Programs (Gross et al. 2016)

Describes a process for program managers, project managers, and 

data analysts to develop and enact a comprehensive safetymanagement program based on contributing factors for FCFTs. 

A Systemic Safety Analysis of Pedestrian 

Crashes: Lessons Learned (Wang et al. 2016)

Compares candidate site-ranking methods that use contributing 

factors for systemic approaches to analyzing pedestrian crashes at 

intersections in Austin, TX. Estimates contributing factors that 

increase crash potential for pedestrian crashes at signalized and 

stop-controlled intersections and compares the effects of the ranking 

and weighting methods.

NCHRP = National Cooperative Highway Research Program; usRAP = United States Road Assessment Program.
30 

The systemic approach to safety involves identifying sites based on site-specific geometric and 

operational attributes rather than observed crashes (Gross et al. 2016). FHWA developed a 

Systemic Safety Project Selection Tool, which identifies a systemic safety-planning process that 

includes the following steps (Preston et al. 2013a): 

1. Identify focus crash types and contributing factors.

2. Screen and prioritize candidate locations.

3. Select countermeasures.

4. Prioritize projects.

The first step relates most closely to this research, and its objective is to identify contributing 

factors commonly associated with each focus crash type experienced across the road system

(Preston et al 2013a). These are the crash types that have the greatest potential for reducing fatal 

and severe injuries. The FHWA Systemic Safety Project Selection Tool notes that the State’s 

Strategic Highway Safety Plan (SHSP) is a good starting point for identifying such contributing 

factors, particularly the State’s documented emphasis areas, which are typically identified 

through a data-driven approach, similar to those recommended by the FHWA tool. An example 

data-driven approach for identifying focus crash types is to disaggregate jurisdiction-wide crash 

data by SHSP emphasis area. The focus crash types may be selected as the most frequently 

occurring statewide, the most frequently occurring crash type by jurisdiction or area type 

(e.g., most frequent crash type for rural areas, counties, and/or urban areas), or the most 

overrepresented when comparing proportions of crashes in a specific jurisdiction or area type to 

the State numbers. 

The second step is to identify where crashes are occurring (i.e., the focus facilities). The FHWA 

Systemic Safety Project Selection Tool recommends using a crash tree diagram for this purpose. 

For a focus crash type, the crash tree begins with total KA crashes and uses available data to 

subdivide each subsequent level (e.g., area type, ownership, segments/intersections, surface type, 

number of lanes, posted speed limit). Each branch of the tree contains nodes that identify the 

number of KA crashes for those conditions. The nodes with the highest counts yield the focus 

facility types (i.e., facility type where the focus crash type most frequently occurs). 

Once analysts identify FCFTs, they can identify and evaluate contributing factors. This can be 

done as an extension of identifying focus facility type (using branches and nodes on crash tree 

diagrams), further focusing on curvature, posted speed limit, traffic volume as data are available, 

or by listing known contributing roadway factors for which data are available. The FHWA 

Systemic Safety Project Selection Tool provides the following list of example factors whose 

presence, absence, and/or characteristics may influence crash potential (Preston et al. 2013a, 

p. 18): 

• Number of lanes.

• Lane width. 

• Shoulder surface width and type. 

• Median width and type. 

• Horizontal curvature, superelevation, delineation, or advance warning devices. 

• Horizontal curve density. 

• Horizontal curve and tangent speed differential.
31 

• Presence of a visual trap at a curve or combinations of vertical grade and horizontal 

curvature.

• Roadway gradient. 

• Pavement condition and friction. 

• Roadside or edge hazard rating (potentially including sideslope design). 

• Driveway presence, design, and density. 

• Presence of shoulder or centerline rumble strips. 

• Presence of lighting. 

• Presence of on-street parking. 

• Intersection skew angle. 

• Intersection traffic control device.

• Number of signal heads versus number of lanes. 

• Presence of backplates.

• Presence of advanced warning signs. 

• Intersection located in or near horizontal curve. 

• Presence of left-turn or right-turn lanes. 

• Left-turn phasing. 

• Allowance of right-turn-on-red.

• Overhead versus pedestal-mounted signal heads. 

• Pedestrian crosswalk presence, crossing distance, signal head type. 

• Average daily traffic volumes.

• Average daily entering vehicles. 

• Proportion of commercial vehicles in traffic stream.

• Posted speed limit or operating speed. 

• Presence of nearby railroad crossing.

• Presence of automated enforcement.

• Adjacent land use type (e.g., schools, commercial, or alcohol-sales establishments).

• Location and presence of bus stops. 

Further, the FHWA Systemic Safety Project Selection Tool suggests using the predictive method 

from the HSM as a resource for identifying potential contributing roadway factors. The HSM 

predictive method includes segment and intersection safety-performance functions and 

adjustment factors for facility types listed in the Facility Types section of the HSM. The HSM 

and HSM supplement contain an extensive list of adjustment factors that have been related to 

crash frequency (AASHTO 2014). However, the HSM predictive method generally focuses on all 

crashes or all crash severities for specific crash types (e.g., all multivehicle crashes). Adjustment 

factors are associated with total crashes and not necessarily severe injury crashes of a highly 

specific type. 

Alternatively, analysts can use contributing roadway factors identified in other research or 

through analysis of their own data (including development of crash-prediction models). The 

following discussion focuses on research that has looked at the process for site selection, 

contributing roadway-factor identification, prioritization, and methods for implementing systemic 

safety approaches. 
32 

National Cooperative Highway Research Program’s (NCHRP’s) Research Results Digest 345: 

Alternate Strategies for Safety Improvement Investments reviewed the “black spot” and 

“systematic” methods for allocating safety resources (National Academy of Sciences 2010). The 

authors distributed a survey to all 50 State DOTs and received responses from 25 State agencies. 

Additionally, the researchers conducted in-depth case studies with four States (Iowa, Minnesota, 

Missouri, and North Carolina). The authors found that most agencies target safety funds at 

high-crash locations; however, there was a trend of increasing the proportion of safety funding 

toward systemwide improvements. The authors also noted that the characteristics associated with 

KA crashes have caused programs to be more focused on rural areas and include more projects 

involving widely deploying proactive, low-cost strategies across systems. The States identified 

two challenges associated with the safety planning process: the analytical process for identifying 

candidate locations for investment in rural areas is not well developed, and while States have 

increased engagement with local road authorities, concerns remain about their lack of 

safety-planning experience.

Gross et al. (2016) developed Reliability of Safety Management Methods: Systemic Safety 

Programs to help program managers, project managers, and data analysts develop comprehensive 

safety-management programs. The objectives of the guide were to raise awareness of the 

systemic approach to safety management, characterize typical projects identified and 

implemented through a comprehensive safety-management program, demonstrate the value of 

integrating systemic approaches as part of a comprehensive safety-management program, and 

provide information on allocating funding to systemic projects within a comprehensive 

safety-management program. The guide described the state of the practice and the latest tools to 

support systemic safety analyses. Additionally, the guide described the systemic approach as 

having the following steps: 

1. Identify FCFTs. 

2. Determine contributing factors.

3. Select countermeasures.

4. Screen network for suitable locations. 

5. Evaluate safety effects.

The authors suggested that contributing factors should be identified using statistical modeling or 

cross-tabulations, identifying the association of specific roadway data with each crash type. If the 

agency does not have data or expertise to determine contributing factors for a focus crash type, 

the guide suggests using the NCHRP Report 500 series (National Academy of Sciences 2003a, 

2003b, 2003c, 2003d, 2003e, 2003f, 2004a, 2004b, 2004c, 2004d, 2004e, 2004f, 2004g, 2005a, 

2005b, 2005c, 2006, 2007, 2008a, 2008b, 2008c, 2008d, 2009) to identify contributing factors 

related to specific crash types. The authors suggested the following common segment and 

intersection features to define contributing factors:

• Segment features: number of lanes, lane width, shoulder type and width, median type and 

width, road edge features and quality, number and type of access points, radius and 

superelevation of horizontal curves, speed limit, speed differential between horizontal 

curves and tangent segments, roadside hazards, and pavement condition and friction. 
33 

• Intersection features: number of approaches, number of approach lanes, traffic control 

devices, skew, proximity to horizontal and vertical curves, signal timing, proximity to 

railroad crossings, presence of street lighting, proximity to nearby access points, and 

presence of commercial developments.

Some transportation agencies have been utilizing their State-specific datasets to conduct systemic 

analyses. For example, the Minnesota Department of Transportation (MnDOT) has been working 

with counties to address fatalities on local roads, where more than 50 percent of Minnesota traffic 

fatalities occur (Preston and Gute 2010). MnDOT recognized that local agencies have less 

experience in conducting systemwide crash analysis or linking crash causes with mitigation 

strategies at specific locations on their system. Therefore, MnDOT began working on an initiative 

to develop a roadway-safety plan for each county within the State, concentrating on information 

specific to individual counties and identifying opportunities to reduce KA crashes. MnDOT funds 

the planning process, which includes the following: 

• Performing crash analyses and conducting a systemwide assessment of crash potential. 

• Identifying unique, low-cost infrastructure-based safety projects that can be deployed 

across the county. 

• Developing unique safety plans for each county. 

The research team completed its review of safety plans for 20 counties with the focus on 

roadway-departure crashes on local roads and ANG crashes at rural stop-controlled intersections

in 2010. The State DOT identified contributing factors for roadway segments, horizontal curves, 

and rural stop-controlled intersections, along with the following contributing factors relevant to 

crashes involving curves (Preston and Gute 2010): 

• Average daily traffic (ADT) volume. 

• Curve radius (most crashes occur on curves with radii ranging between 500 and 1,500 ft).

• History of KA crashes on curves.

• Presence of an intersection or visual trap on a curve.

Additionally, the State DOT identified the following contributing factors for rural paved 

segments (Preston 2012): 

• Density of roadway-departure crashes.

• Traffic volume.

• Curve density. 

• Access density. 

• Pavement edges. 

For rural unsignalized intersections, the State DOT identified the following contributing factors 

(Preston 2012): 

• Skewed minor leg approach. 

• Intersection on/near horizontal curve. 

• Minor and major ADT ratios. 

• Proximity to previous stop sign. 
34 

• Proximity to railroad crossing. 

• Intersection-related crashes.

• Commercial development in quadrants. 

Thurston County, WA, Public Works selected roadway departures along horizontal curves as a 

target crash type based on a review of severe crash data (FHWA 2014). After reviewing crash 

data linked with roadway characteristics, they selected the following contributing factors with

high crash potential to screen and prioritize candidate locations for systemic improvements: 

• Roadway class of major rural collector.

• Presence of an intersection.

• Traffic volume of 3,000 to 7,500 AADT. 

• Edge clearance rating of 3 (on a scale of 1 [widest] to 3 [narrowest]).

• Paved shoulders ≥4 ft in width.

• Presence of a vertical curve.

• Consecutive horizontal curves (windy roads). 

• Speed differential between posted approach speed and curve advisory speed of 0, 5, and 

10 mph. 

• Presence of a visual trap (a minor road on the tangent extended). 

Salem County, NJ, developed a systematic road-safety analysis tool to examine 

roadway-departure crashes at bridges (Cato et al. 2013). The authors found the following 

roadway characteristics to be related to a higher potential of roadway-departure crashes at 

bridges: 

• Pavement width is <22 ft. 

• Shoulder width is <1.5 ft. 

• Lane width is <10.5 ft. 

• Pavement condition is fair or poor. 

• Superelevation is minimal or nonexistent for a horizontal curve. 

• Friction is fair or poor.

• Striping is fair or poor. 

• Advance warning signs are minimal or nonexistent. 

• Object markers are in poor condition or nonexistent. 

• Abutment condition is fair or poor.

• Vertical curve exists.

• Horizontal curve exists.

Preston et al. (2013b) characterized the systemic approach to safety management and summarized 

the cyclical, three-element process in the Systemic Safety Project Selection Tool (Preston et al. 

2013a). To demonstrate the contexts that are suited for systemic safety approaches, the 

researchers conducted a pilot test in three States: Kentucky, New York, and Washington. Table 9

provides a summary of how each State implemented the systematic approach, including the 

facility type, crash type, and contributing factors used for network screening. 
35 

Table 9. Systemic approach summary. 

Method for 

Network

Screening Kentucky New York Washington

Facility type County roadways Rural undivided roadways County arterial and collector 

roads

Crash type Roadway-departure crashes on 

horizontal curves

Lane-departure crashes Roadway-departure crashes

Contributing

factors

Traffic volume, access density, curve 

density for critical radius curves, 

presence of advance signing, 

intersections on the curves, and 

visual traps

Number of lanes, speed, 

traffic volume, shoulder 

width, lighting conditions, 

and curve radius

Speed differential, visual 

trap, intersections, presence 

of advance warning signs, 

and edge assessment

Wang et al. (2017) compared the effects of the ranking and weighting methods using a dataset of 

intersection (signalized and stop controlled) pedestrian crashes from Austin, TX. They obtained 

crash data from the Crash Records Inventory System of the Texas DOT2 and collected 

intersection characteristics data using Google® Street ViewTM (Google 2018). The researchers 

used the following contributing factors for the evaluation: 

• Land-use entropy. 

• Number of bus stops within 0.2 mi. 

• Sidewalk presence/absence. 

• Lighting presence/absence. 

• Pedestrian crosswalk presence/absence. 

• Percentage of one-way streets. 

• Bike lane presence/absence. 

• Number of approaches. 

• Total number of lanes. 

• Percentage of painted/raised medians. 

• Average speed limit.

• Truck composition (percent single truck + percent combo truck). 

• Average of approach ADTs.

• Pedestrian miles traveled.

Wang et al. (2017) used NB regression models to relate pedestrian crashes to traffic volume, 

intersection attributes, and contextual factors. The NB regression models revealed that land-use 

mix, number of approaches, percentage of one-way streets, and number of bus stops within 

0.2 mi are positively correlated to pedestrian-crash potential at stop-controlled intersections. The 

total number of lanes and total number of bus stops within 0.2 mi of intersections are positively 

correlated to pedestrian-crash potential at signalized intersections. The percentage of one-way 

streets and percentage of painted/raised medians is negatively correlated with pedestrian crash 

potential at signalized intersections.

Al-Kaisy et al. (2015) developed an index that estimates crash potential to be used in proactively 

identifying locations or segments for potential safety improvements on low-volume roads in 

2

The Crash Records Inventory System of the Texas DOT is not available to the public. 
36 

Oregon. The index considers three major factors: geometric features, crash history, and traffic 

exposure. The researchers performed cross-tabulations and multivariate linear regression and 

correlation analyses to identify the relationship between road characteristics and observed crashes 

as well as their significance. Identified contributing factors include the following: 

• Degree of curvature.

• Length of vertical curve.

• Lane width. 

• Grade.

• Shoulder width. 

• Driveway density. 

• Side-slope rating. 

• Fixed-object rating. 

• AADT.

• Crash history. 

The final crash index is a function of the roadway geometry and expressed as a value from 0.15 

to 1.0, where higher values correspond to a greater crash potential. The weights of individual 

geometric features are based on the data analysis and not recommended for alteration. However, 

the authors suggested that the overall weights that define the proportion of the crash index due to 

geometric features versus crash history or traffic exposure may benefit from agency input. 

Caldwell and Wilson (1996) described the need for a different approach to safety-improvement 

projects on unpaved roadways. Their approach used a survey of a steering committee to collect 

information about safety on unpaved roadways. The steering committee identified 12 roadway 

elements that should be the focus of unpaved road-safety audits. These elements included surface 

width, consistency, sight distance, signage, surface condition, prudent speed, horizontal curves, 

Percentage of trucks on the roadway, bridges, railroad crossings, vertical curves, foreslope, clear 

zone, and pedestrian/bicyclist presence. 

Mahgoub et al. (2011) developed the rural road safety index (RRSI) using a dataset from 

Brookings County, SD. The RRSI ranks roadway network locations based on their safety features 

to identify deficiencies. The authors proposed the RRSI to support roadway-safety reviews. The 

study found the following factors that contributed to crash potential on the local roads of 

Brookings County: 

• Intersection sight distance. 

• ANG of approach. 

• Signage. 

• Road alignment. 

• Vertical and horizontal curves.

• Culverts. 

• Table drains.

• Location of signage. 

• Visibility and legibility of signs.
37 

Recently, two studies compared the FHWA Systemic Safety Project Selection Tool to other 

methods for selecting systemic safety improvements (Preston et al. 2013a; Knapp et al. 2014). 

Knapp et al. (2014) compared five selection tools/methodologies, including the following, 

through a comprehensive literature review: 

• Minnesota Country Road Safety Plan (CRSP) (MnDOT 2020). 

• FHWA Systemic Safety Project Selection Tool (Preston et al. 2013a). 

• United States Road Assessment Program (usRAP) (usRap 2020). 

• New Jersey Systemic Road Safety Tool.

• Safety Analyst (AASHTO n.d.). 

The researchers compared general availability, required input data, ease of use, basis of 

prioritization, and potential for sensitivity analysis. The tools varied widely in terms of 

data-collection requirements and cost, but all tools used a rating (e.g., stars) of number of 

contributing factors and/or B/C ratios for location prioritization. The authors recommended the 

Minnesota CRSP and usRAP approaches for application on Iowa’s paved secondary rural 

roadways. 

Knapp et al. (2014) implemented the Minnesota CRSP approach by collecting five safety 

contributing factors for horizontal curves, seven for stop-controlled intersections, and five for 

segments, as outlined by Preston and Gute (2010) and Preston (2012). To apply usRAP, they 

collected the following data variables:

• Carriageway/roadway: number of divided and undivided highways and direction of travel. 

• Distance: distance in kilometers from the start of the road segment.

• Length: length of roadway sections generally in kilometers (typically 100 m or 328 ft). 

• Latitude and longitude: coordinates in decimal degrees for the starting point of each 

328-ft roadway section. 

• Landmark: presence of key landmarks (e.g., town/village sign, major bridge, toll booth). 

• Traffic flow: number of vehicles recorded on each section of the road (e.g., AADT).

• Motorcycle percentage: percentage of motorcycles in the traffic flow.

• Observed bicycle flow: number of bicycles in the traffic flow. 

• Pedestrian flow—crossing road: number of pedestrians observed crossing the road. 

• Pedestrian flow—along road: number of pedestrians observed walking along the road. 

• Area type: level of roadside development (e.g., urban, semiurban). 

• Number of lanes for use by through traffic: total number of lanes in one direction of 

travel.

• One-way/two-way flow: traffic-flow operation. 

• Speed: actual posted speed limit in miles per hour. 

• Lane width for lanes serving through traffic: distance from the outside edge of the 

traveled way or the center of the edgeline marking to the center of the adjacent lane or 

centerline marking. 

• Paved shoulder width: paved section of the roadway outside the edgeline that is safe and 

drivable usually measured from the center of the shoulder marking to the outside edge of 

the paved roadway or from the outside edge of the traveled way to the outside edge of the 

paved roadway. 
38 

• Unpaved shoulder width: space available for pedestrians to walk along the side of the 

road. 

• Shoulder rumble strips: presence of rumble strips. 

• Curvature: horizontal alignment (e.g., very sharp, sharp) of the roadway that may be 

based on advisory posted speed limits, if available.

• Quality of curve: measure of drivers’ judgment to determine the sharpness of the curve 

and select a speed to traverse the curve.

• Overtaking demand: developed in the preprocessing stage rather than being coded and

represents the frequency of or possibility that vehicles would undertake passing tactics by 

using the lane in the opposing direction of travel. 

• Delineation: quality of the traffic-control devices.

• Vertical alignment variation: change in the roadway gradient along its length. 

• Road condition: roadway-surface condition with respect to skid resistance. 

• Sidewalk provision—right: presence of a sidewalk on the right side of the road. 

• Sidewalk Provision—left: presence of a sidewalk on the left side of the road. 

• Land use—right and left: measure of the possibility of generating pedestrian activity 

along the roadside. 

• Side friction: extent of interaction between the activities along the roadside and traffic on 

the roadway. 

• Pedestrian-crossing facilities: number of pedestrian-crossing facilities.

• Quality of crossing: measure of road-crossing visibility for drivers and the presence of 

warning signs. 

• Bicycle facilities: presence of facilities (i.e., bike paths or shared lanes) for bicyclists.

• Roadside severity—separated bicycle path: qualitative measure of the severity of the 

roadside present on segregated bicycle facilities based on the presence, type, and location 

of roadside objects, crashworthy barrier, and roadside slopes. 

• Motorcycle facilities: number of facilities for motorcyclists or other motorized two-wheel 

vehicles.

• Roadside severity—separated motorcycle path: qualitative measure of the severity of the 

roadside present on segregated motorcycle facilities based on the presence, type, and 

location of roadside objects, crashworthy barrier, and roadside slopes. 

• Speed limit—separated motorcycle path: posted speed limit for segregated motorcycle 

paths. 

• Median type—separated motorcycle path: presence of roadway infrastructure that 

separates opposing traffic flows for a segregated motorcycle path. 

• Minor access—point density: number of driveways within each 327-ft roadway segment 

for both urban and semiurban areas. 

• Roadside severity—right: distance to the nearest object likely to be struck by an errant 

vehicle on the right side of the road that could result in serious or fatal injury to vehicle 

occupants. 

• Roadside severity—left: distance to the nearest object likely to be struck by an errant 

vehicle on the left side of the road that could result in serious or fatal injury to vehicle 

occupants. 

• Intersection type: type of intersection (e.g., signalized four-leg, unsignalized three-leg).
39 

• Intersection quality: measure of the quality (i.e., condition and location) of intersection 

design features (e.g., advance warnings, signs, markings) and sight distance to the 

intersection for approaching vehicles. 

• Intersecting road volume: approach traffic volume (a rating based on volume, if known). 

• Median type: presence of infrastructure separating opposing traffic flow. 

• Major upgrade cost impact: measure that takes into consideration the influence of 

surrounding land use, environment, and topography on the cost of major developmentalupgrade projects. 

• Comments: (optional) roadway safety and obstacles faced during the coding process. 

• Roadwork (work zones): presence of any major roadway construction or work zones. 

When applying the two approaches, Knapp et al. (2014) changed one or more input variables in 

each tool to adjust the coefficients (or weights) of contributing factors in the Minnesota CRSP 

approach. The researchers recommended that contributing factors should have the objective of 

identifying locations with characteristics known to impact rural roadway safety and locations 

should be differentiated by relatively unique contributing factors or combinations of contributing 

factors.

Harwood et al. (2013) compared three methods for selecting highway-infrastructure 

countermeasures to reduce crash frequency and severity using data from six Kentucky counties. 

The three methods were usRAP, the FHWA Systemic Safety Project Selection Tool, and 

road-safety audits (usRAP 2020; Preston et al. 2013a). The authors applied the FHWA tool to a 

217-mi road network with a focus on ROR crashes on horizontal curves. The contributing factors 

selected as the basis for the network screening and indicating higher crash potential included the 

following: 

• Horizontal curve density greater than mean density for critical curves with radii between 

500 and 1,200 ft. 

• Lane width <10.5 ft. 

• Shoulder type not paved. 

• Shoulder width <6 ft.

• Speed limit >30 mph. 

• Traffic speed (higher than the speed limit or 85th-percentile speed).

• Number of lanes.

• Lane width. 

• Shoulder width. 

• Presence of shoulder rumble strips.

• Delineation.

• Passing demand. 

• Median type. 

• Curvature. 

• Quality of curve signing and marking. 

• Road-surface condition. 

• Roadside severity. 

• Number and type of intersections.
40 

• Quality of intersection signing and marking. 

• Intersecting road volume. 

• Driveway density. 

The results of the correlation analysis suggested that traffic volume should be a mandatory or, at 

least, a strongly considered contributing factor. The study also suggested that traffic volume 

should have a greater weight than other variables in a prioritization scheme. The researchers 

concluded that the FHWA tool is more flexible than usRAP when adapting to specific roadway 

networks and data-availability situations since it requires fewer data variables than usRAP. 

However, the authors noted that the FHWA tool’s greater flexibility may be offset by its inability 

to weigh relevant contributing factors. Road-safety audits were most effective for identifying 

missing safety-related features or those in poor condition. 

SUMMARY

Historically, researchers have attempted to identify contributing roadway factors related to 

various crash types, mainly through datasets developed to answer a specific question or focus on 

a specific set of characteristics (e.g., horizontal curve–related elements). Most research has 

focused on passenger cars or all user types combined. Researchers have used a variety of 

statistical methodologies to examine the relationship, but GLMs are most commonly applied to 

associate roadway characteristics with expected crash frequency. A relatively low number of 

studies focused on identifying contributing roadway factors for systemic safety analysis; 

however, the factors most commonly identified in the studies reviewed in this chapter should be 

prioritized for inclusion in future systemic safety analyses.

Literature that focused on systemic analyses consistently concluded that the analytical process for 

identifying candidate locations for investment in rural areas was not well developed and varied 

based on weights assigned to contributing factors. Multiple proposed strategies for identifying 

sites have strengths and limitations, with some being more data intensive than others. The less 

data-intensive methods may lack the ability to conduct more robust analyses. To this point, the 

ability to identify sites requires robust data of good quality, which may be more difficult to obtain 

for lower-volume roads, roads owned by local jurisdictions, or unpaved roads. Finally, 

researchers identified that local agencies sometimes lack experience in highway-safety planning, 

and staff may not be familiar with methods to conduct systemic safety analyses. Local agencies 

will benefit from the help of State agencies and research that can preidentify contributing factors 

associated with FCFTs. This research will provide a targeted list of data for agencies to collect to 

screen and prioritize locations, as well as identify related countermeasures.
41 

CHAPTER 3. TERMINOLOGY

Drawing on information from the literature review in chapter 2, this chapter includes definitions 

for crash types, facility types, crash potential, and contributing factors. These definitions will 

guide the use of such terms throughout the remainder of this report. 

Crash types are defined by the unique combinations of codes for two variables in crash reports: 

First Harmful Event and Manner of Collision. The First Harmful Event is the first injury- or 

damage-producing event of a crash. First Harmful Events generally fall into one of five 

categories, with more specific event codes under each category: 

1. Noncollision Harmful Events (e.g., rollover/overturn [ROLL], fire/explosion, jackknife). 

2. Collision with Motor Vehicle in Transport. 

3. Collision with Object Not Fixed (e.g., pedestrian, pedalcyclist, railway vehicle, animal). 

4. Collision with Fixed Object (e.g., tree, utility pole, embankment). 

5. Not Reported or Unknown. 

The Manner of Collision variable identifies the orientation of two motor vehicles in transport 

when they were involved in the First Harmful Event of a crash (e.g., front to rear, front to front, 

ANG), providing information on the crash type. Typically, a significant number of options are 

available on crash reports for defining the Manner of Collision. For example, the corresponding 

codes may indicate if the crash type is a particular type of ANG, rear-end, or sideswipe crash. If 

the First Harmful Event is not a collision between two motor vehicles, the Manner of Collision is 

classified as such (e.g., “Not a Collision with Motor Vehicle in Transport”). 

Facility types are defined by surrounding area type/land use, number of lanes, level of access 

control, and median presence, resulting in the following example facility types: 

• Rural four-lane freeway. 

• Urban four-lane freeway. 

• Urban six-lane freeway. 

• Urban eight-lane freeway. 

• Rural four-lane highway (nonfreeway): 

o Divided. 

o Undivided. 

• Rural two-lane highway. 

• Urban six-lane street: 

o Divided. 

o Undivided. 

• Urban four-lane street: 

o Divided. 

o Undivided. 

• Urban two-lane street. 
42 

Facility types are further broken down into site types, such as the following: 

• Urban six-lane freeway: 

o Freeway segment. 

o Freeway-ramp terminal. 

o Ramp. 

o Ramp-crossroad terminal. 

• Rural two-lane highway: 

o Tangent segment. 

o Horizontal curve segment. 

o Intersection. 

The definition of crash potential captures both the probability of an event occurring and its 

negative impact. The event of interest is the occurrence of a specific crash type. Event occurrence 

can be defined in several ways, including the following: 

• Expected frequency. 

• Probability mass (probability of 0, 1, 2, 3, or more events). 

• Cumulative probability (probability of 1 or more, 2 or more, 3 or more events). 

The negative impact of an event refers to its negative impact with respect to cost, health, human 

life, or other personal or societal impacts. This negative impact by the severity of injuries 

resulting from a crash event is defined per the following scale:

• K = fatal injury. 

• A = incapacitating injury. 

• B = nonincapacitating injury. 

• C = possible injury. 

• O = no injury. 

Contributing factors are defined as factors whose presence is associated with increases or 

decreases in expected frequencies of crashes or injury severities resulting from crashes. Chapter 5 

will distinguish contributing factors associated with increases in crash frequency or crash severity 

and contributing factors associated with decreases in crash frequency or crash severity.
43 

CHAPTER 4. FCFTS 

For the purpose of this project, the research team conducted an analysis of the FARS and HSIS 

databases to inform the selection of FCFTs. FARS contains information on all K crashes that 

involved a motor vehicle traveling on a public trafficway in the 50 States, the District of 

Columbia, and Puerto Rico. HSIS contains information on crashes of all severities (fatal, injury, 

and PDO) that occurred on State-operated and -maintained roads for the participating States. This

analysis incorporated data from four States that are part of HSIS (California, Minnesota, Ohio, 

and Washington). This chapter provides additional detail on the data, methods, and results 

regarding the selection of FCFTs.

FARS DATABASE AND ELEMENTS

A K crash is a crash in which the death of at least one person involved in the crash occurred 

within 30 days of the crash. FARS consists of several different files that contain information 

pertaining to K crashes. The research team analyzed three data files—Accident, Vehicle, and 

Person1

—to inform the selection of FCFTs. The following are brief descriptions of the 

information available in these files:

• The Accident data file contains information about crash characteristics and environmental 

conditions at the time of the crash. This file contains one record per crash.

• The Vehicle data file contains information describing the in-transport motor vehicles 

involved in the crash. This file contains one record per in-transport motor vehicle 

involved in the crash. 

• The Person data file contains information describing all persons involved in the crash, 

including both motorists and nonmotorists. This file contains one record per person 

involved in the crash. 

These files can be merged using common variables, such as State, case number, vehicle number, 

and person number. The research team planned to use 6 years of FARS data ranging from 2009 to 

2014; however, due to limitations in the 2009 data with respect to intersection information2

, the 

analysis incorporated FARS data from 2010 to 2014. 

The research team narrowed down a list of key variables for identifying FCFTs for each file. This 

list and a brief description of each variable are provided in appendix A. 

1

Some variables in the Vehicle file used to be in the Accident file. For example, VTRAFCON (a traffic-control 

device) was part of the Accident file but is now in the Vehicle file. 2

In FARS, data on intersection type were not collected prior to 2009.
44 

HSIS DATABASE AND ELEMENTS

HSIS is operated by FHWA and consists of several different files that contain information 

pertaining to crashes of all severities (fatal, injury, and PDO) occurring on State-operated 

and -maintained roads for participating States. The four HSIS data files used for the purpose of 

informing the FCFT selection were the Accident, Roadway, Intersection3

, and Curve4 files, 

which were used to collect data on California, Minnesota, Ohio, and Washington. The research 

team was able to efficiently identify intersections using Intersection files available for California, 

Minnesota, and Ohio and horizontal curves using Curve files available for Ohio and Washington. 

The following are brief descriptions of the information available in these four file types: 

• The Accident data file contains information about crash characteristics and environmental 

conditions at the time of the crash. This file contains one record per crash occurring on 

State-owned or -operated roads. 

• The Roadway data file contains information on homogenous sections of roadway 

(i.e., stretches of road that are consistent in terms of certain characteristics, with new 

sections defined anytime a characteristic changes). Each record represents a homogenous 

roadway segment and contains characteristics of the roadway segment, such as width of 

the traveled way, number of lanes, width of paved shoulder and total shoulder, median 

type, and other variables. 

• The Intersection data file contains information on both mainline routes and crossing 

routes of at-grade intersections. The information includes items such as intersection type, 

traffic control type, lighting, channelization, and AADT. 

• The Curve data file contains information on sections of roadway that are part of 

horizontal curves. Each record includes variables related to deflection ANG, curve 

direction, degree of curvature (and radius), curve length, speed limit, and whether or not 

the horizontal curve overlaps with a preceding curve. 

These HSIS files can be merged using common variables, such as route number, system number, 

milepost (for crashes and intersections), and beginning and end mileposts (for roadway 

segments). The analysis incorporated 6 years of HSIS data ranging from 2009 to 20145

. 

The research team narrowed down a list of key variables for identifying FCFTs for each file. This 

list and a brief description of each variable are provided in appendix A. 

KEY VARIABLES FOR DEFINING POTENTIAL FCFTS

To define potential FCFTs, the research team identified the following variables (from the variable

list in appendix A) as the most relevant: 

3

The Intersection file was only used for California during the FCFT analysis. For the other three States, relevant 

intersection information for identifying FCFTs was obtained from the Accident file. 4

The Curve file was only used for Washington during the FCFT analysis. For the other three States, relevant 

road-alignment information for identifying FCFTs was obtained from the Accident file.

5

The Intersection file for California was not available for 2013. As such, data from 2009 to 2012 and 2014 from 

Intersection files were used. For the other three States, data from 2009 to 2014 were used.
45 

• Crash type. 

• Area type (rural versus urban). 

• Roadway type. 

• Location (intersection versus nonintersection)6. 

• Intersection type. 

• Type of traffic control. 

• Lighting (day versus night). 

• Road alignment (horizontal curve versus straight/tangent). 

The research team explored the possibility of including other variables, such as weather, driver 

demographics, and driver behavior (e.g., speeding), but concluded that these variables would be 

analyzed as potential contributing factors rather than as factors for identifying and defining 

FCFTs. This conclusion is generally consistent with how systemic safety analyses in literature 

and practice have defined FCFTs and potential contributing factors, as well as with the state of 

knowledge, linking infrastructure-related countermeasures to crash types. For example, specific 

infrastructure-related countermeasures exist for single-vehicle ROR crashes at night, but existing 

practices do not distinguish different infrastructure-related countermeasures specific to singlevehicle ROR crashes at night for 16- to 19-year-old drivers versus other age groups. However, 

knowing that the number of 16- to 19-year-old drivers that use a segment is associated with the 

expected frequency of single-vehicle ROR crashes at night (i.e., a contributing factor) could help 

agencies identify which segments to treat systemically with proven countermeasures for ROR

crashes.

The following sections include detailed descriptions of how the research team derived the key 

variables used to define FCFTs, along with information on how these key variables were 

extracted from FARS and HSIS.

Crash Type 

The research team defined crash type based on different variables identifying the manner of 

collision and first harmful event. The information used from each dataset to extract the crash type 

was as follows:

• FARS: first harmful event and manner of collision.

• HSIS, California: accident type. 

• HSIS, Minnesota: accident type and diagram of accident type. 

• HSIS, Ohio: accident type. 

• HSIS, Washington: accident type and first collision type. 

Area Type

The research team defined area type as one of two categories: rural or urban. This information 

was extracted using both the area-type identifier present within the dataset and the roadway 

functional-class variable.

6

In FARS, location type was not available in 2009, so the research team used data from 2010 to 2014.
46 

Roadway Type 

FARS and HSIS define roadway type differently. To define roadway type, FARS uses the 

following variables: roadway functional type, number of lanes, and median type. These variables 

allow for defining roadway type as interstates/freeways/expressways or two-lane/multilane 

divided or undivided roadways. HSIS uses the roadway classification variable, which 

disaggregates road type into freeways or two-lane/multilane divided or undivided roadways. 

Work zone–related crashes were excluded from this analysis. Work zones have specific 

contributing factors linked to temporary traffic-control characteristics and the type of 

construction or maintenance work. This type of information was not readily available for this 

analysis as it is typically pulled from State DOT records and construction diaries.

Location Type 

The research team further identified crashes as being intersection or nonintersection crashes using 

the intersection variable in FARS and the location type variable in HSIS. For this analysis, 

railroad-related crashes, as identified in the Accident files of FARS and HSIS, were excluded

from all analyses. Contributing factors for at-grade railroad crossings often include the frequency 

with which trains pass through the location; however, this type of information was not readily 

available for this analysis.

Intersection Type

The research team classified crashes according to the type of intersection7 (i.e., four-way 

intersection, T-intersection, Y-intersection) using the intersection type variable in FARS and 

location type in HSIS.

Traffic Control Type 

The research team used the traffic control variable in FARS and HSIS8

 to define the type of 

traffic control (i.e., stop controlled, traffic signal, yield) at the location of the crash.

Light Condition 

The research team used the light condition variable in FARS and HSIS to define the light 

condition at the time of the crash. Daytime crashes were defined as any crash that occurred 

during the daytime. To define nighttime crashes, the following categories were combined: 

dark–lighted, dark–not lighted, dark–no lights present, dawn, and dusk. 

Road Alignment Type (Nonintersection Crashes)

The research team defined road alignment as either a straight segment (i.e., horizontal tangent) or 

horizontal curve. This information was extracted using the road alignment variable in FARS and 

7

HSIS data regarding intersection type were not available in the Accident files for Ohio and Washington; thus, 

crashes were identified as intersection- or nonintersection-related crashes for the purpose of informing FCFT 

selection. 8

The traffic control variable was not available in the HSIS Accident files for Ohio and Washington.
47 

road characteristic variable in HSIS. This information was only extracted for nonintersection 

crashes. The road alignment variable was not available for California. In Washington, the Curve 

file was used to determine whether the crash occurred on a curve or straight segment.

Fatal and Incapacitating Injury Crash 

In the HSIS files, KA crashes were identified using the accident severity variable.

POTENTIAL FCFTS

After categorizing the data using the variables identified in the section Key Variables for 

Defining Potential FCFTs, the research team developed a list of potential FCFTs. First, they 

developed separate lists of potential FCFTs using data from FARS and each HSIS State to 

observe and address consistencies and differences across the databases before selecting FCFTs 

that would become the focus of the remainder of this research. 

The research team defined potential FCFTs using a combination of the following variables: 

• Area type (rural versus urban). 

• Crash type. 

• Intersection type. 

• Lighting type (day versus night). 

• Location type (intersection versus nonintersection). 

• Road alignment type (horizontal curve versus straight segment). 

• Roadway type. 

• Traffic control type. 

For example, HEO-D crashes on rural two-lane roads on straight segments represented one 

potential FCFT.

Potential FCFTs were then ranked using the number of K crashes and, for HSIS States, the 

number of KA crashes during the observation period corresponding to each potential FCFT. The 

analysis developed separate ranked lists based on FARS and each HSIS State to observe whether 

certain FCFTs consistently corresponded to higher or lower numbers of K crashes and KA

crashes or whether the rankings varied considerably by database. 

This analysis approach is slightly different from the systemic safety-planning process described 

in FHWA’s Systemic Safety Project Selection Tool (discussion of this publication is in chapter 2) 

(Preston et al. 2013a). The systemic safety-planning process described by Preston et al. (2013a), 

as it applies to identifying FCFTs, is sequential and involves two main tasks. 

The first task is the selection of focus crash types: 

This task involves conducting a system wide analysis of crash types to select those 

representing the greatest potential to reduce fatalities and severe injuries. This effort 

typically results in identifying the crash types that represent the greatest number of severe 

crashes across the system being analyzed. (Preston et al. 2013a, p. 11) 
48 

The second task is the selection of focus facilities for the focus crash types identified in the first 

task: 

After selecting the focus crash types, Task 2 answers the question where are the crashes 

occurring? A ‘crash tree’ diagram is the recommended approach to answer this question. 

The crash tree can have a number of different formats, depending on agency capabilities 

and data availability. One such example is to begin the crash tree with the total number of 

severe crashes at the highest level. Each subsequent level separates the severe crashes by 

facility type. (Preston et al. 2013a, p. 16) 

The research team sought to determine whether the process detailed in this chapter for analyzing

FCFTs yielded the same or different results as the process outlined in FHWA’s Systemic Safety 

Project Selection Tool (Preston et al. 2013a). To accomplish this goal, the research team applied 

FHWA’s approach to data from FARS on nonintersection crashes. The FCFTs selected from 

implementing this approach were consistent to those selected using the process detailed in this 

chapter (the results of this comparison are in appendix B).

Tables detailing potential FCFTs are available in appendix C. Table 93 and table 94 show the 

numbers of K crashes from FARS categorized by intersection and nonintersection crashes and 

sorted by the frequency of K crashes associated with particular combinations of the variables 

identified in the section Key Variables for Defining Potential FCFTs. Table 95 through table 108

show the numbers of K and KA crashes from the HSIS States categorized by intersection and 

nonintersection crashes and sorted by the frequency of K and KA crashes associated with 

particular combinations of the variables identified in the section Key Variables for Defining 

Potential FCFTs. 

Looking at the results from FARS and three HSIS States (Minnesota, Ohio, and Washington) it 

was clear that two-lane roads are the facility type with the highest number of K crashes. More 

specifically, lane-departure (LNDP) crashes on segments (including ROR, ROLL, HEO, and 

ANG crashes) and ANG crashes at intersections seem to have the highest numbers of fatal or KA

crashes. HSIS only includes information from State-maintained roads, and FARS includes K 

crashes from all public roads; however, this did not impact the type of consistency observed 

between the FARS and HSIS State datasets for these potential FCFTs. Freeway crashes were

prevalent in the HSIS California data but not so much in the data from FARS and the other HSIS 

States. The large numbers of higher AADT on urban freeways in the HSIS California data 

partially explains this finding. 

SELECTING FCFTS 

The overall work plan for this project was based on an in-depth analysis of approximately 

15 focus crash type–facility type combinations to identify contributing crash factors. 

Observations made during the FARS and HSIS State-specific analyses described earlier in this

chapter served as the basis for selecting the 15 combinations for further analysis with respect to 

contributing crash factors. The remainder of this section provides additional information on how 

the research team addressed two crash types: pedestrian and ROR. Both of these crash types 

represent a significant number of K crashes and also have unique data-related challenges. The

concluding sections of this chapter provide the nonintersection and intersection FCFTs. 
49 

Based on FARS data from 2010 to 2014, pedestrian crashes represented a significant number of 

K crashes (NHTSA 2018a):

• 5,286 (out of 11,507) K crashes were coded as intersection crashes.

• 14,817 (out of 77,664) K crashes were coded as nonintersection crashes. 

Identifying contributing factors for pedestrian crashes is a challenging task without quality 

exposure data (i.e., the number of pedestrians crossing at segments and intersections or walking 

parallel to segments). Methods for analyzing pedestrian crashes were very limited in the first 

edition of the HSM for this reason, and filling this gap in the second edition is a high priority for 

the research community and AASHTO.

At the time of this project, two other efforts related to identifying factors contributing to 

pedestrian crash frequency and severity were ongoing: 

• NCHRP 17-73, Systemic Pedestrian Safety Analysis, was funded a total of $300,000 for 

2.5 years (Thomas et al. 2018). 

• NCHRP 17-84, Pedestrian and Bicycle Safety Performance Functions for the Highway 

Safety Manual, was funded a total of $500,000 for 2 years (National Academy of Sciences 

n.d.). 

The research team explored an additional original analysis using available data but was not 

confident that a quality analysis of contributing factors could be conducted with the existing data, 

which did not include pedestrian exposure and had some questionable crash-location coding 

(e.g., 350 K crashes and an additional 192 A crashes on California freeways at night). However, 

given California’s significant numbers, chapter 5 includes a discussion of contributing factors for 

pedestrian crashes, which is based on information from related published literature.

For nonintersections, collisions with roadside fixed objects corresponded to the highest number 

of K crashes, but the research team decided not to specifically address fixed-object crashes for 

the following reasons: 

• The HSIS Roadway files have very little information about roadsides, which are 

characterized by a number of factors that likely influence ROR crashes (e.g., roadside 

slopes, types and offsets to fixed objects, types and offsets to barriers). 

• The Low-Cost Safety Improvements Pooled Fund Study, administered through the 

FHWA Office of Safety Research and Development, is sponsoring an effort to quantify 

safety effects of the presence, removal, or shielding of roadside fixed objects. 

This analysis includes ROR and LNDP crashes since they represent a significant number of 

K crashes in the country. ROR crashes are a subset of LNDP crashes. LNDP crashes are crashes 

in which a vehicle departs the travel lane, resulting in a ROR, ROLL, HEO, or ANG crash. 

This definition of LNDP is intended to be synonymous with FHWA’s definition of a 

roadway-departure crash: a crash that occurs after a vehicle crosses an edgeline or centerline or 

otherwise leaves the traveled way (FHWA 2017b). ROR crashes are any crashes in which a 

vehicle strikes a roadside object or otherwise leaves the travel lane and enters the roadside. 
50 

Intersection FCFTs

The following is a list of FCFTs for which an original analysis of contributing factors was 

executed as part of this project: 

• ANG crashes on rural two-lane roads at four-leg stop-controlled intersections (daytime

and nighttime). 

• ANG crashes on urban two-lane roads at four-leg stop-controlled intersections (daytime).

• ANG crashes on urban multilane divided roads at four-leg signalized intersections 

(daytime).

• ANG crashes on urban multilane undivided roads at four-leg signalized intersections 

(daytime).

• ANG crashes on rural two-lane roads at three-leg stop-controlled intersections (daytime).

• ANG crashes on rural multilane divided roads at four-leg minor-road stop-controlled 

intersections (daytime).

Nonintersection FCFTs

The following is a list of FCFTs for which an original analysis of contributing factors was 

executed as part of this project:

• ROR crashes on rural two-lane roads on horizontal curves (daytime and nighttime). 

• ROR crashes on rural two-lane roads on straight segments (daytime and nighttime). 

• LNDP crashes on rural two-lane roads on horizontal curves (daytime and nighttime).

• LNDP crashes on rural two-lane roads on straight segments (daytime and nighttime). 

• HEO crashes on rural two-lane roads on straight segments (daytime and nighttime). 

• ANG crashes on rural two-lane roads on straight segments (daytime). 

• HEO crashes on rural two-lane roads on horizontal curves (daytime and nighttime). 

• ROLL crashes on rural two-lane roads on straight segments (daytime and nighttime). 

• ROLL crashes on rural two-lane roads on horizontal curves (daytime and nighttime). 

This project also addresses contributing factors for two types of pedestrian crashes using 

information from related published literature: 

• All types of pedestrian crashes at intersections.

• Pedestrian crashes at intersections involving a crossing pedestrian and vehicle going 

straight.
51 

CHAPTER 5. CONTRIBUTING FACTORS

This chapter includes descriptions of the data, methodology, and results for identifying and 

evaluating contributing factors corresponding to the selected FCFTs noted in chapter 4. 

According to Systemic Safety Project Selection Tool, “Tasks 1 and 2 [Select Focus Crash Types 

and Select Focus Facilities] relied on data typically in the crash record system. Task 3 [Identify 

and Evaluate Contributing Factors] is the first point where road and intersection inventories are 

likely needed to provide additional levels of detail to support the data analysis” (Preston et al. 

2013a, p. 18).

DATA

Three different data sources were used to conduct the contributing-factor analysis:

• HSIS (FHWA 2018c). 

• Databases from the National Oceanic and Atmospheric Administration’s (NOAA’s) 

National Centers for Environmental Information (2018). 

• Databases from the U.S. Census Bureau (2018). 

The research team initially planned on using HSIS data from four States—California, Minnesota, 

Ohio, and Washington—for the contributing-factor analysis. The plan was to use California and 

Minnesota data for the intersection contributing-factor analysis and Ohio and Washington data 

for the nonintersection contributing-factor analysis. However, the research team encountered 

some challenges using the Minnesota and Ohio datasets for this purpose. In the Minnesota data, 

major and minor roads at intersections were not always clearly defined and a framework to 

efficiently link climate and socioeconomic factors to the Minnesota HSIS Roadway file was not 

available. In the Ohio data, the main issue was that the HSIS Curve file had not been updated 

since 2009. During the analysis, issues with the Ohio data were resolved when the Ohio DOT 

(ODOT) provided their latest Curve Inventory, along with an intersection database. The research 

team resolved the issues with the Minnesota data, but the timing of these issues did not allow the 

research team to incorporate the data into the analysis. Thus, the analysis proceeded using Ohio 

and Washington data for the intersection contributing-factor analysis and California and Ohio 

data for the nonintersection contributing-factor analysis.

The research team assembled data on climate and socioeconomic factors for the sites in 

California, Ohio, and Washington and linked these data to the study locations for use in the 

contributing-factor analysis. This process involved the following steps: 

• Acquire data: The research team obtained climate data from NOAA (2018) and 

socioeconomic data from the U.S. Census Bureau (2018). Both the climate and census 

datasets were available in spatial format.

• Plot study sites: For each State (California, Ohio, and Washington), the research team 

developed a list of locations for the analysis, either intersections, curve segments, or 

tangent segments based on HSIS data. They then plotted each location spatially using a 

spatial roadway network obtained from each State.
52 

• Associate data: The research team joined the climate and census data to the study location 

in the spatial environment. Climate data was joined to the study-site location based on the 

nearest weather station in a straight-line distance. For census data, the join was based on 

the census block that contained the study site. For segments, the census block that housed 

the midpoint of the segment served as the basis for the join. 

• Assemble data fields required for analysis: The research team assembled a working 

database that consisted of selected fields from the climate and census data. In the census 

data, the research team developed certain variables based on the raw data, such as 

“percentage of households with two or more vehicles.” 

POTENTIAL CONTRIBUTING FACTORS

Potential contributing factors were defined for three different categories: roadway, climate, and 

census. Among potential contributing factors, it is possible that some can be direct contributing 

factors while others may be surrogates for other characteristics that were not available (e.g., data 

were not available for roadsides, driveways, how well roads were maintained, or travel patterns 

or behavior other than AADT). In all cases in this report, findings regarding contributing factors 

are interpreted as statistical associations with expected crash frequencies and severities. The 

report does not spend time covering the philosophy of causation or causal inference. That said, 

the following characteristics raise confidence that a specific finding or set of findings are stable 

and transferable:

• Consistency across subsets of related FCFTs. 

• Consistency across multiple States.

• Consistency with previous findings in the literature. 

Roadway 

The analysis explored potential contributing factors associated with roadways. The following list 

contains the contributing factors and their associated variable names: 

• Average AADT (avg_aadt). 

• Mainline AADT or major AADT (ML_AADT or majorAADT). 

• Cross street AADT or minor AADT (XST_AADT or minorAADT). 

• Segment length1

. 

• Curve radius (curv_rad). 

• Percent grade (pct_grad)2. 

• Grade type (grad_typ). 

• Shoulder type (shl_typ). 

• Shoulder width (shldwid). 

1

Instead of using segment length as a direct potential contributing factor in the analysis, the research team used it 

to define crash rate per mile. 2

In the Ohio data, grade information was also available for segments and intersection approaches. The percent 

grade was divided into three categories (level = <3 percent, moderate = 3–6 percent, and steep = >6 percent) and 

used as a categorical variable for the intersection contributing-factor analysis. The segment analysis used the 

absolute value of grade as a continuous variable.
53 

• Terrain (terrain). 

• Speed limit (spd_limt or DESG_SPD). 

• Lane width (lanewidth). 

• Percentage of trucks on the roadway (trkpcts). 

• Median width (MEDWID). 

• Surface width (surfwid). 

• Mainline left-turn channelization (ML_LEFT). 

• Mainline right-turn channelization (ML_RIGHT). 

• Cross street left-turn channelization (xstrtlft). 

• Cross street right-turn channelization (XSTRTRGH). 

• Number of approaches with left-turn lanes (leftLanes). 

• Number of approaches with right-turn lanes (rightLanes). 

Climate

The analysis explored the following potential contributing factors associated with climate:

• Average annual snowfall totals (snowavgyear). 

• Average annual rainfall totals (rainavgyear). 

• Average annual maximum temperatures (tempmaxavg). 

• Average annual minimum temperatures (tempminavg). 

• Average annual winter minimum temperatures (tempwintermin). 

• Average annual number of days with a minimum temperature of ≤32°F (temp32fdays). 

Census

The analysis explored the following potential contributing factors associated with socioeconomic 

characteristics:3

• Percentage of population ages 16+ unemployed (unempl16plus). 

• Percentage of population ages 16–24 working full time (workftage16to24). 

• Percentage of population ages 16–24 working part time (workptage16to24). 

• Percentage of population ages 16–24 unemployed (noworkage16to24). 

• Percentage of population ages 25+ without a high school diploma (noedctn25plus). 

• Percentage of population ages 25+ with a high school diploma (diploma25plus). 

• Percentage of population ages 25+ with a university degree (univ25plus). 

• Percentage of households with income <$50,000 (income50k). 

• Percentage of households with income between $50,000 and $100,000 (income50to100k). 

• Percentage of households with income >$100,000 (income100kplus). 

• Percentage of households with no vehicles (noveh). 

• Percentage of households with one vehicle (X1veh). 

• Percentage of households with two or more vehicles (X2vehplus). 

• Percentage of population ages 15–19 (age15to19). 

3

These percentages represent people or households within the census block that contains the segment or 

intersection.
54 

• Percentage of population ages 20–44 (age20to44). 

• Percentage of population ages 45–64 (age45to64). 

• Percentage of population ages 6–74 (age65to74). 

• Percentage of population ages 75+ (age75plus). 

SUMMARY OF DATA

Table 10 through table 25 provide summary statistics (minimum, maximum, average, and 

standard deviation) for roadway, climate, and socioeconomic variables for California, Ohio, and 

Washington datasets. 

Roadway variables specified as categorical variables in the analysis include lane width, shoulder 

type, surface type, terrain, and curve radius (for segment and intersection analysis using Ohio 

data). The following sections detail the categories used for each variable. 

Lane Width 

The research team divided lane width into three categories:

• <11 ft. 

• 11–12 ft. 

• >12 ft. 

Shoulder Type

The research team divided shoulder type into three categories with information about whether left 

and right shoulders were paved or unpaved: 

• Paved–paved. 

• Paved–unpaved. 

• Unpaved–unpaved. 

Surface Type

The research team divided surface type into six categories for Ohio and five categories for 

Washington. The six categories used for Ohio included the following: 

• Combination surface. 

• Brick.

• Reinforced concrete.

• Plain concrete.

• Dense-graded asphaltic concrete.

• Open-graded road mix. 
55 

The five categories used for Washington included the following: 

• Asphalt. 

• Bituminous. 

• Gravel. 

• Portland concrete. 

• Other. 

Terrain Type 

The research team divided terrain into three categories:

• Level. 

• Rolling. 

• Mountainous. 

Curve Radius

For the analysis of Ohio data only, the research team divided horizontal curve radius data into 

three categories for intersection analysis and four for nonintersection analysis. The three 

categories used for intersection analysis included the following: 

• <500 ft. 

• 500–1,000 ft. 

• >1,000 ft (including intersections on tangent segments). 

The four categories used for nonintersection analysis included the following: 

• <500 ft. 

• 500–1,000 ft. 

• 1,000–1,500 ft. 

• >1,500 ft (including tangent segments). 

Data Quality Checks

The extent of the analysis across a broad range of FCFTs did not allow the research team to 

conduct a site-by-site data check and enhancement using tools like aerial photography and video 

logs. The research team utilized descriptive statistics, including mean, standard deviation, 

minimum, and maximum values, as well as scatter plots and histograms, to identify sites with 

variable values outside of reason. After this initial data-screening process, maximum 

measurements for mainline and cross street AADTs at intersections and shoulder widths at 

nonintersection locations still stood out as “borderline” to the research team. As such, the 

research team talked with ODOT to confirm maximum mainline and cross street AADTs for 

at-grade intersections across the State. They compared ranges of mainline and cross street 

AADTs for at-grade intersections in California to AADT ranges covered by the HSM predictive 

methods and found the California data to be within reason. Finally, the research team explored 

some of the wider shoulder-width segments (e.g., 16-ft shoulder widths). They found these 
56 

measurements to be representative of a shoulder and also found that these segments make up a 

small portion of the overall samples. Therefore, these segments were kept in the analysis.
57 

Table 10. Summary statistics for intersections (all types) in California: roadway inventory data. 

Data Element (Variable Name) Minimum Maximum Average Standard Deviation

Mainline AADT (ML_AADT) (veh/day) 52.00 111,600.00 13,927.96 14,184.35 

Cross street AADT (XST_AADT) (veh/day) 1.00 77,000.00 1,309.18 3,593.86

Mainline number of lanes (ml_lanes) 1.00 8.00 2.82 1.22

Cross street number of lanes (xstlanes) 1.00 8.00 2.04 0.48

Median width (MEDWID) (ft) 0.00 99.00 7.56 17.09

Design speed (DESG_SPD) (mph) 25.00 70.00 51.67 11.19

Table 11. Summary statistics for intersections (all types) in California: climate data. 

Data Element (Variable Name) Minimum Maximum Average Standard Deviation

Average annual snowfall totals (snowavgyear) (inches) 0.00 202.60 11.06 32.94

Average annual rainfall totals (rainavgyear) (inches) 2.36 90.73 23.50 15.37

Average annual maximum temperatures (tempmaxavg) (°F) 56.10 91.40 71.56 6.79

Average annual minimum temperatures (tempminavg) (°F) 18.50 64.90 46.31 6.99

Average annual winter minimum temperatures (tempwintermin) (°F) 5.60 51.20 36.79 7.55

Average annual number of days with a minimum temperature of ≤32°F (temp32fdays) 0.00 307.50 43.43 60.30
58 

Table 12. Summary statistics for intersections (all types) in California: census data.

Data Element (Variable Name) Minimum Maximum Average Standard Deviation

Percentage of population ages 16+ unemployed (unempl16plus) 0.00 72.34 12.27 8.78

Percentage of population ages 16–24 working full time (workftage16to24) 0.00 100.00 13.60 16.47

Percentage of population ages 16–24 working part time (workptage16to24) 0.00 100.00 40.79 23.45

Percentage of population ages 16–24 unemployed (noworkage16to24) 0.00 100.00 45.61 24.68

Percentage of population ages 25+ without a high school diploma (noedctn25plus) 0.00 100.00 54.23 15.27

Percentage of population ages 25+ with a high school diploma (diploma25plus) 0.00 70.27 20.63 9.14

Percentage of population ages 25+ with a university degree (univ25plus) 0.00 98.16 25.14 18.15

Percentage of households with income <$50,000 (income50k) 0.00 100.00 48.63 20.29

Percentage of households with income between $50,000 and $100,000 (income50to100k) 0.00 76.34 27.74 11.01

Percentage of households with income >$100,000 (income100kplus) 0.00 91.93 22.79 17.40

Percentage of households with 0 vehicles (noveh) 0.00 86.29 6.44 8.22

Percentage of households with 1 vehicle (X1veh) 0.00 100.00 31.45 13.28

Percentage of households with ≥2 vehicles (X2vehplus) 0.00 100.00 61.06 18.65

Percentage of population ages 15–19 (age15to19) 0.00 68.01 6.12 4.32

Percentage of population ages 20–44 (age20to44) 0.00 88.26 30.10 11.31

Percentage of population ages 45–64 (age45to64) 0.27 100.00 30.05 9.73

Percentage of population ages 65–74 (age65to74) 0.00 55.77 9.89 6.60

Percentage of population ages 75+ (age75plus) 0.00 74.59 6.85 5.39
59 

Table 13. Summary statistics for curved segments in Washington: roadway inventory data. 

Data Element (Variable Name) Minimum Maximum Average Standard Deviation

Segment length (seg_lngth) (mi) 0.01 0.67 0.061 0.058

Curve radius (curv_rad) (ft) 100.00 12,000.00 2,240.91 3,022.83

Average AADT (avg_aadt) (veh/day) 109.00 25,599.00 2,638.68 2,794.56

Percent grade (pct_grad) 0.00 11.91 2.23 2.07

Shoulder width (shldwid) (ft) 0.00 20.00 3.96 2.31

Speed limit (spd_limt) (mph) 25.00 60.00 49.20 11.60

Percentage of trucks on the roadway (trkpcts) 0.00 61.52 14.50 8.61

Table 14. Summary statistics for curved segments in Washington: climate data.

Data Element (Variable Name) Minimum Maximum Average Standard Deviation

Average annual snowfall totals (snowavgyear) (inches) 0.00 139.90 19.64 27.85

Average annual rainfall totals (rainavgyear) (inches) 0.00 119.72 43.25 33.90

Average annual maximum temperatures (tempmaxavg) (°F) 46.70 66.20 59.61 2.57

Average annual minimum temperatures (tempminavg) (°F) 30.60 45.70 38.76 3.57

Average annual winter minimum temperatures (tempwintermin) (°F) 15.90 38.30 27.58 5.88

Average annual number of days with a minimum temperature of ≤32°F (temp32fdays) 13.30 212.20 103.31 48.44
60 

Table 15. Summary statistics for curved segments in Washington: census data.

Data Element (Variable Name) Minimum Maximum Average Standard Deviation

Percentage of population ages 16+ unemployed (unempl16plus) 0.00 81.08 11.44 8.96

Percentage of population ages 16–24 working full time (workftage16to24) 0.00 100.00 11.51 14.17

Percentage of population ages 16–24 working part time (workptage16to24) 0.00 100.00 47.91 23.79

Percentage of population ages 16–24 unemployed (noworkage16to24) 0.00 100.00 40.58 23.66

Percentage of population ages 25+ without a high school diploma (noedctn25plus) 15.55 91.66 54.12 9.85

Percentage of population ages 25+ with a high school diploma (diploma25plus) 3.10 49.07 24.92 8.44

Percentage of population ages 25+ with a university degree (univ25plus) 0.00 77.34 20.96 10.32

Percentage of households with income <$50,000 (income50k) 0.00 91.04 50.48 14.71

Percentage of households with income between $50,000 and $100,000 (income50to100k) 0.00 100.00 32.19 10.37

Percentage of households with income >$100,000 (income100kplus) 0.00 87.46 16.38 10.59

Percentage of households with 0 vehicles (noveh) 0.00 33.07 3.40 4.26

Percentage of households with 1 vehicle (X1veh) 0.00 62.10 23.21 10.30

Percentage of households with ≥2 vehicles (X2vehplus) 0.00 100.00 72.69 13.70

Percentage of population ages 15–19 (age15to19) 0.00 26.42 5.82 3.39

Percentage of population ages 20–44 (age20to44) 0.00 89.36 24.18 9.62

Percentage of population ages 45–64 (age45to64) 0.00 68.00 34.17 10.52

Percentage of population ages 65–74 (age65to74) 0.00 56.66 12.11 6.53

Percentage of population ages 75+ (age75plus) 0.00 34.64 6.68 4.14

Table 16. Summary statistics for tangent segments in Washington: roadway inventory data. 

Data Element (Variable Name) Minimum Maximum Average Standard Deviation

Segment length (seg_lngth) (mi) 0.01 4.82 0.095 0.137

Average AADT (avg_aadt) (veh/day) 95.00 25,599.00 2,920.16 3,016.06

Percent grade (pct_grad) 0.00 16.13.00 1.85 1.90

Shoulder width (shldwid) (ft) 0.00 20.00 4.02 2.49

Speed limit (spd_limt) (mph) 25.00 60.00 48.65 11.98

Percentage of trucks on the roadway (trkpcts) 0.00 59.62 14.58 9.04
61 

Table 17. Summary statistics for tangent segments in Washington: climate data.

Data Element (Variable Name) Minimum Maximum Average Standard Deviation

Average annual snowfall totals (snowavgyear) (inches) 0.00 139.90 22.74 28.06

Average annual rainfall totals (rainavgyear) (inches) 7.42 119.72 40.80 32.37

Average annual maximum temperatures (tempmaxavg) (°F) 46.70 66.20 59.83 2.66

Average annual minimum temperatures (tempminavg) (°F) 30.60 45.70 38.88 3.57

Average annual winter minimum temperatures (tempwintermin) (°F) 15.90 38.30 27.59 5.79

Average annual number of days with a minimum temperature of ≤32°F (temp32fdays) 13.30 212.20 103.42 47.98

Table 18. Summary statistics for tangent segments in Washington: census data.

Data Element (Variable Name) Minimum Maximum Average Standard Deviation

Percentage of population ages 16+ unemployed (unempl16plus) 0.00 81.08 11.05 8.81

Percentage of population ages 16–24 working full time (workftage16to24) 0.00 100.00 12.62 15.17

Percentage of population ages 16–24 working part time (workptage16to24) 0.00 100.00 46.94 22.93

Percentage of population ages 16–24 unemployed (noworkage16to24) 0.00 100.00 40.44 22.66

Percentage of population ages 25+ without a high school diploma (noedctn25plus) 15.55 88.80 54.77 9.72

Percentage of population ages 25+ with a high school diploma (diploma25plus) 3.10 49.07 25.32 8.03

Percentage of population ages 25+ with a university degree (univ25plus) 0.00 77.34 19.91 9.89

Percentage of households with income <$50,000 (income50k) 0.00 91.04 50.56 14.87

Percentage of households with income between $50,000 and $100,000 (income50to100k) 0.00 100.00 32.38 10.86

Percentage of households with income >$100,000 (income100kplus) 0.00 87.46 16.48 10.53

Percentage of households with 0 vehicles (noveh) 0.00 33.07 3.59 4.20

Percentage of households with 1 vehicle (X1veh) 0.00 59.91 22.89 10.30

Percentage of households with ≥2 vehicles (X2vehplus) 0.00 100.00 73.09 12.93

Percentage of population ages 15–19 (age15to19) 0.00 26.42 6.02 3.26

Percentage of population ages 20–44 (age20to44) 0.00 89.36 24.87 10.04

Percentage of population ages 45–64 (age45to64) 0.00 68.00 33.53 10.02

Percentage of population ages 65–74 (age65to74) 0.00 56.66 11.83 6.48

Percentage of population ages 75+ (age75plus) 0.00 38.30 6.73 4.04
62 

Table 19. Summary statistics for curved segments in Ohio: roadway inventory data. 

Data Element (Variable Name) Minimum Maximum Average Standard Deviation

Segment length (seg_lngth) (mi) 0.02 0.33 0.058 0.031

Curve radius (curv_rad) (ft) 100.00 1,468.00 491.27 224.11

Percent grade (pct_grad) 0.00 20.00 3.63 4.74

Average AADT (avg_aadt) (veh/day) 93.00 12,210.00 1,133.51 1,134.64

Speed limit (spd_limt) (mph) 25.00 55.00 52.45 5.86

Shoulder width (shldwid) (ft) 0.00 11.00 2.33 1.56

Table 20. Summary statistics for curved segments in Ohio: climate data. 

Data Element (Variable Name) Minimum Maximum Average Standard Deviation

Average annual snowfall totals (snowavgyear) (inches) 4.70 109.90 20.16 9.46

Average annual rainfall totals (rainavgyear) (inches) 33.16 48.74 41.10 2.28

Average annual maximum temperatures (tempmaxavg) (°F) 57.00 69.00 62.43 1.99

Average annual minimum temperatures (tempminavg) (°F) 37.00 46.00 40.84 1.61

Average annual winter minimum temperatures (tempwintermin) (°F) 17.00 27.00 21.97 1.80

Average annual number of days with a minimum temperature of ≤32f (temp32fdays) 88.00 148.00 122.60 11.48
63 

Table 21. Summary statistics for curved segments in Ohio: census data.

Data Element (Variable Name) Minimum Maximum Average Standard Deviation

Percentage of population ages 16+ unemployed (unempl16plus) 0.00 54.58 9.04 6.42

Percentage of population ages 16–24 working full time (workftage16to24) 0.00 100.00 16.99 17.65

Percentage of population ages 16–24 working part time (workptage16to24) 0.00 100.00 43.67 22.50

Percentage of population ages 16–24 unemployed (noworkage16to24) 0.00 100.00 39.34 21.44

Percentage of population ages 25+ without a high school diploma (noedctn25plus) 10.85 86.20 44.79 8.94

Percentage of population ages 25+ with a high school diploma (diploma25plus) 5.00 76.36 43.06 8.69

Percentage of population ages 25+ with a university degree (univ25plus) 0.00 65.41 12.16 7.35

Percentage of households with income <$50,000 (income50k) 6.02 95.77 55.00 12.33

Percentage of households with income between $50,000 and $100,000 (income50to100k) 0.00 74.37 32.24 9.83

Percentage of households with income >$100,000 (income100kplus) 0.00 69.78 12.76 7.94

Percentage of households with 0 vehicles (noveh) 0.00 88.11 5.62 6.72

Percentage of households with 1 vehicle (X1veh) 0.00 69.94 23.82 10.11

Percentage of households with ≥2 vehicles (X2vehplus) 5.41 96.38 70.56 11.34

Percentage of population ages 15–19 (age15to19) 0.00 32.10 6.42 3.31

Percentage of population ages 20–44 (age20to44) 1.28 79.23 27.38 6.18

Percentage of population ages 45–64 (age45to64) 2.97 59.86 31.92 7.97

Percentage of population ages 65–74 (age65to74) 0.00 33.27 9.65 4.17

Percentage of population ages 75+ (age75plus) 0.00 58.01 6.68 4.01

Table 22. Summary statistics for tangent segments in Ohio: roadway inventory data. 

Data Element (Variable Name) Minimum Maximum Average Standard Deviation

Segment length (seg_lngth) (mi) 0.02 10.82 0.275 0.581

Percent grade (pct_grad) 0.00 20.00 2.95 3.99

Average AADT (avg_aadt) (veh/day) 90.00 16,623.00 2,267.58 2,112.64

Speed limit (spd_limt) (mph) 20.00 65.00 51.34 7.35

Shoulder width (shldwid) (ft) 0.00 16.00 2.96 2.16

Table 23. Summary statistics for segments in Ohio: climate data. 

Data Element (Variable Name) Minimum Maximum Average Standard Deviation

Average annual snowfall totals (snowavgyear) (inches) 4.70 109.90 25.20 15.61

Average annual rainfall totals (rainavgyear) (inches) 31.48 48.74 40.39 2.37

Average annual maximum temperatures (tempmaxavg) (°F) 57.00 69.00 61.51 2.16

Average annual minimum temperatures (tempminavg) (°F) 37.00 46.00 40.58 1.77

Average annual winter minimum temperatures (tempwintermin) (°F) 17.00 27.00 21.38 1.99

Average annual number of days with a minimum temperature of ≤32°F (temp32fdays) 88.00 148.00 123.84 12.26
64 

Table 24. Summary statistics for segments in Ohio: census data.

Data Element (Variable Name) Minimum Maximum Average Standard Deviation

Percentage of population ages 16+ unemployed (unempl16plus) 0.00 54.66 8.66 6.19

Percentage of population ages 16–24 working full time (workftage16to24) 0.00 100.00 16.65 15.78

Percentage of population ages 16–24 working part time (workptage16to24) 0.00 100.00 45.84 21.09

Percentage of population ages 16–24 unemployed (noworkage16to24) 0.00 100.00 37.51 20.30

Percentage of population ages 25+ without a high school diploma (noedctn25plus) 10.85 86.20 44.50 9.13

Percentage of population ages 25+ with a high school diploma (diploma25plus) 2.75 76.81 41.42 9.03

Percentage of population ages 25+ with a university degree (univ25plus) 0.00 82.82 14.09 8.64

Percentage of households with income <$50,000 (income50k) 3.68 100.00 50.77 13.69

Percentage of households with income between $50,000 and $100,000 (income50to100k) 0.00 74.37 34.14 9.95

Percentage of households with income >$100,000 (income100kplus) 0.00 78.45 15.08 9.41

Percentage of households with 0 vehicles (noveh) 0.00 88.11 5.51 7.38

Percentage of households with 1 vehicle (X1veh) 0.00 78.43 23.38 9.87

Percentage of households with ≥2 vehicles (X2vehplus) 5.41 98.16 71.11 11.85

Percentage of population ages 15–19 (age15to19) 0.00 51.54 6.66 3.28

Percentage of population ages 20–44 (age20to44) 0.56 91.47 27.68 6.09

Percentage of population ages 45–64 (age45to64) 1.68 59.86 30.83 7.42

Percentage of population ages 65–74 (age65to74) 0.00 33.27 9.49 4.08

Percentage of population ages 75+ (age75plus) 0.00 89.47 6.65 3.86

Table 25. Summary statistics for intersection (all types) in Ohio: roadway inventory data. 

Data Element (Variable Name) Minimum Maximum Average Standard Deviation

Major AADT (MajorAADT) (veh/day) 89.00 96,948.00 5,055.14 6,773.42

Minor AADT (minorAADT) (veh/day) 100.00 84,905.00 1,221.91 2,603.86

Median width (medianWidth) (ft) 0.00 201.00 1.57 9.23

Speed limit (postedSpeed) (mph) 20.00 70.00 47.57 10.23

Number of approaches with left-turn lanes (leftLanes) 0.00 4.00 0.18 0.72

Number of approaches with right-turn lanes (rightLanes) 0.00 4.00 0.05 0.32
65 

Table 26. Summary statistics for intersection (all types) in Ohio: climate data. 

Data Element (Variable Name) Minimum Maximum Average Standard Deviation

Average annual snowfall totals (snowavgyear) (inches) 4.70 109.90 26.19 15.67

Average annual rainfall totals (rainavgyear) (inches) 31.48 48.74 39.60 2.66

Average annual maximum temperatures (tempmaxavg) (°F) 57.00 69.00 61.10 2.15

Average annual minimum temperatures (tempminavg) (°F) 37.00 46.00 40.91 1.88

Average annual winter minimum temperatures (tempwintermin) (°F) 17.00 27.00 21.41 2.03

Average annual number of days with a minimum temperature of ≤32°F (temp32fdays) 88.00 148.00 121.70 12.74 

Table 27. Summary statistics for intersections (all types) in Ohio: Census data.

Data Element (Variable Name) Minimum Maximum Average Standard Deviation

Percentage of population ages 16+ unemployed (unempl16plus) 0.00 83.87 8.73 6.69

Percentage of population ages 16–24 working full time (workftage16to24) 0.00 100.00 17.27 16.05

Percentage of population ages 16–24 working part time (workptage16to24) 0.00 100.00 46.98 21.01

Percentage of population ages 16–24 unemployed (noworkage16to24) 0.00 100.00 35.75 20.27

Percentage of population ages 25+ without a high school diploma (noedctn25plus) 0.00 100.00 44.14 9.71

Percentage of population ages 25+ with a high school diploma (diploma25plus) 0.00 76.81 39.67 10.27

Percentage of population ages 25+ with a university degree (univ25plus) 0.00 100.00 16.19 11.31

Percentage of households with income <$50,000 (income50k) 0.00 100.00 50.49 15.53

Percentage of households with income between $50,000 and $100,000 (income50to100k) 0.00 100.00 33.64 10.59

Percentage of households with income >$100,000 (income100kplus) 0.00 96.66 15.87 10.84

Percentage of households with 0 vehicles (noveh) 0.00 93.25 6.05 8.48

Percentage of households with 1 vehicle (X1veh) 0.00 100.00 25.82 11.82

Percentage of households with ≥2 vehicles (X2vehplus) 0.00 100.00 68.12 15.48

Percentage of population ages 15–19 (age15to19) 0.00 83.50 6.65 3.59

Percentage of population ages 20–44 (age20to44) 1.28 94.01 28.50 7.11

Percentage of population ages 45–64 (age45to64) 0.00 68.23 30.10 7.33

Percentage of population ages 65–74 (age65to74) 0.00 33.27 9.10 4.18

Percentage of population ages 75+ (age75plus) 0.00 58.01 6.87 4.25
66 

ANALYSIS METHODOLOGY

The research team used random forests to identify contributing factors corresponding to selected 

FCFTs. As noted in chapter 1, the Highway Safety Statistical Paper Synthesis demonstrated 

CART and random forests within the context of conducting statistical road-safety analyses 

(Persaud et al. 2001). The study concluded that tree-based models hold strong potential for 

road-safety analyses. This current project extends the exploration of potential applications of 

tree-based methods within the context of identifying contributing factors for systemic safety 

analysis.

Tree-based methods are a set of machine-learning and data-mining procedures. They use the form 

of a binary tree and act as predictive models that map values of a dependent variable or response 

variable (e.g., crash frequency) as a function of key explanatory variables (e.g., roadway, 

weather, sociodemographic characteristics). There are two types of tree analyses: a classification 

tree where the dependent variable is categorical and a regression tree where the dependent 

variable is continuous. The output of these analyses is a tree that shows the most predictive 

variable at the top that branches off into combinations of variables that best predict the outcome 

variable. A tree can be useful to determine contributing factors associated with different crash 

types and provide insights into interactions between contributing factors.

Breiman and Cutler (2013) developed the random-forest algorithm, which works within the 

framework of CART. With random forests, instead of having one tree, multiple trees are 

produced using a resampling method, and the aggregate results are then combined. Breiman and 

Cutler believed that a single decision tree may not reveal all variables that contribute to the 

dependent/target variable and that the contributions of some predictive independent variables can 

be masked by other independent variables. Random forests can help identify predictors that may 

not appear in the output of a single classification or regression tree but, nevertheless, are highly 

related to the target variable. The percentage increase in mean squared error (MSE) with the 

removal of a variable from the random-forest model are commonly displayed using random 

forests. The random forests do not directly indicate if the presence of variables correspond to 

increases or decreases in expected crash frequency. However, plots of random forest–predicted 

crash frequencies as a function of variables provide the information needed to identify 

contributing factors and inform countermeasure identification. 

Figure 1 is an example of a random forest developed for crash frequency per mile of ROR-D 

crashes on Washington rural two-lane roads on horizontal curves. The vertical axis displays 

variables, and the horizontal axis shows percent increase in MSE (%IncMSE). Higher values of 

%IncMSE imply that a variable is a stronger predictor of crash frequency per mile. Typically, the 

strongest predictors are shown at the top of the plot. For example, figure 1 illustrates that curve 

radius is the strongest predictor of crash frequency per mile of ROR-D crashes on Washington 

rural two-lane roads on horizontal curves. Taking curve radius out of the analysis can potentially 

increase the MSE of predictions by approximately 40 percent. 

A sample random-forest code used in R software is provided in appendix D. 
67 

Source: FHWA.

Figure 1. Graph. Crash frequency per mile of ROR-D crashes on rural two-lane roads on 

horizontal curves: Washington. 

After identifying the most predictive variables from the random forest, the research team

identified the trend for each variable and created plots of random forest–predicted crash 

frequencies as a function of the predictor variables.

Figure 2 shows an example of such a plot for the random forest–predicted ANG crash frequency 

as a function of mainline AADT for three-leg stop-controlled intersections on rural two-lane 

roads in California. In this case, the plot shows mainline AADT as a contributing factor 

associated with increases in predicted crash frequency (i.e., the positive slope of the linear best-fit 

line indicating that an increase in mainline AADT is associated with more ANG crashes at 

three-leg stop-controlled intersections on rural two-lane roads). Similarly, figure 3 shows an 

example of such a plot for the random forest–predicted ANG crash frequency at four-leg 

stop-controlled intersections on urban two-lane roads in California as a function of percentage of 

households with income <$50,000 within the census block containing the intersection. In this 

case, the plot shows the percentage of households as a factor that decreases predicted crash 

frequency (i.e., the negative slope of the linear best-fit line indicating that an increase in the 
68 

percentage of households with income <$50,000 in the census block containing the intersection is 

associated with fewer ANG crashes).

Source: FHWA.

Figure 2. Graph. ANG crash frequency versus mainline AADT for three-leg stop-controlled 

intersections on rural two-lane roads: California. 

Source: FHWA.

Figure 3. Graph. ANG crash frequency versus percentage of households with income 

<$50,000 for four-leg stop-controlled intersections on urban two-lane roads: California. 

RESULTS

The following sections of this chapter include tables that list the top 10 to 12 most influential 

predictor variables for each intersection and nonintersection FCFT according to the generated 

random forests. These top 10 to 12 variables for each FCFT serve as the focus of discussion of 

contributing factors for the respective FCFT. The tables also identify whether each variable 

contributes to an increase or decrease in the expected number of crashes. Appendix E provides 

the random-forest outputs corresponding to the analyses of all FCFTs. Appendix F provides plots 

of random forest–predicted crash frequencies as a function of selected predictor variables.
69 

There are some counterintuitive results reported in the contributing-factor tables. For example, a 

roadway variable, in some cases, shows up as a contributing factor to an increase in crashes, 

whereas prior knowledge has indicated the variable to contribute to a decrease in crashes

(e.g., wider shoulder width shows up as contributing to an increase in LNDP crashes on tangent 

segments in Washington). Such occurrences of counterintuitive findings for roadway variables 

have been denoted with two asterisks and are discussed in more detail in the following sections of 

this chapter. In general, the research team felt it useful to report the counterintuitive findings for 

the following reasons: 

• There is a much broader discussion occurring at the national level about how to address 

counterintuitive findings, particularly when so little is known about the adaptive behavior 

of drivers (e.g., countermeasures that are thought to increase crashes could induce more 

careful driving and countermeasures that are thought to reduce crashes could induce 

higher-speed driving). 

• There are relatively few examples of crash-data analysis at the level of crash-type 

disaggregation in this report, and therefore, there is not an abundance of existing 

knowledge on which to base a determination of intuitive versus counterintuitive findings 

for certain crash types. It is possible that some of the counterintuitive findings from this 

analysis could set the stage for future investigation, possibly using naturalistic-driving 

data collected as part of SHRP2. 

In a limited number of cases, roadway variables presented in the following tables did not show up 

among the top 10 most influential predictor variables for that particular FCFT but were worth 

reporting in the tables for two reasons. The first reason the variables were included was because it 

made sense from an engineering perspective and the findings (e.g., smaller radius of curve 

increases the expected frequency of ROR crashes) are likely of interest to State and local 

agencies that focus on infrastructure improvements. The second reason the variables were 

included was because their removal would still lead to an increase in MSE of model predictions 

as derived from random-forest outputs even if the variable fell outside the top 10 to 12 predictor 

variables (i.e., the variable still appeared predictive even though it fell outside of the top 10 to 12 

most predictive); when this occurs, the variables are included in the bottom rows of the tables 

below the 10 rows that identify the top most influential predictor variables.

For the nonintersection FCFT contributing-factor analysis, Washington data were separated into 

curved and tangent segments for the analysis and Ohio data for curved and tangent segments 

were analyzed together4 in a single file. 

The contributing-factor analyses of California and Washington data were based on the expected 

number of KAB crashes. The contributing-factor analysis of Ohio data was based on both KAB 

crashes and the expected number of KABCO crashes. This latter approach for Ohio allowed the 

research team to assess whether inclusion of only certain levels of severity (i.e., KAB versus 

KABCO) influenced contributing-factor results.

4

The main reason behind combining the Ohio data was because ODOT defines any curve with a radius >1,800 ft 

as tangent sections, which limited the curve data compared to Washington, where radii ranged from 100 to 12,000 ft.
70 

ROR CRASHES ON RURAL TWO-LANE HIGHWAY SEGMENTS

Ohio

Horizontal Curves and Highway Tangent Segments—Daytime

Table 28 and table 29 summarize the most influential predictor variables for the expected number 

of ROR-D crashes on rural two-lane horizontal curves and highway tangent segments that result 

in fatality, incapacitating injury, or nonincapacitating injury (ROR-KAB-D) or fatality, 

incapacitating injury, nonincapacitating injury, possible injury, or PDO (ROR-KABCO-D) 

according to random forests generated using Ohio data. 

Table 28. Contributing factors for ROR-KAB-D crashes on rural two-lane horizontal 

curves and tangent segments: Ohio.

Variable

Impact on Crash-Frequency 

Predictions

Average AADT Increases

Curve radius Increases/decreases*

Percent grade Increases

Surface width Decreases

Average shoulder width Decreases

Percentage of population ages 16+ unemployed Increases

Percentage of population ages 20–44 Increases

Percentage of households with 0 vehicles Increases

Percentage of population ages 25+ with a high school diploma but no university 

degree

Decreases

Percentage of population ages 75+ Decreases

Speed limit Increases

*Increases crash frequency when comparing curves to tangent segments/decreases crash frequency when comparing 

curves (i.e., a larger radius is associated with fewer crashes).

Table 29. Contributing factors for ROR-KABCO-D crashes on rural two-lane horizontal 

curves and tangent segments: Ohio.

Variable

Impact on Crash-Frequency 

Predictions

Average AADT Increases

Curve radius Increases/decreases*

Surface width Decreases

Average shoulder width Decreases

Percent grade Increases

Speed limit Increases

Percentage of households with income >$100,000 Increases

Percentage of population ages 75+ Decreases

Percentage of population ages 25+ with a university degree Increases

Percentage of population ages 25+ with a high school diploma but no university 

degree

Decreases

*Increases crash frequency when comparing curves to tangent segments/decreases crash frequency when comparing 

curves (i.e., a larger radius is associated with fewer crashes).
71 

Horizontal Curves and Highway Tangent Segments—Nighttime

Table 30 and table 31 summarize the most influential predictor variables for the expected number 

of ROR-N crashes on rural two-lane horizontal curves and highway tangent segments that result 

in fatality, incapacitating injury, or nonincapacitating injury (ROR-KAB-N) or fatality, 

incapacitating injury, nonincapacitating injury, possible injury, or PDO (ROR-KABCO-N) 

according to random forests generated using Ohio data. 

Table 30. Contributing factors for ROR-KAB-N crashes on rural two-lane horizontal 

curves and tangent segments: Ohio.

Variable

Impact on Crash-Frequency 

Predictions

Average AADT Increases

Curve radius Increases/decreases*

Average shoulder width Decreases

Percentage of households with income >$100,000 Increases

Percentage of population ages 45–64 Decreases

Percentage of population ages 25+ with a high school diploma but no university 

degree

Decreases

Percentage of population ages 75+ Decreases

Percentage of population ages 15–19 Increases

Percentage of population ages 20–44 Increases

Surface width Decreases

Percent grade Increases

Speed limit Increases

*Increases crash frequency when comparing curves to tangent segments/decreases crash frequency when comparing 

curves (i.e., a larger radius is associated with fewer crashes).

Table 31. Contributing factors for ROR-KABCO-N crashes on rural two-lane horizontal 

curves and tangent segments: Ohio.

Variable

Impact on Crash-Frequency 

Predictions

Average AADT Increases

Average shoulder width Decreases

Curve radius Increases/decreases*

Speed limit Increases

Percentage of population ages 16–24 working full time Decreases

Surface width Decreases

Percentage of population ages 75+ Decreases

Percentage of population ages 25+ with a high school diploma but no university 

degree 

Decreases

Percentage of population ages 16+ unemployed Decreases

Percentage of population ages 16–24 unemployed Decreases

Percent grade Increases

*Increases crash frequency when comparing curves to tangent segments/decreases crash frequency when comparing 

curves (i.e., a larger radius is associated with fewer crashes).
72 

Washington 

Highway Tangent Segments—Daytime

Table 32 summarizes the most influential predictor variables for the expected number of ROR-D 

crashes on rural two-lane highway tangent segments that result in fatality, incapacitating injury, 

or nonincapacitating injury (ROR-KAB-D) according to random forests generated using 

Washington data. The results in table 32 offer an opportunity to revisit key considerations for 

interpreting the results in this table, as well as subsequent tables:

• Contributing factors are factors whose presence is associated with either increases or 

decreases in the expected frequencies of crashes according to the generated random 

forests.

• Findings regarding contributing factors are interpreted in this project as predictive 

relationships or statistical associations with expected crash frequencies. The report does 

not cover the philosophy of causation or causal inference. That said, the following 

characteristics raise confidence that a specific finding or set of findings are stable and 

transferable:

o Consistency across subsets of related FCFTs. 

o Consistency across multiple States.

o Consistency with previous findings in the literature. 

Knowledge related to the safety impacts of traffic and roadway variables has grown substantially 

over the last two decades and offers a basis to interpret the results of this effort. There is not yet a 

significant amount of theory to support or refute the socioeconomic- and weather-related results 

of this effort. Findings related to socioeconomic variables are likely representative of differences 

in travel behavior, driving behavior, and driving capabilities that seem key for safety analyses but 

are generally not incorporated into analyses that also include traffic and roadway factors. 

Weather-related findings are likely representative of differences in visibility, road conditions, and 

driver experience and behavior. All findings are reported and discussed throughout the remainder 

of this report. In most cases, findings related to socioeconomic and weather variables set the stage 

for future analyses, possibly focused solely on these variables. 

Table 32. Contributing factors for ROR-KAB-D crashes on rural two-lane highway tangent 

segments: Washington. 

Variable

Impact on Crash-Frequency 

Predictions

Percent grade Increases

Average AADT Increases

Percentage of trucks on the roadway Decreases

Average shoulder width Decreases

Average annual rainfall total Increases

Percentage of population ages 20–44 Increases

Percentage of population ages 16–24 unemployed Increases

Annual average maximum temperature Decreases

Percentage of population ages 25+ with a high school diploma but no university 

degree

Decreases

Percentage of population ages 25+ without a high school diploma Decreases
73 

Highway Tangent Segments—Nighttime

Table 33 summarizes the most influential predictor variables for the expected number of ROR-N 

crashes on rural two-lane tangent segments that result in fatality, incapacitating injury, or 

nonincapacitating injury (ROR-KAB-N) according to random forests generated using 

Washington data.

Table 33. Contributing factors for ROR-KAB-N crashes on rural two-lane highway tangent 

segments: Washington. 

Variable

Impact on Crash-Frequency 

Predictions

Average shoulder width Decreases

Percentage of trucks on the roadway Decreases

Percent grade Decreases**

Average AADT Increases

Unpaved shoulders Increases

Percentage of population ages 16+ unemployed Increases

Annual average winter minimum temperature Decreases

Annual average number of days with a minimum temperature of ≤32°F Decreases

Percentage of population ages 65–74 Decreases

Annual average maximum temperature Decreases

**Counterintuitive finding.

Horizontal Curves—Daytime

Table 34 summarizes the most influential predictor variables for the expected number of ROR-D 

crashes on rural two-lane horizontal curves that result in fatality, incapacitating injury, or 

nonincapacitating injury (ROR-KAB-D) according to random forests generated using 

Washington data.

Table 34. Contributing factors for ROR-KAB-D crashes on rural two-lane horizontal 

curves: Washington. 

Variable

Impact on Crash-Frequency 

Predictions

Curve radius Increases/decreases* 

Average AADT Increases

Percentage of trucks on the roadway Decreases

Percent grade Increases

Average shoulder width Decreases

Percentage of population ages 45–64 Increases

Annual average minimum temperature Decreases

Annual Average number of days with a minimum temperature of ≤32°F Decreases

Percentage of population ages 25+ without a high school diploma Decreases

Average annual rainfall total Increases

*Increases crash frequency when comparing curves to tangents/decreases crash frequency when comparing curves 

(i.e., a larger radius is associated with fewer crashes).
74 

Horizontal Curves—Nighttime

Table 35 summarizes the most influential predictor variables for the expected number of ROR-N 

crashes on rural two-lane horizontal curves that result in fatality, incapacitating injury, or 

nonincapacitating injury (ROR-KAB-N) according to random forests generated using 

Washington data.

Table 35. Contributing factors for ROR-KAB-N crashes on rural two-lane horizontal 

curves: Washington. 

Variable

Impact on Crash-Frequency 

Predictions

Curve radius Increases/decreases* 

Average AADT Increases

Average shoulder width Decreases

Percentage of population ages 16+ unemployed Decreases

Percentage of population ages 16–24 working part time Decreases

Percentage of households with more than 2 vehicles Increases

Percent grade Decreases**

Percentage of households with 1 vehicle Decreases

Percentage of trucks on the roadway Decreases

Percentage of population ages 25+ without a high school diploma Decreases

*Increases crash frequency when comparing curves to tangent segments/decreases crash frequency when comparing 

curves (i.e., a larger radius is associated with fewer crashes).

**Counterintuitive finding.

Discussion

Based on the analysis, the research team recommends the following roadway contributing factors 

associated with ROR crashes on rural two-lane horizontal curves and tangent segments:

• Average AADT: an increase in AADT was consistently associated with an increase in the 

frequency of ROR-D and ROR-N crashes on curves and tangent segments. 

• Average shoulder width: an increase in shoulder width was consistently associated with a 

decrease in the frequency of ROR-D and ROR-N crashes (and therefore a decrease in 

shoulder width was consistently associated with an increase in the frequency of ROR 

crashes).

• Curve radius: an increase in curve radius was consistently associated with a decrease in 

the frequency of ROR-D and ROR-N crashes (and therefore a decrease in curve radius 

was consistently associated with an increase in the frequency of ROR crashes).

There were several additional roadway variables that showed relationships with ROR crash 

frequency, but results were inconsistent or could not be validated across the two States due to 

data limitations:

• Percent grade: an increase in percent grade was associated with an increase in the 

frequency of ROR-D crashes in Washington and ROR-D and ROR-N crashes in Ohio; the 

relationship was the opposite for Washington during the nighttime. 

• Surface width: an increase in surface width was associated with a decrease in the 

frequency of ROR-D and ROR-N crashes on curves and tangent segments (and therefore 
75 

a decrease in surface width was consistently associated with an increase in the frequency 

of ROR crashes); this variable was only available for Ohio. 

• Unpaved shoulders or no shoulders: the presence of one or more unpaved shoulders or no 

shoulders showed up as a contributing factor that increases crash frequency when 

compared to paved shoulders but only for ROR-KAB-N crashes on tangent segments in 

Washington. 

• Speed limit: an increase in speed limit was associated with an increase in the frequency of 

ROR-D and ROR-N crashes in Ohio. 

With respect to sociodemographic characteristics, three variables appeared as contributing factors 

in both Ohio and Washington: 

• The percentage of the population ages 16+ that are unemployed appeared to increase

the frequency of ROR-KAB-D crashes on curves and tangent segments in Ohio and 

ROR-KAB-N crashes on tangent segments in Washington. 

• The percentage of the population ages 20–44 appeared to increase ROR-KAB-D and 

ROR-KAB-N crashes on curves and tangent segments in Ohio and ROR-KAB-D crashes

on tangent segments in Washington. 

• The percentage of the population ages 25+ with a high school diploma but no university 

degree appeared to decrease ROR-KAB-D crashes for all crash types in Ohio and on 

tangent segments in Washington. 

Other socioeconomic characteristics of note included the following: 

• The percentage of the population ages 15–19 appeared to increase ROR-KAB-N crashes 

on curves and tangent segments in Ohio. 

• The percentage of the population ages 75+ appeared to decrease ROR-D and ROR-N 

crashes on curves and tangent segments in Ohio. 

• The percentage of households with an annual income >$100,000 also appeared to increase 

crashes for two (i.e., ROR-KABCO-D and ROR-KAB-N) of the four crash types in Ohio, 

possibly serving as a surrogate for travel amount or exposure. 

With respect to weather characteristics, average annual rainfall total appeared to increase RORKAB-D crashes on curves and tangent segments in Washington, but data limitations prevented 

the validation of this finding in Ohio. 

LNDP CRASHES ON RURAL TWO-LANE HIGHWAY SEGMENTS

Ohio

Horizontal Curves and Highway Tangent Segments—Daytime

Table 36 and table 37 summarize the most influential predictor variables for the expected number 

of LNDP-D crashes on rural two-lane horizontal curves and highway tangent segments that result 

in fatality, incapacitating injury, or nonincapacitating injury (LNDP-KAB-D) or fatality, 

incapacitating injury, nonincapacitating injury, possible injury, or PDO (LNDP-KABCO-D) 

according to random forests generated using Ohio data. 
76 

Table 36. Contributing factors for LNDP-KAB-D crashes on rural two-lane horizontal 

curves and tangent segments: Ohio.

Variable

Impact on Crash Frequency 

Predictions

Average AADT Increases

Curve radius Increases/decreases*

Percent grade Increases

Surface width Decreases

Average shoulder width Decreases

Percentage of households with income >$100,000 Increases

Percentage of population ages 25+ with a university degree Increases

Percentage of households with income <$50,000 Decreases

Percentage of households with income between $50,000 and $100,000 Increases

Percentage of population ages 16+ unemployed Increases

Speed limit Increases

*Increases crash frequency when comparing curves to tangent segments/decreases crash frequency when comparing 

curves (i.e., a larger radius is associated with fewer crashes).

Table 37. Contributing factors for LNDP-KABCO-D crashes on rural two-lane horizontal 

curves and tangent segments: Ohio. 

Variable

Impact on Crash-Frequency 

Predictions

Curve radius Increases/decreases*

Average AADT Increases

Average shoulder width Decreases

Surface width Decreases

Percentage of households with income >$100,000 Increases

Percentage of population ages 16+ unemployed Increases

Speed limit Increases

Percentage of population ages 25+ with a university degree Increases

Percentage of population ages 25+ with a high school diploma but no 

university degree

Decreases

Percentage of population ages 75+ Decreases

Percent grade Increases

*Increases crash frequency when comparing curves to tangent segments/decreases crash frequency when comparing 

curves (i.e., a larger radius is associated with fewer crashes).

Horizontal Curves and Highway Tangent Segments—Nighttime

Table 38 and table 39 summarize the most influential predictor variables for the expected number 

of LNDP-N crashes on rural two-lane horizontal curves and highway tangent segments that result 

in fatality, incapacitating injury, or nonincapacitating injury (LNDP-KAB-N) or fatality, 

incapacitating injury, nonincapacitating injury, possible injury, or PDO (LNDP-KABCO-N) 

according to random forests generated using Ohio data. 
77 

Table 38. Contributing factors for LNDP-KAB-N crashes on rural two-lane horizontal 

curves and tangent segments: Ohio.

Variable

Impact on Crash-Frequency 

Predictions

Average AADT Increases

Curve radius Increases/decreases*

Average shoulder width Decreases

Percentage of households with income >$100,000 Increases

Percentage of households with income <$50,000 Decreases

Percentage of population ages 16–24 working full time Decreases

Percentage of population ages 45–64 Decreases

Percent grade Increases

Percentage of population ages 16+ unemployed Increases

Percentage of population ages 20–44 Increases

Surface width Decreases

Speed limit Increases

*Increases crash frequency when comparing curves to tangent segments/decreases crash frequency when comparing 

curves (i.e., a larger radius is associated with fewer crashes).

Table 39. Contributing factors for LNDP-KABCO-N crashes on rural two-lane horizontal 

curves and tangent segments: Ohio.

Variable

Impact on Crash-Frequency 

Predictions

Average AADT Increases

Surface width Decreases

Curve radius Increases/decreases*

Speed limit Increases

Average shoulder width Decreases

Percentage of population ages 16+ unemployed Decreases

Percentage of population ages 16–24 unemployed Decreases

Percentage of households with income >$100,000 Increases

Percentage of population ages 75+ Decreases

Percentage of population ages 65–74 Decreases

Percent grade Increases

*Increases crash frequency when comparing curves to tangent segments/decreases crash frequency when comparing 

curves (i.e., a larger radius is associated with fewer crashes).

Washington 

Highway Tangent Segments—Daytime

Table 40 summarizes the most influential predictor variables for the expected number of 

LNDP-D crashes on rural two-lane highway tangent segments that result in fatality, 

incapacitating injury, or nonincapacitating injury (LNDP-KAB-D) according to random forests 

generated using Washington data.
78 

Table 40. Contributing factors for LNDP-KAB-D crashes on rural two-lane highway 

tangent segments: Washington. 

Variable

Impact on Crash-Frequency 

Predictions

Average AADT Increases

Percent grade Increases

Average shoulder width Increases**

Percentage of trucks on the roadway Decreases

Average annual rainfall total Increases

Percentage of population ages 16–24 unemployed Increases

Percentage of population ages 16+ unemployed Increases

Percentage of population ages 25+ with high school diploma but no university 

degree

Decreases

Percentage of households with 0 vehicles Decreases

Average annual snowfall total Increases

Lane width Increases**

**Counterintuitive finding.

Highway Tangent Segments—Nighttime

Table 41 summarizes the most influential predictor variables for the expected number of 

LNDP-N crashes on rural two-lane highway tangent segments that result in fatality, 

incapacitating injury, or nonincapacitating injury (LNDP-KAB-N) according to random forests 

generated using Washington data.

Table 41. Contributing factors for LNDP-KAB-N crashes on rural two-lane highway 

tangent segments: Washington. 

Variable

Impact on Crash-Frequency 

Predictions

Percent grade Decreases**

Average shoulder width Increases**

Percentage of trucks on the roadway Decreases

Average AADT Increases

Unpaved shoulders Increases

Percentage of population ages 16+ unemployed Increases

Percentage of population ages 65–74 Decreases

Annual average winter minimum temperature Decreases

Percentage of population ages 25+ without a high school diploma Increases

Annual average maximum temperature Decreases

**Counterintuitive finding.

Horizontal Curves—Daytime

Table 42 summarizes the most influential predictor variables for the expected number of 

LNDP-D crashes on rural two-lane horizontal curves that result in fatality, incapacitating injury, 

or nonincapacitating injury (LNDP-KAB-D) according to random forests generated using 

Washington data.
79 

Table 42. Contributing factors for LNDP-KAB-D crashes on rural two-lane horizontal 

curves: Washington. 

Variable

Impact on Crash-Frequency 

Predictions

Curve radius Increases/decreases* 

Average AADT Increases

Percent grade Increases

Percentage of trucks on the roadway Decreases

Average shoulder width Decreases

Average annual rainfall total Increases

Percentage of population ages 45–64 Increases

Annual average maximum temperature Decreases

Percentage of households with income <$50,000 Decreases

Percentage of population ages 25+ with high school diploma but no university 

degree

Decreases

*Increases crash frequency when comparing curves to tangent segments/decreases crash frequency when comparing 

curves (i.e., a larger radius is associated with fewer crashes).

Horizontal Curves—Nighttime

Table 43 summarizes the most influential predictor variables for the expected number of 

LNDP-N crashes on rural two-lane horizontal curves that result in fatality, incapacitating injury, 

or nonincapacitating injury (LNDP-KAB-N) according to random forests generated using 

Washington data.

Table 43. Contributing factors for LNDP-KAB-N crashes on rural two-lane horizontal 

curves: Washington. 

Variable

Impact on Crash-Frequency 

Predictions

Curve radius Increases/decreases* 

Average AADT Increases

Percentage of trucks on the roadway Decreases

Percentage of households with 1 vehicle Decreases

Annual average maximum temperature Increases

Percentage of population ages 16+ unemployed Decreases

Average shoulder width Increases**

Percentage of households with ≥2 vehicles Increases

Percentage of households with 0 vehicles Decreases

Percentage of population ages 65–74 Increases

*Increases crash frequency when comparing curves to tangent segments/decreases crash frequency when comparing 

curves (i.e., a larger radius is associated with fewer crashes).

**Counterintuitive finding.
80 

Discussion

Based on the analysis, the research team recommends the following roadway contributing factors 

associated with LNDP crashes on rural two-lane horizontal curves and tangent segments:

• Average AADT: an increase in AADT was consistently associated with an increase in the 

frequency of LNDP-D and LNDP-N crashes on curves and tangent segments.

• Percent grade: an increase in percent grade was associated with an increase in the 

frequency of LNDP-D and LNDP-N crashes in Ohio and LNDP-D crashes in 

Washington. 

• Curve radius: an increase in curve radius was consistently associated with a decrease in 

the frequency of LNDP-D and LNDP-N crashes (and therefore a decrease in curve radius 

was consistently associated with an increase in the frequency of LNDP crashes).

There were several additional roadway variables that showed relationships with LNDP crash 

frequency, but results were inconsistent or could not be validated across the two States due to 

data limitations:

• Surface width: an increase in surface width was associated with a decrease in the 

frequency of LNDP-D and LNDP-N crashes on curves and tangent segments (and 

therefore a decrease in surface width was consistently associated with an increase in the 

frequency of LNDP crashes); this variable was only available for Ohio. 

• Average shoulder width: an increase in shoulder width was associated with a decrease in 

the frequency of LNDP-D and LNDP-N crashes on horizontal curves in Ohio and 

LNDP-D crashes on horizontal curves in Washington (and therefore a decrease in 

shoulder width was associated with an increase in the frequency of LNDP crashes); the 

relationship was opposite for LNDP-N crashes on horizontal and LNDP-D and LNDP-N 

crashes on tangent segments in Washington. 

• Unpaved shoulders or no shoulders: the presence of one or more unpaved shoulders or no 

shoulders was associated with an increase in crashes when compared to paved shoulders 

but only for LNDP-KAB-N crashes on tangent segments in Washington. 

• Speed limit: an increase in speed limit was associated with an increase in the frequency of 

LNDP-D and LNDP-N crashes in Ohio. 

With respect to sociodemographic characteristics, the percentage of the population ages 16+ that 

is unemployed in Ohio and Washington appeared to increase LNDP-KAB-D crashes (OH curves 

and tangent segments and WA tangent segments), LNDP-KAB-N crashes (OH curves and 

tangent segments and WA tangent segments), and LNDP-KABCO-D crashes (OH curves and 

tangent segments). As an exception to this trend, the percentage of the population ages 16+ that is

unemployed appeared to reduce LNDP-KABCO-N crashes on horizontal curves and tangent 

segments in Ohio and LNDP-KAB-N crashes on horizontal curves in Washington. The 

percentage of the population ages 25+ with a high school diploma but no university degree 

appeared to decrease LNDP-KABCO-D crashes on curves and tangent segments in Ohio and 

LNDP-KAB-D crashes on curves and tangent segments in Washington. 
81 

The following also appeared with respect to LNDP crashes in Ohio and Washington: 

• The percentage of households with an annual income <$50,000 appeared to decrease 

LNDP-KAB-D and LNDP-KAB-N crashes on curves and tangent segments in Ohio and 

LNDP-KAB-D crashes on horizontal curves in Washington. 

• The percentage of households with an annual income >$100,000 appeared to increase 

LNDP crashes in Ohio. 

With respect to weather characteristics, average annual rainfall total appeared to increase

LNDP-KAB-D crashes on horizontal curves and tangent segments in Washington. 

HEO CRASHES ON RURAL TWO-LANE HIGHWAY SEGMENTS

Ohio

Horizontal Curves and Highway Tangent Segments—Daytime

Table 44 and table 45 summarize the most influential predictor variables for the expected number 

of HEO-D crashes on rural two-lane horizontal curves and highway tangent segments that result 

in fatality, incapacitating injury, or nonincapacitating injury (HEO-KAB-D) or fatality, 

incapacitating injury, nonincapacitating injury, possible injury, or PDO (HEO-KABCO-D) 

according to random forests generated using Ohio data. 

Table 44. Contributing factors for HEO-KAB-D crashes on rural two-lane horizontal 

curves and tangent segments: Ohio.

Variable

Impact on Crash-Frequency 

Predictions

Percent grade Increases

Average AADT Increases

Curve radius Increases/decreases*

Percentage of households with income >$100,000 Increases

Percentage of households with ≥2 vehicles Decreases

Percentage of households with 1 vehicle Increases

Percentage of population ages 25+ with a university degree Increases

Percentage of population ages 20–44 Increases

Percentage of population ages 25+ without a high school diploma Increases

Percentage of population ages 25+ with a high school diploma but no university 

degree

Decreases

*Increases crash frequency when comparing curves to tangent segments/decreases crash frequency when comparing 

curves (i.e., a larger radius is associated with fewer crashes).
82 

Table 45. Contributing factors for HEO-KABCO-D crashes on rural two-lane horizontal 

curves and tangent segments: Ohio.

Variable

Impact on Crash-Frequency 

Predictions

Average AADT Increases

Percent grade Increases

Curve radius Increases/decreases*

Percentage of households with income >$100,000 Increases

Percentage of households with income <$50,000 Decreases

Percentage of households with 1 vehicle Increases

Percentage of households with ≥2 vehicles Decreases

Percentage of population ages 25+ with a university degree Increases

Percentage of population ages 25+ with a high school diploma but no university 

degree

Decreases

Percentage of population ages 16–24 unemployed Decreases

*Increases crash frequency when comparing curves to tangent segments/decreases crash frequency when comparing 

curves (i.e., a larger radius is associated with fewer crashes).

Horizontal Curves and Highway Tangent Segments—Nighttime

Table 46 and table 47 summarize the most influential predictor variables for the expected number 

of HEO-N crashes on rural two-lane horizontal curves and highway tangent segments that result 

in fatality, incapacitating injury, or nonincapacitating injury (HEO-KAB-N) or fatality, 

incapacitating injury, nonincapacitating injury, possible injury, or PDO (HEO-KABCO-N) 

according to random forests generated using Ohio data. 

Table 46. Contributing factors for HEO-KAB-N crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio. 

Variable

Impact on Crash-Frequency 

Predictions

Percent grade Increases

Average AADT Increases

Surface width Increases

Percentage of population ages 15–19 Increases

Curve radius Increases/decreases*

Percentage of population ages 75+ Increases

Percentage of population ages 65–74 Decreases

Percentage of population ages 25+ without high school diploma Decreases

Percentage of population ages 16–24 working part time Increases

Speed limit Increases

*Increases crash frequency when comparing curves to highway tangent segments/decreases crash frequency when 

comparing curves (i.e., a larger radius is associated with fewer crashes).
83 

Table 47. Contributing factors for HEO-KABCO-N crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio. 

Variable

Impact on Crash-Frequency 

Predictions

Average AADT Increases

Percent grade Increases

Surface width Increases

Percentage of households with income between $50,000 and $100,000 Increases

Percentage of population ages 16–24 working full time Decreases

Percentage of population ages 15–19 Increases

Curve radius Increases/decreases*

Percentage of population ages 75+ Decreases

Percentage of population ages 16+ unemployed Increases

Percentage of population ages 16–24 unemployed Increases

*Increases crash frequency when comparing curves to highway tangent segments/decreases crash frequency when 

comparing curves (i.e., a larger radius is associated with fewer crashes).

Washington 

Highway Tangent Segments—Daytime

Table 44 summarizes the most influential predictor variables for the expected number of HEO-D 

crashes on rural two-lane highway tangent segments that result in fatality, incapacitating injury, 

or nonincapacitating injury (HEO-KAB-D) according to random forests generated using 

Washington data.

Table 48. Contributing factors for HEO-KAB-D crashes on rural two-lane highway tangent 

segments: Washington. 

Variable

Impact on Crash-Frequency 

Predictions

Percentage of trucks on the roadway Decreases

Annual average maximum temperature Decreases

Average annual rainfall total Increases

Average AADT Increases

Percent grade Decreases**

Percentage of population ages 25+ with a high school diploma but no university 

degree

Decreases

Unpaved shoulders Increases

Percentage of households with 1 vehicle Increases

Percentage of households with ≥2 vehicles Decreases

Percentage of population ages 16–24 working part time Decreases

**Counterintuitive finding.

Highway Tangent Segments—Nighttime

Table 49 summarizes the most influential predictor variables for the expected number of HEO-N 

crashes on rural two-lane highway tangent segments that result in fatality, incapacitating injury, 

or nonincapacitating injury (HEO-KAB-N) according to random forests generated using 

Washington data. 
84 

Table 49. Contributing factors for HEO-KAB-N crashes on rural two-lane highway tangent 

segments: Washington. 

Variable

Impact on Crash-Frequency 

Predictions

Percent grade Decreases**

Annual average maximum temperature Increases 

Average AADT Increases

Percentage of households with income <$50,000 Decreases

Annual average winter minimum temperature Decreases

Annual average number of days with a minimum temperature of ≤32°F Decreases

Percentage of population ages 16–24 unemployed Decreases

Percentage of population ages 20–44 Increases

Annual average minimum temperature Decreases

Percentage of households with income between $50,000 and $100,000 Increases

**Counterintuitive finding.

Horizontal Curves—Daytime

Table 50 summarizes the most influential predictor variables for the expected number of HEO-D 

crashes on rural two-lane horizontal curves that result in fatality, incapacitating injury, or 

nonincapacitating injury (HEO-KAB-D) according to random forests generated using 

Washington data.

Table 50. Contributing factors for HEO-KAB-D crashes on rural two-lane horizontal 

curves: Washington. 

Variable

Impact on Crash-Frequency 

Predictions

Average AADT Increases

Curve radius Increases/decreases*

Percentage of trucks on the roadway Decreases

Percent grade Decreases**

Annual average minimum temperature Decreases

Percentage of population ages 16–24 working part time Decreases

Percentage of population ages 15–19 Increases

Percentage of households with income >$100,000 Increases

Annual average number of days with a minimum temperature of ≤32°F Decreases

*Increases crash frequency when comparing curves to tangent segments/decreases crash frequency when comparing 

curves (i.e., a larger radius is associated with fewer crashes).

**Counterintuitive finding.

Horizontal Curves—Nighttime

Table 51 summarizes the most influential predictor variables for the expected number of HEO-N 

crashes on rural two-lane horizontal curves that result in fatality, incapacitating injury, or 

nonincapacitating injury (HEO-KAB-N) according to random forests generated using 

Washington data.
85 

Table 51. Contributing factors for HEO-KAB-N crashes on rural two-lane horizontal 

curves: Washington. 

Variable

Impact on Crash-Frequency 

Predictions

Percentage of population ages 15–19 Increases

Percentage of trucks on the roadway Decreases

Average AADT Increases

Percentage of population ages 25+ with a high school diploma but not university 

degree

Decreases

Average shoulder width Increases**

Percentage of population ages 16–24 working part time Decreases

Percentage of households with 1 vehicle Decreases

Percentage of households with income >$100,000 Increases

Annual average maximum temperature Decreases

Percentage of population ages 65–74 Decreases

**Counterintuitive finding.

Discussion

Based on the analysis, the research team recommends the following roadway contributing factors 

associated with HEO crashes on rural two-lane horizontal curves and highway tangent segments:

• Average AADT: an increase in AADT was consistently associated with an increase in the 

frequency of HEO-D and HEO-N crashes on curves and highway tangent segments.

• Curve radius: an increase in curve radius was consistently associated with a decrease in 

the frequency of HEO-D and HEO-N crashes (and therefore a decrease in curve radius 

was consistently associated with an increase in the frequency of HEO crashes).

There were several additional roadway variables that showed relationships with HEO crash 

frequency, but results were inconsistent or could not be validated across the two States due to 

data limitations:

• Surface width: an increase in surface width was associated with a decrease in the 

frequency of HEO-N crashes on curves and highway tangent segments (and therefore a 

decrease in surface width was consistently associated with an increase in the frequency of 

HEO-N crashes); this variable was only available for Ohio. 

• Unpaved shoulders: unpaved shoulders appeared to increase HEO-KAB-D crashes on 

highway tangent segments in Washington. 

• Percent grade: an increase in percent grade was associated with an increase in the 

frequency of HEO-D and HEO-N crashes on curves and highway tangent segments in 

Ohio (counterintuitively, an increase in percent grade was associated with a decrease in 

HEO crashes in Washington).

• Speed limit: an increase in speed limit was associated with an increase in HEO-KAB-N 

crashes on curves and highway tangent segments in Ohio. 

With respect to sociodemographic characteristics, several factors showed a consistent relationship 

to HEO crashes in Ohio and Washington. The percentage of the population ages 15–19 appeared 

to increase HEO-KAB-N and HEO-KABCO-N crashes on curves and highway tangent segments 
86 

in Ohio and HEO-KAB-D and HEO-KAB-N crashes on horizontal curves in Washington. The 

percentage of the population ages 20–24 appeared to increase HEO-KAB-D crashes on curves 

and highway tangent segments in Ohio and HEO-KAB-N crashes on highway tangent segments

in Washington. 

The following also appeared with respect to HEO crashes in Ohio and Washington: 

• Percentage of the population ages 25+ with a high school diploma but no university 

degree appeared to decrease HEO crashes (consistent with findings for ROR and LNDP 

crashes). 

• Percentage of households with income <$50,000 appeared to decrease HEO crashes

(consistent with findings for LNDP crashes). 

• Percentage of households with income between $50,000 and $100,000 appeared to 

increase HEO crashes. 

• Percentage of households with income >$100,000 appeared to increase HEO crashes

(consistent with findings for ROR and LNDP crashes). 

The percentage of the population ages 25+ with a university degree appeared to increase HEO-D 

crashes in Ohio. The percentage of households with two or more vehicles appeared to decrease

HEO-D crashes on curves and highway tangent segments in Ohio and highway tangent segments 

in Washington. 

With respect to weather characteristics, the average annual rainfall appeared to increase HEOKAB-D crashes on highway tangent segments in Washington. 

ROLL CRASHES ON RURAL TWO-LANE HIGHWAY SEGMENTS

Ohio

Horizontal Curves and Highway Tangent Segments—Daytime

Table 52 and table 53 summarize the most influential predictor variables for the expected number 

of ROLL-D crashes on rural two-lane horizontal curves and highway tangent segments that result 

in fatality, incapacitating injury, or nonincapacitating injury (ROLL-KAB-D) or fatality, 

incapacitating injury, nonincapacitating injury, possible injury, or PDO (ROLL-KABCO-D) 

according to random forests generated using Ohio data. 
87 

Table 52. Contributing factors for ROLL-KAB-D crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio. 

Variable

Impact on Crash-Frequency 

Predictions

Curve radius Increases/decreases*

Average AADT Decreases**

Surface width Decreases

Percentage of population ages 45–64 Increases

Average shoulder width Decreases

Percentage of population ages 16+ unemployed Increases

Percentage of population ages 16–24 working full time Decreases

Percentage of population ages 20–44 Increases

Percentage of population ages 65–74 Decreases

Percentage of households with income >$100,000 Decreases

Speed limit Increases

Percent grade Increases

*Increases crash frequency when comparing curves to highway tangent segments/decreases crash frequency when 

comparing curves (i.e., a larger radius is associated with fewer crashes).

**Counterintuitive finding.

Table 53. Contributing factors for ROLL-KABCO-D crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio. 

Variable

Impact on Crash-Frequency 

Predictions

Curve radius Increases/decreases*

Average shoulder width Decreases

Percentage of households with 0 vehicles Decreases

Percentage of population ages 20–44 Increases

Percentage of population ages 25+ with a high school diploma but no 

university degree

Decreases

Percentage of population ages 45–64 Increases

Surface width Decreases

Average AADT Decreases

Percentage of population ages 16+ unemployed Increases

Percentage of households with 1 vehicle Increases

Speed limit Increases

Percent grade Increases

*Increases crash frequency when comparing curves to highway tangent segments/decreases crash frequency when 

comparing curves (i.e., a larger radius is associated with fewer crashes).

**Counterintuitive finding.

Horizontal Curves and Highway Tangent Segments—Nighttime

Table 54 and table 55 summarize the most influential predictor variables for the expected number 

of ROLL-N crashes on rural two-lane horizontal curves and highway tangent segments that result 

in fatality, incapacitating injury, or nonincapacitating injury (ROLL-KAB-N) or fatality, 

incapacitating injury, nonincapacitating injury, possible injury, or PDO (ROLL-KABCO-N) 

according to random forests generated using Ohio data. 
88 

Table 54. Contributing factors for ROLL-KAB-N crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio. 

Variable

Impact on Crash-Frequency 

Predictions

Percent grade Increases

Percentage of population ages 65–74 Decreases

Average AADT Increases

Curve radius Increases/decreases*

Percentage of population ages 75+ Decreases

Percentage of population ages 15–19 Increases

Percentage of population ages 25+ without a high school diploma Decreases

Percentage of population ages 45–64 Decreases

Percentage of population ages 16–24 working full time Decreases

Percentage of population ages 25+ with a high school diploma but no 

university degree

Increases

Surface width Increases

Shoulder width Increases**

*Increases crash frequency when comparing curves to highway tangent segments/decreases crash frequency when 

comparing curves (i.e., a larger radius is associated with fewer crashes).

**Counterintuitive finding.

Table 55. Contributing factors for ROLL-KABCO-N crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio. 

Variable

Impact on Crash-Frequency 

Predictions

Average AADT Increases

Percent grade Increases

Surface width Decreases

Percentage of population ages 75+ Increases

Percentage of population ages 45–64 Decreases

Curve radius Increases/decreases*

Percentage of population ages 65–74 Decreases

Percentage of households with income between $50,000 and $100,000 Increases

Percentage of population ages 15–19 Increases

Percentage of households with income <$50,000 Decreases

Speed limit Increases

Average shoulder width Decreases

*Increases crash frequency when comparing curves to highway tangent segments/decreases crash frequency when 

comparing curves (i.e., a larger radius is associated with fewer crashes).

**Counterintuitive finding.

Washington 

Highway Tangent Segments—Daytime

Table 56 summarizes the most influential predictor variables for the expected number of 

ROLL-D crashes on rural two-lane highway tangent segments that result in fatality, 

incapacitating injury, or nonincapacitating injury (ROLL-KAB-D) according to random forests 

generated using Washington data.
89 

Table 56. Contributing factors for ROLL-KAB-D crashes on rural two-lane highway 

tangent segments: Washington. 

Variable

Impact on Crash-Frequency 

Predictions

Percent grade Increases

Average AADT Decreases**

Average shoulder width Increases**

Percentage of trucks on the roadway Decreases

Percentage of population ages 16+ unemployed Increases

Annual average minimum temperature Decreases

Terrain Increases

Average annual rainfall total Increases

Percentage of households with income >$100,000 Increases

Annual average number of days with a minimum temperature of ≤32°F Decreases

**Counterintuitive finding.

Highway Tangent Segments—Nighttime

Table 57 summarizes the most influential predictor variables for the expected number of 

ROLL-N crashes on rural two-lane highway tangent segments that result in fatality, 

incapacitating injury, or nonincapacitating injury (ROLL-KAB-N) according to random forests 

generated using Washington data. 

Table 57. Contributing factors for ROLL-KAB-N crashes on rural two-lane highway 

tangent segments: Washington. 

Variable

Impact on Crash-Frequency 

Predictions

Average AADT Increases

Percentage of trucks on the roadway Increases

Annual average winter minimum temperature Increases

Percentage of population ages 16–24 working full time Increases

Annual average number of days with a minimum temperature of ≤32°F Increases

Percentage of population ages 16–24 working part time Decreases

Percentage of households with income between $50,000 and $100,000 Decreases

Percentage of households with 0 vehicles Decreases

Percentage of population ages 25+ with a university degree Decreases

Percent grade Increases

Horizontal Curves—Daytime

Table 58 summarizes the most influential predictor variables for the expected number of 

ROLL-D crashes on rural two-lane horizontal curves that result in fatality, incapacitating injury, 

or nonincapacitating injury (ROLL-KAB-D) according to random forests generated using 

Washington data.
90 

Table 58. Contributing factors for ROLL-KAB-D crashes on rural two-lane horizontal 

curves: Washington. 

Variable

Impact on Crash-Frequency 

Predictions

Percentage of population ages 16–24 working part time Increases

Percentage of population ages 65–74 Increases

Percentage of trucks on the roadway Decreases

Curve radius Increases/decreases* 

Percentage of population ages 75+ Decreases

Average AADT Increases

Percent grade Increases

Percentage of households with income <$50,000 Decreases

Annual average maximum temperature Decreases

Percentage of population ages 16–24 unemployed Decreases

*Increases crash frequency when comparing curves to highway tangent segments/decreases crash frequency when 

comparing curves (i.e., a larger radius is associated with fewer crashes).

Horizontal Curves—Nighttime

Table 59 summarizes the most influential predictor variables for the expected number of 

ROLL-N crashes on rural two-lane horizontal curves that result in fatality, incapacitating injury, 

or nonincapacitating injury (ROLL-KAB-N) according to random forests generated using 

Washington data. 

Table 59. Contributing factors for ROLL-KAB-N crashes on rural two-lane horizontal 

curves: Washington. 

Variable

Impact on Crash-Frequency 

Predictions

Curve radius Increases/decreases* 

Average shoulder width Increases**

Average AADT Increases

Truck percentage Decreases

Percent grade Increases

Annual average maximum temperature Increases

Percentage of population ages 25+ with a university degree Decreases

Average annual rainfall total Decreases

Percentage of households with income >$100,000 Increases

Percentage of households with income between $50,000 and $100,000 Increases

Percentage of population ages 65–74 Increases

Percentage of population ages 75+ Decreases

*Increases crash frequency when comparing curves to highway tangent segments/decreases crash frequency when 

comparing curves (i.e., a larger radius is associated with fewer crashes).

**Counterintuitive finding.
91 

Discussion

Based on the analysis, the research team recommends the following contributing roadway factors 

associated with ROLL crashes on rural two-lane horizontal curves and highway tangent 

segments:

• Average AADT: an increase in AADT was associated with an increase in the frequency of 

ROLL-N crashes on curves and highway tangent segments in Ohio and Washington; 

interestingly, AADT appeared to ROLL-D crashes in both Ohio (on curves and highway 

tangent segments) and Washington (on highway tangent segments).

• Percent grade: an increase in percent grade was consistently associated with an increase 

in the frequency of ROLL-D and ROLL-N crashes on curves and highway tangent 

segments. 

• Curve radius: an increase in curve radius was consistently associated with a decrease in 

the frequency of ROLL-D and ROLL-N crashes (and therefore a decrease in curve radius 

was consistently associated with an increase in the frequency of ROLL crashes).

There were several additional roadway variables that showed relationships with ROLL crash 

frequency, but results were inconsistent or could not be validated across the two States due to 

data limitations:

• Surface width: an increase in surface width was associated with a decrease in the 

frequency of ROLL-KAB-D crashes and ROLL-KABCO-D and ROLL-KABCO-N 

crashes on curves and highway tangent segments (and therefore a decrease in surface 

width was associated with an increase in the frequency of those types of ROLL crashes); 

this variable was only available for Ohio. 

• Average shoulder width: an increase in shoulder width was associated with a decrease in 

the frequency of ROLL-KAB-D crashes and ROLL-KABCO-D and ROLL-KABCO-N 

crashes on curves and highway tangent segments (and therefore a decrease in surface 

width was associated with an increase in the frequency of ROLL crashes); the relationship 

was the opposite for ROLL-KAB-D crashes on highway tangent segments and 

ROLL-KAB-N crashes on horizontal curves in Washington. 

• Terrain: mountainous terrain appeared to increase ROLL-KAB-D crashes on highway 

tangent segments in Washington. 

• Speed limit: an increase in speed limit was associated with an increase in the frequency of 

ROLL-D and ROLL-N crashes in Ohio. 

With respect to sociodemographic characteristics, there were several predictors of ROLL crashes 

in Ohio and Washington: 

• The percentage of households with incomes between $50,000 and $100,000 appeared to 

increase ROLL-KABCO-N crashes on curves and highway tangent segments in Ohio and 

ROLL-KAB-N crashes on horizontal curves in Washington. It appeared as a factor that 

decreases the crash frequency of ROLL-KAB-N crashes on highway tangent segments in 

Ohio. 

• The percentage of households with an annual income <$50,000 appeared to decrease 

ROLL-KABCO-N crashes on curves and highway tangent segments in Ohio and 
92 

ROLL-KAB-D crashes on horizontal curves in Washington and (consistent with findings 

for LNDP and HEO crashes). 

• The percentage of households with an annual income >$100,000 appeared to increase 

ROLL-KAB-D crashes on highway tangent segments and ROLL-KAB-N crashes on 

horizontal curves in Washington (consistent with findings for ROR, LNDP, and HEO 

crashes). 

With respect to weather characteristics, average annual rainfall total appeared to increase

ROLL-KAB-D crashes on highway tangent segments in Washington. 

ANG CRASHES ON RURAL TWO-LANE HIGHWAY SEGMENTS

Ohio

Horizontal Curves and Highway Tangent Segments—Daytime

Table 60 and table 61 summarize the most influential predictor variables for the expected number 

of ANG-D crashes on rural two-lane horizontal curves and highway tangent segments that result 

in fatality, incapacitating injury, or nonincapacitating injury (ANG-KAB-D) or fatality, 

incapacitating injury, nonincapacitating injury, possible injury, or PDO (ANG-KABCO-D) 

according to random forests generated using Ohio data. 

Table 60. Contributing factors for ANG-KAB-D crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio. 

Variable

Impact on Crash-Frequency 

Predictions

Percent grade Decreases**

Average AADT Increases

Percentage of population ages 25+ with a university degree Increases 

Percentage of households with income >$100,000 Increases

Percentage of population ages 25+ without a high school diploma Increases

Percentage of population ages 45–64 Decreases

Surface width Increases

Percentage of population ages 25+ with a high school diploma but no 

university degree

Decreases

Percentage of population ages 20–44 Increases

Percentage of population ages 75+ Decreases

**Counterintuitive finding.
93 

Table 61. Contributing factors for ANG-KABCO-D crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio. 

Variable

Impact on Crash-Frequency 

Predictions

Average AADT Increases

Average shoulder width Decreases

Percent grade Decreases**

Percentage of population ages 16+ unemployed Increases

Percentage of households with income >$100,000 Increases

Percentage of population ages 25+ with a university degree Increases

Percentage of households with 0 vehicles Decreases

Percentage of households with 1 vehicle Decreases

Percentage of households with ≥2 vehicles Increases

Surface width Increases

Speed limit Decreases**

Curve radius Increases/decreases*

*Increases crash frequency when comparing curves to highway tangent segments/decreases crash frequency when 

comparing curves (i.e., a larger radius is associated with fewer crashes).

**Counterintuitive finding.

Washington 

Highway Tangent Segments—Daytime

Table 62 summarizes the most influential predictor variables for the expected number of ANG-D 

crashes on rural two-lane highway tangent segments that result in fatality, incapacitating injury, 

or nonincapacitating injury (ANG-KAB-D) according to random forests generated using 

Washington data. 

Table 62. Contributing factors for ANG-KAB-D crashes on rural two-lane highway tangent 

segments: Washington. 

Variable

Impact on Crash-Frequency 

Predictions

Average AADT Increases

Lane width Increases

Average shoulder width Increases**

Percentage of population ages 45–64 Increases

Percentage of population ages 20–44 Decreases

Average annual snowfall total Increases

Annual average number of days with a minimum temperature of ≤32°F Decreases

Annual average maximum temperature Increases

Annual average winter minimum temperature Decreases

Percentage of households with income <$50,000 Decreases

**Counterintuitive finding.
94 

Discussion

Based on the analysis, the research team recommends the following contributing roadway factors 

associated with ANG crashes on rural two-lane horizontal curves and highway tangent segments:

• Average AADT: an increase in AADT was consistently associated with an increase in the 

frequency of ANG-D crashes on curves and highway tangent segments.

There were several additional roadway variables that showed relationships with ANG crash 

frequency, but results were inconsistent or could not be validated across the two States due to 

data limitations: 

• Percent grade: an increase in percent grade was consistently associated with a decrease in 

the frequency of ANG-D crashes on curves and highway tangent segments in Ohio 

(and therefore a decrease in percent grade was consistently associated with an increase in 

the frequency of ANG crashes).

• Surface width: an increase in surface width was associated with an increase in the 

frequency of ANG-D crashes on curves and highway tangent segments (and therefore a 

decrease in surface width was consistently associated with a decrease in the frequency of 

ANG-D crashes); this variable was only available for Ohio. 

• Average shoulder width: an increase in shoulder width was associated with a decrease in 

the frequency of ANG-KABCO-D crashes on curves and highway tangent segments in

Ohio (counterintuitively, an increase in shoulder width was associated with an increase in 

the frequency of ANG-KAB-D crashes on highway tangent segments in Washington). 

• Curve radius: an increase in curve radius was associated with a decrease in the frequency 

of ANG-KABCO-D crashes on curves in Ohio (and therefore a decrease in curve radius 

was associated with an increase in the frequency of ANG-KABCO-D crashes).

With respect to sociodemographic characteristics, none of the factors showed a consistent 

relationship to ANG crashes in Ohio or Washington: 

• The percentage of the population ages 25+ with a university degree appears to increase

ANG-D crashes on curves and highway tangent segments in Ohio. 

• The percentage of households with income <$50,000 appears to decrease ANG-D crashes 

on highway tangent segments in Washington. 

• The percentage of households with income >$100,000 appears to increase ANG-D 

crashes on curves and highway tangent segments in Ohio. 

• The percentage of households with no vehicles appears to decrease ANG-D crashes on 

curves and highway tangent segments in Ohio. 

• The percentage of households with one vehicle appears to decrease ANG-D crashes on 

curves and highway tangent segments in Ohio. 

• The percentage of households with two or more vehicles appears to increase ANG-D 

crashes on curves and highway tangent segments in Ohio. 

With respect to weather characteristics, none of the factors showed a consistent relation to ANG

crashes in Ohio or Washington. 
95 

ANG CRASHES AT FOUR-LEG STOP-CONTROLLED INTERSECTIONS ON RURAL 

TWO-LANE ROADS

California

Daytime

Table 63 summarizes the most influential predictor variables for the expected number of ANG-D 

crashes at four-leg stop-controlled intersections (with stop control on the minor road) on rural 

two-lane roads that result in fatality, incapacitating injury, or nonincapacitating injury 

(ANG-KAB-D) according to random forests generated using California data. 

Table 63. Contributing factors for ANG-KAB-D crashes at four-leg stop-controlled 

intersections on rural two-lane roads: California. 

Variable

Impact on Crash-Frequency 

Predictions

Cross street AADT Increases

Mainline AADT Increases

Mainline left channelization Decreases

Lane width Increases

Annual average maximum temperature Decreases

Percentage of population ages 25+ without a high school diploma Decreases

Average annual rainfall total Increases

Percentage of households with income >$100,000 Increases

Annual average number of days with a minimum temperature of ≤32°F Decreases

Percentage of population ages 45–64 Decreases

Nighttime

Table 64 summarizes the most influential predictor variables for the expected number of ANG-N 

crashes at four-leg stop-controlled intersections (with stop control on the minor road) on rural 

two-lane roads that result in fatality, incapacitating injury, or nonincapacitating injury 

(ANG-KAB-N) according to random forests generated using California data. 

Table 64. Contributing factors for ANG-KAB-N crashes at four-leg stop-controlled 

intersections on rural two-lane roads: California. 

Variable

Impact on Crash-Frequency 

Predictions

Design speed Increases

Average annual rainfall total Increases

Annual average maximum temperature Decreases

Mainline AADT Increases

Percentage of population ages 25+ with a university degree Increases

Percentage of households with income <$50,000 Decreases

Cross street AADT Increases

Average annual snowfall total Decreases

Percentage of population ages 45–64 Increases

Annual average minimum temperature Increases

Mainline left channelization Decreases
96 

Ohio

Daytime

Table 65 and table 66 summarize the most influential predictor variables for the expected number 

of ANG-D crashes at four-leg stop-controlled intersections (with stop control on the minor road) 

on rural two-lane roads that result in fatality, incapacitating injury, or nonincapacitating injury 

(ANG-KAB-D) or fatality, incapacitating injury, nonincapacitating injury, possible injury, or 

PDO (ANG-KABCO-D) according to random forests generated using Ohio data. 

Table 65. Contributing factors for ANG-KAB-D crashes at four-leg stop-controlled 

intersections on rural two-lane roads: Ohio. 

Variable

Impact on Crash-Frequency 

Predictions

Cross Street AADT Increases

Speed limit Increases

Mainline AADT Increases

Percentage of households with income <$50,000 Decreases

Percentage of population ages 16–24 unemployed Decreases

Percentage of households with income >$100,000 Increases

Percentage of population ages 16–24 working full time Increases

Percentage of households with ≥2 vehicles Decreases

Annual average maximum temperature Decreases

Percentage of households with income between $50,000 and $100,000 Decreases

Lane width Increases

Table 66. Contributing factors for ANG-KABCO-D crashes at four-leg stop-controlled 

intersections on rural two-lane roads: Ohio. 

Variable

Impact on Crash-Frequency 

Predictions

Cross street AADT Increases

Speed limit Increases

Percentage of households with income <$50,000 Decreases

Mainline AADT Increases

Percentage of households with income >$100,000 Increases

Percentage of population ages 25+ with a high school diploma but no university 

degree

Decreases

Percentage of population ages 16–24 unemployed Decreases

Percentage of population ages 16–24 working part time Increases

Average annual snowfall total Increases

Percentage of households with ≥2 vehicles Decreases

Lane width Increases

Nighttime

Table 67 and table 68 summarize the most influential predictor variables for the expected number 

of ANG-D crashes at four-leg stop-controlled intersections (with stop control on the minor road) 

on rural two-lane roads that result in fatality, incapacitating injury, or nonincapacitating injury 

(ANG-KAB-N) or fatality, incapacitating injury, nonincapacitating injury, possible injury, or 

PDO (ANG-KABCO-N) according to random forests generated using Ohio data.
97 

Table 67. Contributing factors for ANG-KAB-N crashes at four-leg stop-controlled 

intersections on rural two-lane roads: Ohio. 

Variable

Impact on Crash-Frequency 

Predictions

Cross street AADT Increases

Speed limit Increases

Percentage of households with income <$50,000 Decreases

Percentage of households with income between $50,000 and $100,000 Increases

Percentage of households with 1 vehicle Decreases

Mainline AADT Increases

Percentage of households with income >$100,000 Increases

Percentage of population ages 16–24 unemployed Decreases

Percentage of population ages 16–24 working full time Increases

Percentage of population ages 45–64 Increases

Table 68. Contributing factors for ANG-KABCO-N crashes at four-leg stop-controlled 

intersections on rural two-lane roads: Ohio. 

Variable

Impact on Crash-Frequency 

Predictions

Cross street AADT Increases

Percentage of households with income <$50,000 Decreases

Speed limit Increases

Mainline AADT Increases

Percentage of population ages 16–24 working part time Increases

Percentage of households with ≥2 vehicles Decreases

Percentage of households with 1 vehicle Decreases

Percentage of households with 0 vehicles Increases

Percentage of population ages 16–24 unemployed Decreases

Average annual snowfall total Increases

Discussion

Based on the analysis, the research team recommends the following contributing roadway factors 

associated with ANG crashes at four-leg stop-controlled intersections (with stop control on the 

minor road) on rural two-lane roads:

• Mainline AADT: an increase in mainline AADT was consistently associated with an 

increase in the frequency of ANG-D and ANG-N crashes at four-leg stop-controlled 

intersections on rural two-lane roads. 

• Cross street AADT: an increase in cross street AADT was consistently associated with an 

increase in the frequency of ANG-D and ANG-N crashes at four-leg stop-controlled 

intersections on rural two-lane roads.

• Speed: an increase in speed was consistently associated with an increase in the frequency 

of ANG-D and ANG-N crashes at four-leg stop-controlled intersections on rural two-lane 

roads; design speed appeared to increase ANG-KAB-N crashes in California, and speed 

limit appeared to increase ANG-KAB-D, ANG-KABCO-D, ANG-KAB-N, and 

ANG-KABCO-N crashes in Ohio. 
98 

• Lane width: an increase in lane width was associated with an increase in the frequency of 

ANG-D crashes at four-leg stop-controlled intersections on rural two-lane roads in 

California and Ohio. 

There were additional roadway variables that showed relationships with ANG crash frequency, 

but results were inconsistent or could not be validated across the two States due to data 

limitations:

• Mainline left-turn channelization: presence of channelized left-turn lanes on the 

mainline was associated with a decrease in the frequency of ANG crashes at four-leg 

stop-controlled intersections on rural two-lane roads in California. 

With respect to sociodemographic characteristics, two factors showed a consistent relationship to 

ANG crashes in California and Ohio: 

• The percentage of population ages 45–64 appeared to increase ANG-KAB-N crashes at 

four-leg stop-controlled intersections on rural two-lane roads in California and Ohio 

(the relationship was opposite for ANG-KAB-D crashes at four-leg stop-controlled 

intersections on rural two-lane roads in California).

• The percentage of households with income >$100,000 appeared to increase ANG-D 

crashes at four-leg stop-controlled intersections on rural two-lane roads in California and 

Ohio and ANG-KAB-N crashes at four-leg stop-controlled intersections on rural two-lane 

roads in Ohio. 

• The percentage of households with income <$50,000 appeared to decrease ANG-D 

crashes at four-leg stop-controlled intersections on rural two-lane roads in California and 

Ohio; the contributing factor appeared to increase ANG-KAB-N crashes in California and 

all groups of ANG crashes in Ohio. 

The following also appeared with respect to ANG crashes in California and Ohio:

• The percentage of the population ages 16–24 that is unemployed appeared to decrease

ANG crashes at four-leg stop-controlled intersections on rural two-lane roads in Ohio. 

• The percentage of households with two or more vehicles appeared to decrease ANG-D 

crashes at four-leg stop-controlled intersections on rural two-lane roads in Ohio. 

With respect to weather characteristics, two factors showed a consistent relationship to ANG

crashes: 

• Average annual rainfall total appeared to increase ANG crashes at four-leg stopcontrolled intersections on rural two-lane roads in California.

• Annual average maximum temperature appeared to increase ANG-KAB-D and 

ANG-KAB-N crashes in California and ANG-KAB-D in Ohio. 
99 

ANG CRASHES AT FOUR-LEG STOP-CONTROLLED INTERSECTIONS ON URBAN 

TWO-LANE ROADS

California

Daytime

Table 69 summarizes the most influential predictor variables for the expected number of ANG-D 

crashes at four-leg stop-controlled intersections (with stop control on the minor road) on urban 

two-lane roads that result in fatality, incapacitating injury, or nonincapacitating injury 

(ANG-KAB-D) according to random forests generated using California data.

Table 69. Contributing factors for ANG-KAB-D crashes at four-leg stop-controlled 

intersections on urban two-lane roads: California. 

Variable

Impact on Crash-Frequency 

Predictions

Percentage of households with income <$50,000 Decreases

Design speed Increases

Percentage of households with ≥2 vehicles Increases

Percentage of households with income >$100,000 Increases

Annual average maximum temperature Decreases

Annual average minimum temperature Increases

Percentage of population ages 16+ unemployed Decreases

Percentage of population ages 20–44 Decreases

Annual average winter minimum temperature Decreases

Percentage of households with 1 vehicle Decreases

Ohio

Daytime

Table 70 and table 71 summarize the most influential predictor variables for the expected number 

of ANG-D crashes at four-leg stop-controlled intersections (with stop control on the minor road) 

on urban two-lane roads that result in fatality, incapacitating injury, or nonincapacitating injury 

(ANG-KAB-D) or fatality, incapacitating injury, nonincapacitating injury, possible injury, or 

PDO (ANG-KABCO-D) according to random forests generated using Ohio data.
100 

Table 70. Contributing factors for ANG-KAB-D crashes at four-leg stop-controlled

intersections on urban two-lane roads: Ohio. 

Variable

Impact on Crash-Frequency 

Predictions

Speed limit Increases

Percentage of households with income >$100,000 Increases

Percentage of households with income <$50,000 Decreases

Percentage of population ages 25+ with a university degree Increases

Percentage of population ages 25+ without a high school diploma Decreases

Cross street AADT Increases

Percentage of population ages 75+ Decreases

Percentage of households with income between $50,000 and $100,000 Increases

Percentage of households with ≥2 vehicles Increases

Percentage of population ages 25+ with a high school diploma but no university 

degree

Decreases

Mainline AADT Increases

Table 71. Contributing factors for ANG-KABCO-D crashes at four-leg stop-controlled

intersections on urban two-lane roads: Ohio. 

Variable

Impact on Crash-Frequency 

Predictions

Cross street AADT Increases

Percentage of households with ≥2 vehicles Increases

Percentage of households with income >$100,000 Increases 

Speed limit Increases

Percentage of population ages 25+ without a high school diploma Increases

Mainline AADT Increases

Percentage of population ages 16+ unemployed Decreases

Percentage of population ages 16–24 unemployed Decreases

Percentage of households with 1 vehicle Decreases

Percentage of population ages 25+ with a university degree Increases

Discussion

Based on the analysis, the research team recommends the following contributing roadway factors 

associated with ANG crashes at four-leg stop-controlled intersections (with stop control on the 

minor road) on urban two-lane roads:

• Mainline AADT: an increase in mainline AADT was associated with an increase in the 

frequency of ANG-D crashes at four-leg stop-controlled intersections on urban two-lane 

roads in Ohio. 

• Cross street AADT: an increase in cross street AADT was associated with an increase in 

the frequency of ANG-D crashes at four-leg stop-controlled intersections on urban 

two-lane roads in Ohio. 

• Speed: an increase in speed was consistently associated with an increase in the frequency 

of ANG-D crashes at four-leg stop-controlled intersections on urban two-lane roads; 

design speed appeared to increase ANG-KAB-D crashes in California, and speed limit 

appeared to increase ANG-KAB-D and ANG-KABCO-D crashes in Ohio.
101 

With respect to sociodemographic characteristics, two factors showed a consistent relationship to 

ANG crashes in California and Ohio: 

• The percentage of households with two or more vehicles consistently appeared to increase

ANG-D crashes at four-leg stop-controlled intersections on urban two-lane roads in 

California and Ohio. 

• The percentage of households with income >$100,000 consistently appeared to increase 

ANG-D crashes at four-leg stop-controlled intersections on urban two-lane roads in 

California and Ohio. 

The following also appeared with respect to ANG crashes in California and Ohio: 

• The percentage of the population ages 16–24 that is unemployed appeared to decrease

ANG-KABCO-D crashes at four-leg stop-controlled intersections on urban two-lane 

roads in Ohio. The percentage of the population ages 16+ appeared to decrease 

ANG-KAB-D crashes in California and ANG-KABCO-D crashes in Ohio.

• The percentage of households with income <$50,000 appeared to decrease ANG-KAB-D 

crashes at four-leg stop-controlled intersections on urban two-lane roads in California and 

Ohio. 

With respect to weather characteristics, two factors showed a consistent relationship to ANG

crashes:

• Average annual minimum temperature appeared to increase ANG crashes at four-leg

stop-controlled intersections on urban two-lane roads in California.

• Average annual maximum temperature appeared to decrease ANG crashes at four-leg

stop-controlled intersections on urban two-lane roads in California.

ANG CRASHES AT FOUR-LEG SIGNALIZED INTERSECTIONS ON URBAN 

MULTILANE DIVIDED ROADS

California

Daytime

Table 72 summarizes the most influential predictor variables for the expected number of ANG-D 

crashes at four-leg signalized intersections on urban multilane divided roads that result in fatality, 

incapacitating injury, or nonincapacitating injury (ANG-KAB-D) according to random forests 

generated using California data. 
102 

Table 72. Contributing factors for ANG-KAB-D crashes at four-leg signalized intersections 

on urban multilane divided roads: California. 

Variable

Impact on Crash-Frequency 

Predictions

Average annual rainfall total Decreases

Percentage of households with income <$50,000 Increases

Annual average winter minimum temperature Decreases

Annual average minimum temperature Decreases

Annual average number of days with a minimum temperature of ≤32°F Decreases

Design speed Increases

Lane width Increases

Percentage of population ages 75+ Increases

Mainline AADT Increases

Median width Increases

Ohio

Daytime

Table 73 and table 74 summarize the most influential predictor variables for the expected number 

of ANG-D crashes at four-leg signalized intersections on urban multilane divided roads that 

result in fatality, incapacitating injury, or nonincapacitating injury (ANG-KAB-D) or fatality, 

incapacitating injury, nonincapacitating injury, possible injury, or PDO (ANG-KABCO-D) 

according to random forests generated using Ohio data.

Table 73. Contributing factors for ANG-KAB-D crashes at four-leg signalized intersections 

on urban multilane divided roads: Ohio. 

Variable

Impact on Crash-Frequency 

Predictions

Percentage of population ages 16+ unemployed Decreases

Percentage of population ages 45–64 Decreases

Total AADT (mainline AADT + cross street AADT) Increases

Speed limit Increases

Percentage of population ages 16–24 working part time Decreases

Percentage of population ages 25+ without a high school diploma Increases

Percentage of households with income >$100,000 Decreases

Annual average number of days with a minimum temperature of ≤32°F Decreases

Percentage of population ages 25+ with a high school diploma Decreases

Percentage of population ages 16–24 unemployed Increases
103 

Table 74. Contributing factors for ANG-KABCO-D crashes at four-leg signalized

intersections on urban multilane divided roads: Ohio. 

Variable

Impact on Crash-Frequency 

Predictions

Mainline AADT Increases

Percentage of population ages 20–44 Increases

Percentage of population ages 25+ without a high school diploma Increases

Cross street AADT Increases

Percentage of population ages 16–24 unemployed Decreases

Percentage of population ages 45–64 Decreases

Percentage of population ages 25+ with a high school diploma Decreases

Percentage of households with ≥2 vehicles Decreases

Percentage of population ages 16–24 working full time Increases

Median width Decreases

Speed limit Increases

Discussion

Based on the analysis, the research team recommends the following contributing roadway factors 

associated with ANG crashes at four-leg signalized intersections on urban multilane divided 

roads: 

• Mainline AADT: an increase in mainline AADT was consistently associated with an 

increase in the frequency of ANG-D crashes at four-leg signalized intersections on urban 

multilane divided roads. 

• Cross street AADT: an increase in cross street AADT was associated with an increase in 

the frequency of ANG-D crashes at four-leg signalized intersections on urban multilane 

divided roads in Ohio. 

• Speed: an increase in speed was consistently associated with an increase in the frequency 

of ANG-D crashes at four-leg signalized intersections on urban multilane divided roads; 

design speed appeared to increase ANG-KAB-D crashes in California, and speed limit 

appeared to increase ANG-KAB-D and ANG-KABCO-D crashes in Ohio.

There were additional roadway variables that showed relationships with ANG crash frequency, 

but results were inconsistent or could not be validated across the two States due to data 

limitations:

• Lane width: an increase in lane width was associated with an increase in the frequency of 

ANG-D crashes at four-leg signalized intersections on urban multilane divided roads in 

California. 

• Median width: an increase in median width was associated with an increase in the 

frequency of ANG-KAB-D crashes at four-leg signalized intersections on urban multilane 

divided roads in California but a decrease in the frequency of ANG-KABCO-D crashes at 

four-leg signalized intersections on urban multi-lane divided roads in Ohio. 

With respect to sociodemographic characteristics, the percentage of households with income 

<$50,000 appeared to increase ANG-KAB-D crashes at four-leg signalized intersections on urban 

multilane divided roads in California. Interestingly, this factor has generally shown decreases in 
104 

crashes for other FCFTs but appeared to increase crashes for this FCFT. The percentage of 

the population between the ages of 45 and 64 appeared to decrease ANG-KAB-D and 

ANG-KABCO-D crashes in Ohio. The percentage of the population ages 25+ without a high 

school diploma appeared to increase ANG-KAB-D and ANG-KABCO-D crashes in Ohio. 

With respect to weather characteristics, the average annual number of days with a minimum 

temperature of ≤32°F appeared to decrease ANG-KAB-D crashes in both California and Ohio. 

ANG CRASHES AT FOUR-LEG SIGNALIZED INTERSECTIONS ON URBAN 

MULTILANE UNDIVIDED ROADS

California

Daytime

Table 75 summarizes the most influential predictor variables for the expected number of ANG-D 

crashes at four-leg signalized intersections on urban multilane undivided roads that result in 

fatality, incapacitating injury, or nonincapacitating injury (ANG-KAB-D) according to random 

forests generated using California data. 

Table 75. Contributing factors for ANG-KAB-D crashes at four-leg signalized intersections 

on urban multilane undivided roads: California. 

Variable

Impact on Crash-Frequency 

Predictions

Percentage of population ages 25+ with a university degree Increases

Percentage of population ages 45–64 Decreases

Percentage of population ages 65–74 Increases

Percentage of population ages 25+ without a high school diploma Decreases

Percentage of households with income >$100,000 Increases

Percentage of households with ≥2 vehicles Decreases

Percentage of population ages 20–44 Decreases

Lane width Decreases

Cross street right-turn channelization Decreases

Cross street AADT Decreases**

**Counterintuitive finding.

Ohio

Daytime

Table 76 and table 77 summarize the most influential predictor variables for the expected number 

of ANG-D crashes at four-leg signalized intersections on urban multilane undivided roads that 

result in fatality, incapacitating injury, or nonincapacitating injury (ANG-KAB-D) or fatality, 

incapacitating injury, nonincapacitating injury, possible injury, or PDO (ANG-KABCO-D) 

according to random forests generated using Ohio data.
105 

Table 76. Contributing factors for ANG-KAB-D crashes at four-leg signalized intersections 

on urban multilane undivided roads: Ohio. 

Variable

Impact on Crash-Frequency 

Predictions

Mainline AADT Increases

Cross street AADT Increases

Speed limit Increases

Percentage of population ages 25+ with a high school diploma but no university 

degree

Increases

Percentage of households with income <$50,000 Increases

Percentage of households with ≥2 vehicles Increases

Percentage of households with 1 vehicle Decreases

Percentage of population ages 25+ with a university degree Decreases

Percentage of population ages 25+ without a high school diploma Increases

Percentage of households with 0 vehicles Increases

Table 77. Contributing factors for ANG-KABCO-D crashes at four-leg signalized 

intersections on urban multilane undivided roads: Ohio. 

Variable

Impact on Crash-Frequency 

Predictions

Cross street AADT Increases

Mainline AADT Increases

Percentage of households with ≥2 vehicles Decreases

Annual average winter minimum temperature Increases

Annual average number of days with a minimum temperature of ≤32°F Decreases

Percentage of population ages 45–64 Decreases

Percentage of households with income <$50,000 Decreases

Percentage of households with 0 vehicles Increases

Percentage of households with 1 vehicle Decreases

Percentage of population ages 16–24 working full time Increases

Speed limit Increases

Number of channelized left-turn lanes Increases

Discussion

Based on the analysis, the research team recommends the following contributing roadway factors 

associated with ANG crashes at four-leg signalized intersections on urban multilane undivided 

roads: 

• Mainline AADT: an increase in mainline AADT was associated with an increase in the 

frequency of ANG-D crashes at four-leg signalized intersections on urban multilane 

undivided roads in Ohio. 

• Speed: an increase in speed limit was consistently associated with an increase in the 

frequency of ANG-D crashes at four-leg signalized intersections on urban multilane 

undivided roads in Ohio. 
106 

There were additional roadway variables that showed relationships with ANG crash frequency, 

but results were inconsistent or could not be validated across the two States due to data 

limitations:

• Cross street AADT: an increase in cross street AADT was associated with an increase in 

the frequency of ANG-D crashes at four-leg signalized intersections on urban multilane 

undivided roads in Ohio (the relationship was opposite for ANG-D crashes at four-leg

signalized intersections on urban multilane undivided roads in California). 

• Lane width: an increase in lane width was associated with a decrease in the frequency of 

ANG-KAB-D crashes at four-leg signalized intersections on urban multilane undivided 

roads in California. 

• Cross street right-turn channelization: presence of right-turn channelization on cross 

street was associated with a decrease in the frequency of ANG-KAB-D crashes at four-leg

signalized intersections on urban multilane undivided roads in California. 

With respect to sociodemographic characteristics, two factors showed a relationship to ANG

crashes in California and Ohio: 

• The percentage of the population ages 45–64 appeared to decrease ANG-KAB-D crashes 

at four-leg signalized intersections on urban multilane undivided roads in California and 

ANG-KABCO-D crashes at four-leg signalized intersections on urban multilane 

undivided roads in Ohio. 

• The percentage of households with no vehicles appeared to increase ANG-KAB-D and 

ANG-KABCO-D crashes at four-leg signalized intersections on urban multilane 

undivided roads in Ohio. The percentage of households with one vehicle appeared to 

decrease those same crash types.

With respect to weather characteristics, the research did not produce any contributing factors.

ANG CRASHES AT THREE-LEG STOP-CONTROLLED INTERSECTIONS ON 

RURAL TWO-LANE ROADS

California

Daytime

Table 78 summarizes the most influential predictor variables for the expected number of ANG-D 

crashes at three-leg stop-controlled intersections on rural two-lane roads that result in fatality, 

incapacitating injury, or nonincapacitating injury (ANG-KAB-D) according to random forests 

generated using California data.
107 

Table 78. Contributing factors for ANG-KAB-D crashes at three-leg stop-controlled

intersections on rural two-lane roads: California. 

Variable

Impact on Crash-Frequency 

Predictions

Mainline AADT Increases

Percentage of households with 1 vehicle Decreases

Annual average maximum temperature Increases

Percentage of population ages 25+ with a high school diploma but no university 

degree

Decreases

Percentage of households with income >$100,000 Increases

Annual average number of days with a minimum temperature of ≤32°F Decreases

Annual average minimum temperature Decreases

Percentage of population ages 25+ with a university degree Decreases

Annual average winter minimum temperature Decreases

Percentage of households with ≥2 vehicles Increases

Ohio

Daytime

Table 79 and table 80 summarize the most influential predictor variables for the expected number 

of ANG-D crashes at three-leg stop-controlled intersections on rural two-lane roads that result in 

fatality, incapacitating injury, or nonincapacitating injury (ANG-KAB-D) or fatality, 

incapacitating injury, nonincapacitating injury, possible injury, or PDO (ANG-KABCO-D) 

according to random forests generated using Ohio data.

Table 79. Contributing factors for ANG-KAB-D crashes at three-leg stop-controlled

intersections on rural two-lane roads: Ohio. 

Variable

Impact on Crash-Frequency 

Predictions

Percentage of households with income between $50,000 and $100,000 Increases

Percentage of population ages 16–24 working part time Increases

Curve radius Increases/decreases*

Percentage of households with income <$50,000 Decreases

Percentage of population ages 25+ without a high school diploma Decreases

Percentage of households with ≥2 vehicles Increases

Percentage of population ages 16–24 working full time Increases

Percentage of population ages 16–24 unemployed Decreases

Mainline AADT Increases

Percentage of population ages 65–74 Decreases

Lane width Increases

Speed limit Increases

*Increases crash frequency when comparing curves to highway tangent segments/decreases crash frequency when 

comparing curves (i.e., a larger radius is associated with fewer crashes).
108 

Table 80. Contributing factors for ANG-KABCO-D crashes at three-leg signalized 

intersections on rural two-lane roads: Ohio. 

Variable

Impact on Crash-Frequency 

Predictions

Mainline AADT Increases

Percentage of households with ≥2 vehicles Decreases

Percentage of households with income between $50,000 and $100,000 Increases

Percentage of households with income <$50,000 Decreases

Lane width Increases

Percentage of households with 0 vehicles Increases

Average annual rainfall total Decreases

Percentage of population ages 25+ without a high school diploma Increases

Percentage of population ages 20–44 Increases

Annual average number of days with a minimum temperature of ≤32°F Increases

Cross street AADT Increases

Speed limit Decreases**

Percent grade Decreases**

Curve radius Increases/decreases* 

*Increases crash frequency when comparing curves to highway tangent segments/decreases crash frequency when 

comparing curves (i.e., a larger radius is associated with fewer crashes).

**Counterintuitive finding.

Discussion

Based on the analysis, the research team recommends the following contributing roadway factors 

associated with ANG crashes at three-leg stop-controlled intersections on rural two-lane roads:

• Mainline AADT: an increase in mainline AADT was consistently associated with an 

increase in the frequency of ANG-D crashes at three-leg stop-controlled intersections on 

rural two-lane roads.

There were additional roadway variables that showed relationships with ANG crash frequency, 

but results were inconsistent or could not be validated across the two States due to data 

limitations:

• Cross Street AADT: an increase in cross street AADT was associated with an increase in 

the frequency of ANG-KABCO-D crashes at three-leg stop-controlled intersections on 

rural two-lane roads in Ohio. 

• Curve radius: an increase in curve radius was consistently associated with a decrease in 

the frequency of ANG-D crashes (and therefore a decrease in curve radius was 

consistently associated with an increase in the frequency of ANG crashes) at three-leg

stop-controlled intersections on rural two-lane roads in Ohio. 

• Lane width: an increase in lane width was associated with an increase in the frequency of 

ANG-D crashes at three-leg stop-controlled intersections on rural two-lane roads in Ohio. 

• Speed: an increase in speed limit was associated with an increase in the frequency of 

ANG-KAB-D crashes at three-leg stop-controlled intersections on rural two-lane roads 

in Ohio (the relationship was opposite for ANG-KABCO-D crashes at three-leg

stop-controlled intersections on rural two-lane roads in Ohio). 
109 

With respect to sociodemographic characteristics, the percentage of households with two or more 

vehicles appeared to increase ANG-KAB-D crashes at three-leg signalized intersections on rural 

two-lane roads in California and Ohio. 

The following also appeared with respect to ANG crashes in California and Ohio: 

• The percentage of the population ages 16–24 that is unemployed appeared to decrease 

ANG-KAB-D crashes at three-leg stop-controlled intersections on rural two-lane roads in 

Ohio. 

• The percentage of households with income between $50,000 and $100,000 appeared to 

increase ANG-D crashes at three-leg stop-controlled intersections on rural two-lane roads 

in Ohio. The percentage of households with income <$50,000 appeared to decrease those 

same crash types.

With respect to weather characteristics and ANG cashes, average annual rainfall totals appeared 

to decrease ANG-KABCO-D crashes at three-leg stop-controlled intersections on rural two-lane 

roads in Ohio. 

ANG CRASHES AT FOUR-LEG STOP-CONTROLLED INTERSECTIONS ON RURAL 

MULTILANE DIVIDED ROADS

California

Daytime

Table 81 summarizes the most influential predictor variables for the expected number of ANG-D 

crashes at four-leg stop-controlled intersections on rural multilane divided roads that result in 

fatality, incapacitating injury, or nonincapacitating injury (ANG-KAB-D) according to random 

forests generated using California data.

Table 81. Contributing factors for ANG-KAB-D crashes at four-leg stop-controlled

intersections on rural multilane divided roads: California. 

Variable

Impact on Crash-Frequency 

Predictions

Cross street AADT Increases

Percentage of population ages 16+ unemployed Decreases

Percentage of households with income >$100,000 Increases

Percentage of population ages 20–44 Decreases

Median width Increases

Average annual snowfall total Decreases

Percentage of households with income <$50,000 Decreases

Percentage of population ages 16–24 unemployed Decreases

Annual average winter minimum temperature Increases

Percentage of population ages 15–19 Increases
110 

Ohio

Daytime

Table 82 and table 83 summarize the most influential predictor variables for the expected number 

of ANG-D crashes at four-leg stop-controlled intersections on rural multilane undivided roads 

that result in fatality, incapacitating injury, or nonincapacitating injury (ANG-KAB-D) or fatality, 

incapacitating injury, nonincapacitating injury, possible injury, or PDO (ANG-KABCO-D) 

according to random forests generated using Ohio data.

Table 82. Contributing factors for ANG-KAB-D crashes at four-leg stop-controlled

intersections on rural multilane divided roads: Ohio. 

Variable

Impact on Crash-Frequency 

Predictions

Speed limit Decreases**

Mainline AADT Increases

Cross street AADT Decreases**

Average annual snowfall total Increases

Annual average maximum temperature Decreases

Percentage of households with income between $50,000 and $100,000 Decreases

Average annual rainfall total Increases

Percentage of households with income >$100,000 Increases

Percentage of population ages 16–24 unemployed Increases

Percentage of population ages 25+ with a university degree Decreases

**Counterintuitive finding.

Table 83. Contributing factors for ANG-KABCO-D crashes at four-leg stop-controlled

intersections on rural multilane divided roads: Ohio. 

Variable

Impact on Crash-Frequency 

Predictions

Cross street AADT Increases

Speed limit Decreases** 

Mainline AADT Increases

Percentage of households with income >$100,000 Increases

Percentage of population ages 16+ unemployed Increases

Number of channelized left-turn lanes Increases

Average annual snowfall total Increases

Percentage of population ages 25+ with a university degree Increases

Annual average maximum temperature Increases 

Percentage of population ages 16–24 working full time Decreases

**Counterintuitive finding.
111 

Discussion

Based on the analysis, the research team recommends only one contributing roadway factor 

associated with ANG crashes at four-leg stop-controlled intersections on rural multilane divided 

roads: 

• Mainline AADT: an increase in mainline AADT was consistently associated with an 

increase in the frequency of ANG-D crashes at four-leg stop-controlled intersections on 

rural multilane divided roads in Ohio. 

There were additional roadway variables that showed relationships with ANG crash frequency, 

but results were inconsistent or could not be validated across the two States due to data 

limitations:

• Cross street AADT: an increase in cross street AADT was associated with an increase in 

the frequency of ANG-KAB-D crashes at four-leg stop-controlled intersections on rural 

multilane divided roads in California and ANG-KABCO-D crashes at four-leg

stop-controlled intersections on rural multilane divided roads in Ohio (the relationship 

was opposite for ANG-KAB-D crashes at four-leg stop-controlled intersections on rural 

multilane divided roads in Ohio). 

• Median width: an increase in median width was associated with an increase in the 

frequency of ANG-KAB-D crashes at four-leg stop-controlled intersections on rural 

multilane divided roads in California. 

• Speed: an increase in speed limit was associated with a decrease in the frequency of ANG 

crashes at four-leg stop-controlled intersections on rural multilane divided roads in Ohio. 

This finding was opposite of reported speed effects on other intersection FCFTs. 

With respect to sociodemographic characteristics, the percentage of households with income 

>$100,000 appeared to increase ANG-D crashes at four-leg stop-controlled intersections on rural 

multilane divided roads in California and Ohio. 

With respect to weather characteristics, the research did not produce any contributing factors.

PEDESTRIAN CRASHES

This section includes an overview based on information from related published literature of 

contributing factors for pedestrian crashes.

Specifically, a study by Thomas et al. (2017) looked at identifying and screening intersection 

locations with the potential for future pedestrian crashes and injuries to help Seattle, WA, 

broaden treatment priorities beyond high crash locations. The methodology used was similar to 

this current project, as they used random forests to identify potential contributing factors for 

pedestrian crashes.

Thomas et al. (2017) evaluated the following pedestrian crash types:

• All pedestrian crashes at intersections.

• Pedestrians crossing at intersections and struck by motorists going straight. 
112 

The categorical roadway variables used by Thomas et al. (2017) for inputs to the random-forest 

analysis included the following: 

• Highest arterial class for intersection approaches. 

• Type of traffic control at intersection. 

• Presence of a mini-roundabout at the intersection. 

• Total number of motor vehicle lanes on the largest approach leg. 

• Number of through motor-vehicle lanes of all types present on the largest leg of the 

intersection. 

• Number of legs present at the intersection. 

• Number of legs at the intersection that are local streets. 

• Total number of motor-vehicle travel lanes for all legs at the intersection. 

• Sum of through motor-vehicle travel lanes on all legs at the intersection. 

• Presence of parking on any legs of the intersection. 

• Presence of bus-only lanes on any approach. 

• Presence of painted or raised medians on any approach. 

• Presence of right-turn lanes on any approach. 

• Presence of left-turn lanes on any approach. 

• Presence of two-way left-turn lanes on any approach. 

• Presence of bike lanes on any approach. 

• Presence of any type of two-way bicycle track. 

• Presence of bicycle shared-lane markings on any approach. 

• Presence of a shared-use path crossing at the intersection. 

Different measures of exposure and scale factors used by Thomas et al. (2017) for inputs to the 

random-forest analysis included the following: 

• Estimated annual average daily bicycle volume at intersection. 

• Estimated annual average daily pedestrian (AADP) volume at intersection. 

• Presence of one of more K–12 schools within 0.25 mi of the intersection. 

• Presence of university campus within 0.25 mi of the intersection. 

• Mean income within 150 ft of the intersection. 

• Total population within 0.10 mi of the intersection. 

• Total employment within 0.25 mi of the intersection. 

• Number of commercial properties within 0.10 mi of the intersection. 

• Proportion of population ages 65+ within 0.25 mi of the intersection. 

• Proportion of population ages 18 and younger within 0.25 mi of the intersection. 

• Network distance (in miles) to the nearest university campus. 

• Maximum percent slope on any approach. 

• Average slope of terrain within 0.50 mi surrounding the intersection. 

• Number of buses/trains stopping within 150 ft of the intersection on a typical weekday. 

• All building volume (height × area) within 0.10 mi of the intersection. 

• Commercial building volume (height × area) within 0.10 mi of the intersection. 
113 

Table 84 and table 85 provide the most influential predictor variables and their direction of effect 

as identified by Thomas et al.’s framework (2017). 

Table 84. Contributing factors for all pedestrian crashes at intersections. 

Variable

Impact on Crash-Frequency 

Predictions

Number of commercial properties within 0.10 mi of the intersection Increases

Number of buses/trains stopping within 150 ft of the intersection on a typical 

weekday

Increases

All building volume (height × area) within 0.10 mi of the intersection Increases

AADP volume Increases

Commercial building volume (height × area) within 0.10 mi of the intersection Decreases

Number of legs at the intersection that are local streets Decreases

Mean income within 150 ft of the intersection Decreases

Average slope of terrain within 0.50 mi surrounding the intersection Decreases

Total population within 0.10 mi of the intersection Increases

Presence of traffic signal Increases

Number of legs present at the intersection Increases

Total number of motor-vehicle travel lanes for all legs at the intersection Increases

Total number of motor-vehicle lanes on the largest approach leg Increases

Highest arterial class entering the intersection Increases

Presence of parking on any legs of the intersection Increases

Table 85. Contributing factors for pedestrians crossing at intersections and struck by 

motorists going straight. 

Variable

Impact on Crash-Frequency 

Predictions

Number of commercial properties within 0.10 mi of the intersection Increases

Number of buses/trains stopping within 150 ft of the intersection on a typical 

weekday 

Increases

All building volume (height × area) within 0.10 mi of the intersection Increases

AADP volume Increases

Commercial building volume (height × area) within 0.10 mi of the intersection Decreases

Number of legs at the intersection that are local streets Decreases

Mean income within 150 ft of the intersection Decreases

Presence of traffic signal Increases

Number of legs present at the intersection Increases

Total number of motor-vehicle lanes on the largest approach leg Increases

Highest arterial class entering the intersection Increases

As can be seen from table 84 and table 85, the following variables were identified as factors

contributing to potential increases in both types of pedestrian crashes: 

• Number of commercial properties within 0.10 mi of the intersection. 

• Number of buses/trains stopping within 150 ft of the intersection on a typical weekday. 

• All building volume (height × area) within 0.10 mi of the intersection. 

• Estimated AADP volume at intersection. 

• Presence of traffic signal at the intersection. 

• Number of legs present at the intersection. 

• Total number of motor-vehicle travel lanes for all legs at the intersection.
114 

The analyses results presented throughout this chapter, along with the review of a previously 

conducted pedestrian crash analysis by Thomas et al. (2017), provide insights to key 

contributing factors for FCFTs. This information served as the basis for developing a 

countermeasure-selection process, which is presented in the next chapter. While the analyses 

uncovered contributing factors related to traffic, roadway, socioeconomic, and weather 

characteristics, the countermeasure-selection process is focused on the traffic and roadway 

findings. Findings linked to socioeconomic- and weather-related factors show promise, but there 

is not yet a significant amount of theory to support or refute the socioeconomic- and 

weather-related results.
115 

CHAPTER 6. COUNTERMEASURE-SELECTION PROCESS

In addition to identifying contributing factors for FCFTs (presented in chapter 5), another 

objective of this project was to use the contributing-factor findings to assist safety practitioners in 

making informed choices regarding countermeasures to address the focus crash types. This 

chapter describes a process other practitioners can use to identify potential countermeasures for 

each set of focus crash types. The process is accompanied by several examples to demonstrate the 

steps for various crash types and provide a list of countermeasures identified for those crash 

types. The chapter concludes with the countermeasures that address the FCFTs and contributing 

factors covered in chapter 4 and chapter 5. The overview includes multiple potential 

countermeasures for intersection and nonintersection FCFTs, respectively, for consideration by 

State and local transportation agencies as part of an approach to systemic road-safety 

management.

This process recognizes that, although there are certain contributing factors associated with focus 

crash types, selecting countermeasures must be broad and encompass many options for 

addressing the crash type. The usefulness of the contributing-factor identification is to allow a 

practitioner to identify which countermeasures specifically address those factors as a part of their 

safety effectiveness and also to prioritize locations for countermeasures. This identification and 

prioritization of countermeasures may in turn raise the priority for selecting and implementing 

those countermeasures. In addition, the contributing factors will inform practitioners’ decisions 

about where to implement the selected countermeasures as part of their systemic 

safety-management efforts. The following six steps detail the countermeasure-selection process: 

1. Identify a focus crash type.

2. Identify contributing factors for the focus crash type.

3. Assemble a list of potential countermeasures that address the focus crash type.

4. Identify countermeasures that address the contributing factors associated with the focus 

crash type.

5. Identify countermeasures with CMFs. 

6. Select a countermeasure.

BACKGROUND RELEVANT TO COUNTERMEASURE SELECTION

Chapter 4 and chapter 5 provided tables of the most influential predictor variables and identified 

them as factors contributing to increases or decreases in potential future crashes. A brief 

discussion on similarities between the results across States and recommended contributing factors 

for systemic safety analysis based on these results accompanied the tables. 

The research team conducted a contributing-factor analysis for the expected crash frequencies of 

KABCO crashes using Ohio data and compared results to those for KAB crashes in Ohio. Results 

from the contributing-factor analysis were quite similar when using either the KAB or KABCO 

severities. In almost all cases, the most influential predictor variables stayed consistent between 

the KAB and KABCO groupings, except for some changes in the percent increase of predictions 

of MSEs associated with removal of the variables.
116 

The following sections summarize prevalent factors from each main category of factors explored 

in this analysis: roadway, socioeconomic, and climate. 

Roadway Variables 

Recommended roadway variables for contributing-factor analysis from intersection and 

nonintersection FCFT categories are as follows: 

• Intersection: 

o Mainline AADT.

o Cross street AADT.

o Speed.

o Lane width. 

o Curve radius. 

• Nonintersection:

o Average AADT.

o Percent grade.

o Curve radius. 

o Shoulder width. 

A secondary set of roadway factors that also generally showed consistent results and could be 

presented as contributing factors include the following: 

• Surface width (wider surface widths decreased predicted crash frequency).

• Unpaved shoulders or no shoulders (the presence of one or more unpaved shoulders or no 

shoulders increased predicted crash frequency compared to the presence of two paved 

shoulders).

• Terrain (rolling and mountainous terrain increased predicted crash frequency).

• Left- and right-turn channelization (the presence of left- and right-turn channelization at 

intersections decreased predicted crash frequency).

There were some counterintuitive results where a variable was expected to increase crash 

frequency and instead appeared to decrease crash frequency or vice versa. The majority of these 

counterintuitive results were related to percent grade, shoulder width, speed, and cross street 

AADT. The percentage of trucks on the roadway was another variable that was expected to 

increase crashes but consistently came up as a factor expected to decrease crashes. This finding 

was not identified as counterintuitive due to the fact that there have been studies conducted in the 

past by Milton and Mannering (1998), Lord et al. (2005), and Milton et al. (2008) that support 

this finding. Milton and Mannering (1998) looked at the relationship among highway geometrics, 

traffic-related elements, and motor vehicle–accident frequencies using Washington data. They 

concluded that an increase in the percentage of both single-unit trucks and all trucks was 

associated with lower accident frequency. Lord et al. (2005) concluded that an increase in the 

percentage of trucks was associated with a reduction in the number of rear-end and ROR crashes. 

Milton et al. (2008) concluded that an increase in percentages of trucks tends to lower the 

possible amount of injury crashes. 
117 

In most cases, counterintuitive findings would show up in the expected direction (i.e., increasing 

or decreasing crashes) when using data from a different State. For example, average shoulder 

width came up as a factor contributing to increases in LNDP-N crashes on rural two-lane roads 

when using Washington data. However, the same factor showed as contributing to a decrease in 

crash frequency (as expected) for LNDP-N crashes on rural two-lane roads when using Ohio data.

Counterintuitive findings could happen for various reasons. One possible reason could be the 

application of some other treatments for which data were not available for the analysis. Another 

possible reason could be the completeness of data. For example, the research team had the 

information for shoulder widths for Washington; however, information regarding paved and 

unpaved shoulder widths was not available.

Socioeconomic Variables

There were some interesting findings regarding socioeconomic variables. The following variables 

showed as most influential for intersection and nonintersection FCFT categories:

• Percentage of population ages 16–24 working full time (generally appeared to decrease

segment crashes with one occurrence of increasing intersection crashes).

• Percentage of population ages 16–24 working part time (generally appeared to decrease 

segment crashes).

• Percentage of households with income <$50,000 (consistently appeared to decrease all 

crash types, except for crashes at urban signalized intersections).

• Percentage of households with income >$100,000 (consistently appeared to increase 

crashes).

• Percentage of population ages 16–24 that is unemployed (consistently appeared as a 

predictive factor, but whether it was expected to increase or decrease crashes varied by 

FCFT). 

• Percentage of population ages 15–19 (consistently expected to increase ROR, HEO, and 

ROLL crashes, particularly on horizontal curves). 

The research team did not recommend any of the socioeconomic variables for systemic safety 

analysis due to limitations in the data-linkage approach and scarcity of prior research linking 

these variables to crashes at segment and intersection levels. However, these variables showing 

up among the most influential predictor variables does provide a basis for future research to 

explore these as potential contributing factors for systemic analysis efforts. Likely, the variable 

with the strongest theoretical basis would be the percentage of the population ages 15–19 as a 

factor contributing to increased ROR, HEO, and ROLL crashes on rural roads.
118 

Climate Variables

As with the socioeconomic variables, there were some interesting findings regarding climate 

variables. The following variables showed as most influential for intersection and nonintersection 

FCFT categories:

• Average annual snowfall totals (generally appeared to increase crashes).

• Average annual rainfall totals (generally appeared to increase crashes).

• Average annual number of days with a minimum temperature of ≤32°F (generally to 

decrease crashes).

The research team did not recommend any of the climate variables for systemic safety analysis 

due to scarcity of prior research linking these variables to crashes and some of the inconsistencies 

in the directions (i.e., increasing or decreasing crashes) of the effects. However, these variables 

showing up among the most influential predictor variables in some cases does provide a basis for 

future research to explore them as potential contributing factors.

The following sections describe the steps of a countermeasure-selection process for FCFTs based 

on traffic- and roadway-related contributing factors. The traffic- and roadway-related contributing 

factors identified in this process can inform the selection and implementation of countermeasures 

as a part of a systemic safety analysis. The steps include (1) identifying a focus crash type, 

(2) identifying contributing factors for the focus crash type, (3) assembling a list of potential 

countermeasures that address the focus crash type, (4) identifying countermeasures that address 

the contributing factors associated with the focus crash type, (5) identifying countermeasures with 

CMFs, and (6) selecting a countermeasure.

STEP 1. IDENTIFY A FOCUS CRASH TYPE

Approach

Identify the focus crash type of interest. Using the FCFT definitions from chapter 5, the focus 

crash type is defined by the type of maneuver (e.g., ROR), time of day (daytime versus 

nighttime), and type of segment (curve versus tangent) or type of intersection (e.g., four-leg

signalized). Selecting a focus crash type should be done according to the priorities of the 

transportation agency (e.g., as outlined in a State or regional SHSP) and based on an analysis of 

available data. These methods may include the following: 

• Jurisdiction-wide analysis of crash data to identify overrepresented crash types. 

• Site-level analysis to determine overrepresented crash types or crash types in excess of the 

expected performance at a specific site (i.e., through crash-predictive methodologies). 

• Systemic safety analysis of a network to identify crash types that represent the greatest 

number of KA crashes across the system being analyzed. 

Example 

Based on data analysis, an agency selected the following focus crash type: ROR-D crashes on 

curves. The agency conducted an analysis using the approach detailed in chapter 4 and found KA

crashes of this type to be particularly prevalent on rural two-lane roads.
119 

STEP 2. IDENTIFY CONTRIBUTING FACTORS FOR THE FOCUS CRASH TYPE

Approach

Using the results of this project (chapter 7), identify contributing factors associated with the focus 

crash type. Some factors may be expected to increase a particular crash type, such as the presence 

of an unpaved shoulder, whereas others may decrease certain crash types, such as increasing 

(i.e., flattening) the radius of a horizontal curve.

Example 

Given the selected focus crash type of ROR-D crashes on curves, the results of the agency’s

analysis showed that contributing factors for this crash type (ROR-D crashes on curves) were as 

follows: 

1. Percent grade (as grade increases, crash potential increases). 

2. Average shoulder width (as shoulder width increases, crash potential decreases). 

STEP 3. ASSEMBLE A LIST OF POTENTIAL COUNTERMEASURES THAT 

ADDRESS THE CRASH TYPE

Approach

Use countermeasure resources to assemble a list of potential countermeasures that may address 

the focus crash type. Such resources may include the following: 

• NCHRP Report 500 Series (National Academy of Sciences 2003a, 2003b, 2003c, 2003d, 

2003e, 2003f, 2004a, 2004b, 2004c, 2004d, 2004e, 2004f, 2004g, 2005a, 2005b, 2005c, 

2006, 2007, 2008a, 2008b, 2008c, 2008d, 2009).

• HSM (AASHTO 2010).

• CMF Clearinghouse (FHWA 2018a). 

• FHWA Proven Safety Countermeasures (FHWA 2017a).

• Countermeasures That Work (Goodwin et al. 2013). 

• PEDSAFE (FHWA 2018f).

• BIKESAFE (FHWA 2018g).

• State-generated list of common countermeasures within a State (i.e., a toolbox). 

This assembled list should contain a wide pool of potential countermeasures. Eligible 

countermeasures should address any part of the focus crash type. For example, if the focus crash 

type is ROR-N crashes on curves, a countermeasure that addresses ROR crashes should be 

considered eligible even if it does not specifically address curve locations or nighttime 

conditions. 
120 

Example 

Given the selected focus crash type (ROR-D crashes on curves) and an examination of the 

previously listed countermeasure resources, the agency identified the following countermeasures 

as eligible for consideration: 

• Install shoulder rumble strips. 

• Install centerline rumble strips.

• Widen lanes.

• Enhance curve delineation (e.g., add chevrons, large arrows, or delineators on guardrails). 

• Install or improve curve warning signs (e.g., add flashing beacons).

• Install warning arrows on the pavement prior to the curve.

• Install pavement markings to decrease speed prior to the curve.

• Flatten horizontal curves (i.e., increase the curve’s radius).

• Flatten vertical curves (i.e., decrease the curve’s grade).

• Enhance pavement markings (e.g., add edgelines).

• Install skid-resistant pavement or improve pavement friction. 

• Widen paved shoulders. 

• Pave shoulders. 

• Install SafetyEdge. 

• Design safer slopes and ditches to prevent ROLLs. 

• Remove/relocate objects in hazardous locations. 

• Delineate roadside objects.

• Improve the design of roadside hardware (e.g., bridge rails). 

• Improve the design and application of barrier and attenuation systems.

STEP 4. IDENTIFY COUNTERMEASURES THAT EXPLICITLY ADDRESS 

CONTRIBUTING FACTORS ASSOCIATED WITH THE FOCUS CRASH TYPE

Approach

Compare the information known about each countermeasure on the list from step 3 to the 

contributing factors identified in step 2. Identify countermeasures that specifically address one or 

more contributing factors. The degree to which countermeasures address contributing factors is 

subjective; a logical link should be drawn between many countermeasures and contributing 

factors. For the purpose of this process, only clear, explicit relations to contributing factors should 

be indicated.

Example 

The agency used table 86 to summarize which countermeasures address the identified 

contributing factors for the focus crash type (ROR-D crashes on curves). 
121 

Table 86. Example selection process: countermeasure summary with contributing factors. 

Countermeasure

Factor 1: Percent 

Grade

Factor 2: Average 

Shoulder Width

Install shoulder rumble strips — — 

Install centerline rumble strips — — 

Widen lanes — — 

Enhance curve delineation (e.g., add chevrons, large arrows, or 

delineators on guardrails)

— — 

Install or improve curve warning signs (e.g., add flashing beacons) — — 

Install warning arrows on the pavement prior to the curve — — 

Install pavement markings to decrease speed prior to the curve — — 

Flatten horizontal curves (i.e., increase radius) — — 

Flatten vertical curves (i.e., decrease grade) Y — 

Enhance pavement markings (e.g., add edgelines) — — 

Install skid-resistant pavement or improve pavement friction — — 

Widen paved shoulder — Y 

Pave shoulder — Y 

Install SafetyEdge — — 

Design safer slopes and ditches to prevent ROLLs — — 

Remove/relocate objects in hazardous locations — — 

Delineate roadside objects — — 

Improve the design of roadside hardware (e.g., bridge rails) — — 

Improve the design and application of barrier and attenuation systems — — 

—No data available. 

Y = contributing factor is addressed by the countermeasure. 

STEP 5. IDENTIFY COUNTERMEASURES WITH CMFS

Approach

Use CMF resources to determine which countermeasures on the list from step 3 have established 

CMFs that quantify the safety effect. This is a critical part of the process as the systemic approach 

consists of widely implementing low-cost, proven countermeasures to achieve safety benefits 

across a large portion of the system. A countermeasure with a known effect can also be compared 

to other countermeasures in a prioritized selection and used to generate a B/C analysis of 

proposed alternatives. It is possible that countermeasures for which a robust set of CMFs are 

available may have CMFs that specifically address the focus crash type. If this is the case, extra 

consideration should be given to these countermeasures in the final selection.

The most comprehensive and accessible resource for CMF information is the CMF Clearinghouse

(FHWA 2018a). Other resources for CMF information may include State-specific CMF lists 

(FHWA 2018b).

Example 

The agency used table 87, which expands on table 86, to show which countermeasures have 

CMFs available for ROR crashes.
122 

Table 87. Example selection process: countermeasure summary with contributing factors 

and CMF indicators. 

Countermeasure

Factor 1: 

Percent 

Grade

Factor 2: 

Average 

Shoulder 

Width

CMF 

Available

Install shoulder rumble strips — — Y 

Install centerline rumble strips — — Y 

Widen lanes — — Y 

Enhance curve delineation (e.g., add chevrons, large arrows, or 

delineators on guardrails)

— — Y 

Install or improve curve warning signs (e.g., add flashing beacons) — — Y 

Install warning arrows on the pavement prior to the curve — — — 

Install pavement markings to decrease speed prior to the curve — — — 

Flatten horizontal curves (i.e., increase radius) — — Y 

Flatten vertical curves (i.e., decrease grade) Y — Y 

Enhance pavement markings (e.g., add edgelines) — — Y 

Install skid-resistant pavement or improve pavement friction — — Y 

Widen paved shoulder — Y Y 

Pave shoulder — Y Y 

Install SafetyEdge — — Y 

Design safer slopes and ditches to prevent ROLLs — — Y 

Remove/relocate objects in hazardous locations — — Y 

Delineate roadside objects — — — 

Improve the design of roadside hardware (e.g., bridge rails) — — — 

Improve the design and application of barrier and attenuation 

systems

— — Y 

—No data available. 

Y = contributing factor is addressed by the countermeasure or a CMF is available for the countermeasure. 

STEP 6. SELECT A COUNTERMEASURE

Approach

Select a countermeasure to address the focus crash type. This selection should use the pool of 

eligible countermeasures generated in step 3, and how well each countermeasure addresses the 

specific contributing factors for the focus crash type (as determined in step 4) and the extent to 

which CMFs are available for each countermeasure (as determined in step 5) should be 

considered. 

Example 

The agency referenced CMF resources and found that many of the countermeasures on the list 

from step 3 have CMFs that quantify the safety effect. Among these countermeasures, the agency 

determined that those related to paving the shoulder or widening the paved shoulder explicitly 

address the contributing factor of average shoulder width. Additionally, the agency concluded 

that the countermeasure of flattening a vertical curve to decrease the grade was also related to the 

contributing factor of percent grade. The agency first considered vertical curve flattening as it 

could be more effective and address an underlying design issue. However, the agency determined 

that flatting vertical curves would be expensive and decided that it would not be feasible given 

the agency’s existing budget constraints. 
123 

The agency decided that the most appropriate countermeasure to address the focus crash type 

(ROR-D crashes on curves) based explicitly on the contributing factors was paving the shoulder 

or widening the paved shoulder. Alternatively, the agency recognized that it could select a 

countermeasure in table 87 and then use the contributing factors for ROR crashes to identify 

candidate locations at which to install the countermeasure using a systemic approach. 

ADDITIONAL EXAMPLES

This section includes two additional hypothetical examples that demonstrate the process of 

selecting countermeasures to address focus crash types. 

Example 1: HEO Crashes

Complete step 1: identify the focus crash type. An agency determined a focus crash type of 

HEO-D crashes on horizontal curves. The agency used a crash-tree diagram, as suggested in 

FHWA’s Systemic Safety Project Selection Tool, to help identify facility types associated with the 

focus crash type. The agency determined that severe HEO-D crashes are particularly prevalent on 

rural two-lane roads.

Complete step 2: identify contributing factors for the focus crash type. Given the selected focus 

crash type and using the results of this project (chapter 7), the agency determined that the 

contributing factor for this focus crash type is curve radius (as curve radius increases, predicted 

crash frequency decreases). 

Complete step 3: assemble a list of potential countermeasures that address the focus crash type. 

Given the selected focus crash type and an examination of countermeasure resources, the agency 

arrived at the list of potential countermeasures in table 88. 

Complete step 4: identify countermeasures that address the contributing factors associated with 

the focus crash type. The agency identified countermeasures that address the contributing factors 

associated with the focus crash type by comparing the information known about each 

countermeasure on the list from step 3 to the contributing factor identified in step 2. The agency 

used the information in table 88, which summarizes which countermeasures address the identified 

contributing factors for the focus crash type. The agency determined that the only countermeasure 

that will address the contributing factor of curve radius is flatten horizontal curve. 

Complete step 5: identify countermeasures with CMFs. The agency identified which 

countermeasures have CMFs using CMF resources, such as the CMF Clearinghouse or 

State-specific CMF lists (FHWA 2018a). The agency used table 88 to show which 

countermeasures have CMFs available for HEO crashes.

Complete step 6: select a countermeasure. The agency found that many of the countermeasures in 

table 88 have CMFs that quantify the safety effect of the countermeasure. From this pool of 

potential countermeasures, the agency determined in step 4 that only one countermeasure 

(flatten curve radius) explicitly addressed the contributing factor of curve radius. Thus, this 

countermeasure could be one possible selection to address the focus crash type. However, if the 

agency determined it could not afford the high cost of realigning horizontal curves, it could 

instead implement one or more of the other countermeasures identified in table 88, particularly 
124 

those with known CMFs, such as pavement friction or curve delineation. In such cases, the 

agency could select other countermeasures and then use the contributing factors for HEO crashes 

to identify candidate locations at which to install the countermeasures using a systemic approach. 

Table 88. Countermeasure summary for example 1: HEO crashes. 

Countermeasure

Factor 1: Curve 

Radius CMF Available

Install centerline rumble strips — Y 

Install profiled thermoplastic stripes for the centerline — 

Widen lanes — Y 

Install center two-way left-turn lanes — Y 

Reallocate lane and shoulder width to include a narrow buffer median — — 

Install passing relief lanes, alternating passing lanes or four-lane sections at 

key locations

— Y 

Enhance curve delineation (e.g., add chevrons, large arrows, or delineators

on guardrails)

— Y 

Install or improve curve warning signs (e.g., add flashing beacons) — Y 

Install warning arrows on the pavement prior to the curve — — 

Install pavement markings to decrease speed prior to the curve — — 

Flatten horizontal curve (i.e., increase radius) Y Y 

Install skid-resistant pavement or improve pavement friction — Y 

Widen paved shoulder — Y 

Pave shoulder — Y 

Install raised median — Y 

Install cable median barrier — Y 

Install lighting — Y 

—No data available. 

Y = contributing factor is addressed by the countermeasure or a CMF is available for the countermeasure. 

Example 2: ANG Crashes

Complete step 1: identify the focus crash type. An agency determined a focus crash type of 

ANG-N crashes at stop-controlled intersections. The agency used FHWA’s Systemic Safety 

Project Selection Tool, which outlines a process for States or local agencies to identify facility 

types associated with a focus crash type using a crash-tree diagram, to create a crash-tree diagram

(Preston et al. 2013a). Using the diagram, the agency determined that KA crashes of this type are 

particularly prevalent on rural two-lane roads in their jurisdiction.

Complete step 2: identify contributing factors for the focus crash type. Given the selected focus 

crash type and using the results of this project (chapter 7), the agency determined the following 

contributing factors for ANG-N crashes: 

1. Design speed (as the design speed increases, crash potential increases). 

2. Speed limit (as the speed limit increases, crash potential increases). 

3. Mainline left-turn channelization (when present, crash potential decreases). 

Complete step 3: assemble a list of potential countermeasures that address the focus crash type. 

Given the selected focus crash type and an examination of countermeasure resources, the agency 

arrived at the list of potential countermeasures in table 89. 
125 

Complete step 4: identify countermeasures that address the contributing factors associated with 

the focus crash type. The agency identified countermeasures that address the contributing factors 

associated with the focus crash type by comparing the information known about each 

countermeasure on the list from step 3 to the contributing factors identified in step 2. The agency 

used the information in table 89, which summarizes which countermeasures address the identified 

contributing factors for the focus crash type. 

Complete step 5: identify countermeasures with CMFs. The agency identified which 

countermeasures have CMFs using CMF resources, such as the CMF Clearinghouse or 

State-specific CMF lists (FHWA 2018a). The agency used table 89 to show which 

countermeasures have CMFs available for ANG crashes.

Complete step 6: select a countermeasure. The agency found that many of the countermeasures in 

table 88 have CMFs that quantify the safety effect of the countermeasure. From this pool of 

potential countermeasures, the agency determined that the countermeasures of converting the 

intersection to a roundabout, installing or lengthening exclusive left-turn lanes, and providing 

offset left-turn lanes had known CMFs. The agency noted that these three countermeasures 

explicitly address the focus crash type’s contributing factors and identified them as one group of 

priority countermeasures. However, the agency also noted that it could implement one or more of 

the other countermeasures in table 89, particularly those with known CMFs associated with the 

focus crash type. In such cases, the agency could select other countermeasures and then use the 

contributing factors for ANG crashes to identify candidate locations at which to install the 

countermeasures using a systemic approach.

Table 89. Countermeasure summary for example 2: ANG crashes. 

Countermeasure

Factor 1: 

Design Speed

Factor 2: 

Speed Limit

Factor 3: 

Mainline 

Left-Turn 

Channelization

CMF

Available

Replace direct left-turn design with a right 

turn/U-turn

— — — Y 

Convert the intersection to a roundabout Y — — Y 

Install a traffic signal — — — Y 

Install or lengthen an exclusive left-turn lane — — Y Y 

Provide an offset left-turn lane — — Y Y 

Close, relocate, or restrict turns at driveways near 

intersections

— — — — 

Provide an offset right-turn lane at an intersection — — — Y 

Provide a full-width paved shoulder in 

intersection areas

— — — Y 

Restrict or eliminate turning maneuvers with 

signing

— — — — 

Restrict or eliminate turning maneuvers by 

providing channelization or closing median 

openings

— — — — 

Convert a four-leg intersection to two

T-intersections (on a low-volume cross street)

— — — Y 

Convert offset T-intersections to four-leg 

intersection (on a high-volume cross street)

— — — — 

Reduce or eliminate intersection skew — — — Y 
126 

Countermeasure

Factor 1: 

Design Speed

Factor 2: 

Speed Limit

Factor 3: 

Mainline 

Left-Turn 

Channelization

CMF

Available

Improve the sight distance at an intersection — — — Y 

Install a dynamic advance intersection warning 

system

— — — Y 

Provide an automated real-time system to inform 

drivers of the suitability of available gaps for 

making turning and crossing maneuvers

— — — Y 

Provide intersection lighting — — — Y 

Install a splitter island on the minor-road 

approach to an intersection

— — — — 

Provide a stop bar (or provide a wider stop bar) 

on the minor-road approach

— — — — 

Install a larger regulatory and warning sign at the 

intersection

— — — — 

Install transverse rumble strips on the intersection 

approach

— — — Y 

Provide dashed markings (i.e., extended left 

edgelines) for major-road continuity across the 

median opening at a divided highway intersection

— — — — 

Provide a supplementary stop sign mounted over 

the roadway

— — — — 

Install a double stop sign — — — Y 

Provide a pavement marking with a 

supplementary message, such as stop ahead

— — — Y 

Install a flashing beacon at stop-controlled 

intersection

— — — Y 

Replace a standard stop sign with a flashing LED 

stop sign

— — — Y 

Convert a two-way intersection to an all-way 

stop-controlled intersection

— — — Y 

Provide traffic calming on an intersection 

approach through a combination of geometric and 

traffic-control devices

— — — — 

Lower speed on an intersection approach Y Y — — 

Install a median acceleration lane — — — Y 

Provide skid resistance at an intersection — — — Y 

Narrow a lane through rumble strips and a 

painted median

— — — Y 

—No data available. 

Y = contributing factor is addressed by the countermeasure or a CMF is available for the countermeasure. 
127 

TARGETED COUNTERMEASURES FOR CONTRIBUTING FACTORS

Many countermeasures have been researched and evaluated for improving highway safety. 

FHWA’s Proven Safety Countermeasures identifies a small set of these countermeasures, which 

have proven repeatedly to reduce serious injuries and fatalities (FHWA 2017a). As a result, 

FHWA encourages their widespread implementation. Other proven and recommended 

countermeasures, along with additional information on the Proven Safety Countermeasures, are

found in FHWA’s Low-Cost Treatments for Horizontal Curve Safety (Albin et al. 2016), the 

Institute of Transportation Engineers’ Unsignalized Intersection Improvement Guide (ITE 2015), 

FHWA’s Intersection Safety website (FHWA 2015a), FHWA’s Roadway Departure Safety

(FHWA 2017b), and FHWA’s Intersection Safety Strategies Brochure (FHWA 2015b). Table 90

and table 91 show how the Proven Safety Countermeasures applies to various intersection and 

nonintersection contributing factors identified previously in this report. This section is not meant 

to provide complete lists of countermeasures and countermeasure resources. Instead, it is meant 

to be a quick reference and starting point for agencies interested in applying countermeasures 

using the systemic approach to common FCFTs.
128 

Table 90. Systemic countermeasures for intersection contributing factors. 

Countermeasure

Smaller Curve 

Radius 

(Intersection on 

Curve)

Wider Mainline 

Lane Width

Wider Mainline 

Median Width

Absence of 

Mainline 

Left-Turn 

Channelization

Absence of MinorStreet Right-Turn 

Channelization 

(Signalized 

Intersections)

Design 

Speed/Higher 

Speed Limit

Add left- and right-turn lanes — — — ● ● ●

Increase yellow change intervals — — — — — ●

Add backplates with 

retroreflective borders

— ● ● — — ●

Apply multiple low-cost 

countermeasures

● ● ● ● ● ●

Install advance signs ● ● ● — — ●

—Countermeasure does not address contributing factor. 

●Countermeasure addresses contributing factor.

Table 91. Systemic countermeasures for nonintersection contributing factors. 

Countermeasure

Larger 

Percent 

Grade

Narrower 

Lane Width

Narrower 

Paved Surface 

Width

Narrower 

Shoulder 

Width

Unpaved 

Shoulder

Design 

Speed/Higher 

Speed Limit

Mountainous 

Terrain

Smaller 

Curve 

Radius

Install SafetyEdge ● ● ● ● ● ● — ●

Install rumble strips/stripes — ● ● ● ● ● ● ●

Enhance/improve friction 

for horizontal curves

● — ● — — ● — ●

Enhance delineation for 

horizontal curves

— — ● — — ● — ●

Implement roadside design 

improvements at curves

— — — — — ● — ●

Install advance markings 

for curves

— ● ● ● ● ● — ●

Advance signs — ● ● ● — ●a ● ●

—Countermeasure does not address contributing factor. 

●Countermeasure addresses contributing factor. a

Refer to the MUTCD for advance-warning-sign requirements based on speed differentials (FHWA 2012).
129 

The following sections provide brief descriptions of the countermeasures for the intersection 

contributing factors in table 90. For additional information on these countermeasures, refer to the 

Quick Reference Guide that accompanies this report (Porter et al. 2020). The CMF Clearinghouse 

provides additional intersection safety countermeasures, as well as other sources (FHWA 2018a). 

Auxiliary Turn Lanes (FHWA 2017a) 

Providing auxiliary turn lanes at intersection approaches separates slow-moving turning traffic 

from through traffic and provides vehicles space for deceleration prior to turning. With these 

improvements, auxiliary turn lanes can reduce the potential for turning vehicles to be involved in 

ANG and rear-end crashes. This treatment is recommended when there is an absence of mainline 

left-turn channelization, minor-street right-turn channelization, and a high design speed on the 

approaching roadway(s). Additionally, this treatment should be considered for major road 

approaches to intersections with minor stop control where there are operationally warranted 

turning-vehicle volumes and a history of crashes involving turning vehicles. When considering 

auxiliary turn lanes, the impact on pedestrian and bicycle safety and convenience should be 

considered. 

Yellow Change Intervals (FHWA 2017a) 

Evaluating and adjusting yellow change intervals can lead to reduced red-light-running crashes 

(which can arise from yellow change intervals that are too long) and rear-end crashes (which can 

arise from yellow change intervals that are too short). Agencies should regularly reevaluate 

yellow change intervals at all signalized intersections within their system, especially those with 

histories of rear-end crashes and red-light-running crashes. This treatment is recommended for 

intersections with high design speeds and/or speed limits on at least one approach. 

Backplates with Retroreflective Borders (FHWA 2017a) 

Adding backplates with retroreflective borders to signal heads can improve signal visibility by 

providing a contrasting background. These visibility improvements are evident in both daytime 

and nighttime conditions and can improve visibility for all drivers, especially older or color-blind 

drivers. This treatment can reduce crashes caused by signal-visibility issues, such as 

red-light-running and rear-end crashes. Contributing factors for which this treatment is 

recommended include intersections with wide mainline roadways and/or median widths, as well 

as those with high design speeds and/or speed limits on at least one approach. 

Application of Multiple Low-Cost Countermeasures (FHWA 2017a) 

Crashes can be reduced system wide by deploying a suite of low-cost countermeasures at 

multiple stop-controlled intersections. These countermeasures include improving pavement 

markings and signage. By improving the visibility of signage and pavement markings, driver 

awareness is increased and the potential for crashes due to driver inattention or confusion is 

reduced. Systemically, these improvements should be considered for all stop-controlled

intersections and can be effective at intersections with any contributing factors.
130 

Advance Signs (ITE 2015) 

Installing advanced intersection warning signs, especially for unsignalized intersections, can 

reduce intersection crashes by warning approaching drivers of intersections or intersection 

traffic-control devices for which there is limited or inadequate visibility. These signs have the 

potential to reduce right-ANG and rear-end crashes on both major and minor road approaches. 

Specific signage for various intersection configurations and traffic-control devices can be found 

in the MUTCD (FHWA 2012). Contributing factors for which these signs can be effective 

include intersections on curve radii, intersections with wide mainline roadways and/or median 

widths, and high design speeds and/or speed limits on at least one approach.

SafetyEdge (FHWA 2017a) 

Sharp pavement drop-offs at the pavement edge can exacerbate roadway departures. Adding a 

SafetyEdge—a gradual sloped drop from the edge of the paved surface to the roadside 30 degrees 

from the cross-slope—when paving can help drivers maintain control of their vehicles when 

departing the roadway. This treatment only has minimal impact on the cost of paving or 

resurfacing projects while providing a proven reduction in roadside crash frequency and severity. 

Contributing factors for which this treatment should be considered include tight curve radii, 

narrow traveled ways and paved surfaces, narrow and/or unpaved shoulders, and high design 

speeds and/or speed limits. 

Rumble Strips or Stripes (FHWA 2017a) 

Driver inattention, whether through distraction, drowsiness, or another reason, can lead to lane 

and roadway departures, both of which can lead to crashes, often with severe outcomes arising 

from a collision with oncoming traffic or roadside fixed objects. Installing rumble strips or stripes 

along the centerline or edgeline of a roadway can provide an audible alert to drivers straying from 

the traveled way. This treatment is a low-cost and proven countermeasure that can provide 

significant benefits if installed system wide. However, agencies need to consider bicyclists when 

installing shoulder rumble strips; regular gaps to allow for bicyclists to transition between the 

traveled way and shoulder should be provided where there is regular bicycle traffic. Contributing 

factors for which this treatment should be considered include tight horizontal radii; narrow 

traveled ways, shoulder widths, and paved surfaces; high speed limits; and mountainous terrain 

(e.g., steep grades and significant vertical curvature). 

Enhanced Friction for Horizontal Curves (FHWA 2017a) 

Insufficient friction on horizontal curves can lead to crashes. If the existing pavement is unable to 

provide the required friction for cornering, vehicles may lose traction and slide, sometimes off 

the roadway or into opposing traffic. High-friction surface treatments and pavement grooving 

(for concrete pavements) have both been found to improve friction on horizontal curves and 

reduce crash frequency by modifying the existing surface of the pavement. These treatments can 

be especially effective on curves with tight radii, poor cross-slope design, poor drainage, low 

pavement friction, and a history of driving too fast for the design conditions of the curve. Of that 

list, tight curve radii and high speeds are both contributing factors that can be identified 

systemically.
131 

Enhanced Delineation for Horizontal Curves (FHWA 2017a) 

Horizontal curves present a challenge to drivers in that they are a deviation from the existing 

vehicle path. Drivers who are inattentive or unable to see the change in alignment may fail to 

navigate the curve, possibly leading to KA crashes, such as ROR, fixed-object, and HEO crashes. 

Improving the delineation of the curve assists drivers with seeing and preparing for the change in 

alignment. Using one or a combination of pavement markings, post-mounded delineators, larger 

signs, more retroreflective signs, dynamic curve warning signs, and sequential curve signs can 

lead to a reduction in crash severity on a curve. Systemically, curves with small curve radii or 

high speed limits should be considered for this treatment. 

Roadside-Design Improvements at Curves (FHWA 2017a) 

Roadway departures are almost unavoidable on horizontal curves. Given the inevitability, making 

the roadside more forgiving to vehicles departing the roadway can reduce the severity of these 

events. Improvements to the roadside that have proven reliably effective include widening the 

clear zone, flattening side slopes, and paving and widening shoulders. Additionally, if the 

clear-zone width is unable to be obtained, the use of cable barriers, guardrails, or concrete 

barriers (as appropriate) can also reduce the severity of outcomes for departing vehicles. 

Contributing factors for which this treatment should be considered include tight curve radii, 

narrow shoulder widths, high speed limits, and mountainous terrain (e.g., steep grades and 

significant vertical curvature).

Advance Markings for Curves (Albin et al. 2016) 

Various pavement markings can be used to supplement horizontal-curve treatments provided in 

FHWA’s Proven Safety Countermeasures (FHWA 2017). A speed-advisory marking can be 

painted within the travel lane to provide additional guidance in drivers’ line of sight. Optical 

speed bars spaced at gradually decreasing distances on the approach to the curve can manipulate 

drivers’ speed perception and influence them to slow down. Advisory pavement markings are 

most appropriate for curves with advisory speeds much lower than the regular posted speed limit 

on the roadway; optical speed bars are most appropriate on isolated or unexpected horizontal 

curves. Contributing factors for which this treatment should be considered include tight radii and 

high design speeds. 

Advance Signs (Albin et al. 2016) 

A low-cost treatment to improve safety on horizontal curves is the installation of static advance 

curve warning signs. Warning signs at horizontal curves include curve warning signs and 

advisory speed-limit signs. These signs make drivers aware of the impending change in 

alignment and suggested speed at which the curve should be navigated. The MUTCD provides 

guidance for the installation of these signs (FHWA 2012). Contributing factors for which static 

advance signage should be considered include steep grades and tight curve radii. 

The treatments described above and summarized in table 90 and table 91 represent a set of 

countermeasures that have been proven to successfully reduce crash frequency or severity at a 

relatively low cost, leading them to be recommended by FHWA and ITE. For more details on 

these treatments, refer to the Quick Reference Guide accompanying this report (Porter et al. 
132 

2020). For additional countermeasures, refer to resources such as the CMF Clearinghouse 

(FHWA 2018a).
133 

CHAPTER 7. SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS

The main goal of this project was to identify focus crash types, focus facility types, and 

associated contributing factors to inform applications of systemic safety improvements. To 

achieve this goal, the research team executed three main technical tasks: identifying FCFTs, 

identifying contributing factors associated with the FCFTs, and developing a process to assist 

safety practitioners in making informed choices regarding countermeasures to address the focus 

crash types.

IDENTIFYING FCFTS

Selecting FCFTs was guided by the use of FARS and HSIS (NHTSA 2018a; FHWA 2018c). The 

method used by the research team to select potential FCFTs was based on the number of both K 

and KA crashes associated with the combination of various crash-related variables, such as 

location, crash type, area type, roadway type, intersection type, traffic control type, lighting, and 

road alignment. A case study showed that the method employed was analogous to the steps for 

selecting FCFTs outlined in FHWA’s Systemic Safety Project Selection Tool (Preston et al. 

2013a). Its application by the research team resulted in a total of 17 FCFTs (8 intersection FCFTs 

and 9 nonintersection FCFTs) for analysis to identify contributing factors for each FCFT: 

• ROR crashes on rural two-lane roads on horizontal curves. 

• ROR crashes on rural two-lane roads on straight segments.

• LNDP crashes on rural two-lane roads on horizontal curves. 

• LNDP crashes on rural two-lane roads on straight segments. 

• HEO crashes on rural two-lane roads on straight segments. 

• ANG crashes on rural two-lane roads on straight segments. 

• HEO crashes on rural two-lane roads on horizontal curves. 

• ROLL crashes on rural two-lane roads on straight segments. 

• ROLL crashes on rural two-lane roads on horizontal curves. 

• ANG crashes on rural two-lane roads at four-leg stop-controlled intersections.

• ANG crashes on urban two-lane roads at four-leg stop-controlled intersections.

• ANG crashes on urban multilane divided roads at four-leg signalized intersections.

• ANG crashes on urban multilane undivided roads at four-leg signalized intersections.

• ANG crashes on rural two-lane roads at three-leg stop-controlled intersections.

• ANG crashes on rural multilane divided roads at four-leg stop-controlled intersections. 

• All pedestrian crashes at intersections.

• Crashes involving a pedestrian crossing at an intersection struck by a motorist going 

straight.

Original analyses conducted as part of this research was the basis for identifying contributing 

factors for crash types involving only motor vehicles. Identifying contributing factors for 

pedestrian crashes was challenging without quality exposure data. The research team explored 

additional, original analyses of pedestrian crashes using available data, but were not confident 

that a quality analysis of contributing factors could be conducted with existing data, which did 

not include pedestrian exposure and included some questionable crash-location coding. Instead, a 
134 

previous study by Thomas et al. (2017), which looked at identifying and screening intersection 

locations that have the potential for future pedestrian crashes, served as the basis for identifying 

contributing factors for pedestrian crashes.

IDENTIFYING CONTRIBUTING FACTORS ASSOCIATED WITH FCFTS

The research team defined contributing factors as factors whose presence are associated with 

increases or decreases in expected frequencies of crashes or injury severities resulting from 

crashes. The research team used random forests to identify contributing factors corresponding to 

selected FCFTs. As noted in chapter 1, a previous research effort, Highway Safety Statistical 

Paper Synthesis, demonstrated the use of CART and random forests within the context of 

conducting statistical road-safety analyses (Persaud et al. 2001). The study concluded that 

tree-based models have strong potential for effective use in road-safety analyses. This current 

project extended the exploration of potential applications of tree-based methods within the 

context of identifying contributing factors for systemic safety analysis.

The research team used three data sources to conduct the contributing-factor analysis: crash and 

roadway inventory from HSIS, climate data from the NOAA, and socioeconomic census data 

from the U.S. Census Bureau (FHWA 2018c; NOAA 2018; U.S. Census Bureau 2018). All 

linkage of road segments to climate and census data was done in the spatial environment. To 

simplify the joining process, roadway segments were represented as point features according to 

the midpoint of the segment. For climate data, the source data were in a point file, with each 

weather station shown as a point on the map. Each roadway segment was linked to the closest 

weather station by a simple straight-line distance measurement. For census data, the source data 

were in a polygon file, with each census-block group shown as a shape on the map. Each roadway 

segment was linked to the census-block group that contained the midpoint of the segment. 

The research team used HSIS data from California and Ohio for the intersection contributingfactor analysis and HSIS data from Ohio and Washington for the nonintersection contributingfactor analysis. The research team conducted a detailed analysis of crash-severity groupings and 

found that the results of the contributing-factor analyses were quite similar when using either the 

frequencies of KAB or KABCO crashes. In almost all cases, the most influential predictor 

variables were consistent across the two severity levels, except for the changes in the increase of 

predictions of MSE associated with the variables. 

Table 92 lists the most prevalent factors that were found to influence expected crash frequencies 

from each of the three main categories: roadway, socioeconomic, and climate. Findings regarding 

these factors are interpreted in this project as predictive relationships or statistical associations 

with expected crash frequencies. The following characteristics raise confidence that a specific 

finding or set of findings are stable and transferable: 

• Consistency across subsets of related FCFTs. 

• Consistency across multiple States.

• Consistency with previous findings in the literature.
135 

Knowledge related to the safety impacts of traffic and roadway variables has grown substantially 

over the last two decades and offers a basis to interpret the results of this effort. In most cases, the 

traffic- and roadway-related findings were consistent with published literature and practice for 

related crash types.

There is not yet a significant amount of theory to support or refute the socioeconomic- and 

weather-related results of this effort. Findings related to socioeconomic variables likely represent 

differences in travel behavior, driving behavior, and driving capabilities that seem key for safety 

analyses but are generally not incorporated into analyses that also include traffic and roadway 

factors. Weather-related findings likely represent differences in visibility, road conditions, and 

driver experience and behavior. In most cases, findings related to socioeconomic and weather 

variables set the stage for future analyses, possibly focused solely on these variables.

Table 92. Most influential factors found to influence expected crash frequencies. 

Variable Category Factor

Roadway: intersection Mainline AADT

Roadway: intersection Cross street AADT

Roadway: intersection Design speed/speed limit

Roadway: intersection Lane width

Roadway: intersection Median width

Roadway: intersection Left-turn channelization

Roadway: intersection Curve radius

Roadway: nonintersection Average AADT

Roadway: nonintersection Percent grade

Roadway: nonintersection Curve radius

Roadway: nonintersection Surface width

Roadway: nonintersection Shoulder type

Roadway: nonintersection Terrain

Socioeconomic Percentage of population ages 16–24 working full time

Socioeconomic Percentage of population ages 16–24 working part time

Socioeconomic Percentage of population ages 25+ without a high school diploma

Socioeconomic Percentage of population ages 25+ with a high school diploma but no university 

degree

Socioeconomic Percentage of households with income <$50,000

Socioeconomic Percentage of households with 0 vehicles

Socioeconomic Percentage of households with 1 vehicle

Socioeconomic Percentage of households with ≥2 vehicles

Socioeconomic Percentage of population ages 15–19

Socioeconomic Percentage of population ages 45–64

Climate Average annual snowfall totals

Climate Average annual rainfall totals

Climate Average annual maximum temperatures

Climate Average annual number of days with a minimum temperature of ≤32°F

There were some counterintuitive results where a variable was expected to increase crash 

frequency but instead was found to decrease crash frequency or vice versa. In most cases, 

counterintuitive findings would show up in only a single State. For example, increasing the 

average shoulder width appeared to increase the frequency of LNDP-N crashes on rural two-lane 

roads in Washington. However, the same increase in the average shoulder width decreased 

(as expected) the frequency of LNDP-N crashes on rural two-lane roads in Ohio.
136 

Counterintuitive findings can happen for different reasons:

• Data related to the presence and type of safety countermeasures were not available for the 

analysis. For example, an agency may apply chevrons on certain types of curves based on 

curve characteristics (e.g., crash history of a specific curve, radius of a curve). Similarly, 

an agency may have installed centerline or shoulder rumble strips on some corridors. That 

type of countermeasure information was not available to the research team for this 

analysis.

• Details on specifics of some roadway features were not available. The research team had 

information on shoulder widths and types (i.e., paved or unpaved) for Washington. 

However, for paved shoulders, it was not clear if the complete shoulder was paved or only 

part of the shoulder was paved. Data on the nature of the roadside were also not available. 

The road safety research and practitioner communities generally believe that roadside 

design features and hazards are significantly associated with the frequency and severity of 

several of the focus crash types on rural two-lane roads. However, limitations in roadside 

data have made the ability to quantify these relationships elusive. 

• In linking census data to roadway data, there was an implicit assumption that the 

characteristics of the census block represents the characteristics of the drivers at the sites 

investigated in this project. There was no additional analysis to determine whether this 

assumption is reasonable. The findings related to the socioeconomic variables are likely 

representing some complex interaction of travel behavior, driving behavior, and driving 

capabilities. This is an area that needs additional work. 

• There is a possibility of an unexpected interaction of driver behavior with certain roadway 

factors or combinations of roadway factors that this analysis does not fully consider or 

explain. There are multiple examples of these unexpected interactions in road safety 

research, including those highlighted by Smiley (2008). 

Information about these factors could have helped further explore the counterintuitive findings. 

Without full knowledge of the reason behind such counterintuitive findings, the research team did 

not consider these findings in providing guidance on the identification of contributing factors and 

selection of countermeasures. The guidance developed for practitioners focused on traffic and 

roadway findings that were generally consistent across crash types and States, as well as with 

related literature.

PROCESS FOR SELECTING COUNTERMEASURES

The research team laid out a process for identifying and selecting countermeasures for focus 

crash types based on identified contributing factors. This process recognizes that, although there 

are certain contributing factors associated with focus crash types, selecting countermeasures must 

be broad and encompass many options for addressing the crash type. The usefulness of the 

contributing-factor identification is to allow a practitioner to identify which countermeasures 

specifically address those factors as part of their assessment of potential safety effectiveness.
137 

This practitioner identification may in turn raise the priority for selecting and implementing those 

countermeasures. The process is laid out in the following six steps:

1. Identify a focus crash type.

2. Identify contributing factors for the focus crash type.

3. Assemble a list of potential countermeasures that address the focus crash type.

4. Identify countermeasures that address the contributing factors associated with the focus 

crash type.

5. Identify countermeasures with CMFs.

6. Select a countermeasure.

Chapter 6 provides several examples to assist practitioners with implementing this process and

descriptions of proven safety countermeasures for common crash types, facility types, and 

contributing factors.

CONCLUSIONS AND RECOMMENDATIONS

The following sections provide conclusions of this research and recommendations for future work 

with respect to identifying FCFTs, identifying contributing factors, and implementing and 

evaluating countermeasures applied as part of a systemic safety-management approach.

Identifying FCFTs

The methodology developed and applied by the research team to select potential FCFTs based on 

the number of K crashes across the United States, as well as the number of KA crashes in four 

selected States, resulted in a useful list of FCFTs. Agencies with sufficient data and analysis 

capabilities can refer to FHWA’s Systemic Safety Project Selection Tool for discussion about how 

to analyze data to identify jurisdiction-specific FCFTs (Preston et al. 2013a). Agencies with 

limited data or analysis capabilities can refer to State, regional, or local SHSPs to identify focus 

crash types based on emphasis areas. Agencies with limited data or analysis capabilities can also 

refer to the list of FCFTs developed for this research, which contribute to a significant number of 

fatalities and serious injuries across the United States and represent common priorities. 

During initial stages of this research, the research team disaggregated selected FCFTs by time of 

day (e.g., ROR-D crashes on rural two-lane roads on horizontal curves; ROR-N crashes on rural 

two-lane roads on horizontal curves). This approach was to allow the analysis to uncover the 

possibility of different factors influencing the frequency of the same crash type during the 

daytime or nighttime. This disaggregation did not lead to any key insights, likely due to the 

unavailability of traffic information by time of day. Future work should continue to explore this 

direction, as it seems logical that at least some contributing factors would differ by time of day. 

Acquiring traffic exposure by time of day will be critical to this analysis. Musunuru et al. (2017) 

provided one option for estimating day and night traffic volumes on rural two-lane roads in the 

absence of data.
138 

Identifying Contributing Factors

A review of systemic safety practices shows that, to date, systemic safety analyses often include 

subjective approaches to identifying contributing factors and characterizing crash potential. 

Agencies need additional information and guidance to help select and target their systemic safety 

improvements to make the most of limited funds, including detailed and data-driven information 

on situations characterized by contributing factors and facility types where focus crash types are 

more likely to occur. The task is challenging, as the nature of specific crash types that are the 

target of systemic safety management is not necessarily compatible with statistical analyses of 

contributing factors that require significant sample sizes. The following conclusions and 

recommendations pertain to the methodologies and findings with respect to roadway-, 

socioeconomic-, and weather-related factors.

Methodologies 

The research team explored the use of random forests to identify contributing factors for FCFTs 

and noted both strengths and limitations. The strengths of the technique include the following: 

• Unlike traditional regression methods, the random-forest method does not require any 

predefined underlying relationship between the target (dependent) variable and predictors 

(independent variables). 

• Unlike traditional regression methods, the random-forest method does not require any 

formal distributional assumptions.

• Strobl et al. (2009) noted that high-ranked variables on a random forest may appear on 

that list as a result of a complex interaction that cannot be captured in a traditional

regression model. 

• Random forests provided estimates of what independent variables were most predictive in 

their relationship with the dependent variable. Rossi et al. (2005) concluded that randomforest variable rankings with respect to predictive capabilities are more stable than those 

produced by stepwise logistic regressions. 

• In addition to predictive rankings, random forests also provided the percentage increase in 

MSE that would result from removing a particular variable from the analysis. 

The weaknesses of the technique include the following: 

• When random forests are used for regressions, they cannot predict beyond the range in the 

training data. 

• Random forests have been found to overfit datasets that are particularly noisy (Segal 

2004). 

• For data including categorical variables with different numbers of levels, random forests 

are biased in favor of attributes with more levels. Therefore, variable rankings with 

respect to predictive capabilities from random forests are not always reliable for this type 

of data.

• Random forests are primarily a predictive modeling tool, not a descriptive tool, even 

though they can be used to learn about underlying mechanisms. 

• For large datasets, the model size for random forests can be quite large, using hundreds of 

megabytes of memory. 
139 

• Random forests can be black boxes that are difficult to interpret.

• In our application of random forests for segments, the dependent variable was the number 

of crashes per mile, which was calculated as the number of crashes in a segment divided 

by the length of the segment. The results could be unexpectedly and unduly influenced by 

short segments. 

The most challenging aspect of implementing random forests within this context was in the 

limited ability to interpret the direction and form of the relationship between a factor of interest 

and expected crash frequency. This research implemented a relatively simple approach, searching 

for primarily linear trends between each factor and the random forest–predicted crash frequency. 

Future efforts should explore more effective ways to uncover and interpret relationships 

contained in random forests. 

While the research team compared random-forest results to findings of previous research on 

related crash types, it was not able to incorporate prior information or knowledge into the 

analyses. Bayesian approaches hold significant promise, and future efforts should explore 

contributing factors for FCFTs. Causal Bayesian networks, such as those applied by Karwa et al. 

(2011), have significant potential but will need to be further explored. 

Roadway, Socioeconomic, and Weather Factors 

Roadway factors uncovered by the analyses as influencing frequencies of crash types were 

generally consistent with the research team’s expectations based on previous research and 

existing practice. Factors associated with higher crash frequencies included the following: 

• Larger ADT volumes. 

• Steeper vertical grades.

• Sharper curve radii.

• Narrower lane and shoulder widths. 

• Unpaved shoulders or no shoulders. 

• Mountainous terrain. 

• Higher speed limits.

• Wider crossing distances at intersections (captured by lane and median widths on 

approaches).

• Absence of left- and right-turn channelization at intersections. 

Agencies with sufficient data and analysis capabilities can refer to the FHWA Systemic Safety 

Project Selection Tool for discussion of how to analyze data to identify contributing factors given 

a specific FCFT (Preston et al. 2013a). Agencies without sufficient data and analysis capabilities 

can reference factors developed in this research to help identify countermeasures and prioritize 

sites for systemic safety improvements.

Findings related to socioeconomic- and weather-related factors showed promise, but there is not 

yet a significant amount of theory to support or refute the socioeconomic- and weather-related 

results of this effort. Findings related to socioeconomic variables are likely representing 

differences in travel behavior, driving behavior, and driving capabilities that seem key for safety 

analyses but are generally not incorporated into segment- and intersection-specific analyses that 
140 

also include traffic and roadway factors. Weather-related findings are likely representing 

differences in visibility, road conditions, and driver experience and behavior. Both sets of factors 

bring significant potential to the process of making more informed decisions about sites that have 

higher levels of crash potential. (Appendix H provides examples, including an analysis of model 

performance with and without consideration of socioeconomic variables.) A multiyear study 

focused on testing various alternatives and developing safety-analysis guidance on collecting, 

merging, and analyzing crash, traffic, roadway, census, and weather data is needed.

As with many road-safety analyses, the dataset for this project consisted of correlated variables, 

particularly various socioeconomic characteristics. The research team conducted an additional 

analysis that used factor analysis to reduce dimensions of socioeconomic variables. As part of an 

exploratory effort, the research team developed and interpreted factors (e.g., a low socioeconomic 

status factor that weighs low income, no vehicle ownership, unemployment, and so on) and then 

included the factors in the random-forest analysis in place of individual variables. This approach 

was based on a study from transit ridership literature that reduced the dimensionality of a set of 

possibly relevant predictor variables from the same category (e.g., land use) prior to 

implementing a regression-tree approach. The approach and findings were promising but did not 

significantly change the overall conclusions when compared to the original analysis. Appendix G 

provides details of this analysis. Future efforts should explore the contributing factor–regression 

tree approach for correlated roadway, socioeconomic, and weather factors.

Implementing and Evaluating Countermeasures

The research team laid out a process for identifying and selecting countermeasures for focus 

crash types based on contributing factors and identified and proven safety countermeasures for 

common contributing factors. In doing so, the research team observed that most CMFs have not 

been developed for crash types at the level of disaggregation that may be needed for systemic 

safety applications. Similarly, expected changes in crash frequencies reflected by CMFs typically 

represent results of evaluations of site-specific applications of a treatment. Additional work on 

estimating network-wide safety improvements resulting from systemic applications of 

countermeasures is needed.
141 

APPENDIX A. LIST OF VARIABLES USED FROM FARS AND HSIS FOR FCFT

SELECTION

The following sections detail variables the research team used from FARS and HSIS for 

California, Ohio, and Washington for FCFT selection (NHTSA 2018a; FHWA 2018c). 

LIST OF FARS VARIABLES USED FOR FCFT SELECTION

Common Variables

• State number: State in which the crash occurred.

• Consecutive number: Unique case number assigned to each crash. 

• Vehicle number: Number assigned to each vehicle in the case.

• Person number: Number assigned to each person in the case.

Accident Data File

• Number of vehicle forms: Number of contact motor vehicles involved in the crash. 

• Number of forms submitted for persons in motor vehicles: Count of the number of 

persons in motor vehicles that are applicable to the case.

• Number of forms submitted for persons not in motor vehicles: Count of the number of 

persons not in motor vehicles that are applicable to the case.

• Month of crash: Month in which the crash occurred.

• Day of crash: Month on which the crash occurred.

• Day of week: Day of the week on which the crash occurred. 

• Year of crash: Year in which the crash occurred.

• Hour of crash: Hour in which the crash occurred.

• Minute of crash: Minutes after the hour at which the crash occurred.

• First harmful event: First injury or damage-producing event of the crash. 

• Manner of collision: Orientation of two motor vehicles in transport when they were 

involved in the crash.

• Relation to junction—specific location: Location of the crash with respect to its presence 

in or proximity to components in a junction or interchange area.

• Type of intersection: Various intersection type in which the crash occurred. 

• Relation to trafficway: Identifies the location of the crash as it relates to its position on the 

trafficway.

• Work zone: If the crash occurred within the boundaries of a work zone. 

• Light conditions: Type and level of light at the time of the crash.

• Atmospheric conditions: Prevailing atmospheric conditions that existed at the time of the 

crash.

• Drunk drivers: Number of drunk drivers involved in the crash. 

• Fatalities: Number of fatally injured persons in the crash. 

• Roadway function class: Functional classification of the trafficway on which the crash 

occurred.
142 

Vehicle Data File 

• Initial contact point: Area on the vehicle that produced the first instance of injury to 

nonmotorists or damage to other property or to the vehicle itself. 

• Most harmful event: Event that resulted in the most severe injury. 

• Trafficway description: Trafficway flow just prior to the vehicle’s critical precrash event.

• Total lanes in roadway: Number of travel lanes prior to the vehicle’s critical precrash 

event.

• Roadway Alignment: Roadway alignment prior to the vehicle’s critical precrash event.

• Roadway grade: Roadway grade prior to the vehicle’s critical precrash event. 

• Roadway-surface condition: Roadway-surface condition prior to the vehicle’s critical 

precrash event.

• Traffic control device: Traffic controls in the vehicle’s environment prior to the vehicle’s 

critical precrash event.

Person Data File 

• Age: Person’s age at the time of the crash in years.

• Sex: Sex of the person involved in the crash. 

• Person type: Role of the person involved in the crash. 

• Injury severity: Severity of injury (using the KABCO scale) to the person in the crash. 

• Police-reported alcohol involvement: If alcohol was involved for the person. 

• Police-reported drug involvement: If drugs were involved for the person. 

LIST OF HSIS WASHINGTON VARIABLES USED FOR FCFT SELECTION

Common Variables 

• Accident number: Case number of the accident.

• Roadway inventory: Crash location information (i.e., county, route, and milepost) used in 

linkage to other files. 

• Accident reference point: Reference point where the crash occurred.

• Beginning milepost: Beginning milepost of the road segment on which the crash 

occurred. 

• Ending milepost: Ending milepost of the road segment on which the crash occurred. 

Accident Data Files 

• Accident type: Type of accident that occurred.

• Light conditions: Type and level of light at the time of the crash.

• Relation to intersection: Location of the crash in relation to the intersection at which the 

crash occurred. 

• Collision type: Types of first and second collisions in the crash. 

• Roadway surface: Condition of the road surface where the crash occurred. 

• Number of pedestrians/cyclists: Number of pedestrians/cyclists involved in the crash. 

• Crash severity: Most severe injury that resulted from the crash.
143 

• Number of persons injured: Total number of persons injured in the crash. 

• Total killed: Total number of persons killed in the crash.

• Weather conditions: Weather conditions at the time the crash occurred.

• Work zone status: Work zone details if the crash occurred in a work zone. 

• Object struck: Fixed object struck in the crash. 

• Driver’s age: Age of the driver of the vehicle involved in the crash.

• Driver’s injury: Extent of injury to the driver of the vehicle involved in the crash. 

• Driver’s sex: Sex of the driver of the vehicle involved in the crash. 

Roadway and Curve Data Files 

• Calculated average AADT: Calculated average AADT of the location where the crash 

occurred. 

• Control of access: Access control in place at the location where the crash occurred. 

• Functional class: Functional class of roadway segment on which the crash occurred.

• Median barrier type: Type of median barrier on the roadway segment on which the crash 

occurred.

• Total number of lanes: Total number of lanes (in both directions) of the roadway segment 

on which the crash occurred.

• Roadway classification: Roadway classification of the roadway segment on which the 

crash occurred.

• Rural/urban identification: Rural or urban classification of the roadway where the crash 

occurred.

• Curve beginning milepost: Beginning milepost of the curved segment on which the crash 

occurred. 

• Curve ending milepost: Ending milepost of the curved segment on which the crash 

occurred. 

• Grade beginning milepost: Beginning milepost of a grade segment on which the crash 

occurred. 

• Grade ending milepost: Ending milepost of a grade segment on which the crash occurred. 

• Percent grade: Percent grade for the roadway segment on which the crash occurred. 

LIST OF HSIS OHIO VARIABLES USED FOR FCFT SELECTION

Common Variables 

• Accident number: Case number of accident.

• County route: Crash location information used in linkage to other files. 

• Accident reference point: Reference point where the crash occurred.

• Beginning milepost: Beginning milepost of the road segment on which the crash 

occurred. 

• Ending milepost: Ending milepost of the road segment on which the crash occurred. 
144 

Accident Data Files 

• Type of crash (first harmful event): First harmful event in the crash sequence.

• Light conditions: Type and level of light at the time of the crash.

• Relation to intersection: Location of crash in relation to the intersection in which the crash 

occurred. 

• Number of vehicles involved: Number of vehicles involved in the crash.

• Road characteristics: Characteristics of the road on which the crash occurred.

• Number of pedestrians: Number of pedestrians involved in the crash. 

• Crash severity: Most severe injury that resulted from the crash.

• Serious visible injury: Total number of A injuries in the crash. 

• Minor visible injury: Total number of B injuries in the crash. 

• No visible injury: Total number of C injuries in the crash. 

• Total killed: Total number of persons killed in the crash. 

• Weather conditions: Weather conditions at the time the crash occurred.

• Population: Rural/urban population where the crash occurred. 

• Driver’s age: Age of the driver of the vehicle involved in the crash. 

• Driver’s injury: Extent of injury to the driver of the vehicle involved in the crash. 

• Driver’s sex: Sex of the driver of the vehicle involved in the crash. 

Roadway Data Files 

• Calculated average AADT: Calculated average AADT of the crash location. 

• Control of access: Access control in place at the location of crash.

• Functional class: Functional class of the roadway segment on which crash occurred.

• Median type: Type of median on the roadway segment on which the crash occurred.

• Total number of lanes: Total number of lanes (in both directions) of the roadway segment 

on which the crash occurred.

• Roadway classification: Roadway classification of the roadway segment on which the 

crash occurred.

LIST OF HSIS CALIFORNIA VARIABLES USED FOR FCFT SELECTION

Common Variables 

• Accident number: Case number of accident.

• County route: Crash location information used in linkage to other files. 

• Accident reference point: Reference point where the crash occurred.

• Beginning milepost: Beginning milepost of the road segment on which the crash 

occurred. 

• Ending milepost: Ending milepost of the road segment on which the crash occurred. 
145 

Accident Data Files 

• Type of collision: Type of accident that occurred.

• Light conditions: Type and level of light at the time of the crash.

• Relation to intersection: Location of the crash in relation to the intersection at which the 

crash occurred. 

• Number of vehicles: Number of vehicles involved in the crash. 

• Roadway surface: Condition of the road surface on which the crash occurred.

• Pedestrian involvement: Whether or not a pedestrian was involved in the crash.

• Crash severity: Most severe injury that resulted from the crash.

• Number of persons injured: Total number of persons injured in the crash. 

• Total killed: Total number of persons killed in the crash. 

• Weather conditions: Weather conditions at the time the crash occurred.

• Primary collision factor: Primary collision factor of the crash. 

• Motor vehicles involved: Vehicles or nonvehicles that were involved in the crash. 

• Driver’s age: Age of the driver of the vehicle involved in the crash. 

• Driver’s sex: Sex of the driver of the vehicle involved in the crash. 

Roadway and Intersection Data Files 

• Calculated average AADT: Calculated average AADT of the location where the crash 

occurred. 

• Control of access: Access control in place at the location where the crash occurred. 

• Functional class: Functional class of the roadway segment on which the crash occurred. 

• Median barrier type: Type of median on the roadway segment on which the crash 

occurred.

• Total number of lanes: Total number of lanes (in both directions) of the roadway segment 

on which the crash occurred.

• Roadway classification: Roadway classification of the roadway segment on which the 

crash occurred.

• Rural/urban identification: Rural or urban classification of the roadway where the crash 

occurred.

• Traffic control type: Traffic control type at the intersection where the crash occurred. 

LIST OF HSIS MINNESOTA VARIABLES USED FOR FCFT SELECTION

Common Variables 

• Accident number: Case number of accident.

• Combined route system/route number: Combined route system and route number where 

the crash occurred.

• Accident reference point: Reference point where the crash occurred.

• Beginning milepost: Beginning milepost of the road segment on which the crash 

occurred. 

• Ending milepost: Ending milepost of the road segment on which the crash occurred. 
146 

Accident Data Files 

• Diagram of accident code: Accident configuration describing the direction and maneuvers 

of the vehicles involved in the crash. 

• Type of accident: Type of accident that occurred.

• Road design: Design of the roadway where the crash occurred. 

• Light conditions: Type and level of light at the time of the crash.

• Relation to intersection: Location of the crash in relation to the intersection.

• Number of vehicles involved: Number of vehicles involved in the crash. 

• Road characteristics: Characteristics of the road on which the crash occurred. 

• Road surface conditions: Condition of the road surface on which the crash occurred.

• Accident severity: Most severe injury in the crash. 

• Posted speed limit: Posted speed limit where the crash occurred.

• Number of persons injured: Total number of persons injured in the crash. 

• Number of persons killed: Total number of persons killed in the crash. 

• Traffic-control devices: Traffic-control devices where the crash occurred.

• Weather conditions: Weather conditions at the time the crash occurred.

• Work zone marked: Type of work zone where the crash occurred. 

• Driver’s age: Age of the driver of the vehicle involved in the crash. 

• Driver’s injury: Extent of injury to the driver of the vehicle involved in the crash.

• Driver’s sex: Sex of the driver of the vehicle involved in the crash. 

Roadway Data Files 

• Calculated average AADT: Calculated average AADT of the crash location.

• Control of access: Access control in place at the location of crash.

• Functional class: Functional class of the roadway segment on which the crash occurred. 

• Median type: Type of median on the roadway segment on which the crash occurred.

• Total number of lanes: Total number of lanes (in both directions) of the roadway segment 

on which the crash occurred.

• Roadway classification: Roadway classification of the roadway segment on which the 

crash occurred.
147 

APPENDIX B. SYSTEMIC SAFETY-PLANNING PROCESS 

Figure 4 and figure 5 show the selection of focus crash types (i.e., task 1 of the systemic 

safety-planning process). Each box shows a crash or facility type and the number of 

corresponding crashes. The shaded boxes are the selected focus crash types. The crash trees for 

selecting focus facility types for nonintersection crashes (i.e., task 2 of the systemic 

safety-planning process) are shown in figure 5 through figure 13.

Source: FHWA.

Note: Shaded boxes are selected focus crash types.

Figure 4. Chart. Crash types defined by Manner of Collision. 
148 

Source: FHWA.

Note: Shaded boxes are selected focus crash types.

Figure 5. Chart. Crash types defined by First Harmful Event. 
149 

Source: FHWA.

Note: Shaded boxes are selected focus crash types.

Figure 6. Chart. Crash tree for ANG crashes at nonintersection locations.
150 

Source: FHWA.

Note: Shaded boxes are selected focus crash types.

Figure 7. Chart. Crash tree for HEO crashes at nonintersection locations.
151 

Source: FHWA.

Note: Shaded boxes are selected focus crash types.

Figure 8. Chart. Crash tree for rear-end crashes at nonintersection locations. 
152 

Source: FHWA.

Note: Shaded boxes are selected focus crash types.

Figure 9. Chart. Crash tree for fixed-object crashes in rural areas at nonintersection 

locations. 
153 

Source: FHWA.

Note: Shaded boxes are selected focus crash types.

Figure 10. Chart. Crash tree for fixed-object crashes in urban areas at nonintersection 

locations. 
154 

Source: FHWA.

Note: Shaded boxes are selected focus crash types.

Figure 11. Chart. Crash tree for pedestrian crashes in rural areas at nonintersection 

locations. 
155 

Source: FHWA.

Note: Shaded boxes are selected focus crash types.

Figure 12. Chart. Crash tree for pedestrian crashes in urban areas at nonintersection locations.
156 

Source: FHWA.

Note: Shaded boxes are selected focus crash types.

Figure 13. Chart. Crash tree for ROLL crashes at nonintersection locations.
157 

APPENDIX C. POTENTIAL INTERSECTION AND NONINTERSECTION FCFTS

FROM FARS AND HSIS

Table 93 through table 110 list potential intersection and nonintersection FCFTs as derived from 

FARS and HSIS (NHTSA 2018a; FHWA 2018c). Potential FCFTs from HSIS datasets are listed

in order based on both K and KA crashes. 

Table 93. Potential nonintersection FCFTs from FARS. 

No. Crash Type Area Type Roadway Type

Light 

Conditions

Road 

Alignment K Crashes

1 Fixed object Rural 2-lane Nighttime Curve 5,197

2 Fixed object Rural 2-lane Daytime Curve 4,095

3 Fixed object Rural 2-lane Nighttime Straight 4,094

4 Fixed object Rural 2-lane Daytime Straight 3,825

5 HEO Rural 2-lane Daytime Straight 2,789

6 Pedestrian Urban Multilane divided Nighttime Straight 2,174

7 Pedestrian Urban Multilane 

undivided

Nighttime Straight 2,118

8 Pedestrian Urban 2-lane Nighttime Straight 2,051

9 Pedestrian Rural 2-lane Nighttime Straight 1,918

10 ANG Rural 2-lane Daytime Straight 1,815

11 Rollover/

overturn

Rural 2-lane Daytime Curve 1,779

12 HEO Rural 2-lane Nighttime Straight 1,768

13 HEO Rural 2-lane Daytime Curve 1,747

14 Rollover/

overturn

Rural 2-lane Daytime Straight 1,736

15 Rollover/

overturn

Rural 2-lane Nighttime Curve 1,717

16 Rollover/

overturn

Rural 2-lane Nighttime Straight 1,681

17 Fixed object Urban 2-lane Nighttime Straight 1,630

18 Pedestrian Urban Interstates/

freeways/

expressways

Nighttime Straight 1,627

19 Fixed object Urban 2-lane Nighttime Curve 1,544

20 Fixed object Urban Freeways Nighttime Straight 1,500

21 Fixed object Urban Freeways Daytime Straight 1,188

22 Fixed object Urban 2-lane Daytime Straight 1,177

23 Rear end Urban Interstates/

freeways/

expressways

Nighttime Straight 1,053

24 Ditch Rural 2-lane Nighttime Straight 1,026

25 Ditch Rural 2-lane Nighttime Curve 993

26 Fixed object Rural Freeways Daytime Straight 987

27 Fixed object Urban Freeways Nighttime Curve 924

28 Rollover/

overturn

Rural Interstates/

freeways/

expressways

Daytime Straight 886

29 Ditch Rural 2-lane Daytime Straight 868

30 Pedestrian Urban 2-lane Daytime Straight 852

31 ANG Rural 2-lane Daytime Curve 813
158 

No. Crash Type Area Type Roadway Type

Light 

Conditions

Road 

Alignment K Crashes

32 Rear end Urban Interstates/

freeways/

expressways

Daytime Straight 801

33 Fixed object Rural Freeways Nighttime Straight 785

34 ANG Rural 2-lane Nighttime Straight 777

35 Fixed object Urban 2-lane Daytime Curve 756

36 Ditch Rural 2-lane Daytime Curve 755

37 HEO Urban 2-lane Daytime Straight 736

38 HEO Rural 2-lane Nighttime Curve 714

39 Rear end Rural 2-lane Daytime Straight 714

40 Fixed object Urban Multilane divided Nighttime Straight 686

41 Pedestrian Rural Multilane divided Nighttime Straight 680

42 ANG Urban 2-lane Nighttime Straight 625

43 Rollover/

overturn

Rural Interstates/

freeways/

expressways

Nighttime Straight 600

44 Fixed object Urban Freeways Daytime Curve 579

45 ANG Urban Multilane 

undivided

Daytime Straight 549

46 Fixed object Rural Multilane divided Daytime Straight 539

47 Pedestrian Rural 2-lane Daytime Straight 538

48 Rear end Rural Interstates/

freeways/

expressways

Nighttime Straight 526

49 Fixed object Rural Multilane divided Nighttime Straight 519
159 

Table 94. Potential intersection FCFTs from FARS. 

No.

Crash 

Type

Area 

Type Roadway Type Location Type

Traffic 

Control

Light 

Conditions

K 

Crashes

1 ANG Rural 2-lane 4-leg 

intersection

Stop controlled Daytime 2,424

2 ANG Urban 2-lane 4-leg 

intersection

Stop controlled Daytime 1,126

3 ANG Urban Multilane divided 4-leg 

intersection

Traffic signal Daytime 977

4 ANG Urban Multilane 

undivided

4-leg 

intersection

Traffic signal Daytime 864

5 ANG Rural 2-lane 4-leg 

intersection

Stop controlled Nighttime 750

6 ANG Urban Multilane divided 4-leg 

intersection

Traffic signal Nighttime 684

7 ANG Rural 2-lane T intersection Stop controlled Daytime 592

8 Pedestrian Urban Multilane divided 4-leg 

intersection

Traffic signal Nighttime 589

9 ANG Urban Multilane 

undivided

4-leg 

intersection

Traffic signal Nighttime 555

10 ANG Urban 2-lane T intersection Stop controlled Daytime 536

11 Pedestrian Urban Multilane 

undivided

4-leg 

intersection

Traffic signal Nighttime 432

Table 95. Potential nonintersection FCFTs from HSIS: Minnesota (by K crashes). 

No. Crash Type Area Type

Roadway 

Type

Light 

Conditions

Road 

Alignment K Crashes KA Crashes

1 HEO Rural 2-lane Daytime Straight 94 175

2 ROR Rural 2-lane Nighttime Curve 75 104

3 ROR Rural 2-lane Daytime Straight 71 226

4 ROR Rural 2-lane Daytime Curve 68 222

5 ROR Rural 2-lane Nighttime Straight 66 262

6 HEO Rural 2-lane Nighttime Straight 51 231

7 ROLL Rural 2-lane Daytime Straight 44 78

8 HEO Rural 2-lane Daytime Curve 42 71

9 ROLL Rural 2-lane Nighttime Curve 42 84

10 ROLL Rural 2-lane Nighttime Straight 38 103

11 HEO Urban 2-lane Nighttime Straight 33 140

12 ROLL Rural 2-lane Daytime Curve 29 143

13 Pedestrian Urban 2-lane Nighttime Straight 19 140

14 HEO Urban 2-lane Daytime Straight 17 65
160 

Table 96. Potential nonintersection FCFTs from HSIS: Minnesota (by KA crashes). 

No. Crash Type Area Type Roadway Type

Light 

Conditions

Road 

Alignment

K 

Crashes

KA 

Crashes

1 ROR Rural 2-lane Nighttime Straight 66 262

2 HEO Rural 2-lane Nighttime Straight 51 231

3 ROR Rural 2-lane Daytime Straight 71 226

4 ROR Rural 2-lane Daytime Curve 68 222

5 HEO Rural 2-lane Daytime Straight 94 175

6 ROLL Rural 2-lane Daytime Curve 29 143

7 HEO Urban 2-lane Nighttime Straight 33 140

8 Pedestrian Urban 2-lane Nighttime Straight 19 140

9 ROR Rural 2-lane Nighttime Curve 75 104

10 ROLL Rural 2-lane Nighttime Straight 38 103

11 ROLL Rural 2-lane Nighttime Curve 42 84

12 ROLL Rural 2-lane Daytime Straight 44 78

13 HEO Rural 2-lane Daytime Curve 42 71

14 HEO Urban 2-lane Daytime Straight 17 65

Table 97. Potential intersection FCFTs from HSIS: Minnesota (by K crashes). 

No. Crash Type

Area 

Type

Roadway 

Type

Location 

Type

Traffic 

Control

Light 

Conditions

K 

Crashes

KA 

Crashes

1 ANG Rural 2-lane 4-leg 

intersection

Minor road 

stop control

Daytime 97 203

2 ANG Rural Multilane 

divided

4-leg 

intersection

Minor road 

stop control

Daytime 24 42

3 ANG Urban Multilane 

divided

4-leg 

intersection

Traffic 

signal

Daytime 22 80

4 ANG Urban 2-lane 4-leg 

intersection

Minor road 

stop control

Daytime 15 116

5 Pedestrian Urban Multilane 

divided

4-leg 

intersection

Traffic 

signal

Nighttime 13 42

Table 98. Potential intersection FCFTs from HSIS: Minnesota (by KA crashes). 

No. Crash Type

Area 

Type

Roadway 

Type

Location 

Type

Traffic 

Control

Light 

Conditions

K 

Crashes

KA 

Crashes

1 ANG Rural 2-lane 4-leg 

intersection

Minor road 

stop control

Daytime 97 203

2 ANG Urban Multilane 

divided

4-leg 

intersection

Traffic signal Daytime 22 116

3 ANG Urban 2-lane 4-leg 

intersection

Minor road 

stop control

Daytime 15 80

4 ANG Rural Multilane 

divided

4-leg 

intersection

Minor road 

stop control

Daytime 24 42

5 Pedestrian Urban Multilane 

divided

4-leg 

intersection

Traffic signal Nighttime 13 42
161 

Table 99. Potential nonintersection FCFTs from HSIS: Ohio (by K crashes). 

No. Crash Type Area Type

Roadway 

Type

Light 

Conditions

Road 

Alignment

K 

Crashes

KA 

Crashes

1 Fixed object Rural 2-lane Daytime Curve 123 773

2 Fixed object Rural 2-lane Daytime Straight 111 1027

3 Fixed object Rural 2-lane Nighttime Curve 110 569

4 Fixed object Rural 2-lane Nighttime Straight 101 792

5 Sideswipe—

meeting

Rural 2-lane Daytime Straight 85 391

6 HEO Rural 2-lane Daytime Straight 78 181

7 Fixed object Urban Freeways Nighttime Straight 52 522

8 Fixed object Urban Freeways Nighttime Curve 49 291

9 Fixed object Urban Freeways Daytime Straight 48 590

10 Sideswipe—

meeting

Rural 2-lane Daytime Curve 48 196

11 Pedestrian Urban Multilane 

undivided

Nighttime Straight 45 169

12 Sideswipe—

meeting

Rural 2-lane Nighttime Straight 42 199

13 Rear end Urban Freeways Nighttime Straight 35 238

14 Fixed object Urban Freeways Daytime Curve 30 297

15 Rear end Urban Freeways Daytime Straight 28 565

16 Fixed object Urban 2-lane Nighttime Straight 25 191

17 ROLL Rural 2-lane Daytime Curve 22 225

18 Fixed object Rural Freeways Daytime Straight 21 155

19 Fixed object Urban 2-lane Daytime Straight 21 202

20 Sideswipe—

passing

Urban Freeways Daytime Straight 21 348

21 Fixed object Urban Multilane 

undivided

Nighttime Straight 18 158

22 ROLL Rural 2-lane Daytime Curve 18 166

23 Rear end Rural 2-lane Daytime Straight 16 409

24 Sideswipe—

passing

Urban Freeways Daytime Straight 14 169
162 

Table 100. Potential nonintersection FCFTs from HSIS: Ohio (by KA crashes). 

No. Crash Type Area Type

Roadway 

Type

Light 

Conditions

Road 

Alignment K Crashes

KA 

Crashes

1 Fixed object Rural 2-lane Daytime Straight 111 1,027

2 Fixed object Rural 2-lane Nighttime Straight 101 792

3 Fixed object Rural 2-lane Daytime Curve 123 773

4 Fixed object Urban Freeways Daytime Straight 48 590

5 Fixed object Rural 2-lane Nighttime Curve 110 569

6 Rear end Urban Freeways Daytime Straight 28 565

7 Fixed object Urban Freeways Nighttime Straight 52 522

8 Rear end Rural 2-lane Daytime Straight 16 409

9 Sideswipe—

meeting

Rural 2-lane Daytime Straight 85 391

10 Sideswipe—

passing

Urban Freeways Daytime Straight 21 348

11 Rear end Urban Multilane 

undivided

Daytime Straight 6 318

12 Fixed object Urban Freeways Daytime Curve 30 297

13 Fixed object Urban Freeways Nighttime Curve 49 291

14 Rear end Urban 2-lane Daytime Straight 6 270

15 Rear end Urban Freeways Nighttime Straight 35 238

16 ROLL Rural 2-lane Daytime Curve 22 225

17 ANG Urban Multilane 

undivided

Daytime Straight 4 213

18 Fixed object Urban 2-lane Daytime Straight 21 202

19 Sideswipe—

meeting

Rural 2-lane Nighttime Straight 42 199

20 Sideswipe—

meeting

Rural 2-lane Daytime Curve 48 196

21 Fixed object Urban 2-lane Nighttime Straight 25 191

22 HEO Rural 2-lane Daytime Straight 78 181

23 Pedestrian Urban Multilane 

undivided

Nighttime Straight 45 169

24 Sideswipe—

passing

Urban Freeways Daytime Straight 14 169
163 

Table 101. Potential intersection FCFTs from HSIS: Ohio (by K crashes). 

No.

Crash 

Type

Area 

Type Roadway Type Location Type

Light 

Conditions

K 

Crashes

KA 

Crashes

1 ANG Rural 2-lane At or related to 

intersection

Daytime 153 1,152

2 ANG Rural 2-lane At or related to 

intersection

Nighttime 48 296

3 ANG Urban Multilane 

divided

At or related to 

intersection

Daytime 33 511

4 ANG Urban Multilane 

undivided

At or related to 

intersection

Daytime 29 657

5 ANG Urban Multilane 

divided

At or related to 

intersection

Daytime 27 243

6 ANG Rural 2-lane At or related to 

intersection

Daytime 25 213

7 Pedestrian Urban Multilane 

undivided

At or related to 

intersection

Nighttime 20 163

8 ANG Urban 2-lane At or related to 

intersection

Nighttime 17 169

9 Rear end Rural 2-lane At or related to 

intersection

Daytime 13 205

10 Pedestrian Urban Multilane 

undivided

At or related to 

intersection

Daytime 11 184

11 ANG Urban Multilane 

undivided

At or related to 

intersection

Nighttime 10 242

12 Rear end Urban Multilane 

undivided

At or related to 

intersection

Daytime 10 338

13 Rear end Urban 2-lane At or related to 

intersection

Daytime 5 243
164 

Table 102. Potential intersection FCFTs from HSIS: Ohio (by KA crashes). 

No.

Crash 

Type

Area 

Type Roadway Type Location Type

Light 

Conditions

K 

Crashes

KA 

Crashes

1 ANG Rural 2-lane At or related to 

intersection

Daytime 153 1,152

2 ANG Urban Multilane 

undivided

At or related to 

intersection

Daytime 29 657

3 ANG Urban Multilane 

divided

At or related to 

intersection

Daytime 33 511

4 Rear end Urban Multilane 

undivided

At or related to 

intersection

Daytime 10 338

5 ANG Rural 2-lane At or related to 

intersection

Nighttime 48 296

6 ANG Urban Multilane 

divided

At or related to 

intersection

Daytime 27 243

7 Rear end Urban 2-lane At or related to 

intersection

Daytime 5 243

8 ANG Urban Multilane 

undivided

At or related to 

intersection

Nighttime 10 242

9 ANG Rural 2-lane At or related to 

intersection

Daytime 25 213

10 Rear end Rural 2-lane At or related to 

intersection

Daytime 13 205

11 Pedestrian Urban Multilane 

undivided

At or related to 

intersection

Daytime 11 184

12 ANG Urban 2-lane At or related to 

intersection

Nighttime 17 169

13 Pedestrian Urban Multilane 

undivided

At or related to 

intersection

Nighttime 20 163
165 

Table 103. Potential nonintersection FCFTs from HSIS: California (by K crashes). 

No. Crash Type Area Type Roadway Type Light Conditions K Crashes KA Crashes

1 Hit object Urban Freeways Nighttime 530 1,983

2 Pedestrian Urban Freeways Nighttime 350 542

3 Hit object Urban Freeways Daytime 279 1,182

4 Rear end Urban Freeways Nighttime 276 1,172

5 HEO Rural 2-lane Daytime 223 530

6 Hit object Rural 2-lane Daytime 214 867

7 Hit object Rural 2-lane Nighttime 173 547

8 Rear end Urban Freeways Daytime 162 1,182

9 Hit object Rural Freeways Nighttime 137 326

10 ROLL Rural Freeways Daytime 135 418

11 HEO Rural 2-lane Nighttime 129 281

12 Hit object Rural Freeways Daytime 121 394

13 Sideswipe Urban Freeways Daytime 120 738

14 ROLL Rural 2-lane Daytime 107 716

15 Pedestrian Urban Multilane divided Nighttime 106 252

16 ROLL Urban Freeways Nighttime 106 504

17 ROLL Urban Freeways Daytime 96 623

18 Sideswipe Urban Freeways Nighttime 88 436

19 ROLL Rural Freeways Nighttime 84 235

20 Broadside Urban Freeways Nighttime 72 260

21 Rear end Rural Freeways Nighttime 71 215

22 ROLL Rural 2-lane Nighttime 54 221

23 Sideswipe Rural 2-lane Daytime 40 201

Table 104. Potential nonintersection FCFTs from HSIS: California (by KA crashes). 

No. Crash Type Area Type Roadway Type Light Conditions K Crashes KA Crashes

1 Hit object Urban Freeways Nighttime 530 1,983

2 Hit object Urban Freeways Daytime 279 1,182

3 Rear end Urban Freeways Daytime 162 1,182

4 Rear end Urban Freeways Nighttime 276 1,172

5 Hit object Rural 2-lane Daytime 214 867

6 Sideswipe Urban Freeways Daytime 120 738

7 ROLL Rural 2-lane Daytime 107 716

8 ROLL Urban Freeways Daytime 96 623

9 Hit object Rural 2-lane Nighttime 173 547

10 Pedestrian Urban Freeways Nighttime 350 542

11 HEO Rural 2-lane Daytime 223 530

12 ROLL Urban Freeways Nighttime 106 504

13 Sideswipe Urban Freeways Nighttime 88 436

14 ROLL Rural Freeways Daytime 135 418

15 Hit object Rural Freeways Daytime 121 394

16 Hit object Rural Freeways Nighttime 137 326

17 HEO Rural 2-lane Nighttime 129 281

18 Broadside Urban Freeways Nighttime 72 260

19 Pedestrian Urban Multilane divided Nighttime 106 252

20 ROLL Rural Freeways Nighttime 84 235

21 ROLL Rural 2-lane Nighttime 54 221

22 Rear end Rural Freeways Nighttime 71 215

23 Sideswipe Rural 2-lane Daytime 40 201
166 

Table 105. Potential intersection FCFTs from HSIS: California (by K crashes). 

No. Crash Type

Area 

Type

Roadway 

Type

Location 

Type Traffic Control

Light 

Conditions

K 

Crashes

KA 

Crashes

1 Broadside Rural 2-lane 4-leg 

intersection

Minor road stop 

control

Daytime 30 23

2 Broadside Urban Multilane 

divided

4-leg 

intersection

Traffic signal Daytime 13 71

3 Broadside Rural 2-lane 4-leg 

intersection

Minor road stop 

control

Nighttime 12 33

4 Broadside Rural Multilane 

divided

4-leg 

intersection

Minor road stop 

control

Daytime 11 35

5 Broadside Urban Multilane 

divided

4-leg 

intersection

Traffic signal Nighttime 10 48

6 Pedestrian Urban Multilane 

divided

4-leg 

intersection

Traffic signal Nighttime 9 37

7 Hit object Rural 2-lane TintersectionMinor road stop 

control

Nighttime 4 15

Table 106. Potential intersection FCFTs from HSIS: California (by KA crashes). 

No. Crash Type

Area 

Type

Roadway 

Type

Location 

Type

Traffic 

Control

Light 

Conditions

K 

Crashes

KA 

Crashes

1 Broadside Rural 2-lane 4-leg 

intersection

Minor road 

stop control

Daytime 30 23

2 Broadside Urban Multilane 

divided

4-leg 

intersection

Traffic signal Daytime 13 71

3 Broadside Rural 2-lane 4-leg 

intersection

Minor road 

stop control

Nighttime 12 33

4 Broadside Rural Multilane 

divided

4-leg 

intersection

Minor road 

stop control

Daytime 11 35

5 Broadside Urban Multilane 

divided

4-leg 

intersection

Traffic signal Nighttime 10 48

6 Pedestrian Urban Multilane 

divided

4-leg 

intersection

Traffic signal Nighttime 9 37

7 Hit object Rural 2-lane T-intersection Minor road 

stop control

Nighttime 4 15

Table 107. Potential nonintersection FCFTs from HSIS: Washington (by K crashes). 

No. Crash Type

Area 

Type

Roadway 

Type Light Conditions

Road

Alignment

K 

Crashes

KA 

Crashes

1 Hit object Rural 2-lane Daytime Straight 35 118

2 Hit object Rural 2-lane Nighttime Straight 33 111

3 HEO Rural 2-lane Daytime Straight 33 59

4 Hit object Rural 2-lane Nighttime Curve 24 64

5 Hit object Rural 2-lane Daytime Curve 23 92

6 Hit object Urban Freeways Nighttime Straight 22 66

7 Pedestrian Urban Multilane 

undivided

Nighttime Straight 18 57

8 ANG Rural 2-lane Daytime Straight 16 59

9 Rear end Urban Freeways Nighttime Straight 15 63

10 Hit object Urban Freeways Daytime Straight 11 60

11 Rear end Urban Freeways Daytime Straight 8 110

12 Rear end Rural 2-lane Daytime Straight 8 57
167 

Table 108. Potential nonintersection FCFTs from HSIS: Washington (by KA crashes). 

No. Crash Type

Area 

Type Roadway Type

Light 

Conditions

Road 

Alignment

K 

Crashes

KA 

Crashes

1 Hit object Rural 2-lane Daytime Straight 35 118

2 Hit object Rural 2-lane Nighttime Straight 33 111

3 Rear end Urban Freeways Daytime Straight 8 110

4 Hit object Rural 2-lane Daytime Curve 23 92

5 Hit object Urban Freeways Nighttime Straight 22 66

6 Hit object Rural 2-lane Nighttime Curve 24 64

7 Rear end Urban Freeways Nighttime Straight 15 63

8 Hit object Urban Freeways Daytime Straight 11 60

9 ANG Rural 2-lane Daytime Straight 16 59

10 HEO Rural 2-lane Daytime Straight 33 59

11 Pedestrian Urban Multilane 

undivided

Nighttime Straight 18 57

12 Rear end Rural 2-lane Daytime Straight 8 57

Table 109. Potential intersection FCFTs from HSIS: Washington (by K crashes). 

No. Crash Type

Area 

Type

Roadway 

Type Location Type Light Conditions

K 

Crashes

KA 

Crashes

1 ANG Rural 2-lane At or related to intersection Daytime 28 64

2 ANG Urban Multilane 

undivided

At or related to intersection Daytime 13 110

3 Pedestrian Urban Multilane 

undivided

At or related to intersection Nighttime 11 129

4 ANG Rural 2-lane At or related to intersection Nighttime 10 95

5 ANG Urban 2-lane At or related to intersection Daytime 7 42

6 Rear end Urban Multilane 

undivided

At or related to intersection Daytime 5 49

7 ANG Urban Multilane 

undivided

At or related to intersection Nighttime 5 48

8 ANG Urban 2-lane At or related to intersection Daytime 5 40

9 Rear end Rural 2-lane At or related to intersection Daytime 3 49

10 Pedestrian Urban Multilane 

undivided

At or related to intersection Daytime 3 40
168 

Table 110. Potential intersection FCFTs from HSIS: Washington (by KA crashes). 

No. Crash Type

Area 

Type

Roadway 

Type Location Type

Light 

Conditions

K 

Crashes

KA 

Crashes

1 Pedestrian Urban Multilane 

undivided

At or related to intersection Nighttime 11 129

2 ANG Urban Multilane 

undivided

At or related to intersection Daytime 13 110

3 ANG Rural 2-lane At or related to intersection Nighttime 10 95

4 ANG Rural 2-lane At or related to intersection Daytime 28 64

5 Rear end Urban Multilane 

undivided

At or related to intersection Daytime 5 49

6 Rear end Rural 2-lane At or related to intersection Daytime 3 49

7 ANG Urban Multilane 

undivided

At or related to intersection Nighttime 5 48

8 ANG Urban 2-lane At or related to intersection Daytime 7 42

9 ANG Urban 2-lane At or related to intersection Daytime 5 40

10 Pedestrian Urban Multilane 

undivided

At or related to intersection Daytime 3 40
169 

APPENDIX D. RANDOM-FOREST R CODE

Figure 14 provides a sample random-forest code that was used in R software for the 

contributing-factor analysis. In this particular example, the code was used to analyze ANG

crashes at horizontal curves on rural two-lane highway segments. The code can be modified to 

analyze other focus crash types.

Source: FHWA.

Figure 14. Screenshot. Sample random-forest code for R software.

171 

APPENDIX E. RANDOM-FOREST OUTPUTS

Figure 15 through figure 70 show random-forest outputs of the contributing-factor analysis for 

California, Ohio, and Washington data. 

RANDOM FORESTS OF CALIFORNIA DATA

Source: FHWA.

Figure 15. Graph. ANG-D crashes at four-leg stop-controlled intersections on rural 

two-lane roads. 
172 

Source: FHWA.

Figure 16. Graph. ANG-N crashes at four-leg stop-controlled intersections on rural 

two-lane roads. 
173 

Source: FHWA.

Figure 17. Graph. ANG-D crashes at four-leg stop-controlled intersection on urban 

two-lane roads. 
174 

Source: FHWA.

Figure 18. Graph. ANG-D crashes at four-leg signalized intersections on urban multilane 

divided roads. 
175 

Source: FHWA.

Figure 19. Graph. ANG-D crashes at four-leg signalized intersections on urban multilane 

undivided roads. 
176 

Source: FHWA.

Figure 20. Graph. ANG-D crashes at three-leg stop-controlled intersections on rural 

two-lane roads. 
177 

Source: FHWA.

Figure 21. Graph. ANG-D crashes at four-leg stop-controlled intersections on rural 

multilane divided roads. 
178 

RANDOM FORESTS OF WASHINGTON DATA

Source: FHWA.

Figure 22. Graph. ROR-D crashes at tangent segments on rural two-lane highway 

segments. 
179 

Source: FHWA.

Figure 23. Graph. ROR-N crashes at tangent segments on rural two-lane highway 

segments. 
180 

Source: FHWA.

Figure 24. Graph. ROR-D crashes at horizontal curves on rural two-lane highway 

segments. 
181 

Source: FHWA.

Figure 25. Graph. ROR-N crashes at horizontal curves on rural two-lane highway 

segments. 
182 

Source: FHWA.

Figure 26. Graph. LNDP-D crashes at tangent segments on rural two-lane highway 

segments. 
183 

Source: FHWA.

Figure 27. Graph. LNDP-N crashes at tangent segments on rural two-lane highway 

segments. 
184 

Source: FHWA.

Figure 28. Graph. LNDP-D crashes at horizontal curves on rural two-lane highway 

segments. 
185 

Source: FHWA.

Figure 29. Graph. LNDP-N crashes at horizontal curves on rural two-lane highway 

segments. 
186 

Source: FHWA.

Figure 30. Graph. HEO-D crashes at tangent segments on rural two-lane highway 

segments. 
187 

Source: FHWA.

Figure 31. Graph. HEO-N crashes at tangent segments on rural two-lane highway 

segments. 
188 

Source: FHWA.

Figure 32. Graph. HEO-D crashes at horizontal curves on rural two-lane highway 

segments. 
189 

Source: FHWA.

Figure 33. Graph. HEO-N crashes at horizontal curves on rural two-lane highway 

segments. 
190 

Source: FHWA.

Figure 34. Graph. ROLL-D crashes at tangent segments on rural two-lane highway 

segments. 
191 

Source: FHWA.

Figure 35. Graph. ROLL-N crashes at tangent segments on rural two-lane highway 

segments. 
192 

Source: FHWA.

Figure 36. Graph. ROLL-D crashes at horizontal curves on rural two-lane highway 

segments. 
193 

Source: FHWA.

Figure 37. Graph. ROLL-N crashes at horizontal curves on rural two-lane highway 

segments. 
194 

Source: FHWA.

Figure 38. Graph. ANG-D crashes at tangent segments on rural two-lane highway 

segments. 
195 

RANDOM FORESTS OF OHIO DATA

Source: FHWA.

Figure 39. Graph. ROR-KAB-D crashes on rural two-lane highway segments. 
196 

Source: FHWA.

Figure 40. Graph. ROR-KAB-N crashes on rural two-lane highway segments. 
197 

Source: FHWA.

Figure 41. Graph. ROR-KABCO-D crashes on rural two-lane highway segments. 
198 

Source: FHWA.

Figure 42. Graph. ROR-KABCO-N crashes on rural two-lane highway segments. 
199 

Source: FHWA.

Figure 43. Graph. LNDP-KAB-D crashes on rural two-lane highway segments. 
200 

Source: FHWA.

Figure 44. Graph. LNDP-KAB-N crashes on rural two-lane highway segments. 
201 

Source: FHWA.

Figure 45. Graph. LNDP-KABCO-D crashes on rural two-lane highway segments. 
202 

Source: FHWA.

Figure 46. Graph. LNDP-KABCO-N crashes on rural two-lane highway segments. 
203 

Source: FHWA.

Figure 47. Graph. HEO-KAB-D crashes on rural two-lane highway segments. 
204 

Source: FHWA.

Figure 48. Graph. HEO-KAB-N crashes on rural two-lane highway segments. 
205 

Source: FHWA.

Figure 49. Graph. HEO-KABCO-D crashes on rural two-lane highway segments. 
206 

Source: FHWA.

Figure 50. Graph. HEO-KABCO-N crashes on rural two-lane highway segments. 
207 

Source: FHWA.

Figure 51. Graph. ROLL-KAB-D crashes on rural two-lane highway segments. 
208 

Source: FHWA.

Figure 52. Graph. ROLL-KAB-N crashes on rural two-lane highway segments. 
209 

Source: FHWA.

Figure 53. Graph. ROLL-KABCO-D crashes on rural two-lane highway segments. 
210 

Source: FHWA.

Figure 54. Graph. ROLL-KABCO-N crashes on rural two-lane highway segments. 
211 

Source: FHWA. 

Figure 55. Graph. ANG-KAB-D crashes on rural two-lane highway segments. 
212 

Source: FHWA.

Figure 56. Graph. ANG-KABCO-D crashes on rural two-lane highway segments. 
213 

Source: FHWA.

Figure 57. Graph. ANG-KAB-D crashes at four-leg stop-controlled intersections on rural 

two-lane roads. 
214 

Source: FHWA.

Figure 58. Graph. ANG-KAB-N crashes at four-leg stop-controlled intersections on rural 

two-lane roads. 
215 

Source: FHWA.

Figure 59. Graph. ANG-KABCO-D crashes at four-leg stop-controlled intersections on 

rural two-lane roads. 
216 

Source: FHWA.

Figure 60. Graph. ANG-KABCO-N crashes at four-leg stop-controlled intersections on 

rural two-lane roads. 
217 

Source: FHWA.

Figure 61. Graph. ANG-KAB-D crashes at four-leg stop-controlled intersections on urban 

two-lane roads. 
218 

Source: FHWA.

Figure 62. Graph. ANG-KABCO-D crashes at four-leg stop-controlled intersections on 

urban two-lane roads. 
219 

Source: FHWA.

Figure 63. Graph. ANG-KAB-D crashes at four-leg signalized intersections on urban 

multilane divided roads. 
220 

Source: FHWA.

Figure 64. Graph. ANG-KABCO-D crashes at four-leg signalized intersections on urban 

multilane divided roads. 
221 

Source: FHWA.

Figure 65. Graph. ANG-KAB-D crashes at four-leg signalized intersections on urban 

multilane undivided roads. 
222 

Source: FHWA.

Figure 66. Graph. ANG-KABCO-D crashes at four-leg signalized intersections on urban 

multilane undivided roads. 
223 

Source: FHWA.

Figure 67. Graph. ANG-KAB-D crashes at three-leg stop-controlled intersections on rural 

two-lane roads. 
224 

Source: FHWA.

Figure 68. Graph. ANG-KABCO-D crashes at three-leg stop-controlled intersections on 

rural two-lane roads. 
225 

Source: FHWA.

Figure 69. Graph. ANG-KAB-D crashes at four-leg stop-controlled intersections on rural 

multilane divided roads. 
226 

Source: FHWA.

Figure 70. Graph. ANG-KABCO-D crashes at four-leg stop-controlled intersections on 

rural multilane divided roads.
227 

APPENDIX F. SAMPLE PLOTS OF RANDOM FOREST–PREDICTED CRASH 

FREQUENCIES VERSUS PREDICTOR VARIABLES

Figure 71 through figure 89 show sample plots of random forest–predicted crash frequencies 

versus predictor variables for ANG crashes at four-leg stop-controlled intersections on rural 

two-lane roads and ROR crashes at horizontal curves on rural two-lane highway segments. 

ANG-D CRASHES AT FOUR-LEG STOP-CONTROLLED INTERSECTIONS ON 

RURAL TWO-LANE ROADS

Source: FHWA.

Figure 71. Graph. Predicted ANG crash frequency versus cross street AADT. 
228 

Source: FHWA.

Figure 72. Graph. Predicted ANG crash frequency versus mainline AADT. 

Source: FHWA.

Figure 73. Graph. Predicted ANG crash frequency versus lane width. 
229 

Source: FHWA.

Figure 74. Graph. Predicted ANG crash frequency versus average annual maximum 

temperature. 

Source: FHWA.

Figure 75. Graph. Predicted ANG crash frequency versus proportion of people (ages 25+) 

without a high school diploma. 
230 

Source: FHWA.

Figure 76. Graph. Predicted ANG crash frequency versus average annual rainfall. 

Source: FHWA.

Figure 77. Graph. Predicted ANG crash frequency versus proportion of households with 

income >$100,000. 
231 

Source: FHWA.

Figure 78. Graph. Predicted ANG crash frequency versus average annual number of days 

with a minimum temperature of ≤32°F. 

Source: FHWA.

Figure 79. Graph. Predicted ANG crash frequency versus proportion of population 

ages 45–64. 
232 

ROR-D CRASHES AT HORIZONTAL CURVES ON RURAL TWO-LANE HIGHWAY 

SEGMENTS

Source: FHWA.

Figure 80. Graph. Predicted ROR crash frequency per mile versus curve radius. 

Source: FHWA.

Figure 81. Graph. Predicted ROR crash frequency per mile versus AADT. 
233 

Source: FHWA.

Figure 82. Graph. Predicted ROR crash frequency per mile versus percentage of trucks on 

the roadway. 

Source: FHWA.

Figure 83. Graph. Predicted ROR crash frequency per mile versus percent grade. 
234 

Source: FHWA.

Figure 84. Graph. Predicted ROR crash frequency per mile versus average shoulder width. 

Source: FHWA.

Figure 85. Graph. Predicted ROR crash frequency per mile versus proportion of 

population ages 45–64. 
235 

Source: FHWA.

Figure 86. Graph. Predicted ROR crash frequency per mile versus average annual 

minimum temperature. 

Source: FHWA.

Figure 87. Graph. Predicted ROR crash frequency per mile versus average annual number 

of days with a minimum temperature of ≤32°F. 
236 

Source: FHWA.

Figure 88. Graph. Predicted ROR crash frequency per mile versus proportion of people 

(ages 25+) without a high school diploma. 

Source: FHWA.

Figure 89. Graph. Predicted ROR crash frequency per mile versus average annual rainfall.
237 

APPENDIX G. EXPLORATION OF FACTOR ANALYSIS

The research team explored use of a factor analysis to address the correlation between 

socioeconomic characteristics used in the contributing-factor analysis. A study by Li et al. (2016) 

presented a decision tree–based model to forecast rail-transit ridership at the station level 

according to surrounding land-use patterns. To avoid the impact of irrelevant land-use variables, 

Li et al. employed a canonical correlation-analysis method to investigate the relationship between 

all land-use variables and the demand variables in a multivariate framework. As a result, they 

were able to define different factors that were a combination of different land-use variables for 

use in their decision tree.

The research team applied the technique used by Li et al. (2016) to derive factors from a 

combination of various socioeconomic variables for use in the contributing-factor analysis. For 

the purpose of this exploration, the research team used the data from Ohio for the following 

factors: 

• Curves and tangent segments on rural two-lane roads. 

• Four-leg stop-controlled intersections on urban two-lane roads.

• Four-leg signalized intersections on urban multilane undivided roads. 

FACTOR ANALYSIS

The following sections provide results of the factor analysis for Ohio segments and intersections.

Curves and Tangent Segments on Rural Two-Lane Highway Roads 

The factor analysis for curves and tangent segments on rural two-lane highway roads in Ohio 

resulted in three factors for the contributing-factor analysis:

• Factor 1 shows more weight on population/households with less education, low income, 

and less vehicle ownership.

• Factor 2 shows more weight on population/households with less education, high income, 

and less vehicle ownership.

• Factor 3 shows more weight on population/households with more education, high income, 

and more vehicle ownership. 

Table 111 shows the factor-analysis results for curves and tangent segments on rural two-lane 

highway roads in Ohio. 
238 

Table 111. Factor analysis for curves and tangent segments.

Socioeconomic Variable Factor 1 Factor 2 Factor 3

Percentage of population ages 16+ unemployed 0.32420 −0.20406 0.14908

Percentage of population ages 25+ without a high school 

diploma

0.51173 0.64328 −0.34034

Percentage of population ages 25+ with a high school diploma 

but no university degree

0.10912 −0.89584 −0.19936

Percentage of population ages 25+ with a university degree −0.66791 0.26139 0.57936

Percentage of households with income <$50,000 0.87527 −0.24687 0.14978

Percentage of households with income between $50,000 and 

$100,000

−0.52770 0.09029 −0.53644

Percentage of households with income >$100,000 −0.71865 0.26610 0.39014

Percentage of households with 0 vehicles 0.40680 0.54877 −0.30267

Percentage of households with 1 vehicle 0.62705 −0.01826 0.50984

Percentage of households with ≥2 vehicles −0.78055 −0.32344 −0.24351

Four-Leg Stop-Controlled Intersections on Urban Two-Lane Roads 

The factor analysis for four-leg stop-controlled intersections on urban two-lane roads in Ohio 

resulted in two factors for the contributing-factor analysis:

• Factor 1 is associated with the prevalence of population/households with no high school 

education and low income. 

• Factor 2 is associated with the prevalence of population/households with a high school 

education and medium to high income. 

The number of vehicles in a household were not included in the factor analysis due to their 

presence leading to some noninterpretable results. Thus, the research team decided to introduce 

two more factors dealing with household vehicle ownership to the analysis: 

• Factor 3 is associated with the percentage of households with no vehicles. 

• Factor 4 is associated with the percentage of households with any number of vehicles. 

Table 112 shows the factor analysis results for four-leg stop-controlled intersections on urban 

two-lane roads in Ohio. 

Table 112. Factor analysis for four-leg stop-controlled intersections. 

Socioeconomic Variable Factor 1 Factor 2

Percentage of people ages 16+ unemployed 0.58068 −0.30712

Percentage of population ages 25+ without a high school diploma 0.71821 −0.26190

Percentage of population ages 25+ with a high school diploma but 

no university degree

0.48666 0.83686

Percentage of population ages 25+ with a university degree −0.89247 −0.33733

Percentage of households with income <$50,000 0.91095 −0.23481

Percentage of households with income between $50,000 and 

$100,000

−0.41974 0.72009

Percentage of households with income >$100,000 −0.88462 −0.25201
239 

Four-Leg Signalized Intersections on Urban Multilane Undivided Roads

The factor analysis for four-leg signalized intersections on urban multilane undivided roads in 

Ohio resulted in two factors for the contributing-factor analysis:

• Factor 1 is associated with the prevalence of population/households with no high school 

education and low income. 

• Factor 2 is associated with the prevalence of population/households with a high school 

education and medium to high income. 

The number of vehicles in a household were not included in the factor analysis due to their 

presence leading to some noninterpretable results. 

Table 113 shows the factor analysis results for four-leg signalized intersections on urban 

multilane undivided roads in Ohio. 

Table 113. Factor analysis for four-leg signalized intersections. 

Socioeconomic Variable Factor 1 Factor 2

Percentage of people ages 16+ unemployed 0.65462 −0.35922

Percentage of population ages 25+ without a high school diploma 0.79210 −0.10944

Percentage of population ages 25+ with a high school diploma but no university 

degree

0.54037 0.65992

Percentage of population ages 25+ with a university degree −0.90269 −0.34545

Percentage of households with income <$50,000 0.92500 −0.21541

Percentage of households with income between $50,000 and $100,000 −0.50650 0.69851

Percentage of households with income >$100,000 −0.86864 −0.23768

RESULTS FROM THE CONTRIBUTING-FACTOR ANALYSIS

The following sections provide results of the updated contributing-factor analysis for the 

intersection and nonintersection FCFTs based on KAB and KABCO crashes using Ohio data.

ROR Crashes on Rural Two-Lane Highway Roads in Ohio

Horizontal Curves and Highway Tangent Segments—Daytime

Table 114 and table 115 summarize the most influential predictor variables for the expected 

number of ROR-D crashes on rural two-lane horizontal curves and highway tangent segments 

that result in fatality, incapacitating injury, or nonincapacitating injury (ROR-KAB-D) or fatality, 

incapacitating injury, nonincapacitating injury, possible injury, or PDO (ROR-KABCO-D)

according to random forests generated using Ohio data. The random-forest outputs for both crash 

severities are shown in figure 90 and figure 91. 
240 

Table 114. Contributing factors for ROR-KAB-D crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio. 

Variable

Impact on Crash-Frequency 

Predictions

Average AADT Increases

Percent grade Increases

Surface width Decreases

Factor 1 (less education, low income, and less vehicle ownership) Decreases

Curve radius Increases/decreases*

Percentage of population ages 75+ Decreases

Average shoulder width Decreases

Factor 2 (less education, high income, and less vehicle ownership) Increases

Factor 3 (more education, high income, more vehicle ownership) Increases

Percentage of population ages 15–19 Increases

Speed limit Increases

*Increases crash frequency when comparing curves to tangent segments/decreases crash frequency when comparing 

curves (i.e., a larger radius is associated with fewer crashes).

Source: FHWA.

Figure 90. Graph. Output for ROR-KAB-D crashes on rural two-lane horizontal curves 

and highway tangent segments: Ohio. 
241 

Table 115. Contributing factors for ROR-KABCO-D crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio. 

Variable

Impact on Crash-Frequency 

Predictions

Average AADT Increases

Curve radius Increases/decreases* 

Surface width Decreases

Factor 1 (less education, low income, and less vehicle ownership) Decreases

Percentage of population ages 75+ Decreases

Factor 2 (less education, high income, and less vehicle ownership) Increases

Percent grade Increases

Percentage of population ages 15–19 Increases

Factor 3 (more education, high income, and more vehicle ownership) Increases

Speed limit Increases

Average shoulder width Decreases

*Increases crash frequency when comparing curves to tangent segments/decreases crash frequency when comparing 

curves (i.e., a larger radius is associated with fewer crashes).

Source: FHWA.

Figure 91. Graph. Output for ROR-KABCO-D crashes on rural two-lane horizontal curves 

and highway tangent segments: Ohio. 
242 

Horizontal Curves and Highway Tangent Segments—Nighttime

Table 116 and table 117 summarize the most influential predictor variables for the expected 

number of ROR-N crashes on rural two-lane horizontal curves and highway tangent segments 

that result in fatality, incapacitating injury, or nonincapacitating injury (ROR-KAB-N) or fatality, 

incapacitating injury, nonincapacitating injury, possible injury, or PDO (ROR-KABCO-N) 

according to random forests generated using Ohio data. The random-forest outputs for both crash 

severities are shown in figure 92 and figure 93. 

Table 116. Contributing factors for ROR-KAB-N crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio. 

Variable

Impact on Crash-Frequency 

Predictions

Average AADT Increases

Curve radius Increases/decreases* 

Factor 2 (less education, high income, and less vehicle ownership) Increases

Factor 1 (less education, low income, and less vehicle ownership) Decreases

Factor 3 (more education, high income, and more vehicle ownership) Increases

Percentage of population ages 15–19 Increases

Average shoulder width Decreases

Surface width Decreases

Percentage of population ages 75+ Decreases

Percent grade Increases

Speed limit Increases

*Increases crash frequency when comparing curves to tangent segments/decreases crash frequency when comparing 

curves (i.e., a larger radius is associated with fewer crashes).

Source: FHWA.

Figure 92. Graph. Output for ROR-KAB-N crashes on rural two-lane horizontal curves 

and highway tangent segments: Ohio. 
243 

Table 117. Contributing factors for ROR-KABCO-N crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio. 

Variable

Impact on Crash-Frequency 

Predictions

Average AADT Increases

Factor 2 (less education, high income, and less vehicle ownership) Increases

Factor 1 (less education, low income, and less vehicle ownership) Decreases

Average shoulder width Decreases

Factor 3 (more education, high income, and more vehicle ownership) Increases

Percentage of population ages 15–19 Increases

Curve radius Increases/decreases* 

Surface width Decreases

Percentage of population ages 75+ Decreases

Speed limit Increases

Percent grade Increases

*Increases crash frequency when comparing curves to tangent segments/decreases crash frequency when comparing 

curves (i.e., a larger radius is associated with fewer crashes).

Source: FHWA.

Figure 93. Graph. Output for ROR-KABCO-N crashes on rural two-lane horizontal curves 

and highway tangent segments: Ohio. 
244 

Discussion

Based on the analysis, the research team recommends the following roadway contributing factors 

associated with ROR crashes on rural two-lane horizontal curves and highway tangent segments 

in Ohio: 

• Average AADT: an increase in AADT was consistently associated with an increase in the 

frequency of ROR-D and ROR-N crashes.

• Percent grade: an increase in percent grade was consistently associated with an increase 

in the frequency of ROR-D crashes.

• Average shoulder width: an increase in shoulder width was consistently associated with a 

decrease in the frequency of ROR-D and ROR-N crashes (and therefore a decrease in 

shoulder width was consistently associated with an increase in the frequency of ROR 

crashes).

• Curve radius: an increase in curve radius was consistently associated with a decrease in 

the frequency of ROR-D and ROR-N crashes (and therefore a decrease in curve radius 

was consistently associated with an increase in the frequency of ROR crashes).

• Surface width: an increase in surface width was associated with a decrease in the 

frequency of ROR-D and ROR-N crashes on curves and highway tangent segments 

(and therefore a decrease in surface width was consistently associated with an increase in 

the frequency of LNDP crashes).

• Speed: an increase in speed limit was consistently associated with an increase in the 

frequency of ROR-D and ROR-N crashes.

With respect to weather and sociodemographic characteristics, the following factors were 

observed: 

• The percentage of the population ages 15–19 consistently appeared as a factor that 

increases predicted daytime and nighttime crash frequency. 

• The percentage of population ages 75+ consistently appeared as a factor that decreases 

daytime and nighttime predicted crash frequency. 

• Factor 1 (less education, low income, and less vehicle ownership) consistently appeared 

as a factor that decreases daytime and nighttime predicted crash frequency. 

• Factor 2 (less education, high income, and less vehicle ownership) consistently appeared 

as a factor that increase daytime and nighttime predicted crash frequency. 

• Factor 3 (more education, high income, and more vehicle ownership) consistently 

appeared as a factor that increases daytime and nighttime predicted crash frequency. 
245 

LNDP Crashes on Rural Two-Lane Highway Segments in Ohio 

Horizontal Curves and Highway Tangent Segments—Daytime

Table 118 and table 119 summarize the most influential predictor variables for the expected 

number of LNDP-D crashes on rural two-lane horizontal curves and highway tangent segments 

that result in fatality, incapacitating injury, or nonincapacitating injury (LNDP-KAB-D) 

or fatality, incapacitating injury, nonincapacitating injury, possible injury, or PDO

(LNDP-KABCO-D) according to random forests generated using Ohio data. The random-forest 

outputs for both crash severities are shown in figure 94 and figure 95. 

Table 118. Contributing factors for LNDP-KAB-D crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio. 

Variable

Impact on Crash-Frequency 

Predictions

Average AADT Increases

Curve radius Increases/decreases*

Percent grade Increases

Factor 2 (less education, high income, and less vehicle ownership) Increases

Surface width Decreases

Factor 1 (less education, low income, and less vehicle ownership) Decreases

Percentage of population ages 75+ Decreases

Average shoulder width Decreases

Percentage of population ages 15–19 Increases

Factor 3 (more education, high income, and more vehicle ownership) Increases

Speed limit Increases

*Increases crash frequency when comparing curves to tangent segments/decreases crash frequency when comparing 

curves (i.e., a larger radius is associated with fewer crashes).

Source: FHWA.

Figure 94. Graph. Output for LNDP-KAB-D crashes on rural two-lane horizontal curves 

and highway tangent segments: Ohio. 
246 

Table 119. Contributing factors for LNDP-KABCO-D crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio. 

Variable

Impact on Crash-Frequency 

Predictions

Average AADT Increases

Curve radius Increases/decreases* 

Surface width Decreases

Factor 1 (less education, low income, and less vehicle ownership) Decreases

Percentage of population ages 75+ Decreases

Factor 2 (less education, high income, and less vehicle ownership) Increases

Factor 3 (more education, high income, and more vehicle ownership) Increases

Average shoulder width Decreases

Speed limit Increases

Percentage of population ages 15–19 Increases

Percent grade Increases

*Increases crash frequency when comparing curves to tangent segments/decreases crash frequency when comparing 

curves (i.e., a larger radius is associated with fewer crashes).

Source: FHWA.

Figure 95. Graph. Output for LNDP-KABCO-D crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio. 
247 

Horizontal Curves and Highway Tangent Segments—Nighttime

Table 120 and table 121 summarize the most influential predictor variables for the expected 

number of LNDP-N crashes on rural two-lane horizontal curves and highway tangent segments 

that result in fatality, incapacitating injury, or nonincapacitating injury (LNDP-KAB-N) 

or fatality, incapacitating injury, nonincapacitating injury, possible injury, or PDO

(LNDP-KABCO-N) according to random forests generated using Ohio data. The random-forest 

outputs for both crash severities are shown in figure 96 and figure 97. 

Table 120. Contributing factors for LNDP-KAB-N crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio. 

Variable

Impact on Crash-Frequency 

Predictions

Average AADT Increases

Curve radius Increases/decreases* 

Factor 2 (less education, high income, and less vehicle ownership) Increases

Factor 3 (more education, high income, and more vehicle ownership) Increases

Factor 1 (less education, low income, and less vehicle ownership) Decreases

Percentage of population ages 15–19 Increases

Average shoulder width Decreases

Percentage of population ages 75+ Increases

Surface width Decreases

Percent grade Increases

Speed limit Increases

*Increases crash frequency when comparing curves to tangent segments/decreases crash frequency when comparing 

curves (i.e., a larger radius is associated with fewer crashes).

Source: FHWA.

Figure 96. Graph. Output for LNDP-KAB-N crashes on rural two-lane horizontal curves 

and highway tangent segments: Ohio. 
248 

Table 121. Contributing factors for LNDP-KABCO-N crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio. 

Variable

Impact on Crash-Frequency 

Predictions

Average AADT Increases

Factor 2 (less education, high income, and less vehicle ownership) Increases

Factor 1 (less education, low income, and less vehicle ownership) Decreases

Factor 3 (more education, high income, and more vehicle ownership) Increases

Average shoulder width Decreases

Percentage of population ages 15–19 Increases

Surface width Decreases

Percentage of population ages 75+ Decreases

Curve radius Increases/decreases* 

Speed limit Increases

Percent grade Increases

*Increases crash frequency when comparing curves to tangent segments/decreases crash frequency when comparing 

curves (i.e., a larger radius is associated with fewer crashes).

Source: FHWA.

Figure 97. Graph. Output for LNDP-KABCO-N crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio. 
249 

Discussion

Based on the analysis, the research team recommends the following roadway contributing factors 

associated with LNDP crashes on rural two-lane horizontal curves and highway tangent segments 

in Ohio: 

• Average AADT: an increase in AADT was consistently associated with an increase in the 

frequency of LNDP-D and LNDP-N crashes.

• Percent grade: an increase in percent grade was consistently associated with an increase 

in the frequency of LNDP-D and LNDP-N crashes.

• Average shoulder width: an increase in shoulder width was consistently associated with a 

decrease in the frequency of LNDP-D and LNDP-N crashes (and therefore a decrease in 

shoulder width was consistently associated with an increase in the frequency of LNDP 

crashes).

• Curve radius: an increase in curve radius was consistently associated with a decrease in 

the frequency of LNDP-D and LNDP-N crashes (and therefore a decrease in curve radius 

was consistently associated with an increase in the frequency of LNDP crashes).

• Surface width: an increase in surface width was associated with a decrease in the 

frequency of LNDP-D and LNDP-N crashes on curves and tangent segments 

(and therefore a decrease in surface width was consistently associated with an increase in 

the frequency of LNDP crashes).

• Speed: an increase in speed limit was consistently associated with an increase in the 

frequency of LNDP-D and LNDP-N crashes.

With respect to weather and sociodemographic characteristics, the following factors were 

observed: 

• The percentage of the population ages 15–19 consistently appeared as a factor that 

increases daytime and nighttime predicted crash frequency. 

• The percentage of the population ages 75+ consistently appeared as a factor that decreases 

daytime and nighttime predicted crash frequency. 

• Factor 1 (less education, low income, and less vehicle ownership) consistently appeared 

as a factor that decreases daytime and nighttime predicted crash frequency. 

• Factor 2 (less education, high income, and less vehicle ownership) consistently appeared 

as a factor that increases daytime and nighttime predicted crash frequency. 

• Factor 3 (more education, high income, and more vehicle ownership) consistently 

appeared as a factor that increases daytime and nighttime predicted crash frequency. 
250 

HEO Crashes on Rural Two-Lane Highway Segments in Ohio

Horizontal Curves and Highway Tangent Segments—Daytime 

Table 122 and table 123 summarize the most influential predictor variables for the expected 

number of HEO-D crashes on rural two-lane horizontal curves and highway tangent segments 

that result in fatality, incapacitating injury, or nonincapacitating injury (HEO-KAB-D) or fatality, 

incapacitating injury, nonincapacitating injury, possible injury, or PDO (HEO-KABCO-D) 

according to random forests generated using Ohio data. The random-forest outputs for both crash 

severities are shown in figure 98 and figure 99. 

Table 122. Contributing factors for HEO-KAB-D crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio. 

Variable

Impact on Crash-Frequency 

Predictions

Factor 1 (less education, low income, and less vehicle ownership) Decreases

Factor 3 (more education, high income, and more vehicle ownership) Increases

Average AADT Increases

Factor 2 (less education, high income, and less vehicle ownership) Increases

Percentage of population ages 75+ Increases

Percent grade Increases

Average shoulder width Increases**

Percentage of population ages 15–19 Decreases

Curve radius Increases/decreases* 

Surface width Increases

Speed limit Increases

*Increases crash frequency when comparing curves to tangent segments/decreases crash frequency when comparing 

curves (i.e., a larger radius is associated with fewer crashes).

**Counterintuitive finding.

Source: FHWA.

Figure 98. Graph. Output for HEO-KAB-D crashes on rural two-lane horizontal curves 

and highway tangent segments: Ohio. 
251 

Table 123. Contributing factors for HEO-KABCO-D crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio. 

Variable

Impact on Crash-Frequency 

Predictions

Average AADT Increases

Factor 1 (less education, low income, and less vehicle ownership) Decreases

Percent grade Increases

Percentage of population ages 75+ Increases

Factor 3 (more education, high income, and more vehicle ownership) Increases

Factor 2 (less education, high income, and less vehicle ownership) Increases

Percentage of population ages 15–19 Decreases

Average shoulder width Increases**

Curve radius Increases/decreases* 

Surface width Increases

Speed limit Increases

*Increases crash frequency when comparing curves to tangent segments/decreases crash frequency when comparing 

curves (i.e., a larger radius is associated with fewer crashes).

**Counterintuitive finding.

Source: FHWA.

Figure 99. Graph. Output for HEO-KABCO-D crashes on rural two-lane horizontal curves 

and highway tangent segments: Ohio. 
252 

Horizontal Curves and Highway Tangent Segments—Nighttime

Table 124 and table 125 summarize the most influential predictor variables for the expected 

number of HEO-N crashes on rural two-lane horizontal curves and highway tangent segments 

that result in fatality, incapacitating injury, or nonincapacitating injury (HEO-KAB-N) or fatality, 

incapacitating injury, nonincapacitating injury, possible injury, or PDO (HEO-KABCO-N) 

according to random forests generated using Ohio data. The random-forest outputs for both crash 

severities are shown in figure 100 and figure 101. 

Table 124. Contributing factors for HEO-KAB-N crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio. 

Variable

Impact on Crash-Frequency 

Predictions

Percent grade Increases

Average AADT Increases

Curve radius Increases/decreases* 

Percentage of population ages 15–19 Increases

Percentage of population ages 75+ Increases

Factor 2 (less education, high income, and less vehicle ownership) Increases

Surface width Increases

Average shoulder width Increases**

Factor 1 (less education, low income, and less vehicle ownership) Decreases

Factor 3 (more education, high income, and more vehicle ownership) Increases

Speed limit Decreases**

*Increases crash frequency when comparing curves to tangent segments/decreases crash frequency when comparing 

curves (i.e., a larger radius is associated with fewer crashes).

**Counterintuitive finding.

Source: FHWA.

Figure 100. Graph. Output for HEO-KAB-N crashes on rural two-lane horizontal curves 

and highway tangent segments: Ohio. 
253 

Table 125. Contributing factors for HEO-KABCO-N crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio. 

Variable

Impact on Crash-Frequency 

Predictions

Average AADT Increases

Percent grade Increases

Surface width Increases

Curve radius Increases/decreases* 

Factor 2 (less education, high income, and less vehicle ownership) Increases

Percentage of population ages 15–19 Increases

Percentage of population ages 75+ Decreases

Average shoulder width Increases**

Factor 3 (more education, high income, and more vehicle ownership) Increases

Factor 1 (less education, low income, and less vehicle ownership) Decreases

Speed limit Decreases**

*Increases crash frequency when comparing curves to tangent segments/decreases crash frequency when comparing 

curves (i.e., a larger radius is associated with fewer crashes).

**Counterintuitive finding.

Source: FHWA.

Figure 101. Graph. Output for HEO-KABCO-N crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio. 
254 

Discussion

Based on the analysis, the research team recommends the following roadway contributing factors 

associated with HEO crashes on rural two-lane horizontal curves and highway tangent segments 

in Ohio: 

• Average AADT: an increase in AADT was consistently associated with an increase in the 

frequency of HEO-D and HEO-N crashes.

• Percent grade: an increase in percent grade was consistently associated with an increase 

in the frequency of HEO-D and HEO-N crashes.

• Curve radius: an increase in curve radius was consistently associated with a decrease in 

the frequency of HEO-D and HEO-N crashes (and therefore a decrease in curve radius 

was consistently associated with an increase in the frequency of LNDP crashes).

• Surface width: an increase in surface width was associated with an increase in the 

frequency of HEO-D and HEO-N crashes.

There were additional roadway variables that showed relationships with HEO crash frequency, 

but they were not recommended as contributing factors due inconsistent results: 

• Average shoulder width: an increase in shoulder width was consistently associated with 

an increase in the frequency of HEO-D and HEO-N crashes (this is a counterintuitive 

finding, as an increase in shoulder width is generally associated with a decrease in 

crashes).

• Speed: an increase in speed limit was associated with an increase in the frequency of 

HEO-D crashes (during the nighttime, an increase in speed limit was associated with a 

decrease in the frequency of HEO crashes).

With respect to weather and sociodemographic characteristics, the following factors were 

observed: 

• The percentage of the population ages 15–19 appeared as a factor that increases the 

predicted crash frequency of nighttime crashes and a factor that decreases the predicted 

crash frequency of daytime crashes.

• The percentage of the population ages 75+ appeared as a factor that increases the 

predicted crash frequency of HEO-KAB-D, HEO-KAB-N, and HEO-KABCO-D crashes. 

It also appeared as a factor that decreases the predicted crash frequency of 

HEO-KABCO-N crashes.

• Factor 1 (less education, low income, and less vehicle ownership) consistently appeared 

as a factor that decreases daytime and nighttime predicted crash frequency. 

• Factor 2 (less education, high income, and less vehicle ownership) consistently appeared 

as a factor that increases daytime and nighttime predicted crash frequency. 

• Factor 3 (more education, high income, and more vehicle ownership) consistently 

appeared as a factor that increases daytime and nighttime predicted crash frequency. 
255 

ROLL Crashes on Rural Two-Lane Highway Segments in Ohio

Horizontal Curves and Highway Tangent Segments—Daytime

Table 126 and table 127 summarize the most influential predictor variables for the expected 

number of ROLL-D crashes on rural two-lane horizontal curves and highway tangent segments 

that result in fatality, incapacitating injury, or nonincapacitating injury (ROLL-KAB-D) 

or fatality, incapacitating injury, nonincapacitating injury, possible injury, or PDO

(ROLL-KABCO-D) according to random forests generated using Ohio data. The random-forest 

outputs for both crash severities are shown in figure 102 and figure 103. 

Table 126. Contributing factors for ROLL-KAB-D crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio. 

Variable

Impact on Crash-Frequency 

Predictions

Curve radius Increases/decreases* 

Average AADT Decreases**

Factor 1 (less education, low income, and less vehicle ownership) Increases

Factor 3 (more education, high income, and more vehicle ownership) Decreases

Factor 2 (less education, high income, and less vehicle ownership) Increases

Surface width Decreases

Percentage of population ages 15–19 Increases

Percentage of population ages 75+ Decreases

Average shoulder width Increases**

Speed limit Increases

Percent grade Increases

*Increases crash frequency when comparing curves to tangent segments/decreases crash frequency when comparing 

curves (i.e., a larger radius is associated with fewer crashes).

**Counterintuitive finding.

Source: FHWA.

Figure 102. Graph. Output for ROLL-KAB-D crashes on rural two-lane horizontal curves 

and highway tangent segments: Ohio. 
256 

Table 127. Contributing factors for ROLL-KABCO-D crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio. 

Variable

Impact on Crash-Frequency 

Predictions

Curve radius Increases/decreases* 

Factor 1 (less education, low income, and less vehicle ownership) Decreases

Factor 3 (more education, high income, and more vehicle ownership) Increases

Factor 2 (less education, high income, and less vehicle ownership) Increases

Surface width Decreases

Average shoulder width Decreases

Percentage of population ages 75+ Decreases

Average AADT Decreases**

Percentage of population ages 15–19 Increases

Percent grade Increases

Speed limit Increases

*Increases crash frequency when comparing curves to tangent segments/decreases crash frequency when comparing 

curves (i.e., a larger radius is associated with fewer crashes).

**Counterintuitive finding.

Source: FHWA.

Figure 103. Graph. Output for ROLL-KABCO-D crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio. 
257 

Horizontal Curves and Highway Tangent Segments—Nighttime 

Table 128 and table 129 summarize the most influential predictor variables for the expected 

number of ROLL-N crashes on rural two-lane horizontal curves and highway tangent segments

that result in fatality, incapacitating injury, or nonincapacitating injury (ROLL-KAB-N) 

or fatality, incapacitating injury, nonincapacitating injury, possible injury, or PDO

(ROLL-KABCO-N) according to random forests generated using Ohio data. The random-forest 

outputs for both crash severities are shown in figure 104 and figure 105. 

Table 128. Contributing factors for ROLL-KAB-N crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio. 

Variable

Impact on Crash-Frequency 

Predictions

Curve radius Increases/decreases* 

Factor 2 (less education, high income, and less vehicle ownership) Decreases

Factor 1 (less education, low income, and less vehicle ownership) Decreases

Percentage of population ages 75+ Decreases

Average AADT Increases

Factor 3 (more education, high income, and more vehicle ownership) Increases

Percent grade Increases

Surface width Increases

Percentage of population ages 15–19 Increases

Average shoulder width Increases**

Speed limit Increases

*Increases crash frequency when comparing curves to tangent segments/decreases crash frequency when comparing 

curves (i.e., a larger radius is associated with fewer crashes).

**Counterintuitive finding.

Source: FHWA.

Figure 104. Graph. Output for ROLL-KAB-N crashes on rural two-lane horizontal curves 

and highway tangent segments: Ohio. 
258 

Table 129. Contributing factors for ROLL-KABCO-N crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio. 

Variable

Impact on Crash-Frequency 

Predictions

Average AADT Increases

Surface width Decreases

Factor 1 (less education, low income, and less vehicle ownership) Decreases

Factor 2 (less education, high income, and less vehicle ownership) Increases

Curve radius Increases/decreases* 

Percent grade Increases

Factor 3 (more education, high income, and more vehicle ownership) Increases

Percentage of population ages 75+ Increases

Percentage of population ages 15–19 Decreases

Speed limit Increases

Average shoulder width Decreases

*Increases crash frequency when comparing curves to tangent segments/decreases crash frequency when comparing 

curves (i.e., a larger radius is associated with fewer crashes).

Source: FHWA.

Figure 105. Graph. Output for ROLL-KABCO-N crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio. 
259 

Discussion

Based on the analysis, the research team recommends the following roadway contributing factors 

associated with ROLL crashes on rural two-lane horizontal curves and highway tangent segments

in Ohio: 

• Percent grade: an increase in percent grade was consistently associated with an increase 

in the frequency of ROLL-D and ROLL-N crashes. 

• Curve radius: an increase in curve radius was consistently associated with a decrease in 

the frequency of ROLL-D and ROLL-N crashes (and therefore a decrease in curve radius 

was consistently associated with an increase in the frequency of LNDP crashes).

• Speed: an increase in speed limit was associated with an increase in the frequency of 

ROLL-D and ROLL-N crashes.

There were additional roadway variables that showed relationships with HEO crash frequency, 

but they were not recommended as contributing factors due to inconsistent results: 

• Average AADT: an increase in AADT was associated with an increase in the frequency of 

ROLL-D and ROLL-N crashes (during the daytime, an increase in AADT was associated 

with a decrease in the frequency of ROLL crashes).

• Surface width: an increase in surface width was associated with a decrease in the 

frequency of ROLL-KAB-D, ROLL-KACO-D, and ROLL-KABCO-N (and therefore a 

decrease in surface width was associated with an increase in the frequency of these 

crashes).

• Average shoulder width: an increase in shoulder width was consistently associated with 

an increase in the frequency of ROLL-KAB-D and ROLL-KAB-N crashes (this is a 

counterintuitive finding, as an increase in shoulder width is generally associated with a 

decrease in crashes).

With respect to weather and sociodemographic characteristics, the following factors were 

observed: 

• The percentage of the population ages 15–19 appeared as a factor that increases the 

predicted crash frequency of ROLL-KAB-D, ROLL-KABCO-D, and ROLL-KAB-N 

crashes. It also appeared as a factor that decreases the predicted crash frequency of 

HEO-KABCO-N crashes.

• The percentage of the population ages 75+ appeared as a factor that decreases the 

predicted crash frequency of ROLL-KAB-D, ROLL-KAB-N, and ROLL-KABCO-D 

crashes. It also appeared as a factor that decreases the predicted crash frequency of 

ROLL-KABCO-N crashes.

• Factor 1 (less education, low income, and less vehicle ownership) consistently appeared 

as a factor that decreases the predicted crash frequency of ROLL-KABCO-D, 

ROLL-KAB-N, and ROLL-KABCO-N crashes. It also appeared as a factor that increases 

the predicted crash frequency of ROLL-KAB-D crashes.

• Factor 2 (less education, high income, and less vehicle ownership) appeared as a factor 

that increases the predicted crash frequency of ROLL-KAB-D, ROLL-KABCO-D, and 
260 

ROLL-KABCO-N crashes. It also appeared as a factor that decreases the predicted crash 

frequency of ROLL-KAB-N crashes.

• Factor 3 (more education, high income, and more vehicle ownership) appeared as a factor 

that increases the predicted crash frequency of ROLL-KABCO-D, ROLL-KAB-N, and 

ROLL-KABCO-N crashes. It also appeared as a factor that decreases the predicted crash 

frequency of ROLL-KAB-D crashes.

ANG Crashes on Rural Two-Lane Highway Segments in Ohio

Horizontal Curves and Highway Tangent Segments—Daytime 

Table 130 and table 131 summarize the most influential predictor variables for the expected 

number of ANG-D crashes on rural two-lane horizontal curves and highway tangent segments 

that result in fatality, incapacitating injury, or nonincapacitating injury (ANG-KAB-D) or fatality, 

incapacitating injury, nonincapacitating injury, possible injury, or PDO (ANG-KABCO-D) 

according to random forests generated using Ohio data. The random-forest outputs for both crash 

severities are shown in figure 106 and figure 107. 

Table 130. Contributing factors for ANG-KAB-D crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio. 

Variable

Impact on Crash-Frequency 

Predictions

Percent grade Decreases**

Surface width Increases

Average AADT Increases

Factor 1 (less education, low income, and less vehicle ownership) Decreases

Factor 2 (less education, high income, and less vehicle ownership) Increases

Percentage of population ages 15–19 Increases

Percentage of population ages 75+ Decreases

Factor 3 (more education, high income, and more vehicle ownership) Increases

Average shoulder width Increases**

Speed limit Decreases**

**Counterintuitive finding.
261 

Source: FHWA.

Figure 106. Graph. Output for ANG-KAB-D crashes on rural two-lane horizontal curves 

and highway tangent segments: Ohio. 

Table 131. Contributing factors for ANG-KABCO-D crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio. 

Variable

Impact on Crash-Frequency 

Predictions

Factor 2 (less education, high income, and less vehicle ownership) Increases

Average AADT Increases

Average shoulder width Increases**

Factor 3 (more education, high income, and more vehicle ownership) Increases

Percentage of population ages 15–19 Increases

Factor 1 (less education, low income, and less vehicle ownership) Decreases

Percent grade Decreases**

Surface width Increases

Percentage of population ages 75+ Increases

Curve radius Increases/decreases* 

*Increases crash frequency when comparing curves to tangent segments/decreases crash frequency when comparing 

curves (i.e., a larger radius is associated with fewer crashes).

**Counterintuitive finding.
262 

Source: FHWA.

Figure 107. Graph. Output for ANG-KABCO-D crashes on rural two-lane horizontal 

curves and highway tangent segments: Ohio. 

Discussion

Based on the analysis, the research team recommends the following roadway contributing factors 

associated with ANG crashes on rural two-lane horizontal curves and highway tangent segments 

in Ohio: 

• Average AADT: an increase in AADT was consistently associated with an increase in the 

frequency of ANG-D crashes.

• Curve radius: an increase in curve radius was associated with a decrease in the frequency 

of ANG-KABCO-D crashes (and therefore a decrease in curve radius was consistently 

associated with an increase in the frequency of ANG-KABCO-D crashes).

• Surface width: an increase in surface width was consistently associated with an increase 

in the frequency of ANG-D crashes.

There were additional roadway variables that showed relationships with HEO crash frequency,

but they were not recommended as contributing factors due to inconsistent results: 

• Percent grade: an increase in percent grade was consistently associated with a decrease in 

the frequency of ANG-D crashes (this is a counterintuitive finding, as an increase in 

percent grade is generally associated with an increase in crashes). 

• Average shoulder width: an increase in shoulder width was consistently associated with 

an increase in the frequency of ANG-D crashes (this is a counterintuitive finding, as an 

increase in shoulder width is generally associated with a decrease in crashes).

• Speed: an increase in speed limit was consistently associated with a decrease in the 

frequency of ANG-D crashes.
263 

With respect to weather and sociodemographic characteristics, the following factors were 

observed: 

• The percentage of the population ages 15–19 consistently appeared as a factor that 

increases predicted crash frequency. 

• The percentage of the population ages 75+ appeared as a factor that decreases the 

predicted crash frequency of ANG-KAB-D crashes and a factor that increases the 

predicted crash frequency of ANG-KABCO-D crashes. 

• Factor 1 (less education, low income, and less vehicle ownership) consistently appeared 

as a factor that decreases predicted crash frequency. 

• Factor 2 (less education, high income, and less vehicle ownership) consistently appeared 

as a factor that increases predicted crash frequency. 

• Factor 3 (more education, high income, and more vehicle ownership) consistently 

appeared as a factor that increases predicted crash frequency. 

ANG Crashes at Four-Leg Stop-Controlled Intersections on Urban Two-Lane Roads in 

Ohio

Daytime

Table 132 and table 133 summarize the most influential predictor variables for the expected 

number of ANG-D crashes at four-leg stop-controlled intersections (with stop control on the 

minor road) on urban, two-lane roads that result in fatality, incapacitating injury, or 

nonincapacitating injury (ANG-KAB-D) or fatality, incapacitating injury, nonincapacitating 

injury, possible injury, or PDO (ANG-KABCO-D) according to random forests generated using 

Ohio data. The random-forest outputs for both crash severities are shown in figure 108 and 

figure 109. 

Table 132. Contributing factors for ANG-KAB-D crashes at four-leg stop-controlled

intersections on urban two-lane roads: Ohio. 

Variable

Impact on Crash-Frequency 

Predictions

Speed limit Increases

Factor 1 (less education, low income, and less vehicle ownership) Decreases

Cross street AADT Increases

Factor 2 (less education, high income, and less vehicle ownership) Decreases

Percentage of households with any number of vehicles Increases

Percentage of households with 0 vehicles Decreases

Percentage of population ages 75+ Decreases

Annual average number of days with a minimum temperature of ≤32°F Increases

Mainline AADT Decreases**

Average annual rainfall totals Decreases

Percentage of population ages 15–19 Decreases

**Counterintuitive finding.
264 

Source: FHWA.

Figure 108. Graph. Output for ANG-KAB-D crashes at four-leg stop-controlled

intersections on urban two-lane roads: Ohio. 

Table 133. Contributing factors for ANG-KABCO-D crashes at four-leg stop-controlled

intersections on urban two-lane roads: Ohio. 

Variable

Impact on Crash-Frequency 

Predictions

Cross street AADT Increases

Speed limit Increases

Mainline AADT Increases

Factor 1 (no education and low income) Decreases

Percentage of households with 0 vehicles Increases**

Percentage of households with any number of vehicles Decreases**

Percentage of population ages 75+ Decreases

Average annual rainfall totals Decreases

Annual average number of days with a minimum temperature of ≤32°F Increases

Factor 2 (diploma education and medium income) Decreases

Average annual snowfall totals Increases

Average annual winter minimum temperature Decreases**

Percentage of population ages 15–19 Decreases

**Counterintuitive finding.
265 

Source: FHWA.

Figure 109. Graph. Output for ANG-KABCO-D crashes at four-leg stop-controlled

intersections on urban two-lane roads: Ohio. 

Discussion

Based on the analysis, the research team recommends the following roadway contributing factors 

associated with ANG crashes at four-leg stop-controlled intersections (with stop control on the 

minor road) on urban two-lane roads in Ohio: 

• Cross Street AADT: an increase in cross street AADT was associated with an increase in 

the frequency of ANG-D crashes at four-leg stop-controlled intersections on urban 

two-lane roads.

• Speed: an increase in speed was consistently associated with an increase in the frequency 

of ANG-D crashes at four-leg stop-controlled intersections on urban two-lane roads. 

There were additional roadway variables that showed relationships with HEO crash frequency, 

but they were not recommended as contributing factors due to inconsistent results: 

• Mainline AADT: an increase in mainline AADT was associated with an increase in the 

frequency of ANG-KABCO-D crashes at four-leg stop-controlled intersections on urban 

two-lane roads. 
266 

With respect to weather and sociodemographic characteristics, the following factors were 

observed: 

• The percentage of the population ages 15–19 consistently appeared as a factor that 

decreases predicted crash frequency. 

• The percentage of the population ages 75+ consistently appeared as a factor that decreases 

predicted crash frequency. 

• Factor 1 (no education and low income) consistently appeared as a factor that decreases 

predicted crash frequency. 

• Factor 2 (diploma education and medium income) consistently appeared as a factor that 

decreases predicted crash frequency. 

• The percentage of households with no vehicles appeared as a factor that increases the 

predicted crash frequency of ANG-KAB-D crashes and as a factor that decreases the 

predicted crash frequency of ANG-KABCO-D crashes.

• The percentage of households with any number of vehicles appeared as a factor that 

increases the predicted crash frequency of ANG-KAB-D crashes and as a factor that 

decreases the predicted crash frequency of ANG-KABCO-D crashes.

• Average annual rainfall totals consistently appeared as a factor that decreases predicted 

crash frequency. 

• Average annual snowfall totals appeared as a factor that increases the predicted crash 

frequency of ANG-KABCO-D crashes.

• Average annual number of days with a minimum temperature of ≤32°F appeared 

consistently as a factor that increases predicted crash frequency. 

ANG Crashes at Four-Leg Stop-Controlled Intersections on Urban Two-Lane Roads in 

Ohio

Daytime

Table 134 and table 135 summarize the most influential predictor variables for the expected 

number of ANG-D crashes at four-leg signalized intersections on urban multilane undivided 

roads that result in fatality, incapacitating injury, or nonincapacitating injury (ANG-KAB-D) 

or fatality, incapacitating injury, nonincapacitating injury, possible injury, or PDO

(ANG-KABCO-D) according to random forests generated using Ohio data. The random-forest 

outputs for both crash severities are shown in figure 110 and figure 111. 
267 

Table 134. Contributing factors for ANG-KAB-D crashes at four-leg signalized 

intersections on urban multilane undivided roads: Ohio. 

Variable

Impact on Crash-Frequency 

Predictions

Mainline AADT Increases

Cross street AADT Increases

Factor 1 (no education and low income) Increases**

Speed limit Increases

Annual average number of days with a minimum temperature of ≤32°F Increases

Percentage of households with any number of vehicles Decreases**

Percentage of households with 0 vehicles Increases**

Average annual winter minimum temperature Decreases**

Factor 2 (diploma education and medium income) Decreases**

Average annual snowfall totals Decreases**

Average annual minimum temperature Increases

Lane width Decreases

**Counterintuitive finding.

Source: FHWA.

Figure 110. Graph. Output for ANG-KAB-D crashes at four-leg signalized intersections on 

urban multilane undivided roads: Ohio. 
268 

Table 135. Contributing factors for ANG-KABCO-D crashes at four-leg signalized 

intersections on urban multilane undivided roads: Ohio. 

Variable

Impact on Crash-Frequency 

Predictions

Cross street AADT Increases

Mainline AADT Increases

Annual average number of days with a minimum temperature of ≤32°F Decreases**

Annual average winter minimum temperature Increases

Percentage of households with 0 vehicles Increases**

Percentage of households with any number of vehicles Decreases**

Factor 1 (no education and low income) Decreases

Average annual snowfall totals Decreases**

Speed limit Increases

Average annual maximum temperature Increases

Number of channelized left-turn lanes Increases

Average annual minimum temperature Increases

Factor 2 (diploma education and medium income) Decreases**

**Counterintuitive finding.

Source: FHWA.

Figure 111. Graph. Output for ANG-KABCO-D crashes at four-leg signalized intersections 

on urban multilane undivided roads: Ohio. 
269 

Discussion

Based on the analysis, the research team recommends the following roadway contributing factors 

associated with ANG crashes at four-leg signalized intersections on urban multilane undivided 

roads in Ohio: 

• Mainline AADT: an increase in mainline AADT was associated with an increase in the 

frequency of ANG-D crashes at four-leg signalized intersections on urban multilane

undivided roads. 

• Cross Street AADT: an increase in cross street AADT was associated with an increase in 

the frequency of ANG-D crashes at four-leg signalized intersections on urban multilane 

undivided roads. 

• Speed: an increase in speed was consistently associated with an increase in the frequency 

of ANG-D crashes at four-leg signalized intersections on urban multilane undivided 

roads. 

There were additional roadway variables that showed relationships with HEO crash frequency, 

but they were not recommended as contributing factors due to inconsistent results: 

• Lane width: an increase in lane width was associated with a decrease in the frequency of 

ANG-KAB-D crashes at four-leg stop signalized intersections on urban multilane

undivided roads. 

• Number of approaches with left turn lanes: an increase in the number of approaches with 

left-turn lanes at an intersection was associated with an increase in the frequency of 

ANG-KABCO-D crashes at four-leg stop signalized intersections on urban multilane 

undivided roads. 

With respect to weather and sociodemographic characteristics, the following factors were 

observed: 

• Factor 1 (no education and low income) appeared as a factor that decreases the predicted 

crash frequency of ANG-KABCO-D crashes and a factor that increases the predicted 

crash frequency of ANG-KAB-D crashes. 

• Factor 2 (diploma education and medium income) consistently appeared as a factor that 

decreases predicted crash frequency. 

• The percentage of households with no vehicles consistently appeared as a factor that 

increases predicted crash frequency. 

• The percentage of households with any number of vehicles consistently appeared as a 

factor that decreases predicted crash frequency. 

• Average annual snowfall totals consistently appeared as a factor that decreases predicted 

crash frequency. 

• Average annual minimum temperature consistently appeared as a factor that increases 

predicted crash frequency. 
270 

SUMMARY OF RESULTS FROM THE CONTRIBUTING-FACTOR ANALYSIS

The previous sections provided the updated analysis for Ohio data using factors to reduce the 

number of socioeconomic variables and to account for high correlation between them. The 

updated analysis showed good results with respect to nonintersection FCFTs, but the results for 

the intersections FCFTs were not as expected.

Intersection FCFTs

The contributing-factor analysis for urban intersections produced consistent results with AADT 

and speed limit showing as roadway contributing factors. The analysis, however, did not show 

consistent results with respect to socioeconomic variables. The percentage of households with no 

vehicles came up as a factor that increases predicted crash frequency, whereas the percentage of 

households with any number of vehicles came up as factor that decreases predicted crash 

frequency. Similarly, Factor 1 (no education and low income) showed up as a factor that 

increases predicted crash frequency at urban signalized intersections. Weather variables also 

showed some counterintuitive results; for example, the average annual winter minimum 

temperature and average annual snowfall totals showed up as factors that decrease crash 

frequencies for urban signalized intersections when, for the same site type, the average annual 

minimum temperature showed up as a factor that increases predicted crash frequency. 

Nonintersection FCFTs

The contributing-factor analysis for rural highway segments produced consistent results with 

AADT, percent grade, curve radius, and average shoulder width showing as roadway contributing 

factors. In almost all cases, the percentage of the population ages 15–19 showed up as a factor 

that increases predicted crash frequency, while the percentage of the population ages 75+ showed 

up as factor that decreases predicted crash frequency. For socioeconomic factors, in almost all 

cases, Factor 1 (less education, low income, and less vehicle ownership) showed up as a factor 

that decreases predictive crash frequency, whereas Factor 2 (less education, high income, and less 

vehicle ownership) and Factor 3 (more education, high income, and more vehicle ownership) 

showed up as factors that increase predicted crash frequency.
271 

APPENDIX H. OBSERVING PREDICTION MSEs TO EXPLORE CONTRIBUTIONS 

OF SOCIOECONOMIC VARIABLES

To further explore the contributions of socioeconomic variables to the random-forest analysis, the 

research team calculated and compared prediction MSEs using random-forest predictions from 

analyses using data with and without socioeconomic variables. For these comparisons, the 

research team used daytime crashes on horizontal curves and tangent segments on rural two-lane 

roads in Ohio. Random forests were estimated using only roadway variables and compared to the 

models reported in appendix G (the roadway-only random-forest outputs are provided in 

figure 112 and figure 113). 

MSE values for predictions from the roadway-only random forests were compared to MSE 

values for predictions from random forests presented in appendix G, which included 

socioeconomic variables as factors to account for the high correlation between the different 

socioeconomic variables. 

Table 136 provides a comparison of the MSEs for the two analyses.

Table 136. Comparison of MSEs for analyses using data with and without socioeconomic 

variables. 

Crash-Severity Type

Predictions with Socioeconomic 

Variables

Predictions without Socioeconomic 

Variables

ROR-KAB-D 7.19 9.39

ROR-KABCO-D 30.99 41.65

LNDP-KAB-D 9.11 12.18

LNDP-KABCO-D 35.05 47.80

ROLL-KAB-D 1.47 1.85

ROLL-KABCO-D 2.36 3.04

HEO-KAB-D 0.23 0.29

HEO-KABCO-D 0.29 0.37

ANG-KAB-D 0.27 0.31

ANG-KABCO-D 1.37 1.77

When comparing MSEs, lower values are better as they suggest that predicted values were closer 

to observed values. It can be seen from table 136 that MSEs for predictions using data with 

socioeconomic variables are consistently 20- to 30-percent lower than MSEs for predictions 

using data without socioeconomic variables. 

Based on this analysis, inclusion of socioeconomic variables in analyses leads to improved crash 

predictions. 
272 

Source: FHWA.

Figure 112. Graph. Output for ROR-KAB-D crashes on rural two-lane horizontal curves 

and tangent segments: Ohio. 

Source: FHWA.

Figure 113. Graph. Output for ROR-KABCO-D crashes on rural two-lane horizontal 

curves and tangent segments: Ohio. 
273 

Source: FHWA.

Figure 114. Graph. Output for LNDP-KAB-D crashes on rural two-lane horizontal curves 

and tangent segments: Ohio. 

Source: FHWA.

Figure 115. Graph. Output for LNDP-KABCO-D crashes on rural two-lane horizontal 

curves and tangent segments: Ohio. 
274 

Source: FHWA.

Figure 116. Graph. Output for ROLL-KAB-D crashes on rural two-lane horizontal curves 

and tangent segments: Ohio. 

Source: FHWA.

Figure 117. Graph. Output for ROLL-KABCO-D crashes on rural two-lane horizontal 

curves and tangent segments: Ohio. 
275 

Source: FHWA.

Figure 118. Graph. Output for HEO-KAB-D crashes on rural two-lane horizontal curves 

and tangent segments: Ohio. 

Source: FHWA.

Figure 119. Graph. Output for HEO-KABCO-D crashes on rural two-lane horizontal 

curves and tangent segments: Ohio. 
276 

Source: FHWA.

Figure 120. Graph. Output for ANG-KAB-D crashes on rural two-lane horizontal curves 

and tangent segments: Ohio. 

Source: FHWA.

Figure 121. Graph. Output for ANG-KABCO-D crashes on rural two-lane horizontal 

curves and tangent segments: Ohio.
277 

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