DRIVER DISTRACTION IN 

COMMERCIAL VEHICLE OPERATIONS 

September 2009
FOREWORD 

The Federal Motor Carrier Safety Administration awarded a contract to investigate driver 

distraction in commercial motor vehicle drivers. The purpose of this study was to characterize 

driver inattention in safety-critical and baseline events and to determine the relative risk of 

driving while distracted. The purpose of this report was to document the method, results, and 

conclusions from this study. 

NOTICE 

This document is disseminated under the sponsorship of the Department of Transportation in the 

interest of information exchange. The United States Government assumes no liability for its 

contents or use thereof. 

This report does not constitute a standard, specification, or regulation. 

The United States Government does not endorse products or manufacturers. Trade or 

manufacturers’ names appear herein only because they are considered essential to the objective 

of this document. 
Technical Report Documentation Page—Form DOT F 1700.7 (8-72) 

1. Report No. 

FMCSA-RRR-09-042 

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

4. Title and Subtitle 

DRIVER DISTRACTION IN COMMERCIAL 

OPERATIONS 

VEHICLE 

5. Report Date 

September 2009 

6. Performing Organization Code 

7. Author(s) 

Rebecca L. Olson, Richard 

Joseph Bocanegra 

J. Hanowski, Jeffrey S. Hickman, and 

8. Performing Organization Report No. 

9. Performing Organization Name and Address 

Center for Truck and Bus Safety 

Virginia Tech Transportation Institute 

3500 Transportation Research Plaza (0536) 

Blacksburg, VA 24061 

10. Work Unit No. (TRAIS) 

11. Contract or Grant No. 

DTMC75-07-D-00006 

12. Sponsoring Agency Name and Address 

U.S. Department of Transportation 

Federal Motor Carrier Safety Administration 

1200 New Jersey Avenue, SE 

Washington, DC 20590 

13. Type of Report and Period Covered 

Final Report 

July 2007–July 2009 

14. Sponsoring Agency Code 

FMCSA 

15. Supplementary Notes 

This program was sponsored by the Federal Motor Carrier Safety Administration. 

Martin Walker, PhD, and Theresa Hallquist served as Task Order Managers. 

16. Abstract 

This study investigated the impact of driver distraction in commercial motor vehicle (CMV) operations. Data from 

two earlier naturalistic studies were combined to create a data set of 203 CMV drivers and 55 trucks from seven 

trucking fleets operating at 16 locations. A total of 4,452 safety-critical events (i.e., crashes, near-crashes, crashrelevant conflicts, and unintentional lane deviations) were identified in the data set, along with 19,888 baseline 

(uneventful, routine driving) epochs. Data analyses included odds ratio calculations and population attributable 

risk estimates. Key findings were that drivers were engaged in non-driving related tasks in 71 percent of crashes, 46 

percent of near-crashes, and 60 percent of all safety-critical events. Also, performing highly complex tasks while 

driving lead to a significant increase in risk. Eye glance analyses examined driver eye location while performing 

tasks while operating a CMV. Tasks associated with high odds ratios (increased risk) were also associated with high 

eyes off forward road times. This suggests that tasks that draw the driver’s visual attention away from the forward 

roadway should be minimized or avoided. Based on the results of the analyses, a number of recommendations are 

presented that may help address the issue of driver distraction in CMV operations. 

17. Key Words 

CMV, commercial motor vehicle, distraction, 

naturalistic data, safety-critical events 

18. Distribution Statement 

No restrictions. 

19. Security Classif. (of this report) 

Unclassified 

20. Security Classif. (of this page) 

Unclassified 

21. No. of Pages 

285 

22. Price 

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized. 
SI* (MODERN METRIC) CONVERSION FACTORS 

Table of 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 yards 0.836 square meters m2 

ac acres 0.405 hectares ha 

mi2 square miles 2.59 square kilometers km2 

VOLUME Note: Volumes greater than 

1000 L shall be shown in m3 

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 

MASS 

oz ounces 28.35 grams g 

lb pounds 0.454 kilograms kg 

T short tons (2000 lb) 0.907 megagrams (or “metric ton”) Mg (or “t”) 

TEMPERATURE Temperature is in exact degrees 

°F Fahrenheit 5 × (F-32) ÷ 9 

or (F-32) ÷ 1.8 

Celsius °C 

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 

Table of 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 (2000 lb) T 

TEMPERATURE Temperature is in 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 & Pressure or Stress 

N newtons 0.225 poundforce lbf 

kPa kilopascals 0.145 poundforce per square inch lbf/in2 

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

(Revised March 2003, Section 508-accessible version September 2009)

ii 
TABLE OF CONTENTS 

LIST OF ACRONYMS .............................................................................................................XV

EXECUTIVE SUMMARY ....................................................................................................XVII

1. INTRODUCTION.................................................................................................................1

1.1 PROJECT OVERVIEW ................................................................................................1 

1.1.1 Overview of Commercial Motor Vehicle Crash Statistics ............................... 1 

1.2 DRIVER ERROR ..........................................................................................................2 

1.2.1 Driver Distraction as a Contributing Factor in Critical Events......................... 2 

2. REVIEW OF THE LITERATURE ....................................................................................5

2.1 DEFINING DRIVER DISTRACTION .........................................................................5 

2.2 KEY DISTRACTION STUDIES ..................................................................................8 

2.3 NATURALISTIC DATA COLLECTION ..................................................................10 

2.4 DISTRACTION-RELA TED STUDIES USING NATURALISTIC DATA 

COLLECTION IN HEAVY VEHICLES....................................................................12 

2.5 SUMMARY .................................................................................................................15 

3. OVERVIEW OF DDWS FOT AND NTDS......................................................................17

3.1 DROWSY DRIVER WARNING SYSTEM FIELD OPERATIONAL TEST ...........17 

3.1.1 Project Overview ............................................................................................ 17 

3.1.2 Experim ental Design....................................................................................... 17 

3.1.3 Participants and Setting................................................................................... 17 

3.1.4 Data Collection Process .................................................................................. 18 

3.1.5 Data Reduction................................................................................................ 20 

3.2 NATURALISTIC TRUCK DRIVING STUDY..........................................................21 

3.2.1 Project Overview ............................................................................................ 21 

3.2.2 Experim ental Design....................................................................................... 21 

3.2.3 Participants and Setting................................................................................... 21 

3.2.4 Data Collection Process .................................................................................. 21 

3.2.5 Data Reduction................................................................................................ 22 

4. DATA REDUCTION..........................................................................................................25

4.1 CHARACTERIZE SAFETY-CRITICAL EVENTS...................................................25 

4.1.1 Running the Event Trigger Program............................................................... 25 

4.1.2 Checking the Validity of the Additional Triggered Events ............................ 27 

4.1.3 Applying the Data Directory to the Validated Events .................................... 28 

iii 
4.2 CHARACTERIZE 20,000 BASELINE EPOCHS ......................................................28 

4.2.1 Quality Control ............................................................................................... 29 

4.3 EYE GLANCE REDUCTION ....................................................................................30 

4.4 DRIVER TASKS AND INATTENTION ...................................................................31 

4.5 RESEARCH QUESTIONS .........................................................................................32 

5. DATA ANALYSIS AND RESULTS .................................................................................33

5.1 DATA ANALYSIS CAVEATS ..................................................................................33 

5.2 DATA ANALYSIS METHODS .................................................................................34 

5.2.1 Odds Ratios..................................................................................................... 34 

5.2.2 Population Attributable Risk........................................................................... 35 

5.3 RESEARCH QUESTION 1: WHAT ARE THE TYPES AND FREQUENCY OF 

TASKS IN WHICH DRIVERS ENGAGE PRIOR TO INVOLVEMENT IN 

SAFETY-CRITICAL EVENTS? WHAT ARE THE ODDS RATIOS AND THE 

PAR PERCENTAGE FOR EACH TASK TYPE?......................................................38 

5.3.1 Frequency of Tasks......................................................................................... 38 

5.3.2 Odds Ratios of Driver Tasks........................................................................... 40 

5.3.3 Population Attributable Risk........................................................................... 46 

5.3.4 Summary of Key Findings.............................................................................. 48 

5.4 RESEARCH QUESTION 2: WHAT ENVIRONMENTAL CONDITIONS ARE 

ASSOCIATED WITH DRIVER CHOICE OF ENGAGEMENT IN TASKS? WHAT 

ARE THE ODDS OF BEING IN A SAFETY-CRITICAL EVENT WHILE 

ENGAGING IN TASKS WHILE ENCOUNTERING THESE CONDITIONS? ......50 

5.4.1 Lighting Levels ............................................................................................... 51 

5.4.2 Weather Conditions ........................................................................................ 55 

5.4.3 Roadway Surface Conditions.......................................................................... 60 

5.4.4 Relation to Junction ........................................................................................ 65 

5.4.5 Trafficway Flow.............................................................................................. 71 

5.4.6 Roadway Alignment ....................................................................................... 76 

5.4.7 Road Profile .................................................................................................... 80 

5.4.8 Traffic Density................................................................................................ 85 

5.4.9 Summ ary......................................................................................................... 91 

5.5 RESEARCH QUESTION 3: WHAT ARE THE ODDS RATIOS OF EYES-OFFFORWARD-ROADWAY? DOES EYES-OFF-FORWARD-ROADWAY 

SIGNIFICANTLY AFFECT SAFETY AND/OR DRIVING PERFORMANCE? ....91 

5.5.1 Eyes off Forward Roadway ............................................................................ 91 

5.5.2 Duration of Eyes off Forward Roadway......................................................... 95 

5.5.3 Number of Glances Away From Forward Roadway .................................... 130 

iv 
v 

5.5.4 Length of Longest Glance Away From Forward Roadway.......................... 136 

5.5.5 Summ ary....................................................................................................... 142 

6. SUMMARY AND CONCLUSIONS ...............................................................................145

6.1 RISK ASSOCIATED WITH DISTRACTING TASKS............................................145 

6.2 POPULATION RISK FOR DISTRACTING TASKS ..............................................148 

6.3 VISUAL DEMAND FOR DISTRACTING TASKS ................................................150 

6.4 LENGTH OF GLANCES..........................................................................................150 

6.5 FINAL CONSIDERATIONS ....................................................................................152 

6.6 STUDY LIMITATIONS AND NEXT STEPS..........................................................152 

6.7 SUMMARY FINDINGS AND RECOMMENDATIONS........................................154 

ACKNOWLEDGEMENTS ......................................................................................................231

REFERENCES...........................................................................................................................233

ANNOTATED BIBLIOGRAPHY ...........................................................................................239

LIST OF APPENDICES 

APPENDIX A. DATA CODING DIRECTORY.....................................................................157

APPENDIX B. BASELINE EPOCH DATA DIRECTORY..................................................199

APPENDIX C. DRIVER TASKS, TASK CATEGORIES AND MANUAL/VISUAL 

COMPLEXITY.................................................................................................................205

APPENDIX D. T TEST TABLES ............................................................................................209
LIST OF FIGURES 

Figure 1. Diagram. Camera Directions and Approximate Fields of View ....................................19 

Figure 2. Photo. Split-Screen Presentation of the Four Camera Views.........................................19 

Figure 3. Photo. Five Camera Images Multiplexed Into a Single Image.......................................22 

Figure 4. Image. Eye Glance Location Window in DART............................................................30 

Figure 5. Graph. Mean Duration of Eyes off Forward Roadway by Event Type for All Tasks....96 

Figure 6. Graph. Mean Duration of Eyes off Forward Roadway by Event Type for All 

Tertiary Tasks.................................................................................................................97 

Figure 7. Graph. Mean Duration of Eyes off Forward Roadway by Event Type for Complex 

Tertiary Tasks.................................................................................................................98 

Figure 8. Graph. Mean Duration of Eyes off Forward Roadway for Text Message on Cell 

Phone............................................................................................................................100 

Figure 9. Graph. Mean Duration of Eyes off Forward Roadway for Other—Complex Tertiary 

Task ..............................................................................................................................101 

Figure 10. Graph. Mean Duration of Eyes off Forward Roadway for Interact with 

Dispatching Device ......................................................................................................102 

Figure 11. Graph. Mean Duration of Eyes Off Forward Roadway for Write on Pad, Notepad, 

etc. ................................................................................................................................103 

Figure 12. Graph. Mean Duration of Eyes off Forward Roadway for Use Calculator................104 

Figure 13. Graph. Mean Duration of Eyes off Forward Roadway for Look at Map...................105 

Figure 14. Graph. Mean Duration of Eyes off Forward Roadway for Dial Cell Phone ..............106 

Figure 15. Graph. Mean Duration of Eyes off Forward Roadway for Read Book, Newspaper, 

Paperwork, etc..............................................................................................................107

Figure 16. Graph. Mean Duration of Eyes off Forward Roadway by Event Type for Moderate 

Tertiary Tasks...............................................................................................................108 

Figure 17. Graph. Mean Duration of Eyes off Forward Roadway for Use/Reach for Other 

Electronic Device .........................................................................................................109 

Figure 18. Graph. Mean Duration of Eyes off Forward Roadway for Other—Moderate 

Tertiary Task ................................................................................................................110 

Figure 19. Graph. Mean Duration of Eyes off Forward Roadway for Use/Reach for Personal 

Grooming .....................................................................................................................111 

Figure 20. Graph. Mean Duration of Eyes off Forward Roadway for Use/Reach for Reach for 

Object in Vehicle..........................................................................................................112 

Figure 21. Graph. Mean Duration of Eyes off Forward Roadway for Use/Reach for Look 

Back in Sleeper Berth...................................................................................................114 

Figure 22. Graph. Mean Duration of Eyes off Forward Roadway for Smoking-Related—

Lighting ........................................................................................................................115 

Figure 23. Graph. Mean Duration of Eyes off Forward Roadway for Talk/Listen to CB Radio 116 

Figure 24. Graph. Mean Duration of Eyes off Forward Roadway for External Distraction .......117 

Figure 25. Graph. Mean Duration of Eyes off Forward Roadway for Talk/Listen to HandsFree Phone....................................................................................................................118 

vi 
vii 

Figure 26. Graph. Mean Duration of Eyes off Forward Roadway by Event Type for Simple 

Tertiary Tasks...............................................................................................................119 

Figure 27. Graph. Mean Duration of Eyes off Forward Roadway for Put on/remove/adjust 

Sunglasses or Glasses...................................................................................................121 

Figure 28. Graph. Mean Duration of Eyes off Forward Roadway for Adjust Instrument Panel.122 

Figure 29. Graph. Mean Duration of Eyes off Forward Roadway for Other Personal Hygiene .123 

Figure 30. Graph. Mean Duration of Eyes off Forward Roadway for Bite Nails........................124 

Figure 31. Graph. Mean Duration of Eyes off Forward Roadway for Interact with Other 

Occupant(s) ..................................................................................................................125 

Figure 32. Graph. Mean Duration of Eyes off Forward Roadway by Event Type for 

Secondary Tasks...........................................................................................................126 

Figure 33. Graph. Mean Duration of Eyes off Forward Roadway for Look at Left-side 

Mirror/Out Left Window..............................................................................................127 

Figure 34. Graph. Mean Duration of Eyes off Forward Roadway for Look at Right-side 

Mirror/Out Right Window ...........................................................................................128 

Figure 35. Graph. Mean Duration of Eyes off Forward Roadway for Check Speedometer........129 

Figure 36. Graph. Mean Number of Glances Away from Forward Roadway by Event Type 

for All Tasks.................................................................................................................131 

Figure 37. Graph. Mean Number of Glances Away from Forward Roadway by Event Type 

for All Tertiary Tasks...................................................................................................132 

Figure 38. Graph. Mean Number of Glances Away from Forward Roadway by Event Type 

for Complex Tertiary Tasks .........................................................................................133 

Figure 39. Graph. Mean Number of Glances Away from Forward Roadway by Event Type 

for Moderate Tertiary Tasks.........................................................................................134 

Figure 40. Graph. Mean Number of Glances Away from Forward Roadway by Event Type 

for Simple Tertiary Tasks.............................................................................................135 

Figure 41. Graph. Mean Number of Glances Away from Forward Roadway by Event Type 

for Secondary Tasks .....................................................................................................136 

Figure 42. Graph. Mean Length of Longest Glance Away from Forward Roadway by Event 

Type for All Tasks........................................................................................................137 

Figure 43. Graph. Mean Length of Longest Glance Away from Forward Roadway by Event 

Type for All Tertiary Tasks..........................................................................................138 

Figure 44. Graph. Mean Length of Longest Glance Away from Forward Roadway by Event 

Type for Complex Tertiary Tasks ................................................................................139 

Figure 45. Graph. Mean Length of Longest Glance Away from Forward Roadway by Event 

Type for Moderate Tertiary Tasks ...............................................................................140 

Figure 46. Graph. Mean Length of Longest Glance Away from Forward Roadway by Event 

Type for Simple Tertiary Tasks ...................................................................................141 

Figure 47. Graph. Mean Length of Longest Glance Away from Forward Roadway by Event 

Type for Secondary Tasks............................................................................................142 

Figure 48. Vehicle/Non-Motorist 2 Position ...............................................................................160 

Figure 49. Critical Crash Envelope..............................................................................................161 

Figure 50. Description of the Accident Types.............................................................................175 
LIST OF TABLES 

Table 1. Frequency and Percentage of Any Secondary and/or Tertiary Task in “All” and 

“Vehicle 1 At-Fault” Events ...................................................................................... xviii 

Table 2. Frequency and Percentage of Any Tertiary Tasks in “All” and “Vehicle 1 At-Fault” 

Events ........................................................................................................................... xix 

Table 3. Odds Ratios and 95% Confidence Intervals to Assess Likelihood of a Safety-Critical 

Event While Engaging In Tertiary Tasks for All Events ............................................. xxi 

Table 4. Population Attributable Risk and 95% Confidence Intervals for Driver Tasks across 

All Events................................................................................................................... xxiii 

Table 5. Odds Ratios and 95% Confidence Intervals for All Events to Assess Likelihood of a 

Safety-Critical Event While Eyes Off Forward Roadway ...........................................xxv 

Table 6. Distraction Taxonomy .......................................................................................................7 

Table 7. Trigger Values Used in the DDWS FOT.........................................................................20 

Table 8. Trigger Values Used in the NTDS...................................................................................23 

Table 9. Comparison of the Trigger Values Used in the DDWS FOT Phase I Analysis, 

NTDS, and the current study..........................................................................................26 

Table 10. Assignment of Task Categories into Three Levels of Manual/Visual Complexity.......32 

Table 11. 2 × 2 Contingency Table Used to Calculate Odds Ratio ...............................................34 

Table 12. Odds Ratio Example ......................................................................................................34 

Table 13. Population Attributable Risk—Confidence Limits Example ........................................36 

Table 14. Frequency and Percentage of Any Secondary and/or Tertiary Task in “All” and 

“Vehicle 1 At-Fault” Events ..........................................................................................38 

Table 15. Frequency and Percentage of Any Tertiary Tasks in “All” and “Vehicle 1 At-Fault” 

Events .............................................................................................................................40 

Table 16. Odds Ratios and 95% Confidence Intervals to Assess Likelihood of a SafetyCritical Event While Engaging In a Task across “All” and “Vehicle 1 At-Fault” 

(V1) Events ....................................................................................................................42 

Table 17. Odds Ratios and 95% Confidence Intervals to Assess Likelihood of a SafetyCritical Event While Engaging In Tasks across “All” and “Vehicle 1 At-Fault” 

(V1) Events ....................................................................................................................44 

Table 18. Population Attributable Risk and 95% Confidence Intervals for Driver Tasks 

across “All” and “Vehicle 1 At-Fault” (V1) Events.......................................................47 

Table 19. The Frequency of Tertiary and Secondary Tasks during Safety-Critical Events and 

Baseline Epochs Across “All” and “Vehicle 1 At-Fault” Events for each Lighting 

Level...............................................................................................................................52 

Table 20. Odds Ratios and 95% Confidence Intervals for the Interaction of Any Secondary 

and/or Tertiary Tasks by Lighting Level across “All” and “Vehicle 1 At-Fault” 

(V1) Events ....................................................................................................................53 

Table 21. Odds Ratios and 95% Confidence Intervals for the Interaction of Tertiary Tasks by 

Lighting Level across “All” and “Vehicle 1 At-Fault” (V1) Events..............................53 

Table 22. Odds Ratios and 95% Confidence Intervals for the Interaction of Complex Tertiary 

Tasks by Lighting Level across “All” and “Vehicle 1 At-Fault” (V1) Events ..............54 

viii 
Table 23. Odds Ratios and 95% Confidence Intervals for the Interaction of Moderate Tertiary 

Tasks by Lighting Level across “All” and “Vehicle 1 At-Fault” (V1) Events ..............54 

Table 24. Odds Ratios and 95% Confidence Intervals for the Interaction of Simple Tertiary 

Tasks by Lighting Level across “All” and “Vehicle 1 At-Fault” (V1) Events ..............55 

Table 25. Odds Ratios and 95% Confidence Intervals for the Interaction of Secondary Tasks 

by Lighting Level across “All” and “Vehicle 1 At-Fault” (V1) Events.........................55 

Table 26. The Frequency of Secondary and Tertiary Tasks during Safety-Critical Events and 

Baseline Epochs across “All” and “Vehicle 1 At-Fault” (V1) Events for each 

Weather Condition .........................................................................................................57 

Table 27. Odds Ratios and 95% Confidence Intervals for the Interaction of Secondary and/or 

Tertiary Tasks by Weather Condition across “All” and “Vehicle 1 At-Fault” (V1) 

Events .............................................................................................................................58 

Table 28. Odds Ratios and 95% Confidence Intervals for the Interaction of Tertiary Tasks by 

Weather Condition across “All” and “Vehicle 1 At-Fault” (V1) Events.......................58 

Table 29. Odds Ratios and 95% Confidence Intervals for the Interaction of Complex Tertiary 

Tasks by Weather Condition across “All” and “Vehicle 1 At-Fault” (V1) Events........59 

Table 30. Odds Ratios and 95% Confidence Intervals for the Interaction of Moderate Tertiary 

Tasks by Weather Condition across “All” and “Vehicle 1 At-Fault” (V1) Events........59 

Table 31. Odds Ratios and 95% Confidence Intervals for the Interaction of Simple Tertiary 

Tasks by Weather Condition across “All” and “Vehicle 1 At-Fault” (V1) Events........60 

Table 32. Odds Ratios and 95% Confidence Intervals for the Interaction of Secondary Tasks 

by Weather Condition across “All” and “Vehicle 1 At-Fault” (V1) Events..................60 

Table 33. The Frequency of Secondary and Tertiary Task during Safety-Critical Events and 

Baseline Epochs across “All” and “Vehicle 1 At-Fault” (V1) Event for each 

Roadway Surface Condition...........................................................................................62 

Table 34. Odds Ratios and 95% Confidence Intervals for the Interaction of Secondary and/or 

Tertiary Tasks by Roadway Surface Condition across “All” and “Vehicle 1 AtFault” (V1) Events .........................................................................................................63 

Table 35. Odds Ratios and 95% Confidence Intervals for the Interaction of Tertiary Tasks by 

Roadway Surface Condition across “All” and “Vehicle 1 At-Fault” (V1) Events ........63 

Table 36. Odds Ratios and 95% Confidence Intervals for the Interaction of Complex Tertiary 

Tasks by Roadway Surface Condition across “All” and “Vehicle 1 At-Fault” (V1) 

Events .............................................................................................................................64 

Table 37. Odds Ratios and 95% Confidence Intervals for the Interaction of Moderate Tertiary 

Tasks by Roadway Surface Condition across “All” and “Vehicle 1 At-Fault” (V1) 

Events .............................................................................................................................64 

Table 38. Odds Ratios and 95% Confidence Intervals for the Interaction of Simple Tertiary 

Tasks by Roadway Surface Condition across “All” and “Vehicle 1 At-Fault” (V1) 

Events .............................................................................................................................65 

Table 39. Odds Ratios and 95% Confidence Intervals for the Interaction of Secondary Tasks 

by Roadway Surface Condition across “All” and “Vehicle 1 At-Fault” (V1) Events ...65 

Table 40. The Frequency of Secondary and Tertiary Tasks during Safety-Critical Events and 

Baseline Epochs across “All” and “Vehicle 1 At-Fault” (V1) for each Relation to 

Junction ..........................................................................................................................67 

ix 
Table 41. Odds Ratios and 95% Confidence Intervals for the Interaction of Secondary and/or 

Tertiary Tasks by Relation to Junction across “All” and “Vehicle 1 At-Fault” (V1) 

Events.............................................................................................................................68 

Table 42. Odds Ratios and 95% Confidence Intervals for the Interaction of Tertiary Tasks by 

Relation to Junction across “All” and “Vehicle 1 At-Fault” (V1) Events .....................69 

Table 43. Odds Ratios and 95% Confidence Intervals for the Interaction of Complex Tertiary 

Tasks by Relation to Junction across “All” and “Vehicle 1 At-Fault” (V1) Events......69 

Table 44. Odds Ratios and 95% Confidence Intervals for the Interaction of Moderate Tertiary 

Tasks by Relation to Junction across “All” and “Vehicle 1 At-Fault” (V1) Events......70 

Table 45. Odds Ratios and 95% Confidence Intervals for the Interaction of Simple Tertiary 

Tasks by Relation to Junction across “All” and “Vehicle 1 At-Fault” (V1) Events......70 

Table 46. Odds Ratios and 95% Confidence Intervals for the Interaction of Secondary Tasks 

by Relation to Junction across “All” and “Vehicle 1 At-Fault” (V1) Events ................71 

Table 47. The Frequency of Secondary and Tertiary Tasks during Safety-Critical Events and 

Baseline Epochs across “All” and “Vehicle 1 At-Fault” (V1) Events for each 

Trafficway Flow Condition............................................................................................72 

Table 48. Odds Ratios and 95% Confidence Intervals for the Interaction of Secondary and/or 

Tertiary Tasks by Trafficway Flow across “All” and “Vehicle 1 At-Fault” (V1) 

Events .............................................................................................................................73 

Table 49. Odds Ratios and 95% Confidence Intervals for the Interaction of Tertiary Tasks by 

Trafficway Flow across “All” and “Vehicle 1 At-Fault” (V1) Events...........................73 

Table 50. Odds Ratios and 95% Confidence Intervals for the Interaction of Complex Tertiary 

Tasks by Trafficway Flow across “All” and “Vehicle 1 At-Fault” (V1) Events ...........74 

Table 51. Odds Ratios and 95% Confidence Intervals for the Interaction of Moderate Tertiary 

Tasks by Trafficway Flow across “All” and “Vehicle 1 At-Fault” (V1) Events ...........74 

Table 52. Odds Ratios and 95% Confidence Intervals for the Interaction of Simple Tertiary 

Tasks by Trafficway Flow across “All” and “Vehicle 1 At-Fault” (V1) Events ...........75 

Table 53. Odds Ratios and 95% Confidence Intervals for the Interaction of Secondary Tasks 

by Trafficway Flow across “All” and “Vehicle 1 At-Fault” (V1) Events......................75 

Table 54. The Frequency of Secondary and Tertiary Tasks during Safety-Critical Events and 

Baseline Epochs across “All” and “Vehicle 1 At-Fault” (V1) Events for each 

Roadway Alignment.......................................................................................................77 

Table 55. Odds Ratios and 95% Confidence Intervals for the Interaction of Secondary and/or 

Tertiary Tasks by Roadway Alignment across “All” and “Vehicle 1 At-Fault” (V1) 

Events .............................................................................................................................78 

Table 56. Odds Ratios and 95% Confidence Intervals for the Interaction of Tertiary Tasks by 

Roadway Alignment across “All” and “Vehicle 1 At-Fault” (V1) Events ....................78 

Table 57. Odds Ratios and 95% Confidence Intervals for the Interaction of Complex Tertiary 

Tasks by Roadway Alignment across “All” and “Vehicle 1 At-Fault” (V1) Events.....79 

Table 58. Odds Ratios and 95% Confidence Intervals for the Interaction of Moderate Tertiary 

Tasks by Roadway Alignment across “All” and “Vehicle 1 At-Fault” (V1) Events.....79 

Table 59. Odds Ratios and 95% Confidence Intervals for the Interaction of Simple Tertiary 

Tasks by Roadway Alignment across “All” and “Vehicle 1 At-Fault” (V1) Events.....80 

x 
Table 60. Odds Ratios and 95% Confidence Intervals for the Interaction of Secondary Tasks 

by Roadway Alignment across “All” and “Vehicle 1 At-Fault” (V1) Events ...............80 

Table 61. The Frequency of Secondary and Tertiary Tasks during Safety-Critical Events and 

Baseline Epochs across “All” and “Vehicle 1 At-Fault” (V1) Events for each Road 

Profile .............................................................................................................................82 

Table 62. Odds Ratios and 95% Confidence Intervals for the Interaction of Secondary and/or 

Tertiary Tasks by Road Profile across “All” and “Vehicle 1 At-Fault” (V1) Events....83 

Table 63. Odds Ratios and 95% Confidence Intervals for the Interaction of Tertiary Tasks by 

Road Profile across “All” and “Vehicle 1 At-Fault” (V1) Events .................................83 

Table 64. Odds Ratios and 95% Confidence Intervals for the Interaction of Complex Tertiary 

Tasks by Road Profile across “All” and “Vehicle 1 At-Fault” (V1) Events..................84 

Table 65. Odds Ratios and 95% Confidence Intervals for the Interaction of Moderate Tertiary 

Tasks by Road Profile across “All” and “Vehicle 1 At-Fault” (V1) Events..................84 

Table 66. Odds Ratios and 95% Confidence Intervals for the Interaction of Simple Tertiary 

Tasks by Road Profile across “All” and “Vehicle 1 At-Fault” (V1) Events..................85 

Table 67. Odds Ratios and 95% Confidence Intervals for the Interaction of Secondary Tasks 

by Road Profile across “All” and “Vehicle 1 At-Fault” (V1) Events ............................85 

Table 68. The Frequency of Secondary and Tertiary Tasks during Safety-Critical Events and 

Baseline Epochs across “All” and “Vehicle 1 At-Fault” (V1) Events for each LOS ....87 

Table 69. Odds Ratios and 95% Confidence Intervals for the Interaction of Secondary and/or 

Tertiary Tasks by Traffic Density across “All” and “Vehicle 1 At-Fault” (V1) 

Events .............................................................................................................................88 

Table 70. Odds Ratios and 95% Confidence Intervals for the Interaction of Tertiary Tasks by 

Traffic Density across “All” and “Vehicle 1 At-Fault” (V1) Events.............................89 

Table 71. Odds Ratios and 95% Confidence Intervals for the Interaction of Complex Tertiary 

Tasks by Traffic Density across “All” and “Vehicle 1 At-Fault” (V1) Events..............89 

Table 72. Odds Ratios and 95% Confidence Intervals for the Interaction of Moderate Tertiary 

Tasks by Traffic Density across “All” and “Vehicle 1 At-Fault” (V1) Events..............90 

Table 73. Odds Ratios and 95% Confidence Intervals for the Interaction of Simple Tertiary 

Tasks by Traffic Density across “All” and “Vehicle 1 At-Fault” (V1) Events..............90 

Table 74. Odds Ratios and 95% Confidence Intervals for the Interaction of Secondary Tasks 

by Traffic Density across “All” and “Vehicle 1 At-Fault” (V1) Events........................91 

Table 75. Contingency Tables Used to Calculate Eyes off Forward Roadway Odds Ratios ........92 

Table 76. Odds Ratios and 95% Confidence Intervals to Assess Likelihood of a SafetyCritical Event While Eyes Off Forward Roadway across “All” and “Vehicle 1 AtFault” (V1) Events .........................................................................................................94 

Table 77. Odds Ratios and 95% Confidence Intervals to Assess Likelihood of a SafetyCritical Event While Engaging In Tasks for “All” Events...........................................147 

Table 78. Population Attributable Risk and 95% Confidence Intervals for Driver Tasks 

Across All Events.........................................................................................................149 

Table 79. Odds Ratios and 95% Confidence Intervals for All Events to Assess Likelihood of 

a Safety-Critical Event While Eyes Off Forward Roadway.........................................151 

Table 80. Descriptions and Diagrams of Incident Types.............................................................179 

xi 
Table 81. Potentially Distracting Driver Behaviors.....................................................................188 

Table 82. Driver Tasks, Task Categories and Manual/Visual Complexity .................................205 

Table 83. Mean Duration of Eyes off Forward Roadway—Any Task— across All Events and 

Vehicle 1 At-Fault (V1) Events ...................................................................................209 

Table 84. Mean Duration of Eyes off Forward Roadway—All Tertiary Tasks— across All 

Events and Vehicle 1 At-Fault (V1) Events.................................................................210 

Table 85. Mean Duration of Eyes off Forward Roadway—Complex Tertiary Tasks— across 

All Events and Vehicle 1 At-Fault (V1) Events...........................................................210 

Table 86. Mean Duration of Eyes off Forward Roadway—Moderate Tertiary Tasks— across 

All Events and Vehicle 1 At-Fault (V1) Events...........................................................211 

Table 87. Mean Duration of Eyes off Forward Roadway—Simple Tertiary Tasks— across 

All Events and Vehicle 1 At-Fault (V1) Events...........................................................211 

Table 88. Mean Duration of Eyes off Forward Roadway—Secondary Tasks— across All 

Events and Vehicle 1 At-Fault (V1) Events.................................................................212 

Table 89. Mean Number of Glances Away from Forward Roadway—Any Task— across All 

Events and Vehicle 1 At-Fault (V1) Events.................................................................212 

Table 90. Mean Number of Glances Away from Forward Roadway—All Tertiary Tasks— 

across All Events and Vehicle 1 At-Fault (V1) Events................................................213 

Table 91. Mean Number of Glances Away from Forward Roadway—Complex Tertiary 

Tasks— across All Events and Vehicle 1 At-Fault (V1) Events .................................213 

Table 92. Mean Number of Glances Away from Forward Roadway—Moderate Tertiary 

Tasks— across All Events and Vehicle 1 At-Fault (V1) Events .................................214 

Table 93. Mean Number of Glances Away from Forward Roadway—Simple Tertiary 

Tasks— across All Events and Vehicle 1 At-Fault (V1) Events .................................214 

Table 94. Mean Number of Glances Away from Forward Roadway—Secondary Tasks— 

across All Events and Vehicle 1 At-Fault (V1) Events................................................215 

Table 95. Mean Duration of Longest Glance Away from Forward Roadway—Any Task— 

across All Events and Vehicle 1 At-Fault (V1) Events................................................215 

Table 96. Mean Duration of Longest Glance Away from Forward Roadway— All Tertiary 

Tasks— across All Events and Vehicle 1 At-Fault (V1) Events .................................216 

Table 97. Mean Duration of Longest Glance Away from Forward Roadway— Complex 

Tertiary Tasks— across All Events and Vehicle 1 At-Fault (V1) Events ...................216 

Table 98. Mean Duration of Longest Glance Away from Forward Roadway—Moderate 

Tertiary Tasks—across All Events and Vehicle 1 At-Fault (V1) Events ....................217 

Table 99. Mean Duration of Longest Glance Away from Forward Roadway—Simple 

Tertiary Tasks —across All Events and Vehicle 1 At-Fault (V1) Events ...................217 

Table 100. Mean Duration of Longest Glance Away from Forward Roadway—Secondary 

Tasks— across All Events and Vehicle 1 At-Fault (V1) Events .................................218 

Table 101. Complex Tertiary Tasks—Text Messaging— across All Events and Vehicle 1 AtFault (V1) Events .........................................................................................................218 

Table 102. Complex Tertiary Task—Other— across All Events and Vehicle 1 At-Fault (V1) 

Events ...........................................................................................................................219 

xii 
Table 103. Complex Tertiary Task—Interact with Dispatching Device— across All Events 

and Vehicle 1 At-Fault (V1) Events.............................................................................219 

Table 104. Complex Tertiary Task—Write on pad, notebook, etc. — across All Events and 

Vehicle 1 At-Fault (V1) Events ...................................................................................220 

Table 105. Complex Tertiary Task—Use Calculator— across All Events and Vehicle 1 AtFault (V1) Events .........................................................................................................220 

Table 106. Complex Tertiary Task—Look at Map— across All Events and Vehicle 1 AtFault (V1) Events .........................................................................................................221 

Table 107. Complex Tertiary Task—Dial Cell Phone— across All Events and Vehicle 1 AtFault (V1) Events .........................................................................................................221 

Table 108. Complex Tertiary Task—Read book, newspaper, etc. — across All Events and 

Vehicle 1 At-Fault (V1) Events ...................................................................................222 

Table 109. Moderate Tertiary Tasks—Use/Reach for Other Electronic Device— across All 

Events and Vehicle 1 At-Fault (V1) Events.................................................................222 

Table 110. Moderate Tertiary Tasks—Other— across All Events and Vehicle 1 At-Fault 

(V1) Events ..................................................................................................................223 

Table 111. Moderate Tertiary Tasks—Personal Grooming— across All Events and Vehicle 1 

At-Fault (V1) Events....................................................................................................223 

Table 112. Moderate Tertiary Tasks—Reach for Object— across All Events and Vehicle 1 

At-Fault (V1) Events....................................................................................................224 

Table 113. Moderate Tertiary Tasks—Look Back in Sleeper Berth— across All Events and 

Vehicle 1 At-Fault (V1) Events ...................................................................................224 

Table 114. Moderate Tertiary Tasks—Smoking–Lighting— across All Events and Vehicle 1 

At-Fault (V1) Events....................................................................................................225 

Table 115. Moderate Tertiary Tasks—Talk/Listen to CB— across All Events and Vehicle 1 

At-Fault (V1) Events....................................................................................................225 

Table 116. Moderate Tertiary Tasks—External Distraction— across All Events and Vehicle 

1 At-Fault (V1) Events.................................................................................................226 

Table 117. Moderate Tertiary Tasks—Talk/Listen to Hands-Free Phone— across All Events 

and Vehicle 1 At-Fault (V1) Events.............................................................................226 

Table 118. Moderate Tertiary Tasks—Put on/remove/adjust sunglasses or glasses— across 

All Events and Vehicle 1 At-Fault (V1) Events...........................................................227 

Table 119. Moderate Tertiary Tasks—Adjust Sunglasses— across All Events and Vehicle 1 

At-Fault (V1) Events....................................................................................................227 

Table 120. Simple Tertiary Tasks—Other Personal Hygiene— across All Events and Vehicle 

1 At-Fault (V1) Events.................................................................................................228 

Table 121. Simple Tertiary Tasks—Bite Nails— across All Events and Vehicle 1 At-Fault 

(V1) Events ..................................................................................................................228 

Table 122. Simple Tertiary Tasks—Interact with Other Occupant(s)— across All Events and 

Vehicle 1 At-Fault (V1) Events ...................................................................................229 

Table 123. Secondary Tasks—Look at Left Mirror/Out Left Window— across All Events 

and Vehicle 1 At-Fault (V1) Events.............................................................................229 

xiii 
xiv 

Table 124. Secondary Tasks—Look at Right Mirror/Out Right Window— across All Events 

and Vehicle 1 At-Fault (V1) Events.............................................................................230 

Table 125. Secondary Tasks—Check Speedometer— across All Events and Vehicle 1 AtFault (V1) Events .........................................................................................................230 
LIST OF ACRONYMS 

Acronym Definition 

ANOVA analysis of variance 

CB radio Citizens Band radio 

CMV commercial motor vehicle 

DART data analysis and reduction tool 

DAS data acquisition system 

DDWS FOT Drowsy Driver Warning System Field Operational Test 

EYESOFF eyes off roadway 

EYETRANS eye transition 

GPS global positioning system 

GVWR gross vehicle weight rating 

FMCSA Federal Motor Carrier Safety Administration 

LCL lower confidence limit 

LOS level of service 

L/SH local/short haul 

LTCCS Large Truck Crash Causation Study 

NHTSA National Highway Traffic Safety Administration 

NTDS Naturalistic Truck Driving Study 

OR odds ratio 

ORD observer rating of drowsiness 

PAR population attributable risk 

SD standard deviation 

TTC time to collision 

UCL upper confidence limit 

USDOT U. S. Department of Transportation 

xv 

EXECUTIVE SUMMARY 

Promoting safe operation of commercial motor vehicles (CMVs) and reducing the number and 

severity of crashes on U.S. roadways is critical to the mission of the Federal Motor Carrier 

Safety Administration (FMCSA). The most recently published crash data indicate that 41,059 

people were killed in road crashes in 2007 (FMCSA, 2009a). Of these fatalities, 12 percent 

(4,808) involved large trucks. Although this represented a net decrease in fatalities, down 7.5 

percent from 1998 to 2007, it represents far too many deaths for our nation’s road users. 

In direct support of FMCSA’s mission, the current study involved a detailed investigation of 

CMV “driver distraction,” a prominent type of “driver error” known to contribute to motor 

vehicle crashes. Past research has suggested that driver distraction and driving inattention may be 

involved in 78 percent of light-vehicle crashes (Klauer, Dingus, Neale, Sudweeks, & Ramsey, 

2006). It should be noted that most of the research that has been conducted to investigate driver 

distraction has occurred with light-vehicle (e.g., passenger automobile) drivers and, as such, the 

impact of driver distraction of CMV crashes has not been well-understood. 

The purpose of the current study was to address this gap in the literature by investigating driver 

distraction in CMV operations. To accomplish this, data from two large-scale CMV naturalistic 

truck driving studies (Hanowski et al., 2008; Blanco et al., in press) were combined and 

analyzed. Naturalistic data collection is a method used to study driver behavior and performance 

by installing sensors and video cameras in fleet trucks and providing these vehicles to truck 

drivers to use as part of their normal revenue-producing deliveries. Taken together, these data 

sets represent 203 CMV drivers, seven trucking fleets, and 16 fleet locations. In terms of data, 

the data set used includes approximately 3 million miles of continuously collected kinematic and 

video data, and represents the most comprehensive naturalistic CMV driving set in the world. 

DRIVER DISTRACTION IN CMV SAFETY-CRITICAL EVENTS 

To investigate distracting tasks that were present in the CMV data set, the data were filtered for 

safety-critical events. These events are defined as crashes, near-crashes, crash-relevant conflicts 

(less severe near-crashes), and unintentional lane deviations. This filtering process, using 

kinematic data thresholds and video review and validation, resulted in 4,452 safety-critical 

events: 21 crashes, 197 near-crashes, 3,019 crash-relevant conflicts, and 1,215 unintentional lane 

deviations. These safety-critical events were combined into a single data set. In addition, 19,888 

baseline epochs (uneventful, routine driving) of normal driving were randomly selected. The 

amount of time a driver was in the study was used to weight the frequency of baseline epochs per 

driver. As such, drivers who were in the study for a longer duration (e.g., 12 weeks) had more 

baseline epochs than drivers in the study for less time (e.g., 8 weeks). 

Following the method used in Klauer et al. (2006), of the 4,452 safety-critical events, 81.5 

percent had some type of driver distraction listed as a potential contributing factor. Table 1

displays the percentage of any secondary and/or tertiary tasks that were present in all safetycritical events and all events where the Vehicle 1 driver (i.e., the participant driver) was judged 

to be at-fault in the safety-critical event. Tasks were categorized as tertiary (non-driving related) 

xvii 
and secondary (driving related, but not required for vehicle control; Ablassmeier, Poitschke, 

Wallhoff, Bengler, & Rigoll, 2007). 

Table 1. Frequency and Percentage of Any Secondary and/or Tertiary Task in “All” and “Vehicle 1 

At-Fault” Events 

Event Type All SafetyCritical Events

Frequency 

and Percent of 

All SafetyCritical Events

All Vehicle 1 

At-Fault 

Events 

Frequency 

and Percent of 

All Vehicle 1 

At-Fault 

Events 

All safety-critical events 81.5% n = 4,452 

(100.0%) 83.4% n = 3,618 (100.0%) 

Crashes 100.0% n = 21 

(0.5%) 100.0% n = 10 (0.3%) 

Near-crashes 78.7% n = 197 

(4.4%) 83.0% n = 112 (3.1%) 

Crash-relevant conflicts 79.1% n = 3,019 

(67.8%) 81.1% n = 2,281 (63.0%) 

Unintentional lane 

deviations 87.7% n = 1,215 (27.3%) 87.7% n = 1,215 (33.6%) 

Baseline epochs 76.9% n = 19,888 

(100.0%) 76.9% n = 19,888 (100.0%) 

Though a breakdown of each Event Type is provided in Table 1, caution must be used in 

interpreting individual Event Types. While Klauer et al. (2006) found that 78 percent of crashes 

contained at least one type of inattention category (i.e., secondary task distraction; drivingrelated inattention to forward roadway; drowsiness; and non-specific eye glance away from the 

forward roadway), the current study, following the Klauer et al. method, found that 100 percent 

of crashes contained at least one type of inattention task (either secondary or tertiary). It is 

important to point out a few caveats in comparing these two studies. First, and perhaps most 

importantly, the percentages in Table 1 include any task that was present within the 6-s interval; 

often times the task was driving-related such as checking the side mirror. Because Klauer et al. 

included checking mirrors as a distraction type, this approach was followed in the current study. 

However, based on training received by CMV drivers, who are instructed to check mirrors every 

5–8 s (FMCSA, 2009b), it would be expected that video of the drivers would show them 

regularly checking their mirrors. This would, in turn, inflate the percentages seen in the current 

study and may not represent an accurate picture of “driver distraction” in CMV operations. 

A second caveat when comparing the results from the Klauer et al. (2006) study and the current 

study is the data collection time frames of the studies. The Klauer et al. study was conducted 

from January 2003 to July 2004, while the Drowsy Driver Warning System Field Operational 

Test (DDWS FOT) was conducted from May 2004 to September 2005 (Hanowski et al., 2008), 

and the Naturalistic Truck Driving Study (NTDS) study was conducted from November 2005 to 

May 2007 (Blanco et al., in press). Because of these time period differences, the specific types of 

distraction across studies were similar, but not identical. For example, as will be described, a key 

finding in the current study was the high risk associated with texting. However, because texting 

is a relatively recent phenomenon, there were no cases of texting in the Klauer et al. study. 

However, we know that light-vehicle drivers engage in texting. As such, if the Klauer et al. study 

xviii 
were conducted in present times, it would be expected that the distraction percentages may be 

different (or, at least, texting would be represented). 

Third, while the distraction categories used were similar across studies, they were not exactly the 

same and the current study had additional non-driving related distractions (e.g., texting, using 

calculator, using dispatching device) that were not cited in Klauer et al. (2006). 

Finally, it should be noted that crashes were a rare occurrence in the current study (<0.5 percent 

of all safety-critical events). Klauer et al. (2006) had 69 crashes in the light-vehicle data set; 

approximately three times as many collected in the CMV data sets. Also, the majority of the 

crashes in the CMV data sets were relatively minor, including deer hits (n = 5) and contact with 

an object (e.g., construction cone, piece of debris) in the road or on the side of the road (n = 9). 

Collectively, these caveats underline the need for caution when comparing results from the 

current study with Klauer et al. (2006) and interpreting the results of individual event types, 

particularly those with small sample sizes (crashes). Table 2 provides an alternative approach, 

which the authors believe to be more appropriate, to evaluating the impact of driver distraction in 

the current CMV study. 

Table 2 shows the percentage of all safety-critical events, and events where the Vehicle 1 driver 

(i.e., the participant driver) was judged to be at-fault, and where the driver was engaged in a nondriving related, tertiary, task. As shown, driver distraction due to non-driving related tertiary 

tasks was a contributing factor in 71 percent of crashes, 46 percent of near-crashes, and 60 

percent of all events. Table 2 may capture the effects of “driver distraction” as many people 

think of it. That is, the events in Table 2 represent driving while also engaged in a non-driving 

related activity such as using a cell-phone, texting, eating, etc. As noted, Klauer et al. (2006) did 

not distinguish between secondary and tertiary tasks in the light-vehicle study so a direct 

comparison to the light-vehicle data is not possible. 

Table 2. Frequency and Percentage of Any Tertiary Tasks in “All” and “Vehicle 1 At-Fault” Events 

Event Type 

All SafetyCritical 

Events 

Frequency 

and Percent of 

All SafetyCritical Events

All Vehicle 1 

At-Fault 

Events 

Frequency 

and Percent of 

All Vehicle 1 

At-Fault 

Events 

All safety-critical events 59.9% n = 4,452 

(100.0%) 63.9% n = 3,618 (100.0%) 

Crashes 71.4% n = 21 

(0.5%) 40.0% n = 10 (0.3%) 

Near-crashes 46.2% n = 197 

(4.4%) 50.0% n = 112 (3.1%) 

Crash-relevant conflicts 53.6% n = 3,019 

(67.8%) 57.4% n = 2,281 (63.0%) 

Unintentional lane 

deviations 77.5% n = 1,215 (27.3%) 77.5% n = 1,215 (33.6%) 

Baseline epochs 56.5% n = 19,888 

(100.0%) 56.5% n = 19,888 (100.0%) 

xix 
Three research questions were identified as critical to the current analysis effort: 

 Research Question 1: What are the types and frequency of tasks which drivers engage in 

prior to involvement in safety-critical events? What are the odds ratios (OR) and the 

population attributable risk (PAR) percentages for each task type? 

 Research Question 2: What environmental conditions are associated with driver choice of 

engagement in tasks? What are the odds of being in a safety-critical event while engaging 

in tasks while encountering these conditions? 

 Research Question 3: What are the odds ratios of eyes-off-forward-roadway? Does eyesoff-forward-roadway significantly affect safety and/or driving performance? 

RISK ASSOCIATED WITH DISTRACTING TASKS 

Odds ratio analyses were calculated to identify tasks that were high risk. That is, tasks that were 

associated with an increased likelihood of involvement in a safety-critical event compared to 

uneventful baseline driving. Odds of occurrence were defined as the probability of event 

occurrence (safety-critical event) divided by the probability of non-occurrence (baseline epoch). 

These probability estimates were conditioned on the presence/absence of the behavior of interest 

and then compared via ratios. For a given task, an odds ratio of 1.0 indicated the outcome was 

equally likely to occur given the condition (i.e., equally likely to occur in the safety-critical event 

data as in the baseline, uneventful/routine driving data). An odds ratio greater than 1.0 indicated 

the outcome was more likely to occur given the presence of the task, and odds ratios of less than 

1.0 indicated the outcome was less likely to occur (Pedhazur, 1997). When considering odds 

ratios, it was also important to look at calculated confidence limits. Along with an odds ratio 

statistic, lower confidence limits (LCL) and upper confidence limits (UCL) were calculated. To 

interpret the odds ratio, the range of the LCL and UCL must not include 1.0 to be considered 

statistically significant (a 95 percent confidence interval was used in this study). Table 3 shows 

the results from the analyses that included all safety-critical events (similar findings resulted 

from analyses of events where the truck driver was judged to have been at-fault). Tasks were 

considered individually, but were also grouped by tertiary and secondary classifiers. For tertiary 

tasks, the level of complexity of the task (as outlined by Klauer et al., 2006) was used as a 

grouping factor. Odds ratios, along with the LCL and UCL are shown in Table 3. Large odds 

ratios (greater than 1.0) that have LCL and UCL ranges that do not include 1.0 indicate that the 

task is risky. As shown in Table 3, the most risky behavior identified was “text message on cell 

phone,” with a significant odds ratio of 23.2 (as the LCL and UCL range does not include 1.0). 

This means that drivers who text message while driving were 23.2 times more likely to be 

involved in a safety-critical event, compared to a baseline epoch, than if they were not text 

messaging while driving. 

xx 
Table 3. Odds Ratios and 95% Confidence Intervals to Assess Likelihood of a Safety-Critical Event 

While Engaging In Tertiary Tasks for All Events 

TASK Odds 

Ratio LCL UCL 

Complex Tertiary Task 

Text message on cell phone 23.24* 9.69 55.73 

Other—Complex 

(e.g., cleaning side mirror, rummaging through a grocery bag) 10.07* 3.10 32.71 

Interact with/look at dispatching device 9.93* 7.49 13.16 

Write on pad, notebook, etc. 8.98* 4.73 17.08 

Use calculator 8.21* 3.03 22.21 

Look at map 7.02* 4.62 10.69 

Dial cell phone 5.93* 4.57 7.69 

Read book, newspaper, paperwork, etc. 3.97* 3.02 5.22 

Moderate Tertiary Task 

Use/reach for other electronic device 

(e.g. video camera, 2-way radio) 6.72* 2.74 16.44 

Other—Moderate 

(e.g., opening a pill bottle to take medicine, exercising in the cab) 5.86* 2.84 12.07 

Personal grooming 4.48* 2.01 9.97 

Reach for object in vehicle 3.09* 2.75 3.48 

Look back in Sleeper Berth 2.30* 1.30 4.07 

Talk or listen to hand-held phone 1.04 0.89 1.22 

Eating 1.01 0.83 1.21 

Smoking-related behavior—reaching, lighting, extinguishing 0.60* 0.40 0.89 

Talk or listen to CB radio 0.55* 0.41 0.75 

Look at outside vehicle, animal, person, object, or undetermined 0.54* 0.50 0.60 

Talk or listen to hands-free phone 0.44* 0.35 0.55 

Simple Tertiary Task 

Put on/remove/adjust sunglasses or reading glasses 3.63* 2.37 5.58 

Adjust instrument panel 1.25* 1.06 1.47 

Remove/adjust jewelry 1.68 0.44 6.32 

Other—Simple 

(e.g., opening and closing driver’s door) 2.23 0.41 12.20 

Put on/remove/adjust hat 1.31 0.69 2.49 

Use chewing tobacco 1.02 0.51 2.02 

Put on/remove/adjust seat belt 1.26 0.60 2.64 

Talk/sing/dance with no indication of passenger 1.05 0.90 1.22 

Smoking-related behavior—cigarette in hand or mouth 0.97 0.82 1.14 

Drink from a container 0.97 0.72 1.30 

Other personal hygiene 0.67* 0.59 0.75 

Bite nails/cuticles 0.45* 0.28 0.73 

Interact with or look at other occupant(s) 0.35* 0.22 0.55 

Secondary Task 

Look at left-side mirror/out left window 1.09* 1.01 1.17 

Look at right-side mirror/out right window 0.95 0.86 1.05 

Check speedometer 0.32* 0.28 0.38 

* Asterisk indicates a significant odds ratio. These ratios are also shown in bold. 

xxi 
Several other tasks had significantly high odds ratios. Interacting with a dispatching device (the 

dispatching devices observed in this data set featured a small keyboard, which drivers often 

placed on their steering wheel and typed with one or both hands while driving) (OR = 9.9) and 

dialing a cell phone (OR = 5.9) were two noteworthy complex tertiary tasks associated with 

substantially elevated risk in being involved in a safety-critical event. Reaching for objects—

electronic devices such as a video camera (OR = 6.7) or other objects (OR = 3.1)—is noteworthy 

because of their common occurrence as found in the PAR analysis (highlighted later). 

An interesting finding from the analyses was the result for cell phone use. As indicated, reaching 

for or dialing a cell phone was indicated to be a high-risk task. However, talking or listening on a 

hand-held phone was found to have an odds ratio that was not significantly different than 1.0 

(thus, it did not elevate the likelihood of being involved in a safety-critical event). Furthermore, 

talking or listening on a hands-free phone (defined as the driver talking into a headset when it 

was apparent he/she was not talking to a passenger) provided a significant protective effect (OR 

= 0.4), as did Citizens Band (CB) radio use (OR = 0.6). It is noteworthy that recent empirical 

studies have shown benefits of hands-free phone interfaces (Shutko, Mayer, Laansoo, & Tijerina, 

2009). This finding from the current study may provide support for “hands-free” cell phone 

policies and regulations; as of August 2009, six States have banned hand-held cell phone use, but 

allow hands-free cell phone use; however, no State has banned hands-free cell phone use 

(Governor’s Highway Safety Association, 2009). Furthermore, the positive findings for 

“listening and talking” are consistent with results of two recent naturalistic studies with lightvehicle drivers. In the first study, protective effects (defined as decreasing the risk of a safetycritical event) were found for moderately complex tasks, which included talking/listening to 

handheld devices (F. Guo, personal communication, July 7, 2009). In the second study, when 

drivers were using a cell phone, they had lower speed variance (i.e., speeds changed more 

smoothly) and they maintained their eyes on the forward roadway (Sayer, Devonshire, & 

Flannagan, 2007). One hypothesis for the results in the current study is that reaching for a phone 

and dialing a phone, like texting, requires manual manipulation (i.e., hand off wheel) and 

substantial visual attention to complete the task. This visual attention is directed away from the 

forward roadway such that the driver is not effectively, or safely, operating the CMV. Listening 

and talking, on the other hand, does not draw the eyes away from the forward road. However, 

this hypothesis does not consider the impact that “cognitive distraction” may have with listening 

and talking tasks and further research is required to investigate this finding. Nonetheless, based 

on the analysis of safety-critical events from the current study, talking/listening was not found to 

be a risk factor. 

POPULATION ATTRIBUTABLE RISK FOR DISTRACTING TASKS 

Odds ratios and confidence limits only inform part of the story, that is, which tasks are shown to 

increase the likelihood of involvement in a safety-critical event. The other part of the story 

considers the frequency of occurrence of each task (i.e., which task, if removed, would reduce 

the percentage of safety-critical events). For example, tasks that are rare occurrences (as 

indicated by low PAR percentages), even though they might be risky, may not significantly 

increase crashes in the population, nor would their elimination reduce crashes by much. Table 4

shows the results from the PAR analysis for the tertiary and secondary tasks with an odds ratio 

greater than 1.0. As shown in Table 4, tasks are ordered from largest PAR percentage to smallest 

xxii 
PAR percentage. Specific tasks with the largest PAR percentage included: reaching for an object 

(PAR = 7.6), interacting with a dispatching device (PAR = 3.1), and dialing a cell phone (PAR = 

2.5). Why were the PAR percentages for these tasks greater than the other tasks? The reason was 

that these tasks were commonly performed by drivers in the current study. Text messaging, on 

the other hand, though it had a very high odds ratio, was a task performed infrequently by drivers 

in the current study, thus it does not have a high PAR percentage. However, this does not mean 

that it should be ignored. On the contrary, it suggests that if texting while driving becomes more 

prevalent, the frequency of safety-critical events is likely to increase. 

Table 4. Population Attributable Risk and 95% Confidence Intervals for 

Driver Tasks across All Events 

Task PAR 

Percentage LCL UCL 

Complex Tertiary Task 27.46 27.24 27.67 

Interact with/look at dispatching device 3.13 2.84 3.42 

Dial cell phone 2.46 2.02 2.91 

Read book, newspaper, paperwork, etc. 1.65 0.96 2.34 

Look at map 1.08 0.48 1.68 

Text message on cell phone 0.67 0.29 1.04 

Write on pad, notebook, etc. 0.56 -0.16 1.28 

Use calculator 0.22 -1.00 1.43 

Other—Complex 

(e.g., cleaning side mirror, rummaging through a grocery bag) 

0.18 -0.99 1.35 

Moderate Tertiary Task 11.77 11.32 12.23 

Reach for object in vehicle 7.64 7.27 8.02 

Other—Moderate 

(e.g., opening a pill bottle to take medicine, exercising in the cab) 

0.32 -0.92 1.55 

Use/reach for other electronic device 0.23 -1.10 1.56 

Personal grooming 0.21 -1.58 2.00 

Look back in sleeper berth 0.23 -2.24 2.70 

Talk or listen to hand-held phone 0.18 -1.29 1.64 

Eating 0.02 -1.80 1.83 

Simple Tertiary Task 5.96 5.20 6.73 

Adjust instrument panel 0.82 -0.47 2.11 

Put on/remove/adjust sunglasses or reading glasses 0.62 -0.56 1.80 

Talk/sing/dance with no indication of passenger 0.23 -1.12 1.59 

Put on/remove/adjust hat 0.06 -4.85 4.98 

Use chewing tobacco 0.00 -6.75 6.76 

Put on/remove/adjust seat belt 0.04 -5.84 5.92 

Remove/adjust jewelry 0.03 -7.89 7.95 

Other—Simple 

(e.g., opening and closing driver’s door) 

0.02 -7.57 7.62 

Secondary Task 11.71 11.29 12.13 

Look at left-side mirror/out left window 2.25 1.77 2.75 

xxiii 
VISUAL DEMAND FOR DISTRACTING TASKS 

The eye glance analyses conducted on the various tasks provided the needed “why” for the 

findings in the odds ratio analysis. Put simply, tasks that draw drivers’ eyes away from the 

forward roadway were those with high odds ratios. For example, texting, which had the highest 

odds ratio of 23.2, also had the longest duration of eyes off forward roadway (4.6 s over a 6-s 

interval). This equates to a driver traveling the length of a football field, at 55 mi/h, without 

looking at the roadway. Other high visual attention tasks included those that involved the driver 

interacting with technology: calculator (4.4 s), dispatching device (4.1 s), and cell phone dialing 

(3.8 s). 

Technology-related tasks were not the only ones with high visual demands. Non-technology 

tasks, including mundane or common activities, with high visual demands included: writing (4.2 

s), reading a book/newspaper/other (4.3 s), looking at a map (3.9 s), and reaching for an object 

(2.9 s). 

LONG GLANCES AND SHORT GLANCES 

Eye glance analysis was conducted to determine, during the 6-s interval, the drivers’ mean 

duration of eyes off forward roadway (i.e., any time the driver’s eyes were not on the forward 

roadway, either from a single glance or multiple glances). In the current study, CMV drivers’ 

mean duration of eyes off forward roadway was 2.1 s prior to the onset of a crash, 1.7 s prior to 

the onset of a near-crash, 1.6 s prior to the onset of a crash-relevant conflict, and 1.2 s during the 

baseline epoch. Klauer et al. (2006) reported that light-vehicle drivers’ mean duration of eyes off 

forward roadway was 1.8 s prior to the onset of a crash, 1.3 s prior to the onset of a near-crash, 

1.1 s prior to the onset of a crash-relevant conflict, and 0.9 s during the baseline epoch. 

One of the analyses in the current study calculated the odds ratios of the total time eyes off 

forward roadway for five different time durations. Table 5 illustrates the odds ratios across “All” 

events in each of the five time durations. As noted, the total eyes off forward roadway time was 

measured over a 6-s interval for safety-critical events and baseline epochs. Not surprising, longer 

glances of more than 1.5 s were associated with high risk (OR= 1.3) and very long glances of 

more than 2 s had the highest risk (OR = 2.9). These findings (i.e., that long eye glance durations 

away from the forward roadway increase risk) were consistent with light-vehicle results (Klauer 

et al., 2006). For example, Klauer et al. reported that light-vehicle drivers were 2.19 times more 

likely to be involved in a crash/near-crash (compared to a baseline epoch) when total eyes off 

forward roadway time was greater than 2 s. 

xxiv 
Table 5. Odds Ratios and 95% Confidence Intervals for All Events to Assess 

Likelihood of a Safety-Critical Event While Eyes Off Forward Roadway 

Total Eyes Off Forward Roadway Odds 

Ratio LCL UCL 

Less than or equal to 0.5 s 1.36* 1.16 1.58 

Greater than 0.5 s but less than or equal to 1.0 s 0.91 0.80 1.03 

Greater than 1.0 s but less than or equal to 1.5 s 1.07 0.94 1.23 

Greater than 1.5 s but less than or equal to 2.0 s 1.29* 1.12 1.49 

Greater than 2.0 s 2.93* 2.65 3.23 

* Asterisk indicates a significant odds ratio. These ratios are also shown in bold 

An additional significant result was found for very short total eyes off road durations (less than 

or equal to 0.5 s). Klauer et al. (2006) found a similar trend with light-vehicle drivers; however, 

the odds ratio in the Klauer et al. study was not statistically significant. As shown in Table 5, a 

significant odds ratio was found when the total time eyes off forward roadway was less than or 

equal to 0.5 s (OR = 1.4). Though this may be a spurious finding, one possible explanation is the 

scanning behavior of CMV drivers is likely to be different than the scanning behavior of lightvehicle drivers. More specifically, CMV drivers are taught to continually monitor their 

environment and regularly scan their mirrors (FMCSA, 2009b). Moreover, large trucks have 

many blind spots and it can be difficult for CMV drivers to locate other vehicles in their mirrors. 

It is possible these mirror-checking behaviors lasted longer than 0.5 s in the current study and 

more complex tasks required many short duration glances. There is some support for this 

contention in the eye glance analyses results as the mean length of longest glance for secondary 

tasks (e.g., checking mirrors) was greater than 0.5s: 2.8 s for crashes, 1.5 s for near-crashes, 1.1 s 

for crash-relevant conflicts and 1.5 s for unintentional lane deviations (discussed in section 

5.5.4.6). Also, the mean number of glances away from the forward roadway was 2.7 for nearcrashes, 3.1 for crash-relevant conflicts, and 3.2 for unintentional lane deviations when complex 

tertiary tasks were considered compared to 1.3 for crashes, 1.6 for near-crashes, 1.7 for crashrelevant conflicts and 2.3 for unintentional lane deviations when moderate tertiary tasks were 

considered (see sections 5.5.3.1 and 5.5.3.2). It is also possible that the significant finding for 

glances under 0.5 s was because drivers may have been in situations (e.g., following too closely 

behind a lead vehicle) that would require longer and more frequent glances to monitor the 

forward roadway. Such situations would likely result in more safety-critical events and may help 

explain the significant odds ratio. Further analysis would be required to test these hypotheses. 

The data set is available to conduct a more detailed eye glance analysis with drivers who rarely 

scanned the driving environment and/or mirrors (i.e., primarily focused on the forward roadway). 

Such an analysis could investigate the risk implications of not regularly scanning the driving 

environment. At this point, it is an interesting finding and would require further research. 

xxv 
FINAL CONSIDERATIONS 

One of the objectives in this study was to compare results between this CMV study and the lightvehicle study performed by Klauer et al. (2006). Though a few comparisons have been 

described, perhaps the most important finding, common across both studies, is that driver 

distraction is prevalent in both light vehicles and CMV operations. It is difficult to make clear 

comparisons across studies because of the caveats noted previously, including: mirror check as a 

distraction type and the expected mirror use differences between light-vehicle and CMV drivers, 

different data collection time frames, different distraction types, and small number of crashes in 

the CMV study. Nonetheless, an important take-away is that driver distraction is an important 

contributing factor in safety-critical events for both light-vehicle drivers and CMV drivers. 

The current study resulted in a number of important findings related to driver distraction and 

CMV driver safety. Because this is one of the first naturalistic studies focused on CMV drivers, 

it will be important to conduct follow-on research to assess the robustness of these findings. As 

outlined, many of the results were consistent with previous distraction studies with light-vehicle 

drivers. However, there were also some results, such as the high risk associated with short 

glances, that may be novel to CMV operations. 

Finally, it is important to highlight that some results of the current study and other recent 

naturalistic driving studies (e.g., Klauer et al., 2006; Sayer et al., 2007) are at odds with results 

obtained from simulator studies (e.g., Beede & Kass, 2006; Strayer, Drews, & Johnston, 2003). 

Future research may explore the reasons why such studies often do not reflect studies conducted 

in actual driving conditions (i.e., the full context of the driving environment). It may be, as Sayer 

et al. note, that controlled investigations cannot account for driver choice behavior and risk 

perception as it actually occurs in real-world driving. If this assessment is accurate, the 

generalizability of simulator findings, at least in some cases, may be greatly limited outside of 

the simulated environment. 

SUMMARY FINDINGS AND RECOMMENDATIONS 

The following findings and recommendations by the authors to address driver distraction in 

CMV operations were formulated through a review of this study. These findings and 

recommendations provide a summarized list of critical issues and are ordered from general 

recommendations (e.g., maintain eyes on forward roadway) to more specific recommendations 

(e.g., no texting). These recommendations focus on improving CMV safety by reducing driver 

distraction and are intended to provide key take-aways for fleet-safety managers on how they 

might improve safety by applying the findings from the current study. The authors found and 

recommended that: 

 Fleet safety managers engage and educate their drivers, and discuss the importance of 

being attentive and not engaging in distracting tasks or behaviors. Even routine types of 

behaviors (e.g., reaching for an object, putting on sunglasses, or adjusting the instrument 

panel) can distract and may lead to a safety-critical event. 

xxvi 
xxvii 

 Fleet safety managers develop policies to minimize or eliminate the use of in-vehicle 

devices while driving. The authors also urge fleet safety managers to be cognizant of 

devices that drivers may bring in the truck cab and use while driving. These may seem 

innocuous (e.g., calculator), but may increase crash risk, if used while driving. 

 Drivers not use dispatching devices while driving and that fleet safety managers educate 

drivers on the danger of interacting with these devices while driving. Similar to manually 

dialing a cell phone, if drivers must interact with a dispatching device, the authors 

recommend that drivers do so only when the truck is stopped. 

 Drivers not text while driving. This is a relatively new phenomenon, but data from the 

current study clearly show an increased risk when drivers text while driving. 

 Drivers not manually dial cell phones while driving. If a call must be made, the authors 

suggest that drivers pull off the road to a safe area, and then dial to make the phone call. 

Another option, requiring further study, is the use of voice-activated, hands-free dialing, 

which would allow the driver to maintain eyes on the forward roadway. However, this 

approach may have implications for “cognitive distraction” (though visual distraction 

would be expected to be reduced). 

 Drivers not read, write, or look at maps while driving. What may seem like quick, 

commonly performed tasks, such as reading, writing, and looking at maps, were found to 

significantly draw visual attention away from the forward roadway. These activities, 

which may be integral to the driver’s job, are not integral to operating the vehicle and the 

authors recommend that such tasks never be performed while the vehicle is on and in 

motion. 

 Drivers not be prohibited from talking on a cell phone or CB radio as this was not found 

to increase risk. Regarding cell phones, the findings from the current study clearly 

indicated that manual device interaction, and the associated high eyes off forward road 

time, were the key factors to increased risk. Though “visual distraction” is foremost in 

manual device interaction, potential “cognitive distraction” of talking/listening was not 

measured in the current study. However, based on the analysis of safety-critical events 

from the current study, talking or listening were not risk factors. 

 Designers of dispatching devices consider the increased risk associated with using their 

devices and work to develop more user-friendly interfaces that do not draw the driver’s 

eyes from the forward roadway. Possible solutions include a hands-free interface and/or 

blocking manual use while the vehicle is in motion. 

 Designers of instrument panels consider the increased risk of adjusting panel controls. 

The authors suggest that designs be intuitive, user-friendly, and not require long glances 

away from the forward roadway. 

 Further research be undertaken into the protective effects of performing certain tasks. 

Identifying the characteristics of tasks that had protective effects may lead to safety 

countermeasures. 

1. INTRODUCTION 

1.1 PROJECT OVERVIEW 

The objective of this study was to characterize commercial motor vehicle (CMV) driver 

inattention in crashes, near-crashes, crash-relevant conflicts, unintentional lane deviations, and 

baseline (i.e., uneventful, routine driving) epochs that were recorded in two studies sponsored by 

the Federal Motor Carrier Safety Administration (FMCSA): the Drowsy Driver Warning System 

Field Operational Test (DDWS FOT) (Hanowski et al., 2008) and the Naturalistic Truck Driving 

Study (NTDS) (Blanco et al., in press). The characterization of these events focused on 

identifying secondary and tertiary tasks (i.e., tasks that may divert the driver’s attention away 

from the primary task of driving) and other activities that drivers engaged in prior to events as 

well as the frequency and percentage of these secondary tasks, tertiary tasks, and other activities. 

As outlined by Ablassmeier et al. (2007), secondary tasks are related to the driving task (e.g., 

turn-signal use), but are not necessary to keeping the vehicle on course; tertiary tasks are 

extraneous tasks (e.g., eating) that are not related to driving. 

1.1.1 Overview of Commercial Motor Vehicle Crash Statistics 

In the Federal Motor Carrier Safety Regulations (2007), a CMV is defined as a self-propelled or 

towed motor vehicle used on a highway in interstate commerce to transport passengers or 

property when the vehicle meets any of these standards: 

 Has a gross vehicle weight rating or gross combination weight rating, or gross vehicle 

weight or gross combination weight, of 4,536 kg (10,001 pounds) or more, whichever is 

greater. 

 Is designed or used to transport more than eight passengers (including the driver) for 

compensation. 

 Is designed or used to transport more than 15 passengers, including the driver, and is not 

used to transport passengers for compensation. 

 Is used in transporting material found by the Secretary of Transportation to be hazardous 

under 49 U.S.C. 5103 and transported in a quantity requiring placarding under 

regulations prescribed by the Secretary under 49 CFR, subtitle B, chapter I, subchapter C. 

In 2007, 413,000 large trucks were involved in traffic crashes in the U.S. and 4,584 were 

involved in fatal crashes; 4,808 people were killed in these crashes (National Highway Traffic 

Safety Administration, 2008). Of the fatalities resulting from a large truck crash, 75 percent of 

those killed were occupants of other vehicles, 8 percent were non-occupants, and 17 percent 

were occupants of large trucks. Thus, trucking safety impacts all road users, and all who share 

the road with large trucks will benefit from the identification of issues and contributing factors 

associated with these crashes. Only with a clear understanding of the factors that contribute to 

crashes can countermeasures, aimed at improving safety, be identified, developed, and deployed. 

1 
1.2 DRIVER ERROR 

For any given safety-critical event (e.g., crashes, near-crashes, crash-relevant conflicts, and 

unintentional lane deviations), various contributing factors may play a role. These factors include 

environmental, vehicle, and driver factors. Research has found that driver factors (including 

driver errors) are by far the most prominent contributing factor in traffic crashes (Treat et al., 

1977; Wierwille et al., 2002). The focus of the current study was to investigate the role that one 

type of driver error, driver distraction, plays in large-truck safety-critical events. The next section 

describes why driver distraction is a key contributing factor in safety-critical events, while 

section 2 provides several definitions of driver distraction, taken from the literature, and 

highlights key distraction-related studies. 

1.2.1 Driver Distraction as a Contributing Factor in Critical Events 

Driver distraction occurs when inattention leads to a delay in recognition of information 

necessary to accomplish the driving task. Crash database analyses have estimated that driver 

distraction is a primary contributing factor in 25–30 percent of crashes (Wang, Knipling, & 

Goodman, 1996). This statistic was based on police accident reports completed at the crash 

scene. In these cases, the investigating police officer indicated "distraction" or "inattention" on 

the report if the driver admitted to being distracted/inattentive and/or if distraction/ 

inattentiveness was readily apparent based on eyewitness observation. Because this method has 

the potential for recording inaccurate or incomplete information, most transportation researchers 

believe the actual percentage of distraction-related crashes may be higher than 25 to 30 percent. 

A study by Hanowski, Perez, and Dingus (2005) investigated CMV driver distraction by 

studying distraction-related critical events with instrumented vehicles on normal, revenueproducing deliveries (i.e., a naturalistic approach). Hanowski et al. analyzed 178 distractionrelated critical events and identified 34 unique distraction types. Further, they found that a small 

percentage of the participating drivers were responsible for a disproportionate number of these 

distraction-related critical events. Analysis of the video data collected prior to, during, and after 

the critical event found that the duration of the secondary or tertiary task, along with the visual 

demand associated with performing that task, contributed to the occurrence of distraction-related 

critical events. There were several limitations with the Hanowski et al. study. Most significant 

was the lack of exposure data (i.e., the analysis did not compare the critical event data to 

baseline, or normal/normative/uneventful driving data). 

A noteworthy light-vehicle naturalistic study that did include exposure data was conducted using 

100 instrumented light vehicles (i.e., passenger automobiles) (Klauer, Dingus, Neale, Sudweeks, 

& Ramsey, 2006). The “100-Car Study” found that driving inattention was present in 78 percent 

of light-vehicle crashes (n = 69). This finding was much higher than the 25–30 percent identified 

in previous crash database analyses (Wang et al., 1996). The naturalistic approach used by 

Klauer et al. (2006) provided an example of how the limitations in crash databases may be 

overcome to generate an accurate, more valid picture of the real impact of driver distraction. 

At present, information related to the impact of driver distraction on CMV safety-critical events 

is unclear. Klauer et al. (2006) described prior research using naturalistic data from light vehicles 

to investigate the issue of driver distraction; however, little research is directed at investigating 

this issue in CMVs. The objective of the current study was to fill this gap using data from two 

2 
3 

FMCSA-sponsored CMV studies (Hanowski et al., 2008; Blanco et al., in press), which provided 

a large, naturalistic data set that allowed researchers to study pre-critical event driver behavior 

and assess the impact that driver distraction has on critical event occurrence. 

Before outlining the methods used to analyze these two CMV data sets, it is worthwhile to 

highlight previous key research that has focused on assessing driver distraction. The information 

gleaned from this literature review informed the development of the methodology used to 

analyze the data in the current study. 

2. REVIEW OF THE LITERATURE 

2.1 DEFINING DRIVER DISTRACTION 

The literature defines “driver distraction” several different ways. Smiley (2005, p. 1) defined it 

as “misallocated attention.” Ranney, Mazzai, Garrott, and Goodman (2000, p. 1) indicated that 

“driver distraction may be characterized as any activity that takes a driver’s attention away from 

the task of driving.” Ranney et al. grouped driver distraction into four separate categories: visual, 

auditory, biomechanical, and cognitive distraction. Stutts et al. (2005, p. 1094) defined driver 

distraction as “an object or event that draws one’s attention from the task of driving.” 

Pettitt, Burnett, and Stevens (2005) argued for a more comprehensive definition of distraction in 

order to compare data across studies and assist in the categorization of crash data. For this study, 

the authors compared several different definitions of distraction to determine what components 

need to be strengthened or added. The following definitions of distraction were used for 

comparison by Pettitt et al.: 

 Something that distracts the attention and prevents concentration (Pollarad, 1994; p. 234). 

 Attention given to a non-driving related activity, typically to the detriment of driving 

performance (International Organization for Standardization, 2007). 

 A driver is delayed in the recognition of information needed to safely accomplish the 

driving task because some event, activity, object or person within or outside the vehicle 

compelled or tended to induce the driver’s shifting attention away from the driving task 

(American Automobile Association Foundation for Traffic Safety, as cited in Young, 

Regan, & Hammer [2003]). 

After comparing the definitions above, Pettitt et al. (2005) indicated that a more comprehensive 

definition of distraction included the following four components: the difference between 

distraction and inattention; the recognition that distraction can be internal or external to the 

vehicle; that distraction can be categorized into four types (visual, cognitive, biomechanical, and 

auditory); and the effect of distraction on the driving task. 

In order to develop a new, more comprehensive definition, Pettitt et al. (2005) assessed a workrelated road traffic crash database that contained data on 2,114 vehicle crashes from 1996 to 

2004 in the Midlands region of the United Kingdom. The authors grouped the data in several 

different ways, including: across all crashes, all distraction-related crashes, all distraction crashes 

without inattention by crash severity (slight damage, serious damage, and fatal), and all 

distraction sources (internal versus external). 

The results illustrated the importance of differentiating between distraction and inattention. For 

example, when grouping all crashes together, 5 percent were shown to be fatal; however, when 

grouping all distraction-related crashes (including inattention), 8 percent were shown to be fatal. 

And, when grouping all distraction-related crashes (excluding inattention), 4 percent were shown 

to be fatal. In addition, when all crashes were grouped by the distraction source, 30 percent of the 

distractions were found to be external, while 15 percent were found to be internal. 

5 
From these results, Pettitt et al. (2005) developed a new, more comprehensive definition of 

distraction that accounted for all four key components, which indicated that driver distraction 

occurred: 

 When a driver is delayed in the recognition of information necessary to safely maintain 

the lateral and longitudinal control of the vehicle (the driving task) (Impact). 

 Due to some event, activity, object or person, within or outside the vehicle (Agent). 

 That compels or tends to induce the driver’s shifting attention away from fundamental 

driving tasks (Mechanism). 

 By compromising the driver’s auditory, biomechanical, cognitive or visual faculties, or 

combinations thereof (Type) (Pettitt et al., 2005; p. 11). 

A study by Hanowski et al. (2005) used the concepts from Pettitt et al. (2005) and provided a 

definition that could be implemented in the analysis of naturalistic driving data. In addition to 

providing a definition of driver distraction that could guide naturalistic data analysis, Hanowski 

et al. developed a taxonomy (Table 6) of secondary/tertiary tasks by analyzing naturalistic 

critical incident data. To accomplish this, video of critical incidents collected during a 

naturalistic heavy-vehicle study were reviewed to determine what behaviors the driver engaged 

in prior to the occurrence of a critical incident. These behaviors reflect the Agents and 

underlying Mechanisms, as described by Pettitt et al., that can distract and lead to a safetycritical event (or Impact). 

In the current study, safety-critical events (Impact) and baseline epochs (i.e., normative routine 

driving) were filtered from a continuous CMV naturalistic data set and reviewed for potential 

distractions (Agents). Because the data set included video of the driver, biomechanical and visual 

distraction was the Type of distraction evaluated. As there was no audio with the video 

recording, auditory distraction could not be investigated. In addition, as described later, though it 

may be possible to investigate cognitive distraction, it was not considered in the current study. 

6 
Table 6. Distraction Taxonomy 

Distraction Definition 

Talking on CB radio Driver is holding CB to mouth and talking; usually looking forward; one hand off 

the wheel 

Adjusting CB Driver is adjusting knobs, with right arm extended up, on CB receiver located on 

ceiling at the front and center of cab; glancing at CB periodically; one hand off 

the wheel 

Looking at CB Driver is looking up at CB receiver located on ceiling at the front and center of 

cab; both hands on the wheel 

Adjusting radio Driver is reaching to the music radio, on center console of cab, adjusting station 

or volume; glancing at radio periodically; one hand off the wheel 

Looking at radio Driver is looking at the music radio, down and to the right, on center console of 

cab; both hands on the wheel 

Dialing cell phone Driver is looking down at cell phone in hands, dialing number; one hand off the 

wheel 

Plugging in cell 

phone 

Driver is plugging in battery charger to bottom of cell phone; usually looking at 

the phone; one hand off the wheel 

Talking on cell 

phone 

Driver is holding cell phone up to ear and talking on it; usually looking forward; 

one hand off the wheel 

Answering ringing 

cell phone/Looking 

at cell phone display 

Driver is answering ringing cell phone; reaches to middle console, picks up 

phone, looks down at phone several times, but never puts it to ear; one hand off 

the wheel 

Phone call/hanging 

up cell phone 

Driver makes phone call and is hanging up cell phone; reaches down to floor to 

put phone back; usually looks down; one hand off the wheel 

Lighting cigarette Driver is lighting a cigarette; often looking at cigarette; one or both hands off the 

wheel 

Getting cigarette Driver is removing a cigarette from rest of pack; often looking at pack; one hand 

off the wheel 

Blowing smoke Driver has head turned, blowing smoke out the window; usually holding 

cigarette with one hand off the wheel 

Drinking Driver is drinking out of a soda bottle or mug; usually looking forward; one hand 

off the wheel 

Getting Food Driver is getting food out of a bag in his/her lap; often looking at bag/food; one 

or both hands off the wheel 

Eating/Talking Driver is eating food and looking at passenger; one hand off the wheel 

Talking to 

passenger 

Driver is talking to another person in the cab; sometimes looking to the right at 

passenger; both hands on the wheel 

Reaching in pocket Driver is reaching for something in either front shirt pocket or back pant pocket; 

usually looking forward but moving around in seat; one hand off the wheel 

Reaching to floor Driver is reaching for something either on the floor of the cab (down and to the 

right) or somewhere in the cab; usually looking forward; takes one hand off the 

wheel 

Looking at 

paperwork 

Driver is holding paperwork on steering wheel and is looking down at it; one or 

both hands off the wheel 

Looking at floor Driver is looking at/for something on the floor (down and to the right); both 

hands on the wheel 

Looking at 

instrument panel 

Driver is looking down, through steering wheel, at instrument panel containing 

speedometer and gauges; both hands on the wheel 

7 
Distraction Definition 

Looking down Driver is looking down; either in lap at something unknown, or at hands; may 

have one or both hands off the wheel 

Looking up Driver is looking up at the visor; both hands on the wheel 

Toothpick/Visor 

mirror 

Driver is looking up in the visor mirror, while picking teeth with a toothpick; one 

hand off the wheel 

Looking right—

outside 

Driver has head turned to the right, either looking in passenger side mirror, or 

out passenger window; usually both hands are on the wheel 

Looking left—

outside 

Driver has head turned to the left, either looking in driver side mirror, or out 

driver window; usually both hands are on the wheel 

Looking outside Driver is looking at a road sign, something along side of the road, or another 

car, but is still looking out front window; both hands on the wheel 

Adjusting in seat Driver is adjusting himself/herself in driver seat; usually looking forward; both 

hands on the wheel 

Taking off jacket Driver is taking off jacket; usually looking forward; one hand off the wheel 

Let go of wheel Driver is looking forward but does not have either hand on wheel while dancing 

in seat; is not holding anything 

Wiping dash Driver is wiping off dash of cab with a cloth; usually looking at dash; one hand 

off the wheel 

Rubbing face Driver is wiping face off or rubbing eyes; usually looking forward but eyes may 

close for a few moments; one hand off the wheel 

Brushing hair Driver is using a hairbrush to brush hair; looking forward; one hand off the 

wheel 

Coughing Driver is coughing; usually closes eyes for a short period of time; both hands on 

the wheel 

Yawning Driver is yawning; usually closes eyes for a short period of time; both hands on 

the wheel 

Source: Hanowski, Perez, and Dingus (2005) 

2.2 KEY DISTRACTION STUDIES 

The following section reviews several key distraction studies from the extant literature. Although 

most of these studies provide data from light vehicles only (due to the lack of heavy-vehicle 

studies in this area), they demonstrate past and present issues related to driver distraction and the 

contention that driver distraction will continue with the proliferation of new in-vehicle 

technologies. See the Annotated Bibliography for a list of distraction studies from the 

literature search. 

One of the earliest, and perhaps most cited, driver distraction studies was conducted by Indiana 

University by Treat et al. (1977). Data were collected between 1972 and 1975 and grouped into 

three “levels.” Level A was a collection of baseline data and included vehicle registration and 

driver’s license information as well as surveys from the general population. Level B was a data 

set collected from police accident reports. Investigators identified crashes by listening to police 

scanners and then went to the scene of the crash to collect data. A total of 2,258 crashes were 

investigated (crashes involving heavy vehicles and vehicles pulling trailers were not included). 

Level C data was an in-depth investigation of Level B data and included 420 crashes. For each 

crash in Level C, there was an investigation of human, environment, and vehicle factors that may 

8 
have contributed to the crash. The drivers were interviewed by a psychologist or sociologist and 

participated in dynamic vision and driver knowledge tests. An automotive engineer also 

inspected the vehicle(s) involved in the crash. The data from these crashes were divided into 

three sections: accident summary, identification of causal factors, and a probability assessment to 

determine how likely the factor was the reason for the accident. 

The results of this study found that human factors were most often (71–93 percent) cited as the 

cause in the crashes, followed by environment (12–34 percent) and vehicle factors (5–13 

percent). Five major categories of human direct causes were identified: recognition errors, 

decision errors, performance errors, critical non-performance errors, and non-accident/intentional 

involvement. In addition, five specific human causes were identified: improper lookout (18–23 

percent), excessive speed (8–17 percent), inattention (10–15 percent), improper evasive action 

(5–13 percent), and internal distraction (6–9 percent). It can be seen that two of the five specific 

human causes were related to inattention and distraction, indicating their prevalence during 

vehicle crashes. 

A second key distraction study, conducted by Goodman, Tijerina, Bents, and Wierwille (1999), 

provided more up-to-date research looking at the then-growing use of cell phones while driving. 

The cell phone has since become an integral and, for some, essential communication tool. The 

CTIA–The Wireless Association (2007) reported approximately 50 million U.S. cell phone users 

in 1996; this number skyrocketed to an estimated 241 million U.S. cell phone subscribers in 

2007. Approximately 85 percent of cell phone owners use their cell phones while driving and the 

rate of cell phone-related crashes has increased over the years (Goodman et al., 1999). Several 

researchers have also found that cell phone use while driving increases the risk of having a crash 

(Redelmeier & Tibshirani, 1997; Goodman et al.; Laberge-Nadeau et al., 2003; Strayer et al., 

2003). The dangers of cell phone use while driving may not be limited to the manipulation of the 

cell phone itself (i.e., answering a cell phone, holding a cell phone, etc.), but may also relate to 

the cognitive processing while engaged in a conversation (Redelmeier & Tibshirani, 1997; 

Goodman et al.; Strayer & Johnston, 2001; Harbluk, Noy, & Eizenman, 2002; Strayer et al., 

2003; Patten, Kircher, Östlund, & Nilsson, 2004). Though cognitive processing may play a role, 

naturalistic data (Klauer et al., 2006) clearly show that keeping a driver’s eyes on the forward 

roadway is a critical component in safe driving and avoiding vehicle crashes (Hanowski, 2009). 

Therefore, any evaluation of driver distraction must consider the impact that secondary and/or 

tertiary tasks have on drawing the driver’s eyes away from the forward roadway. 

Goodman et al. (1999) investigated North Carolina police accident report data in order to 

determine the rate of cell phone use during traffic crashes. The authors developed a list of key 

words that police officers may have used in a police accident report to describe the use of a cell 

phone. These terms included “answer,” “cell,” “handset,” and “ring.” Databases were retrieved 

from 1989, 1992, 1993, 1994, and the first part of 1995 (limited data prohibited use of the 1990 

and 1991 databases). A database search using the list of key words noted above was conducted. 

Each narrative was carefully reviewed to determine if cell phone use was present during the 

crash. If cell phone use was present during the crash, the valid narratives from the police accident 

reports were classified into categories. The results of that study showed that the task with the 

highest frequency of being reported across all years was “using cellular telephone” (i.e., talking 

on a cell phone). In 1989, 6 crashes were related to talking on a cell phone, 8 crashes in 1992, 5 

crashes in 1993, 12 crashes in 1994, and 11 crashes in 1995. The overall trend of all cell phone9 
related tasks (e.g., answering a cell phone, dialing a cell phone, reaching for a cell phone, etc.) 

increased over time (13.2 total cell phone-related crashes in 1989 and 30 cell phone-related 

crashes in 1995), reinforcing the need for additional research to support the association between 

these tasks and traffic safety. As noted, analyses on heavy-vehicle drivers were not included, so 

the extent to which these data compare to the CMV industry is unknown. 

A final distraction study worth noting is the Large Truck Crash Causation Study (LTCCS), 

which assessed the causal factor as well as associated factors for fatal crashes involving large 

trucks (FMCSA, 2005). Considered the most comprehensive safety database for crashes 

involving large trucks, the LTCCS collected data on crashes at 24 sites in 17 states from 2001 

through 2003. Investigators traveled to crash sites to collect crash scene data and conducted 

thorough interviews with drivers about their conditions before the crash and inspected the trucks. 

Critical events, critical reason, and other crash-associated factors to assess crash risk are coded in 

the LTCCS. Each of these terms is defined below: 

 Critical Event: The action or event that put the vehicle or vehicles on a course that made 

the collision unavoidable. The critical event is assigned to the vehicle that took the action 

that made the crash inevitable. 

 Critical Reason: The immediate reason for the critical event (i.e., the failure leading to 

the critical event). The critical reason is assigned to the vehicle coded with the critical 

event in the crash. It can be coded as a driver error, vehicle failure, or environmental 

condition (roadway or weather). 

 Associated Factors: The person, vehicle, and environmental conditions present at the time 

of the crash. No judgment is made as to whether any factor is related to the reason for a 

particular crash, just whether the factor was present. The list of the many factors that can 

be coded provides enough information to describe the circumstances of the crash. 

The results of the LTCCS indicate that 9 percent of the crashes studied were attributed to driver 

inattention, 8 percent were attributed to an external distraction (i.e., the driver was looking at 

something outside of his/her truck), and 2 percent were attributed to an internal distraction. It is 

important to note that these driver errors were determined to be the causal factor of the crash 

(i.e., had they not been present, the crash would not have happened), but if these driver errors 

had also been considered as an associated factor, they would likely result in higher percentages. 

2.3 NATURALISTIC DATA COLLECTION 

As noted earlier, much of the distraction-while-driving research is based on police accident 

reports. This section describes the naturalistic data collection method and explains how it 

addresses several of the inherent limitations of a database analysis approach that relies on police 

accident reports. 

While police accident reports provide useful information to assess crash occurrence, these 

reports were not designed to determine, with reliability, issues related to pre-crash driver 

behavior or eye-glance patterns. For example, police-reported data is retrieved after a crash has 

occurred. Police officers attempt to re-create the crash scene by interviewing drivers and 

witnesses. However, drivers may not remember details of what happened prior to the crash (e.g., 

10 
reaching for the radio, feeling drowsy, looking away from the forward road) or may be hesitant 

to report it to the officer for fear of embarrassment or getting in additional trouble. Additionally,

crashes are rare occurrences, compared to near-crashes or other close-calls, providing only a 

limited amount of data to use for such research. 

Underlining the importance of naturalistic data is a conclusion made by Sayer et al. (2007) after 

conducting a naturalistic driving study with 36 light-vehicle drivers. The authors noted that the 

driver behavior observed in a real-world environment was not necessarily consistent with what 

was observed in controlled data collection environments (e.g., test track or driving simulator). As 

such, naturalistic data is a key method for understanding driver behavior as controlled studies 

cannot account for the effects of driver choice and perceived risk (Sayer et al.). 

A naturalistic data collection method has been used in several studies (Hanowski, Wierwille, 

Garness & Dingus, 2000; Klauer et al., 2006; Hanowski et al., 2008; Blanco et al., in press) to 

provide a more complete picture of the driver’s behavior prior to a crash. In the naturalistic 

approach research participants are asked to drive an instrumented vehicle as they would drive 

their personal/company vehicle. Each vehicle typically contains several video cameras (e.g., 

recording views of the face, over-the-shoulder, front view, rear-view, right/left side view, and 

foot pedals) and vehicle sensors to collect data on vehicle speed, global positioning system 

(GPS), braking intensity, steering input, forward range to a lead vehicle, and many additional 

measures. These data are generally collected continuously; that is, the data collection system is 

started as soon as the vehicle ignition starts and continues to record until the vehicle is turned off. 

This enlightening method enables researchers to see video of exactly what the driver was doing 

prior to a crash, in addition to assessing the driving environment (e.g., road type, traffic 

conditions, weather conditions, etc.). Continuous data collection also provides a greater amount 

of data for use in analyses as it captures more than just crash data. For instance, all near-crashes 

and close calls are recorded as well as baseline (normative/uneventful) data to be used as a 

comparison or control. 

One of the first large-scale studies to use this data collection method was the 100-Car Study 

(Klauer et al., 2006). In this study, naturalistic data were collected over an 18-month period from 

100 light vehicles in the Washington, DC, metropolitan area. The purpose of the study was to 

collect information on critical events. The naturalistic data collection method allowed researchers 

to obtain specific pre-crash data from video cameras installed inside the vehicle. The data set for 

that analysis included all crashes (n = 69) and near-crashes (n = 761) as well as 20,000 baseline 

epochs of normal driving to use for comparison. During the data reduction process, data analysts 

were able to mark various distracting tasks and behaviors that occurred prior to a critical event. 

In addition, eye glance analysis was conducted prior to each crash. Results showed that 78 

percent of light-vehicle crashes and 65 percent of near-crashes contained at least one of the four 

types of inattention listed below: 

 Secondary task distraction—Driver behavior that diverts the driver’s attention away from 

the driving task. This may include talking/listening to hand-held device, eating, talking to 

a passenger, etc. 

 Driver drowsiness—Driver behavior that includes eye closures, minimal body/eye 

movement, repeated yawning, etc. 

11 
 Driving-related inattention to the forward roadway—Driver behavior that is directly

related to the driving task, but diverts driver’s attention away from the forward field of 

nt 

. 

shes, and 5,000 of the 20,000 baseline 

prior 

ology. A similar distraction-analysis approach was used in this current 

section 

 naturalistic data collection approach. 

e., 

tified and categorized (and the main cause of the distraction was noted). 

 if 

view. This includes checking the speedometer, checking blind spots, observing adjace

traffic prior to or during a lane change, looking for a parking spot, and checking mirrors

 Non-specific eye glance away from the forward roadway—Driver behavior that includes 

moments when the driver glances, usually momentarily, away from the roadway, but at 

no discernable object, person, or known location. 

During this study, an eye glance analysis was also conducted to determine eyes off forward 

roadway time. Data analysts reviewed all crashes, near-cra

epochs to determine eye glance position for 5 s prior to the event and 1 s after the event (6 s 

to the trigger for baseline epochs). Crashes and near-crashes where the driver was not considered 

to be at-fault or where the driver was rear-ended by another vehicle were not included in the 

analysis. Odds ratios were calculated using the eye-glance data and showed that more than 18 

percent of at-fault crashes and near-crashes in an urban environment were attributed to eyes off 

the forward roadway. 

The 100-Car Study gathered a great deal of useful data because of the continuous, naturalistic 

data-collection method

study; however, naturalistic CMV data, rather than light-vehicle data, were used. 

2.4 DISTRACTION-RELATED STUDIES USING NATURALISTIC DATA 

COLLECTION IN HEAVY VEHICLES 

Several CMV studies have used the naturalistic data collection method. The following 

outlines three key CMV distraction studies using the

Hanowski et al. (2005) conducted the first known distraction-related analysis using naturalistic 

CMV data. The data were collected in a previous study in which approximately 140,000 miles of 

naturalistic data were collected from 41 different CMV drivers (Dingus et al., 2002). Each 

participant drove an instrumented heavy vehicle for approximately 10 days. The data collected 

included video of the driver’s face, the forward roadway and adjacent lanes, along with 

performance data such as speed, braking, and steering. Data were not collected continuously; 

rather, only critical events were recorded based on a trigger method. For example, if the driver’s

longitudinal acceleration was greater than or equal to the pre-set threshold for that measure (i.

indicating hard braking), the data recording system would save buffered data for a period of time 

surrounding the event. This method led to a set of 2,737 safety-related critical events that were 

later used for analysis. 

During the first step of the analysis, the critical event data were reviewed and 178 distractionrelated events were iden

Next, exposure data were determined. Baseline data were collected by having the driver press a 

button and provide a self-assessment of drowsiness at timed intervals. Baseline epochs were 

selected for each driver based on the number of critical events from each driver. For example, if 

a driver had between 4 and 9 distraction-related events, one baseline epoch was selected; if a 

driver had between 10 and 20 distraction-related events, 2 baseline epochs were selected; and

12 
drivers had more than 20 distraction-related events, 3 baseline epochs were selected. The final 

step in the analysis was to conduct eye glance analysis on the 20 s surrounding the critical event 

(10 s prior to the trigger and 10 s after the trigger). Trained data analysts reviewed the video and 

marked the glance location and duration during the 20-second interval. 

Thirty-six different distraction types across the 178 distraction-related critical events were 

identified, as shown in Table 6. Thirty-three of the 41 drivers had one or more distraction-related 

critical events and two of the drivers accounted for 24 percent of the total distraction-related 

events. This distribution of distraction-related critical events was consistent with a second study 

in which it was found that a relatively small number of the drivers (6 percent) were responsible 

for a disproportionate number of the distraction-related critical events (24 percent; Hanowski et 

al., 2000). Study results also showed that tertiary tasks can impact a driver’s situation awareness 

(eyes on the road) and, potentially, adversely affect other road users. 

A second study by Barr, Yang, Hanowski, and Olson (2005) investigated the prevalence of 

driver drowsiness in local/short-haul (L/SH) drivers and the relationship between drowsiness and 

distraction. This was a data mining effort using data previously collected, as outlined in 

Hanowski et al. (2000). Data from 42 L/SH drivers were analyzed, totaling approximately 900 

hours of continuous video. Barr et al. reviewed the entire video library and identified 2,745 

drowsy events (i.e., the driver exhibited some form of drowsiness such as yawning, heavy eyes, 

slow eye closures, rubbing the face/eyes). The drowsy event began with the first indication of 

driver drowsiness and continued until the driver displayed some sort of “alerting behavior.” Each

drowsy event in the Barr et al. study was reviewed by data analysts who were instructed to watch 

the driver’s face and body language for 1 min prior to the event trigger and record an observer 

rating of drowsiness (ORD). 

ORD is a subjective measurement of drowsiness developed by Wierwille and Ellsworth (1994), 

who described it as signs indicative of drowsiness that include rubbing the face or eyes, facial 

contortions, moving restlessly in the seat, and slow eyelid closures. Data analysts in Barr et al. 

(2005) were trained to look for these signs of drowsiness and make a subjective, but specific, 

assessment of the level of drowsiness. After watching the video data, data analysts classified 

each drowsy episode into one of the following categories: 

 ORD 2: slightly drowsy. 

 ORD 3: moderately drowsy. 

 ORD 4: very drowsy. 

 ORD 5: extremely drowsy. 

An ORD rating of 1 indicated a baseline (non-drowsy) epoch. It should be noted that an event 

with an ORD 5 indicated there was an observable impact on the driver’s performance (e.g., a 

lane deviation) due to drowsiness. 

Once all drowsy events were identified and classified, a sample of drowsy and baseline epochs 

were selected for analysis. All high-severity events (ORD 4, n = 160; and ORD 5, n = 125) were 

included in the sample, along with an equal number of baseline epochs (baseline epochs were 

matched based on time of day, road type/conditions and weather conditions). The remaining 

13 
events were comprised of an equal number of ORD 2 and ORD 3 events and a 3 to 1 ratio of 

drowsy-to-baseline epochs were used. The final data set contained 607 drowsy events and 393 

baseline epochs. Once the events were selected, each event was analyzed for 3 min prior to the

alerting event. During this 3-min period, the following measures were determined: PERCLOS

(percent closure of eyes is a measure of drowsiness), EYETRANS (eye transition measures 

inattention), and EYESOFF (eyes off roadway measures inattention). PERCLOS was defined as

the percentage of time the driver’s eyes were closed or nearly closed over a 3-min interval. 

EYETRANS was defined as the number of eye transitions made by the driver over a 3-min 

interval, and EYESOFF was the proportion of time the driver looked away from the forward

roadway over a 3-min interval. Environmental, road, and traffic conditions were also recorded 

during the interval. 

Although the main objective of the Barr et al. (2005) study was to investigate driver 

fatigue/drowsiness in L/SH drivers, an additional analysis was conducted to determine the 

relationship between driver drowsiness and distraction. More specifically, researchers assessed 

whether a driver was more likely to be distracted while he/she was drowsy. An analysis of 

F for 

 have 

eading, 

sis 

ng 

study. Using naturalistic data collected 

during the DDWS FOT, continuous data were collected from 95 heavy-vehicle drivers from May 

d 

) 

eo and kinematic 

data from flagged critical events were reviewed to ensure they represented actual safety-

-

uction 

variance (ANOVA) was conducted on the mean values of EYETRANS and EYESOF

drowsy versus baseline epochs to assess driver distraction. Results showed that both 

EYETRANS and EYESOFF were higher during baseline or alert driving than during periods of 

drowsiness, indicating that drivers scanned the environment more often while alert and may

experienced gaze concentration (Reagan, Lee, & Young, 2009) while drowsy. This also 

suggested that drivers did not engage in distracting behaviors (e.g., tuning the radio, r

etc.) while drowsy. Next, a detailed video analysis was performed on a sample of 300 events, 

including ORD 4 and 5 events along with matching baseline epochs, to compare the frequency of 

distracting behaviors during baseline epochs and drowsy events. The results of this analy

showed that drivers engaged in the most distracting behaviors (such as reading paperwork, usi

a cell phone, or eating) only during baseline and/or alert driving. It was also found that drivers

engaged in some distracting behaviors (e.g., taking a drink, smoking a cigarette) as an alerting 

activity to reduce and/or end their drowsiness episode. 

The most recent CMV distraction study was a preliminary analysis using data from the DDWS 

FOT (Hickman et al., in press). Note that, along with a more recent naturalistic truck study, the

complete DDWS FOT data set was used in this current 

2004 through May 2005. Participants were volunteer drivers who each drove an instrumented 

heavy vehicle for up to 4 months. Each vehicle was instrumented with four video cameras 

(recording views of the face, forward, right lane, and left lane) and various sensors that collecte

information such as the truck’s speed, braking intensity, and steering patterns. 

The data were collected and processed using the Data Analysis and Reduction Tool (DART

software program. Driving behaviors, such as hard braking, sharp steering movements, and close 

time-to-collisions, were flagged in DART for later review by data analysts. Vid

significant events and then categorized into one of three categories: crash, near-crash, or crash

relevant conflicts. The total of these three types resulted in 915 safety-critical events. An 

additional 1,072 baseline epochs were created to represent normal driving (one baseline epoch 

was randomly selected for each week a participant drove an instrumented truck). Data red

14 
was performed on all 915 safety-critical events and 1,072 baseline epochs. Data analysts 

reviewed each safety-critical event and baseline epoch and provided information—such as

cause of the conflict between vehicles, animals, pedestrians, etc. (critical events only); number o

vehicles involved; potential distracting behaviors; road and traffic conditions; and weather 

conditions (a complete list of variables can be found in Hickman et al., in press). 

Of particular interest to the current study was the variable “potential distracting behaviors.” Data 

analysts were instructed to watch the video for 10 s prior to the event trigger (i.e., an evasiv

maneuver by the driver, such as hard braking, steering, etc.) and note up to four po

 the 

f 

e 

tentially 

distracting behaviors that occurred (in no particular order). The most frequent behaviors coded 

les 

earch 

focuses on CMV driver distraction. As such, it is unclear the extent to which driver distraction is 

 it was 

during safety-critical events were “look at left-side mirror/out left side window” (34.8 percent) 

and “look at right-side mirror/out right side window” (25.1 percent). 

2.5 SUMMARY 

As noted earlier, 413,000 large trucks were involved in traffic crashes in 2007, killing 4,808 

people (National Highway Traffic Safety Administration, 2008). Between 25 percent (Wang et 

al., 1996) and 78 percent (Klauer et al., 2006) of light-vehicle crashes were believed to have been 

related to some form of driver distraction. This discrepancy suggests the naturalistic approach, 

through the availability of video “instant replay,” provides a more thorough assessment of preevent driver behavior than can be determined through police accident report databases. 

The benefits of the naturalistic data collection method in assessing pre-event driver behavior was 

demonstrated in the studies described above. With continuous, naturalistic data it was possible to 

view the driver in his/her normal driving/working environment and assess the driver’s tasks, 

behaviors, and environment prior to a critical event. Also, as highlighted by Sayer et al. (2007), 

naturalistic studies provide understanding of driver choice and perceived risk in actual real-world 

driving situations. As such, it may be expected that some results found in laboratory and 

simulators studies may not be replicated in real-world driving. 

The current study used data from two CMV naturalistic studies, totaling approximately 60,000 

hours and 3 million miles of continuous data (Hanowski et al., 2008; Blanco et al., in press), 

which provided an extremely rich data set and followed the safety-critical event and baseline 

analysis method highlighted previously by Klauer et al. (2006) in the 100-Car Study. 

Previous research using naturalistic data and critical events has been conducted for light vehic

to better understand the adverse impact of driver distraction on crashes and near-crashes, 

especially as the use of in-vehicle technologies increases; however, little published res

a problem in CMVs. For example, CMV drivers have many opportunities to be distracted due to 

work-related technologies in their vehicles (e.g., cell phones, Citizens Band [CB] radios, 

navigation devices, and messaging systems). To underscore this point, Llaneras, Singer, and 

Bowers-Carnahan (2005) found that 48 percent of the CMV drivers they interviewed admitted to 

having a close call while using a device while driving. However, the same study found that CMV

industry personnel and drivers believed that truck drivers made good decisions as to when

15 
16 

e secondary/tertiary tasks and other activities that drivers 

engaged in prior to involvement in critical events. The impact of these driver distractions was 

d 

as 

ashes, 

safe to use these devices (when compared to light-vehicle drivers) due to their professionalism 

and high level of safety training. 

The current study followed, to a large extent, the analysis approach used in the 100-Car Study 

(Klauer et al., 2006) to identify th

assessed and compared to baseline driving (uneventful data). As noted, the current study 

characterized crashes, near-crashes, crash-relevant conflicts, unintentional lane deviations an

baseline epochs that were recorded in the DDWS FOT and the NTDS. The goal of this study w

to gain a better understanding of the impact of driver distraction on CMV crashes, near-cr

and lane keeping. 
3. OVERVIEW OF DDWS FOT AND NTDS 

The current project was a secondary analysis, or data mining effort, using two recently 

completed naturalistic heavy-vehicle data collection studies, including the DDWS FOT 

(Hanowski et al., 2008) and NTDS (Blanco et al., in press). Below is a detailed description of 

each of these two studies. 

3.1 DROWSY DRIVER WARNING SYSTEM FIELD OPERATIONAL TEST 

3.1.1 Project Overview 

The DDWS FOT was a naturalistic data collection study in which data were collected for 18 

months from 103 CMV drivers. The purpose of the DDWS FOT was to determine the safety 

benefits and operational capabilities, limitations, and characteristics of a DDWS that monitored 

drivers’ drowsiness. The methodological details of the project are described below and have been 

abstracted from the DDWS FOT Phase I report (Hickman et al., in press) and the DDWS FOT 

draft final report (Hanowski et al., 2008). 

3.1.2 Experimental Design 

Data were collected from 103 drivers; 24 drivers were randomly assigned to the Control group 

and 79 drivers were randomly assigned to the Experimental group. The experimental design for 

the Control group was A9

, while the Experimental group followed an A2B9 design. In this design, 

A refers to the Baseline (passive) condition and B refers to the Treatment condition. The 

superscripts refer to the number of weeks each participant drove an instrumented truck. In the 

Baseline condition, the DDWS monitored the driver, but did not provide any alerts (either 

auditory or visual). Conversely, the DDWS monitored the driver and provided the driver with 

alerts in the Treatment condition. 

3.1.3 Participants and Setting 

Three for-hire companies participated in this study (a for-hire company transports goods for 

several customers for a fee). Driver volunteers were selected based on the following 

qualifications: a significant proportion of their driving was at night, they did not wear glasses 

while driving, they had a low risk of dropping out or leaving the company, and they passed 

vision and hearing tests. The first two qualifications were important as the DDWS device being 

tested did not work in the daytime or with drivers wearing glasses. 

One hundred and three drivers participated in this study; 102 were male drivers and one was a 

female driver. Their average age was 40.0 years old (SD = 8.24 years), with ages ranging from 

24 to 60 years old. Drivers had an average of 10.6 years of total self-reported driving experience 

(SD = 8.37 years), which ranged from 0.5 to 42 years of driving experience. 

Drivers were employed at one of three fleets at nine different locations. Fleets A and B were 

line-haul operations, whereby a driver typically returned to the home base once per 24-hour 

period (5 days per week). For example, these drivers may have taken their truck out in the 

evening of Day 1, drove to their delivery location, delivered their load, and returned to their 

home base the morning of Day 2. They would leave again in the evening of Day 2 and repeat the 

17 
process to complete their work week. Fleet C was involved in over-the-road, truckload 

operations. For the over-the-road drivers, a typical schedule may have included starting on 

Sunday evening and returning to their home base the following Friday afternoon. 

3.1.4 Data Collection Process 

Three types of data were collected by the data acquisition system (DAS): video, dynamic 

performance (i.e., kinematic), and audio. Each driver drove for approximately 60 h in a 7-day 

period. Approximately 48,000 driving-data hours covering 2.4 million miles traveled were 

collected. Forty-six trucks were instrumented with the DAS and the DDWS. Typically, drivers 

would rotate into an instrumented truck, and each driver drove, on average, for 12 weeks. 

3.1.4.1 Data Acquisition System 

The DAS computer received and stored data from a network of sensors distributed around the 

vehicle. The DAS consisted of five major components: an encased unit that housed the computer 

and external hard drive, dynamic sensors, vehicle network, incident box, and video cameras. In 

addition, the DAS interfaced with the DDWS and recorded data from it. Each component was 

active when the vehicle ignition system was turned on. The system remained active and recorded 

data as long as the engine was on and the vehicle was in motion. The system shut down in an 

orderly manner when the ignition was turned off. The system paused if the vehicle ceased motion 

for 10 min or longer. 

There were three main DAS output files: truck dynamic performance data file, digital video, and 

audio. These files were stored on the DAS’s external hard drive. The truck performance file 

contained the kinematic driver input measures (e.g., lateral and longitudinal acceleration, 

braking) and the truck-related measures (e.g., GPS, light level). The digital video file contained 

the video recorded continuously during the trip. The audio file resulted from the driver pressing 

the Critical Incident Button, which enabled drivers to comment on incidents they believed were 

notable. 

Vehicle Network: The Society of Automotive Engineers’ J1587 defines the format of messages 

and data collected by large truck on-board microprocessors. These microprocessors are installed 

in the vehicle at the truck manufacturing facility. Thus, the vehicle network refers to a from-thefactory on-board data collection system. Depending upon the truck model, year, and 

manufacturer, several data network protocols or standards are used with heavy-vehicles. An 

interface was developed to access the data from the network and merge it into the DAS data set. 

Typical measures found on the vehicle network of most trucks include, but are not limited to: 

vehicle speed, distance since vehicle start-up, ignition signal, throttle position, and brake 

pressure. In addition to the truck network measures, other driver input measures that were 

collected with sensors include right and left turn-signal use, and headlight status (on/off). 

Incident Box: The Incident Box contained a light meter that recorded the in-cab ambient 

illumination level. Note that the ambient light level was also measured. The incident box also 

contained an incident pushbutton which the participants were instructed to push when they were 

involved in a safety-critical event. When pushed, the button opened an audio channel for 30 s 

during which the driver could provide a verbal report of what occurred. 

18 
Video Cameras: Digital video cameras were used to continuously record the driver and the 

driving environment. Four video cameras were multiplexed into a single image. The four camera 

views were: forward, driver’s face, rear-facing-left, and rear-facing-right. The forward and rearfacing camera views provided good coverage of the driving environment. The face view 

provided coverage of the driver’s face and will allow researchers to conduct eye glance analysis 

and manual PERCLOS assessment. Figure 1 shows the camera positions and approximate fieldsof-view for the four cameras used in the study. 

Figure 1. Diagram. Camera Directions and Approximate Fields of View 

As shown in Figure 2, the four camera images were multiplexed into a single image. The top left 

quadrant in Figure 2 displays the driver’s face, while the top right quadrant displays the forward 

view out the truck’s windshield. The bottom right and left quadrants in Figure 2 display the rearfacing-left and rear-facing-right views, respectively. A time-stamp (.mpg frame number) was 

included in the .mpg data file, but not displayed on the screen. The frame number was used to 

time-synchronize the video (in .mpg format) and the truck/performance data (in .dat format). 

Figure 2. Photo. Split-Screen Presentation of the Four Camera Views 

19 
The digital video files did not contain continuous audio. However, as noted previously, the driver 

could press an Incident Pushbutton and record a verbal comment for 30 s. This audio data was 

recorded together with the video data. 

3.1.5 Data Reduction 

As noted, the DDWS FOT collected naturalistic data from 103 participants, totaling 

approximately 2.2 million driving miles. Once these data were collected, trained data analysts 

identified safety-critical events (i.e., crashes, near-crashes, and crash-relevant conflicts) that 

occurred during data collection. To do this, the data were processed through a specialized 

software program to flag potential events of interest based on trigger threshold values (Table 7). 

Table 7. Trigger Values Used in the DDWS FOT 

Trigger Type Trigger Values 

Longitudinal 

Acceleration (hard 

braking) 

Deceleration greater than or equal to -0.35 g†

. Speed greater than or 

equal to 15 mi/h. 

Deceleration greater than or equal to -0.5 g. Speed less than or 

equal to 15 mi/h. 

Time-to-Collision A forward time-to-collision (TTC) value of less than or equal to 1.8 s, 

coupled with a range of less than or equal to 150 ft, a target speed of 

greater than or equal to 5 mi/h, a yaw rate of less than or equal to 

│4/sec│, and an azimuth of less than or equal to │0.8°│. 

A forward TTC value of less than or equal to 1.8 s, coupled with a 

deceleration greater than or equal to -0.35 g, a forward range of less 

than or equal to 150 ft, a yaw rate of less than or equal to │4°/sec│, 

and an azimuth of less than or equal to │0.8°│. 

Swerve* Swerve value of greater than or equal to 3 rad/s2. Speed greater 

than or equal to 15 mi/h. 

Critical Incident Button Activated by the driver upon pressing a button, located by the 

driver’s visor, when an incident occurred that he/she deemed critical. 

Analyst Identified Event that was identified by a data analyst viewing video footage; no 

other trigger listed above identified the event (i.e., Longitudinal 

Acceleration, TTC, etc.). 

* The swerve variable looks for a large change in yaw rate in a short amount of time where the heading of the vehicle 

returns to its original heading when the swerve starts. 

† The lowercase and italicized letter “g” is the force of gravity. 

Next, data analysts identified valid safety-critical events and removed spurious events (i.e., 

invalid safety-critical events), resulting in 1,217 valid safety-critical events. Details of this 

filtering process are described in more detail in section 4. Once the valid safety-critical events 

were identified, data analysts answered specific questions for each event (e.g., type of conflict, 

potential distractions, driver behaviors, and road and environmental conditions). 

20 
3.2 NATURALISTIC TRUCK DRIVING STUDY 

3.2.1 Project Overview 

This study collected naturalistic data to investigate CMV crash risk by identifying safety-critical 

events. The details of the NTDS described below are abstracted from the NTDS draft final report 

(Blanco et al., in press). 

3.2.2 Experimental Design 

As this was an on-road driving study conducted in normal operations, there were no 

experimental manipulations. Each participant in this on-road study was observed for 

approximately 4 consecutive work weeks. One hundred participants were recruited from four 

different trucking fleets across seven terminals and one to three trucks at each trucking fleet were 

instrumented (nine trucks total). After a participant finished 4 consecutive weeks of data 

collection, another participant started driving the instrumented truck. 

3.2.3 Participants and Setting 

Four companies and 100 drivers participated in this study. From this total, 95 were male drivers 

and five were female drivers. The average age of the drivers was 44.5 years old (SD = 12.20 

years), with ages ranging from 21 to 73 years old. Drivers had an average of 9.1 years of total 

self-reported driving experience (SD = 10.46 years), which ranged from 0.1 to 54 years of 

driving experience. Drivers were employed at one of four carriers at seven different locations. 

Fleets A, B, and C were line-haul operations, while Fleet D was involved in over-the-road, 

truckload operations. 

3.2.4 Data Collection Process 

Three forms of data were collected by the NTDS DAS: video, dynamic performance, and audio. 

Each driver drove for approximately 45 h in a 7-day period. Approximately 14,500 driving-data 

hours covering 735,000 miles traveled were collected. Nine trucks were instrumented with the 

DAS. Each truck was driven by 6–14 different drivers for approximately 4 weeks each. 

3.2.4.1 Data Acquisition System 

Many aspects of the DAS were identical in the DDWS FOT and the NTDS. The DAS in the 

NTDS was comprised of the same five components in the DAS used in the DDWS FOT, 

including an encased unit that housed the computer and external hard drive, dynamic sensors 

(identical as the DDWS FOT except with a more robust lane tracker), vehicle network, incident 

box, and video cameras, with the exception of one additional camera view described below. No 

DDWS was included. 

Lane Tracker: A lane tracker was included in the DAS, and consisted of a single analog blackand-white camera, a personal computer (PC) with a frame grabber card, and an interface-tovehicle network for obtaining ground speed (note the “grabbed” video frames were not stored, 

but were processed algorithmically in real time to calculate the vehicle position relative to road 

lane markings). Once installed, software automatically calibrated itself to determine camera 

position (no elaborate calibration procedure was required). The following variables were 

reported: 

21 
 Distance from center of truck to left and right lane markings (estimated max error < 6 

inches, average error < 2 inches). 

 Angular offset between truck centerline and road centerline (estimated max error < 1 

deg). 

 Approximate road curvature. 

 Confidence in reported values for each marking found. 

 Marking characteristics, such as dashed vs. solid and double vs. single. 

 Status information, such as in-lane or solid line crossed. 

Video Cameras: Data analysts in the DDWS FOT reported that the drivers would often reach 

for an object outside the camera view, thus they were unable to determine what the driver was 

trying to reach. As such, an additional camera view looking over the drivers’ shoulder into their 

lap was added in the NTDS (this can be seen in the lower left quadrant in Figure 3). This view 

provided information on many potentially distracting behaviors the driver was engaging in (e.g., 

eating, reading, and using electronic devices). The top left quadrant in Figure 3 displays the 

driver’s face, while the top right quadrant displays the forward view. The bottom right quadrant 

in Figure 3 is split and displays the rear-facing-right and rear-facing-left views. The quality of 

the video data was also improved in the NTDS to provide clearer video for data reduction. 

Figure 3. Photo. Five Camera Images Multiplexed Into a Single Image 

Face View Forward View 

Over-the-Shoulder 

Right Mirror 

Left Mirror 

3.2.5 Data Reduction 

The NTDS data reduction process was similar to that of the DDWS FOT. The first step was to 

process the data using modified trigger values (Table 8) to flag potential events of interest. The 

lane deviation trigger in the NTDS was not included in the DDWS FOT. 

22 
23 

Table 8. Trigger Values Used in the NTDS 

Trigger Type Trigger Values 

Longitudinal 

Acceleration 

(hard braking) 

Deceleration greater than or equal to -0.20 g. Speed greater than or 

equal to 1 mi/h (1.6 km/h). 

Time-to-Collision A forward TTC value of less than or equal to 2 s, coupled with a 

range of less than or equal to 250 ft, a target speed of greater than 

or equal to 5 mi/h (8 km/h), a gyro rate of less than or equal to 

│6°/s│, and an azimuth of less than or equal to │0.12°│. 

Swerve* Swerve value of greater than or equal to 2 rad/s2. Speed greater 

than or equal to 5 mi/h (8.05 km/h). 

Lane Deviation Lane tracker status = abort. Distance from center of lane to outside 

of lane line < 44 inches 

Critical Incident Button Activated by the driver upon pressing a button, located by the 

driver’s visor, when an incident occurred that he/she deemed critical.

Analyst Identified Event that was identified by a data analyst viewing video footage; no 

other trigger listed above identified the event (i.e., Longitudinal 

Acceleration, TTC, etc.). 

*The swerve variable looks for a large change in yaw rate in a short amount of time where the heading of the vehicle 

returns to its original heading when the swerve starts. 

The remaining data reduction steps were identical to those described in the DDWS FOT data 

reduction. There were a total of 2,899 valid safety-critical events in the NTDS. Trained data 

analysts used a data coding directory to reduce all these safety-critical events. 

4. DATA REDUCTION 

4.1 CHARACTERIZE SAFETY-CRITICAL EVENTS 

As noted, the current study involved combining naturalistic data sets from two CMV studies. 

Part of this process involved processing each data set with the same set of sensor trigger values 

in order to identify safety-critical events across data sets that had the same trigger signatures. 

Each valid safety-critical event was classified as a crash, near-crash, crash-relevant conflict 

(Hickman et al., in press), or unintentional lane deviation (Blanco et al., in press) as defined 

below. 

 Crash: Any contact with an object, either moving or fixed, at any speed. Included other 

vehicles, roadside barriers, objects on or off of the roadway, pedestrians, pedalcyclists, or 

animals. 

 Near-crash: Any circumstance that required a rapid, evasive maneuver (e.g., hard 

braking, steering) by the subject vehicle or any other vehicle, pedestrian, pedalcyclist, or 

animal, in order to avoid a crash. 

 Crash-relevant conflict: Any circumstance that required a crash-avoidance response on 

the part of the subject vehicle, any other vehicle, pedestrian, pedalcyclist, or animal that 

is less severe than a rapid evasive maneuver (as defined above), but greater in severity 

than a normal maneuver. A crash-avoidance response can include braking, steering, 

accelerating, or any combination of control inputs. 

 Unintentional lane deviation: Any circumstance where the subject vehicle crosses over a 

solid lane line (e.g., onto the shoulder) where no hazard (e.g., guardrail, ditch, vehicle, 

etc.) is present. 

The methods used in this current study to complete the data reduction on safety-critical events 

are described below. 

4.1.1 Running the Event Trigger Program 

To find safety-critical events of interest, DART was used to scan the data set for notable actions, 

including hard braking, quick steering maneuvers, short times-to-collision (TTC), and lane 

deviations. To identify these actions, threshold values (called “triggers”) were created to flag 

instances in the video and quantitative data where the threshold values were met or exceeded. 

Since the trigger threshold values between the DDWS FOT and NTDS data sets differed, it was 

important to obtain a common set of threshold values for comparison across data sets. The lower 

trigger threshold values used in the NTDS was used to process the DDWS FOT data set. For 

example, in the DDWS FOT data set, a longitudinal acceleration trigger (to identify hard braking 

events) was initially created when a driver braked at or exceeded -0.35 g. However, in the NTDS 

data set, a longitudinal acceleration trigger was created any time a driver braked at or exceeded -

0.20 g. So that each data set would have the same safety-critical event signatures 

-0.20 g was used for both data sets. 

25 
In the original DDWS FOT study, trigger threshold values were selected based on values used in 

the 100-Car Study (Klauer et al., 2006) and from suggestions from the software programmers 

who developed DART and were familiar with the data. Before data reduction began on the 

NTDS, a sensitivity analysis was conducted on various trigger values to determine the best 

combination of values to obtain the fewest number of false alarms (i.e., triggers created with no 

conflict threat) and missed valid events. Table 9 shows a comparison of the original trigger 

values that were used in the DDWS FOT data set during the Phase I study (Hickman et al., in 

press), NTDS (Blanco et al, in press), and the new trigger values that were used in the current 

study. As can be seen in Table 9, the longitudinal acceleration, time-to-collision, and swerve 

triggers in the DDWS FOT were different than those used in the NTDS. Thus, the trigger values 

in the DDWS FOT were revised to be the same as those used in the NTDS; this resulted in the 

creation of an additional 221,687 triggers (potential events). It should also be noted that 

instrumented trucks in the NTDS were equipped with a lane tracking device that was not reliable 

in the DDWS FOT; therefore, valid lane deviation triggers were present in the NTDS data set but 

not in the DDWS FOT data set. 

Table 9. Comparison of the Trigger Values Used in the DDWS FOT Phase I Analysis, 

NTDS, and the current study 

Trigger 

Type 

Trigger Values Used in Phase I 

of the DDWS FOT 

Trigger Values Used 

in the NTDS 

Trigger Values Used 

in the Current Study 

Longitudinal 

Acceleration 

Deceleration greater than or equal 

to -0.35 g. Speed greater than or 

equal to 15 mi/h. 

Deceleration greater than or equal 

to -0.5 g. Speed less than or equal 

to 15 mi/h. 

Deceleration greater 

than or equal to -0.20 g. 

Speed greater than or 

equal to 1 mi/h (1.6 

km/h). 

Deceleration greater 

than or equal to -0.20 g. 

Speed greater than or 

equal to 1 mi/h (1.6 

km/h). 

Time-toCollision A forward TTC value of less than 

or equal to 1.8 s, coupled with a 

range of less than or equal to 150 

ft, a target speed of greater than 

or equal to 5 mi/h, a yaw rate of 

less than or equal to │4/sec│, 

and an azimuth of less than or 

equal to │0.8│. 

A forward TTC value of less than 

or equal to 1.8 s, coupled with an 

acceleration or deceleration 

greater than or equal to │0.35 g│, 

a forward range of less than or 

equal to 150 ft, a yaw rate of less 

than or equal to │4/sec│, and an 

azimuth of less than or equal to 

│0.8│. 

A forward TTC value of 

less than or equal to 2 

s, coupled with a range 

of less than or equal to 

250 ft, a target speed of 

greater than or equal to 

5 mi/h (8 km/h), a gyro 

rate of less than or 

equal to │6°/s│, and an 

azimuth of less than or 

equal to │0.12°│. 

A forward TTC value of 

less than or equal to 2 

s, coupled with a range 

of less than or equal to 

250 ft, a target speed of 

greater than or equal to 

5 mi/h (8 km/h), a gyro 

rate of less than or 

equal to │6°/s│, and an 

azimuth of less than or 

equal to │0.12°│. 

Swerve Swerve value of greater than or 

equal to 3 rad/s2. Speed greater 

than or equal to 15 mi/h. 

Swerve value of greater 

than or equal to 2 

rad/s2. Speed greater 

than or equal to 5 mi/h 

(8.05 km/h). 

Swerve value of greater 

than or equal to 2 

rad/s2. Speed greater 

than or equal to 5 mi/h 

(8.05 km/h). 

26 
Trigger Trigger Values Used in Phase I Trigger Values Used Trigger Values Used 

Type of the DDWS FOT in the NTDS in the Current Study 

Lane 

Deviation 

Lane tracker data not available. Lane tracker status = 

abort. Distance from 

center of lane to outside 

of lane line less than 44 

inches. 

Lane tracker status = 

abort. Distance from 

center of lane to outside 

of lane line less than 44 

inches. 

Critical 

Incident 

Button 

Activated by the driver upon 

pressing a button, located by the 

driver’s visor, when an incident 

occurred that he/she deemed 

critical. 

Activated by the driver 

upon pressing a button, 

located by the driver’s 

visor, when an incident 

occurred that he/she 

deemed critical. 

Activated by the driver 

upon pressing a button, 

located by the driver’s 

visor, when an incident 

occurred that he/she 

deemed critical. 

Analyst 

Identified 

(AI) 

Event that was identified by a data 

analyst viewing video footage; no 

other trigger listed above identified 

the event (i.e., Longitudinal 

Acceleration, TTC, etc.). 

Event that was 

identified by a data 

analyst viewing video 

footage; no other trigger 

listed above identified 

the event (i.e., 

Longitudinal 

Acceleration, TTC, 

etc.). 

Event that was 

identified by a data 

analyst viewing video 

footage; no other trigger 

listed above identified 

the event (i.e., 

Longitudinal 

Acceleration, TTC, 

etc.). 

4.1.2 Checking the Validity of the Additional Triggered Events 

The software scanned the data set and potential safety-critical events of interest were identified 

for review, based on the trigger criteria. A 75-s epoch was created for each identified safetycritical event (60 s prior to trigger, 15 s after trigger). The result of the automatic scan was a data 

set that included both valid and invalid events. 

Valid events were those events where recorded dynamic-motion values actually occurred and 

were verifiable in the video and other sensor data (also identified by Critical Incident Button or 

Analyst Identified). Invalid events were those events where sensor readings were spurious due to 

a transient spike or some other anomaly such as driving over a pothole (false positive). The 

validity of all events was determined through video review. 

During this process, an additional 534 valid events were identified in the DDWS FOT data set (2 

crashes, 16 near-crashes, and 516 crash-relevant conflicts). Events determined to be invalid were 

not analyzed further. Valid events were analyzed further and classified as conflicts or nonconflicts. Conflicts were valid events that also represented a traffic conflict (i.e., crash, nearcrash, crash-relevant conflict, unintentional lane deviation). Non-conflicts were events that were 

not safety-critical per se, even though their trigger values were valid (“true trigger”). Nonconflicts were analogous to nuisance alarms—where the threshold value for that particular event 

was set ineffectually. Examples of valid events that were non-conflicts included hard braking by 

a driver in the absence of a specific crash threat or a high swerve value from a lane change not 

resulting in any loss-of-control, lane departure, or proximity to other vehicles. While such 

situations may have reflected at-risk driving habits and styles, they did not result in a discernible 

crash-relevant conflict. To determine the validity of the events, data analysts observed the 

recorded video and data plots of the various sensor measures associated with each 75-s epoch. 

27 
4.1.3 Applying the Data Directory to the Validated Events 

An event coding Data Directory was used to reduce and analyze all new valid safety-critical 

events in the DDWS FOT (as was used in Phase I of the DDWS FOT and NTDS). See appendix 

A for the data coding directory used in the DDWS FOT and NTDS. This data directory was 

originally developed for the Phase I analysis (Hickman et al., in press) and also used in the 

NTDS analysis (Blanco et al., in press). The DART software presented the data analyst with a 

series of variables consisting either of a blank space for entry of specific comments (e.g., 

Element #39, Event Comments) or provided pull-down menus for the analyst to select the most 

applicable code (i.e., number corresponding to a data element). Different variables had different 

coding rules. For most variables, only one code was selected; however, for a few variables, the 

data analyst could select up to four codes that were applicable. For example, analysts could 

select multiple Potential Distraction Behaviors (e.g., Element #25, Potential Distractions). 

The DART software automatically coded many of the variables. These automatically coded 

variables reflect data recorded from sensors in the instrumented vehicle (e.g., vehicle number, 

driver subject number, date, and time). Although these variables were coded automatically by 

DART, they are listed in the Data Directory to provide readers with a full picture of the variables 

that were available to support analyses of the data (see appendix A). 

4.1.3.1 Drowsiness 

It is important to discuss the method used to determine drowsiness during the data reduction 

process. Because the focus of this study was on “driver distraction” and the Klauer et al. (2006) 

method was used as a model approach, each of the video segments surrounding an event were 

reduced to 6 s. Video review derived measures of drowsiness, such as PERCLOS or ORD 

(Wierwille & Ellsworth, 1994), require at least 1-min of video review. As data were collected 

continuously, this data is available for a data mining effort that might focus specifically on 

drowsiness (i.e., it is possible, in a future effort, to conduct PERCLOS and ORD analysis on this 

naturalistic data). Reducing the video to 6 s was suitable for investigating secondary and tertiary 

tasks that the driver was engaged in immediately prior to or during an event. However, the 6-s 

duration precluded conducting PERCLOS or ORD analysis. Note that it was initially decided 

that a high-level assessment of driver drowsiness would be performed by analysts by viewing the 

6-s video segment and providing a subjective “yes” or “no” indication of the presence of 

drowsiness. However, this was dropped as a measure of interest because it was later decided that 

6 s was not a sufficient duration to reliably assess the drowsiness state of a driver. Future 

research is recommended to more fully address this issue. 

4.2 CHARACTERIZE 20,000 BASELINE EPOCHS 

In addition to the safety-critical events described above, approximately 20,000 baseline epochs 

(i.e., uneventful driving) were created. The creation of a baseline data set enabled the current 

study to describe and characterize “normal” driving for the study sample, thereby infer the 

increased or decreased risk associated with various conditions and driver tasks with comparisons 

between the control (baseline) data set and the safety-critical event data set. For example, if 20 

percent of safety-critical events but only 10 percent of baseline epochs occurred during rain, one 

28 
could infer that rain was associated with an increased safety-critical event rate, and therefore, 

increased risk. Baseline epochs were defined as follows (Hickman et al., in press): 

Baseline epoch: Brief time periods (e.g., 6 s) that are randomly selected from the 

recorded data set. Baseline epochs will be described using many of the same 

variables and data elements used to describe and classify crashes, near-crashes, 

and crash-relevant conflicts. Examples of such variables included ambient 

weather, roadway type, and driver behaviors. 

A random sampling method was used to obtain baseline epochs. Baseline epochs were selected 

based on driver exposure. That is, the more time a given driver spent in the study, the more 

baseline epochs that driver had included in the baseline data set. In addition, all baseline epochs 

involved the truck traveling at a minimum speed of 15 mi/h. More specifically, the proportion of 

an individual driver’s driving time (when the truck was traveling faster than 15 mi/h) was 

divided by the total driving time across the DDWS FOT and NTDS (when the truck was 

traveling faster than 15 mi/h) and multiplied by 100 percent. This percentage reflected an 

individual driver’s exposure and was used to determine the frequency of baseline epochs needed 

(i.e., 1 percent exposure reflected 200 baseline epochs). As with safety-critical events, data 

analysts used the Baseline Epoch Data Directory to reduce and analyze baseline epochs. See 

appendix B for the Baseline Epoch Data Directory used in the current study. 

4.2.1 Quality Control 

Because of the large number of baseline epochs that were reduced, it was necessary to implement 

quality control to ensure accurate coding among data analysts. Data analysts were trained on how 

to code the baseline epochs using the Baseline Epoch Data Directory (appendix B). Data analysts 

were typically trained at the beginning of their shift and then asked to spend the remaining time 

of their shift (e.g., usually 2–3 hours) working on data reduction. After their first day of work, 

the data analyst’s manager (i.e., an experienced data analyst) checked 100 percent of the work 

completed on that day and left comments in a log regarding any mistakes. On the following day, 

data analysts were asked to review all mistakes and make corrections before working on new 

baseline epochs. The quality control schedule was as follows: check 100 percent of the work 

completed by data analysts each day during the first two days, check 20 percent of the work 

completed by data analysts each day for the following five days, and check 10 percent of work 

completed by data analysts each day for the remainder of the data reduction process. If it became 

apparent that a data analyst was making the same mistake over multiple days, the data analyst’s 

manager would have a brief meeting with the data analyst to discuss the issue(s) and formulate a 

corrective action plan that involved re-training (e.g., review of additional video examples). The 

data analysts would then re-review all events done prior to the meeting and make any necessary 

corrections. 

Once all baseline epochs were coded, an additional 10 percent of the baselines epochs that had 

not been previously reviewed during quality control were assessed for accuracy (with appropriate 

changes made where necessary). The same procedure was used to assess eye glance quality 

control. 

29 
4.3 EYE GLANCE REDUCTION 

To measure visual attention (or inattention), eye glance analysis was conducted for all safetycritical events and baseline epochs in the DDWS FOT and NTDS for a period of 6 s, following 

the approach outlined in Klauer et al. (2006). For safety-critical events, this 6 s was broken into 5 

s prior to the onset of the safety-critical event and 1 s after. For baseline epochs, the entire 6-s 

epoch was analyzed. Data analysts viewed the video through DART and held down the 

appropriate letter/key when the drivers’ eye glance was in a specific direction. The following eye 

glance locations were adapted from Klauer et al. (2006) and were used in the current analysis: 

 Forward. 

 Right mirror/out right window. 

 Left mirror/out left window. 

 External object—through front windshield. 

 Instrument panel (including speedometer, radio and heating, ventilating, and air 

conditioning-HVAC). 

 Cell phone. 

 Interior object (e.g., food/drink, map, seatbelt, door/window control, CB radio, passenger, 

etc.). 

 Eyes closed (eyes had to be closed for at least 5 syncs; 10 syncs = 1 s). 

 Other. 

 No eyes visible—glance location unknown. 

 No eyes visible—eyes are off road. 

Each glance location was assigned a different letter (i.e., on the keyboard) as shown in Figure 4

below. For example, the data analysts would input an “f” when the driver was looking at the 

forward roadway. 

Figure 4. Image. Eye Glance Location Window in DART 

30 
Though these various eye glance locations were assessed as part of the eye glance reduction 

process, it was determined that the key location to assess (visual) attention was Forward; that is, 

eyes on the forward roadway. Therefore, for the statistical analyses conducted, all locations other 

than Forward were grouped together (and considered “inattention” to the forward roadway). 

Although it would have been optimal to consider glances to the mirrors also, it was difficult 

through the video review to reliably assess mirror glances separate from glances out the 

windows; as such, these categories were grouped together (e.g., “Right mirror/out right 

window”). Not being able to reliably discern between mirror glances and looking out the side 

windows is a limitation of the current study. 

4.4 DRIVER TASKS AND INATTENTION 

Across both the safety-critical event and baseline epoch data sets, driver tasks—depending on the 

context, these can also be considered “behaviors;”for consistency, the term “task” will be used 

throughout— that were identified during the 6-s interval were grouped into one of two task 

categories: (1) secondary and (2) tertiary. As outlined by Ablassmeier et al. (2007), secondary 

and tertiary tasks, in addition to primary tasks, comprise a complete taxonomy of driving tasks. 

As noted previously, the primary task for a driver is driving (i.e., operating the vehicle). 

Secondary tasks are related to the driving task (e.g., turn-signal use), but are not necessary to 

keeping the vehicle on course. Tertiary tasks are extraneous tasks (e.g., eating) that are not 

related to driving. Appendix C lists all tasks and definitions, grouped into secondary and tertiary 

task categories that were identified in the DDWS and NTDS data sets. Note that these do not 

necessarily represent the universe of secondary and tertiary tasks, but only those that were 

observed in the video from the DDWS FOT and NTDS data sets. The Klauer et al. (2006) 

categorization scheme, used in the 100-Car study with light-vehicle data, was employed as much 

as possible in the grouping of CMV driver tasks in the current study. 

Once each task had been grouped into a secondary or tertiary category, the task category was 

broken down further into three distinct groups based on the manual/visual complexity of the task: 

simple, moderate, or complex (Klauer et al., 2006). These three categories were defined by 

Klauer et al.: 

Complex tertiary tasks are defined as a task that requires either multiple steps, 

multiple eye glances away from the forward roadway, and/or multiple button 

presses (Dingus, Antin, Hulse, & Wierwille, 1989). Moderate tertiary tasks are 

those that require, at most, two glances away from the roadway and/or at most 

two button presses. Simple tertiary tasks are those that require none or one button 

press and/or one glance away from the forward roadway. (p. 25) 

Examples of specific tasks in each category are shown in Table 10. The key point to note is that 

though analyses considered individual behaviors and tasks, grouping strategies were also used to 

parse the data. This provided both detailed (i.e., at the task level) and higher-level (i.e., at the 

task category level) approaches in assessing CMV driver behavior and inattention. 

31 
32 

Table 10. Assignment of Task Categories into Three Levels of Manual/Visual Complexity 

Simple Moderate Complex 

Talk/sing/dance with no 

indication of passenger 

Reach for object in vehicle Use calculator 

Interact with/look at other 

occupant(s) 

Look back in sleeper berth Read book, newspaper, 

paperwork, etc. 

Put on/remove/adjust seat belt Talk/listen to hand-held phone Look at map 

Put on/remove/adjust 

sunglasses or glasses 

Talk/listen to hands-free 

phone 

Write on pad, notebook, etc. 

Put on/remove/adjust hat Talk/listen to CB radio Dial cell phone 

Drink from a container Use/reach for other device Text message on cell phone 

Smoking-related—cigarette in 

hand or mouth 

Eating Interact with/look at 

dispatching device 

Use chewing tobacco Smoking-related—reaching, 

lighting, extinguishing 

Bite nails/cuticles Personal grooming 

Remove/adjust jewelry Look at outside vehicle, 

animal, object, etc. 

Other personal hygiene 

Adjust instrument panel 

4.5 RESEARCH QUESTIONS 

Once the safety-critical event and baseline data had been reduced, the data were ready to conduct 

statistical analyses and answer key research questions related to driver distraction. More 

specifically, these data were used to answer the specific research questions listed below. Section 

5 provides the results of each question. 

 Research Question 1: What are the types and frequency of tasks which drivers engage in 

prior to involvement in safety-critical events? What are the odds ratios and the PAR 

percentage for each task type? 

 Research Question 2: What are the environmental conditions associated with driver 

choice of engagement in tasks? What are the odds of being in a safety-critical event while 

engaging in tasks while encountering these conditions? 

 Research Question 3: What are the odds ratios of eyes-off-forward-roadway? Does eyesoff-forward-roadway significantly affect safety and/or driving performance? 
5. DATA ANALYSIS AND RESULTS 

Once all safety-critical events and baseline epochs were characterized, and associated tasks had 

been identified, statistical analyses were performed to assess the risk associated with the various 

tasks and the visual impact (i.e., eyes off road time) associated with each task. The data analysis 

procedures followed those outlined in the 100-Car Study (Klauer et al., 2006). Odds ratios were 

calculated to approximate relative safety-critical event risk compared to normal, baseline driving 

for various driver tasks. In addition, population attributable risk (PAR) calculations were used to 

determine what percentage of safety-critical events occurring in the population was attributable 

to driver distraction. Definitions of odds ratios and PAR, adapted from Klauer et al., are included 

later in this section. 

5.1 DATA ANALYSIS CAVEATS 

Several caveats regarding the data analyses are presented below. As previously noted, the data 

set used in the current analysis was comprised of two separate data sets, which differed on 

several points. First, the DDWS FOT data collection took place from May 2004 to September 

2005, while the NTDS data collection took place from November 2005 to May 2007. While cell 

phones were capable of text messaging during the DDWS FOT, text messaging has since 

become a prevalent communication behavior on cell phones. Thus, there were far more instances 

of text messaging in the NTDS data set than in the DDWS FOT data. Therefore, it is important to 

keep in mind that, across the two data sets, technology use by drivers was seen to have changed 

substantially over this short time span. 

Second, the NTDS had an additional camera view that was not present in the DDWS FOT. An 

over-the-shoulder camera installed in each instrumented vehicle in the NTDS recorded the 

driver’s steering wheel, hands, and lap, thereby providing the data analysts with more detailed 

information as to what the driver was doing at any given time. In the DDWS FOT data analysts 

could only see the driver’s face and shoulders. This is important to note as drivers would often 

hold an object (e.g., cell phone, map, calculator, dispatching device, etc.) in their lap that may 

not have been visible in the DDWS FOT, but would be visible in the NTDS data. As such, it may 

appear that drivers in the NTDS were more distracted by known (or identifiable) devices than 

drivers in the DDWS FOT. 

Third, because the primary purpose of the DDWS FOT was to test equipment that only worked 

at night, most DDWS FOT drivers typically drove in the middle of the night, on divided 

highways, in low levels of traffic. Because of these conditions, drivers may not have engaged in 

as many distracting behaviors, such as talking on a cell phone or reading paperwork. However, 

this is a hypothesis and an open question. 

Lastly, drivers’ attention was measured by assessing the amount of time the driver’s eyes were 

looking at the forward roadway. This was considered an objective, proxy measure of driver 

attention. However, this does not preclude the possible effects of cognitive processing while 

engaged in distracting behaviors (e.g., Goodman et al., 1999; Strayer et al., 2003). Though 

cognitive processing may play a role, naturalistic data (Klauer et al., 2006) clearly show that 

keeping a driver’s eyes on the forward roadway is a critical component in safe driving and 

33 
avoiding vehicle crashes (Hanowski, 2009). Therefore, any evaluation of driver distraction must 

consider the impact of the secondary and/or tertiary tasks have on drawing the driver’s eyes 

away from the forward roadway 

5.2 DATA ANALYSIS METHODS 

5.2.1 Odds Ratios 

Odds ratios were calculated to approximate relative safety-critical event risk compared to 

normal, baseline driving for various driver tasks. The odds ratio is a way of comparing the odds 

of some outcome (e.g., a crash) occurring, given the presence of some predictor factor, condition, 

or classification (e.g., CB use). It is usually a comparison of the presence of a condition to its 

absence (e.g., driver inattention versus no driver inattention). As shown in Table 11, an odds 

ratio is a measure of association commonly employed in the analysis of 2 × 2 contingency tables 

(Agresti, 1996). 

Table 11. 2 × 2 Contingency Table Used to Calculate Odds Ratio 

Driver Inattention No Driver Inattention 

Incidence Occurrence N11 N12

No Incidence Occurrence n21 n22

Odds of occurrence are defined as the probability of event occurrence (safety-critical event) 

divided by the probability of non-occurrence (baseline epoch). The following formula was used 

to perform the calculation to determine the odds ratio in order to assess the increase (or decrease) 

in the probability of having a safety-critical event, compared to a baseline epoch, in the presence 

of driver inattention versus no driver inattention: 

Odds Ratio = (n11)(n22)/(n21)(n12) 

Odds ratios of 1.0 indicate the outcome is equally likely to occur given the condition. An odds 

ratio greater than 1.0 indicates the outcome is more likely to occur given the condition. Odds 

ratios of less than 1.0 indicate the outcome is less likely to occur (Pedhazur, 1997). The 

hypothetical data presented in Table 12 will be used to illustrate how odds ratios are calculated. 

For this hypothetical example, assume there were a total of 100 safety-critical events and 100 

baseline epochs. The driver was found to be talking on a cell phone while driving during 45 of 

the safety-critical events, while the driver was talking on the cell phone while driving in 23 of the 

baseline epochs. 

Table 12. Odds Ratio Example 

Cell Phone Use No Cell Phone Use 

Safety-critical events 45 (A) 55 (B) 

Baseline epochs 23 (C) 77 (D) 

34 
The formula for this calculation would be as follows: 

B C

 DA OR

  (1a) 

 5523

OR   7745

OR  74.2

 (1b) 

 (1c) 

In order to determine if the odds ratio of 2.74 is significant, a 95 percent confidence interval is 

calculated, including the upper confidence limits (UCL) and lower confidence limits (LCL). The 

formulas to calculate the UCL and LCL are shown below: 

dcba eORUCL

96.1 



1111

 (2a) 

77235545

96.1

74.2 LCL  e

1111

UCL  04.5

 (2b) 

 (2c) 

dcba eORLCL

96.1 



1111

 (3a) 

77235545

96.1

74.2 LCL  e

1111

LCL  49.1

 (3b) 

 (3c) 

Since 1.0 is not included between the LCL and the UCL, the odds ratio is significantly different 

than 1.0. Thus, we are 95 percent certain the true odds ratio lies somewhere between 1.49 and 

5.04. Therefore, this example using data from Table 12 can be interpreted that drivers who talk 

on a cell phone while driving were 2.74 times more likely to be involved in a safety-critical 

event, compared to a baseline epoch, than if they were not talking on a cell phone while driving. 

5.2.2 Population Attributable Risk 

PAR is defined as the “risk of disease in the total population (pt) minus the risk in the unexposed 

group (pu)” (Sahai & Khurshid, 1996; p. 205). For each odds ratio with an outcome greater than 

1.0, the PAR percentage was also calculated. While the odds ratio is measured at the individual

level, the PAR is measured at the population level. This analysis provided an assessment of the 

percentage of safety-critical events that are occurring in the population and that are directly 

attributable to the specific behavior measured (i.e., driver inattention). 

35 
The PAR percentage is defined as the “proportion of the risk to the disease in the study 

population that is attributable to the exposure, and thus could be avoided by limiting the 

exposure to the risk factor” (Sahai & Khurshid, 1996; p. 205). Since the disease, or safety-critical 

events, occur rarely in the population, odds ratios may be substituted for relative risk and the 

PAR percentage is calculated as follows: 

100

))1(1(





 ))1((  ORP

PAR percentage

e

e ORP (4a) 

Where: Pe = population exposure estimate (e.g., number of baseline epochs with complex tertiary 

task/total number of baseline epochs) and OR = odds ratio estimate for a safety-critical event 

This calculation provides a percentage value which can then be generalized to the entire 

population. For example, if drivers who talk on cell phones while driving are two times as likely 

to be involved in a safety-critical event as when not talking on a cell phone, but cell phone use 

while driving is a rare occurrence in the entire population, this is explained by calculating the 

PAR percentage. Again, using the hypothetical data presented in Table 13, the PAR percentage 

is calculated below where: 

23.0

100   total baseline epochs

Pe

23 baseline whileusephonecellwithepochs driving present

OR  74.2

 (4b) 

 (4c) 

100

))174.2(23.01(



 

2(23.0(  ))174. PAR percentage  (4d) 

PAR percentage  70.18 (4e) 

In order to interpret the PAR percentage, the estimated sample variance and the UCL and LCL 

must first be calculated. Table 13 displays the hypothetical data used above in the odds ratio 

example; these data will be used to explain the calculations shown below. 

Table 13. Population Attributable Risk—Confidence Limits Example 

Cell Phone Use No Cell Phone Use Row Total 

Safety-critical events 45 (A) 55 (B) 100 (m1) 

Baseline epochs 23 (C) 77 (D) 100 (m2) 

Column Total 68 (n1) 132 (n2) (n) 

36 
First, it is necessary to calculate the estimated sample variance using the following formula: 

( ) 100

1 1 2

2

 



 





 













  Dm

C

Bm

A

Dm

Bm PARVar percentage

2

 (5a) 

100

10077

23

10055

45

10077

10055 ( ) 



 















 









PARVar percentage 

2

(PARVar percentage  57.0)

 (5b) 

 (5c) 

Next, the 95 percent UCL and LCL can be calculated using the estimated sample variance. This 

formula is as follows: 

 PARUCL percentage (96.1 PARVar percentage) (6a) 

UCL  57.096.170.18 (6b) 

UCL  18.20 (6c) 

 PARLCL percentage (96.1 PARVar percentage) (7a) 

LCL  57.096.170.18 (7b) 

LCL  22.17 (7c) 

Therefore, it can be reported that cell phone use while driving leads to a safety-critical event in 

17–20 percent of the population when compared to driving while not using a cell phone. 

37 
5.3 RESEARCH QUESTION 1: 

WHAT ARE THE TYPES AND FREQUENCY OF TASKS IN WHICH DRIVERS 

ENGAGE PRIOR TO INVOLVEMENT IN SAFETY-CRITICAL EVENTS? 

WHAT ARE THE ODDS RATIOS AND THE PAR PERCENTAGE FOR EACH 

TASK TYPE? 

5.3.1 Frequency of Tasks 

As noted in the previous chapter, each task was grouped into one of two task categories: 

secondary and tertiary. Additionally, tertiary tasks were further grouped by manual/visual 

complexity into complex, moderate, and simple (defined on page 31). Table 14 shows the 

percentage of safety-critical events that involved any type of distraction based on the list of 

distractions used in this study (see appendix C). These percentages reflect events when the driver 

was involved in any secondary or tertiary task in the 6-s interval. That is, a driver could be 

talking on a cell phone, checking a side mirror, or scratching an ear, and that would be reflected 

in the results. 

Following the method used in Klauer et al. (2006), of the 4,452 safety-critical events, 81.5 

percent had some type of driver distraction listed as a potential contributing factor. Table 14

displays the percentage of any secondary and/or tertiary tasks that were present in all safetycritical events and all events where the Vehicle 1 driver (i.e., the participant driver) was judged 

to be at-fault in the safety-critical event. 

Table 14. Frequency and Percentage of Any Secondary and/or Tertiary Task in 

“All” and “Vehicle 1 At-Fault” Events 

Event Type All SafetyCritical Events 

Frequency 

and Percent of 

All SafetyCritical Events 

All Vehicle 1 

At-Fault (V1) 

Events 

Frequency 

and Percent of 

All Vehicle 1 

At-Fault (V1) 

Events 

All safety-critical events 81.5% n = 4,452 

(100.0%) 83.4% n = 3,618 (100.0%) 

Crashes 100.0% n = 21 

(0.5%) 100.0% n = 10 

(0.3%) 

Near-crashes 78.7% n = 197 

(4.4%) 83.0% n = 112 

(3.1%) 

Crash-relevant conflicts 79.1% n = 3,019 

(67.8%) 81.1% n = 2,281 (63.0%) 

Unintentional lane 

deviations 87.7% n = 1,215 (27.3%) 87.7% n = 1,215 (33.6%) 

Baseline epochs 76.9% n = 19,888 

(100.0%) 76.9% n = 19,888 (100.0%) 

Though a breakdown of each Event Type is provided in Table 14, caution must be used in 

interpreting individual Event Types. While Klauer et al. (2006) found that 78 percent of crashes 

contained at least one type of inattention category (i.e., secondary task distraction; drivingrelated inattention to forward roadway; drowsiness; and non-specific eye glance away from the 

forward roadway), the current study, following the Klauer et al. method, found that 100 percent 

38 
of crashes contained at least one type of inattention task (either secondary or tertiary). It is 

important to point out a few caveats in comparing these two studies. First, and perhaps most 

importantly, the percentages in Table 14 include any task that was present within the 6-s interval; 

often the task was driving-related such as checking the side mirror. Because Klauer et al. 

included checking mirrors as a distraction type, this approach was followed in the current study. 

However, based on training received by CMV drivers, who are instructed to check mirrors every 

5–8 s (FMCSA, 2009b), it would be expected that video of the drivers would show them 

regularly checking their mirrors. This would, in turn, inflate the percentages seen in the current 

study and may not represent an accurate picture of “driver distraction.” 

A second caveat when comparing the results from the Klauer et al. (2006) study and the current 

study is the data collection time frames of the studies. The Klauer et al. study was conducted 

from January 2003 to July 2004, while the DDWS FOT was conducted from May 2004 to 

September 2005 and the NTDS study was conducted from November 2005 to May 2007. 

Because of these time period differences, the specific types of distraction across studies were 

similar, but not identical. For example, as will be described, a key finding in the current CMV 

study was the high risk associated with texting. However, because texting is a relatively recent 

phenomenon, there were no cases of texting in the Klauer et al. study. However, we know that 

light-vehicle drivers do engage in texting. As such, if the Klauer et al. study were conducted in 

present times, it would be expected that the distraction percentages may be different (or, at least, 

texting would be represented). 

Third, while the distraction categories used were similar across studies, they were not exactly the 

same and the current study had additional non-driving related distractions (e.g., texting, use 

calculator, using dispatching device) that were not cited in Klauer et al. (2006). 

Finally, it should be noted that crashes were a rare occurrence in the current study (less than 0.5 

percent of all safety-critical events). Klauer et al. (2006) had 69 crashes in the light-vehicle data 

set; approximately three times as many collected in the CMV data sets. Also, the majority of the 

crashes in the CMV data sets were relatively minor including deer hits (n = 5), contact with an 

object (e.g., construction cone, piece of debris) in the road or on the side of the road (n = 9). 

Collectively, these caveats underline the need for caution when comparing results from the 

current study with Klauer et al. and interpreting the results of individual event types, particularly 

those with small sample sizes (crashes). Table 15 provides an alternative approach, which the 

authors believe to be more appropriate, to evaluating the impact of driver distraction. 

Table 15 shows the percentage of all safety-critical events, and events where the Vehicle 1 driver 

was judged to be at-fault, where the driver was engaged in a non-driving related, tertiary task. As 

shown, driver distraction due to non-driving related tertiary tasks was a contributing factor in 71 

percent of crashes, 46 percent of near-crashes, and 60 percent of all events. Table 15 may capture 

the effects of “driver distraction” as many people think of it. That is, these events represent 

driving while also engaged in a non-driving related activity (e.g., using a cell phone, texting, 

eating). 

39 
Table 15. Frequency and Percentage of Any Tertiary Tasks in “All” and “Vehicle 1 At-Fault” Events 

Event Type All SafetyCritical Events 

Frequency 

and Percent of 

All SafetyCritical Events 

All Vehicle 1 

At-Fault (V1) 

Events 

Frequency 

and Percent of 

All Vehicle 1 

At-Fault (V1) 

Events 

All safety-critical events 59.9% n = 4,452 

(100.0%) 63.9% n = 3,618 (100.0%) 

Crashes 71.4% n = 21 

(0.5%) 40.0% n = 10 (0.3%) 

Near-crashes 46.2% n = 197 

(4.4%) 50.0% n = 112 (3.1%) 

Crash-relevant conflicts 53.6% n = 3,019 

(67.8%) 57.4% n = 2,281 (63.0%) 

Unintentional lane 

deviations 77.5% n = 1,215 (27.3%) 77.5% n = 1,215 (33.6%) 

Baseline epochs 56.5% n = 19,888 

(100.0%) 56.5% n = 19,888 (100.0%) 

5.3.2 Odds Ratios of Driver Tasks 

5.3.2.1 Task Categories 

In order to approximate safety-critical event risk, compared to normal, baseline driving, odds 

ratios were calculated on the different task categories. Odds ratios for each task category (tertiary 

task [complex, moderate, and simple] and secondary task) were calculated with the absence and 

presence of each task category. 

Each of these calculations was performed across all safety-critical events*

 (n = 4,452) and on 

those events where the Vehicle 1 driver was judged to be at-fault in the safety-critical event†

 (n = 

3,618). The results from these calculations are in Table 16, which shows the odds ratios, LCL, 

UCL, frequency of safety-critical events, and frequency of baseline epochs for each driver task 

(i.e., complex tertiary task, moderate tertiary task, simple tertiary tasks, and secondary tasks) 

across “All” and “Vehicle 1 At-Fault” safety-critical events. 

“All” Events: As shown in Table 16, odds ratios were significant for all four driver task types 

when “All” events were considered. As compared to baseline epochs, drivers were 10.4 times 

more likely to be involved in a safety-critical event while engaging in a complex tertiary task. 

For moderate tertiary, simple tertiary, and secondary tasks, the increased likelihood was 1.3, 1.2, 

and 1.3, respectively. 

 

*

 Analyses that included all safety-critical events are referred to as ‘All’ from here on. †

 Analyses that included all safety-critical events where the Vehicle 1 driver was judged to be at-fault in the safety -critical event are referred to 

as ‘Vehicle 1 At-Fault’ from here on. 

40 
41 

“Vehicle 1 At-Fault”: When “Vehicle 1 At-Fault” events were considered, odds ratios were also 

significant for all four driver task types. As compared to baseline epochs, drivers were 14.0 times 

more likely to be involved in a safety-critical event while engaging in a complex tertiary task. 

For moderate tertiary, simple tertiary, and secondary tasks, the increased likelihood was 1.6, 1.4, 

and 1.3, respectively. 
Table 16. Odds Ratios and 95% Confidence Intervals to Assess Likelihood of a Safety-Critical Event While Engag

“All” and “Vehicle 1 At-Fault” (V1) Events 

Task 

ALL 

Odds 

Ratio 

ALLLCL 

ALL 

UCL 

ALL 

Frequency of 

SafetyCritical 

Events 

ALL 

Frequency 

of 

Baselines 

V1 

Odds 

Ratio 

V1 

LCL 

V1 

UCL 

Frequ

Sa

Cr

Ev

Complex Tertiary Task 10.34* 8.55 12.50 359 194 13.92* 11.50 16.92 3Moderate Tertiary Task 

1.30* 1.17 1.44 876 3,776 1.55* 1.38 1.74 7

Simple Tertiary Task 1.22* 1.07 1.39 408 1,869 1.41* 1.22 1.62 3Secondary Task 1.32* 1.20 1.47 964 4,066 1.33* 1.18 1.50 7

* Asterisk indicates a significant odds ratio. These ratios are also shown in bold. 

42 
5.3.2.2 Manual/Visual Complexity 

Odds ratios were calculated for each tertiary task (each tertiary task is operationally defined in 

appendix C). Because of the small sample size for some of these tasks, each task of interest may 

occur in addition to another task during a safety-critical event or baseline epoch (i.e., if the task 

of interest is talking on a hands-free phone, the driver may also be smoking at the same time); 

therefore the results should be interpreted considering that at least the particular task was present. 

“All” Events: The results for these calculations are presented in Table 17 and suggest that 

engaging in any, and all, of the complex tertiary tasks increased the risk of being involved in a 

safety-critical event when compared to baseline epoch. While most of the tasks listed in Table 17

are self-evident from their title (e.g., text messaging on a cell phone), some of the tasks may not 

be as obvious. For example, the “Other—Complex” tertiary task was used to describe tasks that 

the driver engaged in that were not part of the task list, but were considered a complex task and 

worth noting (e.g., the driver cleaning his/her side mirror, rummaging through a grocery bag). 

The “Other—Moderate” tertiary task was used to describe tasks that the driver engaged in that 

were not part of the task list, but were considered a moderate task and worth noting (e.g., taking 

medicine by opening a pill bottle and taking a pill, exercising in the cab). The “Other—Simple” 

tertiary task was used to describe other tasks that the driver engaged in that were not part of the 

task list, but were considered a simple task and worth noting (e.g., the driver opening and closing 

the driver-side door). Lastly, some incidents of “personal grooming” included a driver shaving 

his head with an electric razor and drivers brushing their hair with a comb or brush; incidents of 

“use/reach for other electronic device” included reaching for or using a digital camera or video 

camera. 

A few highlights from Table 17 show that texting was a significant safety risk. Drivers were 23.2 

times more likely to be involved in a safety-critical event while text messaging. Using a 

dispatching device increased risk significantly by 9.9 times, while writing, using a calculator, 

looking at a map, dialing a cell phone, and reading significantly increased risk by 9.0, 8.2, 7.0, 

5.9, and 4.0, respectively. 

43 
Table 17. Odds Ratios and 95% Confidence Intervals to Assess Likelihood of a Safety-Critical Event While En

across “All” and “Vehicle 1 At-Fault” (V1) Events 

Task 

ALL 

Odds 

Ratio 

ALLLCL 

ALL

UCL 

ALL 

Frequency 

of SafetyCritical 

Events 

ALL 

Frequency 

of 

Baselines 

V1 

Odds 

Ratio 

V1 

LCL 

V1 

UCL 

F

Complex Tertiary Task

Text message on cell phone 23.24* 9.69 55.73 31 6 27.71* 11.52 66.61 Other—Complex (e.g., cleaning side mirror, rummaging through a grocery bag)

10.07* 3.10 32.71 9 4 12.40* 3.82 40.28 

Interact with/look at dispatching device 9.93* 7.49 13.16 155 72 11.90* 8.97 15.80 Write on pad, notebook, etc. 8.98* 4.73 17.08 28 14 11.07* 5.82 21.05 Use calculator 8.21* 3.03 22.21 11 6 10.11* 3.73 27.34 Look at map 7.02* 4.62 10.69 56 36 8.67* 5.70 13.20 Dial cell phone 5.93* 4.57 7.69 132 102 7.06* 5.42 9.18 Read book, newspaper, paperwork, etc. 3.97* 3.02 5.22 98 112 4.76* 3.61 6.27 

Moderate Tertiary Task

Use/reach for other electronic device 6.72* 2.74 16.44 12 8 7.58* 3.05 18.85 Other—Moderate (e.g., opening a pill bottle to take medicine, exercising in the cab)

5.86* 2.84 12.07 17 13 7.22* 3.50 14.87 

Personal grooming 4.48* 2.01 9.97 12 12 5.05* 2.23 11.46 Reach for object in vehicle 3.09* 2.75 3.48 503 787 3.65* 3.24 4.12 Look back in Sleeper Berth 2.30* 1.30 4.07 18 35 2.52* 1.39 4.56 Talk or listen to hand-held phone 1.04 0.89 1.22 195 837 1.16 0.99 1.37 Eating 1.01 0.83 1.21 137 609 1.16 0.96 1.41 Smoking-related behavior—reaching, lighting, extinguishing 0.60* 0.40 0.89 28 208 0.63* 0.41 0.97 Talk or listen to CB radio 0.55* 0.41 0.75 50 399 0.46* 0.33 0.66 Look at outside vehicle, animal, person, object, or undetermined 0.54* 0.50 0.60 625 4,590 0.51* 0.46 0.57 Talk or listen to hands-free phone 0.44* 0.35 0.55 91 901 0.40* 0.31 0.51 

44 
45 

Task 

ALL 

Odds 

Ratio 

ALLLCL 

ALL

UCL 

ALL 

Frequency 

of SafetyCritical 

Events 

ALL 

Frequency 

of 

Baselines 

V1 

Odds 

Ratio 

V1 

LCL 

V1 

UCL 

F

Simple Tertiary Task

Put on/remove/adjust sunglasses or reading glasses 3.63* 2.37 5.58 38 47 4.00* 2.57 6.24 Adjust instrument panel 1.25* 1.06 1.47 185 668 1.38* 1.16 1.65 Remove/adjust jewelry 1.68 0.44 6.32 3 8 2.06 0.55 7.78 Other—Simple (e.g., opening and closing driver’s door) 2.23 0.41 12.20 2 4 1.37 0.15 12.30 Put on/remove/adjust hat 1.31 0.69 2.49 12 41 1.34 0.67 2.68 Use chewing tobacco 1.02 0.51 2.02 10 44 1.12 0.55 2.31 Put on/remove/adjust seat belt 1.26 0.60 2.64 9 32 1.55 0.74 3.24 Talk/sing/dance with no indication of passenger 

1.05 0.90 1.22 225 961 0.93 0.78 1.10 

Smoking-related behavior—cigarette in hand or mouth 0.97 0.82 1.14 178 820 0.94 0.78 1.12 Drink from a container 0.97 0.72 1.30 54 249 1.13 0.83 1.53 Other personal hygiene 0.67* 0.59 0.75 359 2,313 0.73* 0.64 0.82 Bite nails/cuticles 0.45* 0.28 0.73 18 178 0.43* 0.25 0.74 Interact with or look at other occupant(s) 0.35* 0.22 0.55 20 256 0.36* 0.22 0.59 

Secondary Task

Look at left-side mirror/out left window 1.09* 1.01 1.17 1,211 5,077 1.03 0.95 1.12 Look at right-side mirror/out right window 0.95 0.86 1.05 493 2,306 0.74* 0.66 0.84 Check speedometer 0.32* 0.28 0.38 166 2,127 0.34* 0.29 0.41 

* Asterisk indicates a significant odds ratio. These ratios are also shown in bold. 
The results in Table 17 showed that five of the moderate complexity tasks significantly increased 

the risk of being involved in a safety-critical event (again, as compared to baseline epochs). It is 

noteworthy that talking/listening to a cell phone was not associated with increased risk (though, 

as noted above, dialing a cell phone was). Other interesting results from this category are in 

regard to the protective effect (defined as decreasing the risk of a safety-critical event) of some 

tasks. That is, tasks that had an OR less than 1.0 (and a UCL of less than 1.0) indicated that 

engaging in the task or behavior provided a safety benefit. Smoking-related behaviors were 

found to be protective, as was talking or listening to a CB radio, and talking or listening with a 

hands-free phone (which was defined as the driver talking into a headset when it was apparent 

he/she was not talking to a passenger). 

Two of the simple complexity tasks significantly increased risk, including interacting with eye 

wear and adjusting the instrument panel. Once again, certain tasks had a significant protective 

effect, including interacting with other occupants. 

For secondary tasks, drivers were 1.1 times more likely to be involved in a safety-critical event 

(compared to a baseline epoch) while looking out the left-side mirror/out left window. The 

results also show that checking the speedometer had a protective effect and was considered a 

safe behavior. 

“Vehicle 1 At-Fault”: When “Vehicle 1 At-Fault” events were considered, all complex tertiary 

tasks were found to be significantly riskier. As when considering “All” events, “Vehicle 1 AtFault” events where the driver was text messaging presented a substantial safety risk; drivers 

were 27.7 times more likely to be involved in a safety-critical event (compared to a baseline 

epoch) while text messaging. As noted, all other complex tertiary tasks were associated with a 

significant increase in risk. 

For moderately complex and simple tasks, the results for the “Vehicle 1 At-Fault” events were 

similar to the “All” events data. However, when differences in risk ratios were found, they were 

usually more robust. 

For secondary tasks, none of the tasks resulted in an increase in risk; however, a significant 

protective effect was found when drivers’ looked at the right-side mirror/out right window or 

checked the speedometer. This suggests these activities may be indicative of scanning the driving 

environment and heightened situation awareness. 

5.3.3 Population Attributable Risk 

The last step in answering Research Question 1 was to calculate the PAR percentages. Recall 

that the PAR provides an assessment of the percentage of safety-critical events that occurred in 

the population and that were directly attributable to the specific task or behavior measured. The 

PAR was calculated on all odds ratios greater than 1.0; the results from these calculations are 

presented in Table 18. 

46 
Table 18. Population Attributable Risk and 95% Confidence Intervals for Driver Tasks 

across “All” and “Vehicle 1 At-Fault” (V1) Events 

Task 

ALL 

PAR 

Percentage 

ALL 

LCL 

ALL 

UCL 

V1 

PAR 

Percentage 

V1 

LCL 

V1 

UCL 

Complex Tertiary Task 27.46 27.24 27.67 34.38 34.20 34.56

Interact with/look at dispatching 

device 

3.13 2.84 3.42 3.80 3.55 4.04

Dial cell phone 2.46 2.02 2.91 3.01 2.64 3.39

Read book, newspaper, 

paperwork, etc. 

1.65 0.96 2.34 2.07 1.49 2.66

Look at map 1.08 0.48 1.68 1.37 0.88 1.86

Text message on cell phone 0.67 0.29 1.04 0.80 0.48 1.12

Write on pad, notebook, etc. 0.56 -0.16 1.28 0.70 0.12 1.29

Use calculator 0.22 -1.00 1.43 0.27 -0.71 1.26

Other—Complex 

(e.g., cleaning side mirror, 

rummaging through a grocery bag) 

0.18 -0.99 1.35 0.23 -0.72 1.18

Moderate Tertiary Task 11.77 11.32 12.23 19.77 19.35 20.20

Reach for object in vehicle 7.64 7.27 8.02 9.49 9.16 9.82

Other—Moderate 

(e.g., opening a pill bottle to take 

medicine, exercising in the cab) 

0.32 -0.92 1.55 0.40 -0.60 1.41

Look back in sleeper berth 0.23 -2.24 2.70 0.27 -2.08 2.62

Use/reach for other electronic 

device 

0.23 -1.10 1.56 0.26 -0.94 1.47

Personal grooming 0.21 -1.58 2.00 0.24 -1.38 1.87

Talk or listen to hand-held phone 0.18 -1.29 1.64 0.68 -0.69 2.06

Eating 0.02 -1.80 1.83 0.49 -1.13 2.11

Simple Tertiary Task 5.96 5.20 6.73 10.56 9.83 11.30

Adjust instrument panel 0.82 -0.47 2.11 1.27 0.06 2.49

Put on/remove/adjust sunglasses 

or reading glasses 

0.62 -0.56 1.80 0.71 -0.40 1.81

Talk/sing/dance with no indication 

of passenger 

0.23 -1.12 1.59 – – –

Put on/remove/adjust hat 0.06 -4.85 4.98 0.07 -5.08 5.22

Put on/remove/adjust seat belt 0.04 -5.84 5.92 0.09 -4.69 4.87

Remove/adjust jewelry 0.03 -7.89 7.95 0.04 -6.39 6.48

Other—Simple 

(e.g., opening and closing driver’s 

door) 

0.02 -7.57 7.62 0.01 -15.95 15.94

Use chewing tobacco 0.00 -6.75 6.76 0.03 -6.34 6.40

Drink from a container 0.16 -2.50 2.82

Secondary Task 11.71 11.29 12.13 11.91 11.43 12.39

Look at left-side mirror/out left 

window 

2.25 1.77 2.75 0.80 0.21 1.38

47 
Note that in Table 18, the tasks are listed, within each complexity category, from highest to 

lowest PAR. As shown in Table 18, combining all complex tasks resulted in a PAR percentage 

of 27.5, with a LCL of 27.2 and an UCL of 27.7. Interpreted from statistics, this indicates that 

engaging in a complex tertiary task led to 27 percent of the safety-critical events in the 

population when compared to driving while not engaged in a complex tertiary task. When 

looking at specific tasks, interacting with a dispatching device and dialing a cell phone resulted 

in the highest percentage of safety-critical events with PAR percentages of 3.1 and 2.5, 

respectively. Recall that text messaging was associated with the highest odds ratio in the 

previous analyses. When calculating the PAR percentage and confidence levels for text 

messaging, it can be seen that driving while texting leads to a safety-critical event in 0.3 to 1.0 

percent of the population. This indicates that albeit dangerous (in the sense of increased risk), it 

is not a prominent activity. However, as texting becomes more commonplace, it would be 

expected that more safety-critical events will result. 

For moderate complexity tertiary tasks, reaching for an object was associated with the highest 

PAR percentage of 7.7 (with LCL of 11.3 and UCL of 12.2). Because this is such a frequently 

occurring behavior, it is represented as a relatively high PAR percentage (in fact, this is the 

highest for any of the individual tasks across all complexity categories). 

It should be noted that negative confidence level values in the table are, in some cases, the result 

of relatively few data points for a particular task; in other cases, when the original odds ratio was 

not significant, the PAR may be negative. As such, the resulting confidence level becomes 

unstable. When additional naturalistic data is added to the data set, and this additional data 

contains tasks that currently have negative confidence level for PAR analyses, the distribution 

will become more stable and a more accurate confidence level can be constructed. 

5.3.4 Summary of Key Findings 

The analysis of tasks resulting in driver distraction provided some intriguing findings. In general, 

the results showed that drivers engaging in any complex tertiary task will have an increased risk 

of being involved in a safety-critical event. In addition, several of the moderate complexity 

tertiary tasks and two of the simple tertiary tasks also resulted in elevated risk. 

After examining specific tasks further, some stand out as being particularly risky for CMV 

drivers: 

 Texting. 

 Interacting with dispatching devices. 

 Writing. 

 Using a calculator. 

 Looking at a map. 

 Dialing a cell phone. 

 Reading a book/newspaper. 

In addition, the increased risk when reaching for electronic devices and other objects in the cab 

was also noteworthy. 

48 
Though many of the secondary and tertiary tasks could be performed by both light-vehicle and 

CMV drivers (such as cell phone use), several of the tertiary tasks are specific to CMV drivers. 

For example, interacting with a dispatching device was associated with an odds ratio of 9.93 for 

CB 

 device. CB radios are 

simply designed communication devices and the analyses indicated that drivers are able to use 

ded 

 cell phone were both 

found to be high-risk behaviors. However, talking or listening to a hand-held phone was not 

 a 

ot 

sults 

a 

uency with which drivers engaged in these tasks. As noted, 

drivers frequently interacted with dispatching devices and the increased risk, in combination with 

AR 

“All” events and 11.9 for “Vehicle 1 at-fault’ events. Although this was a typical task for CMV 

drivers, as indicated by the PAR percentage, this tertiary task had one of the highest associated 

risks. These results indicate that this particular tertiary task should not be performed while 

driving and/or design improvements in the dispatch system are needed. 

Another tertiary task that was prevalent with CMV drivers was CB radio use. Interestingly, 

radio use had a protective effect, indicating a safety benefit of using this

them while driving without increased risk of being involved in a safety-critical event. Drivers in 

the DDWS FOT and the NTDS typically kept these devices within close reach (within an arm 

length). Moreover, the protective effect, or decreased risk of being involved in a safety-critical 

event, found while using CB radios suggests that drivers may be more alert or attentive to 

driving (a hypothesis supported by the eye glance analysis shown later). 

The use of cell phones while driving is a popular research topic and the current study provi

an interesting perspective. Reaching for an electronic device and dialing a

associated with increased risk, and talking or listening to a hands-free phone had a protective 

effect (similar to a CB radio). Again, the current study did not assess the possible effects of 

cognitive processing while engaged in a cell phone conversation, which has been shown to be

distracting behavior by other researchers (Goodman et al., 1999; Strayer et al., 2003), though n

in naturalistic studies. The positive findings for “listening and talking” are consistent with re

of two naturalistic studies with light-vehicle drivers. In the first study, protective effects were 

found for moderately complex tasks, which included talking/listening to handheld devices (F. 

Guo, personal communication, July 7, 2009). In the second study, drivers’ speed variance was 

better (i.e., speeds changed more smoothly) when drivers were using (i.e., talking or listening) 

cell phone (Sayer et al., 2007). 

The PAR percentages provided an interesting perspective and accounted for the increased risk of 

different tasks as well as the freq

the frequency of use, indicated this task to be especially risky. Interacting with a dispatching 

device led to a safety-critical event in 3.6–4.0 percent of the population when compared to 

driving while not interacting with a dispatching device. Dialing a cell phone was also a common 

task for CMV drivers and was found to have a high PAR percentage. More specifically, the P

percentage indicates which tasks, if removed, would provide the largest reduction in safetycritical events. Texting, though a high-risk behavior, was not a prominently occurring behavior 

during the data collection. However, it would be expected that safety-critical events that result 

from texting will increase in frequency as more drivers engage in this behavior. 

49 
5.4 RESEARCH QUESTION 2: 

WHAT ENVIRONMENTAL CONDITIONS ARE ASSOCIATED WITH DRIVER 

CHOICE OF ENGAGEMENT IN TASKS? WHAT ARE THE ODDS OF BEING 

IN A SAFETY-CRITICAL EVENT WHILE ENGAGING IN TASKS WHILE 

ENCOUNTERING THESE CONDITIONS? 

The second research question focused on task involvement as a function of environmental 

conditions. As a follow-up, an odds ratio analysis was performed to approximate the increased 

risk of being involved in a safety-critical event, as compared to baseline epoch, while engaging 

in various tasks in different environmental conditions. 

The eight environmental conditions listed below were assessed for each safety-critical event and 

baseline epoch during data reduction: 

 Lighting Levels. 

 Weather Conditions. 

 Roadway Surface Conditions. 

 Relation to Junction. 

 Trafficway Flow. 

 Roadway Alignment. 

 Road Profile. 

 Traffic Density. 

For each environmental condition, a frequency table was created. From this table, odds ratios and 

95 percent confidence limits were calculated. The odds ratios provide information as to whether 

a driver was more likely to be involved in a safety-critical event, compared to a baseline epoch, 

while engaged in a task during specific environmental conditions compared to not being engaged 

in a task in that environment. The following tasks were considered: 

 All tasks. 

 Tertiary tasks. 

– Complex tertiary tasks. 

– Moderate tertiary tasks. 

– Simple tertiary tasks. 

 Secondary tasks. 

Odds ratios were calculated with the absence or presence of each task category. The data were 

parsed for analysis in two ways: “All” events and “Vehicle 1 At-Fault” events. Each of the eight 

environmental conditions was considered in turn. 

50 
51 

5.4.1 Lighting Levels 

Lighting levels indicate the atmospheric light condition during the safety-critical event or 

baseline epoch. Note that “Dark but lighted” indicates the atmospheric lighting conditions were 

dark; however, the road had active artificial lighting. The lighting level at the time of the safetycritical event or baseline epoch was assessed. Data analysts were instructed to use the video data 

as well as the time stamp from the data files to assist in determining the appropriate lighting 

level. During data reduction, data analysts selected one of the five lighting conditions: 

 Daylight. 

 Dark. 

 Dark but lighted (i.e., street lights). 

 Dawn. 

 Dusk. 

Table 19 shows the frequency of tertiary and secondary tasks during safety-critical events and 

baseline epochs across “All” and “Vehicle 1 At-Fault” events for each lighting level. 
Table 19. The Frequency of Tertiary and Secondary Tasks during Safety-Critical Events and Baseline 

Across “All” and “Vehicle 1 At-Fault” Events for each Lighting Level 

Lighting 

Levels 

ALL 

Frequency 

of Tertiary 

Task 

SafetyCritical 

Events 

ALL 

Frequency 

of 

Secondary 

Task 

SafetyCritical 

Events 

ALL 

Frequency 

of Tertiary 

Task 

Baselines 

ALL 

Frequency 

of 

Secondary 

Task 

Baselines 

V1 

Frequency 

of Tertiary 

Task 

SafetyCritical 

Events 

V1 

Frequency 

of 

Secondary 

Task 

SafetyCritical 

Events 

V

Freq

of Te

Ta

Base

Daylight 1,586 800 4,004 2,305 1,431 581 4,0Dark 302 92 2,502 1,327 265 71 2,5Dark but lighted 46 46 405 261 29 33 4Dawn 8 9 84 68 8 9 Dusk 20 17 166 105 16 13 1

52 
Table 20–Table 25 show the results of the odds ratio calculations for each lighting level analysis. 

Table 20 displays the odds ratio calculations for engaging in a secondary and/or tertiary task 

across “All” and “Vehicle 1 At-Fault” events for each lighting level. The results in Table 20

suggest that engaging in any secondary or tertiary task significantly increased the risk of a driver 

being involved in a safety-critical event (compared to a baseline epoch) in two of the five 

lighting conditions. When “All” events were considered, drivers were 1.2 times more likely to be 

involved in a safety-critical event (compared to a baseline epoch) when driving in daylight. 

When “Vehicle 1 At-Fault” events were considered, drivers were 1.4 times more likely to be 

involved in a safety-critical event (compared to a baseline epoch) when driving in daylight and 

1.3 times more likely when driving in the dark. 

Table 20. Odds Ratios and 95% Confidence Intervals for the Interaction of Any Secondary and/or 

Tertiary Tasks by Lighting Level across “All” and “Vehicle 1 At-Fault” (V1) Events 

Lighting 

Levels 

ALL 

Odds Ratio 

ALL 

LCL 

ALL 

UCL 

V1 

Odds Ratio 

V1 

LCL 

V1 

UCL 

Daylight 1.19* 1.08 1.32 1.37* 1.22 1.53 

Dark 1.13 0.95 1.36 1.34* 1.09 1.64 

Dark but lighted 1.35 0.84 2.15 1.40 0.79 2.48 

Dawn 0.56 0.28 1.12 0.64 0.31 1.33 

Dusk 1.24 0.65 2.37 0.99 0.50 1.95 

 * Asterisk indicates a significant odds ratio. These ratios are also shown in bold. 

Table 21 displays the odds ratio calculations for engaging in any tertiary task across “All” and 

“Vehicle 1 At-Fault” events for each lighting level. The results in Table 21 suggest that engaging 

in any of the tertiary tasks significantly increased the risk of a driver being involved in a safetycritical event in two of the five lighting conditions. When “All” events were considered, drivers 

were 1.4 times more likely to be involved in a safety-critical event (compared to a baseline 

epoch) when driving in daylight and 1.4 times more likely when driving in the dark. When 

“Vehicle 1 At-Fault” events were considered, drivers were 1.8 times more likely to be involved 

in a safety-critical event (compared to a baseline epoch) when driving in daylight and 1.7 times 

more likely when driving in the dark. 

Table 21. Odds Ratios and 95% Confidence Intervals for the Interaction of Tertiary Tasks by 

Lighting Level across “All” and “Vehicle 1 At-Fault” (V1) Events 

Lighting 

Levels 

ALL 

Odds Ratio 

ALL 

LCL 

ALL 

UCL 

V1 

Odds Ratio 

V1 

LCL 

V1 

UCL 

Daylight 1.41* 1.26 1.57 1.75* 1.55 1.97 

Dark 1.42* 1.17 1.73 1.73* 1.38 2.15 

Dark but lighted 1.27 0.75 2.14 1.23 0.65 2.33 

Dawn 0.45 0.18 1.12 0.52 0.20 1.33 

Dusk 1.04 0.50 2.17 0.90 0.41 1.97 

 * Asterisk indicates a significant odds ratio. These ratios are also shown in bold. 

Table 22 displays the odds ratio calculations for engaging in any complex tertiary task across 

“All” and “Vehicle 1 At-Fault” events for each lighting level. The results in Table 22 suggest 

that engaging in a complex tertiary task significantly increased the risk of a driver being involved 

53 
in a safety-critical event (compared to a baseline epoch) in four of the five lighting conditions. 

This was true when “All” and “Vehicle 1 At-Fault” events were considered. Complex tertiary 

tasks resulted in significant odds ratios in all but one of the lighting conditions. Odds ratios for 

the dusk category was particularly high, indicating drivers were 21.5 times more likely to be 

involved in a safety-critical event (compared to a baseline epoch) when driving at dusk when 

considering “All” events and 18.7 times more likely for “Vehicle 1 At-Fault” events. 

Table 22. Odds Ratios and 95% Confidence Intervals for the Interaction of Complex Tertiary Tasks 

by Lighting Level across “All” and “Vehicle 1 At-Fault” (V1) Events 

Lighting 

Levels 

ALL 

Odds Ratio 

ALL 

LCL 

ALL 

UCL 

V1 

Odds Ratio 

V1 

LCL 

V1 

UCL 

Daylight 8.55* 6.83 10.70 11.55* 9.18 14.54 

Dark 8.36* 5.33 13.10 11.60* 7.33 18.35 

Dark but lighted 7.45* 2.44 22.78 11.42* 3.59 36.32 

Dawn 2.37 0.20 27.82 2.73 0.23 32.36 

Dusk 21.54* 3.79 122.40 18.67* 3.09 112.79 

 * Asterisk indicates a significant odds ratio. These ratios are also shown in bold. 

Table 23 displays the odds ratio calculations for engaging in any moderate tertiary task across 

“All” and “Vehicle 1 At-Fault” events for each lighting level. The results in Table 23 suggest 

that engaging in any of the moderate tertiary tasks significantly increased the risk of a driver 

being involved in a safety-critical event in two of the five lighting conditions. When “All” events 

were considered, drivers were 1.2 times more likely to be involved in a safety-critical event 

(compared to a baseline epoch) when driving in daylight. When “Vehicle 1 At-Fault” events 

were considered, drivers were 1.5 times more likely to be involved in a safety-critical event 

(compared to a baseline epoch) when driving in daylight and 1.3 times more likely when driving 

in the dark. 

Table 23. Odds Ratios and 95% Confidence Intervals for the Interaction of Moderate Tertiary Tasks 

by Lighting Level across “All” and “Vehicle 1 At-Fault” (V1) Events 

Lighting 

Levels 

ALL 

Odds Ratio 

ALL 

LCL 

ALL 

UCL 

V1 

Odds Ratio 

V1 

LCL 

V1 

UCL 

Daylight 1.20* 1.06 1.36 1.46* 1.28 1.68 

Dark 1.12 0.88 1.42 1.33* 1.02 1.74 

Dark but lighted 0.91 0.49 1.70 0.63 0.27 1.46 

Dawn 0.57 0.18 1.86 0.66 0.20 2.19 

Dusk 0.70 0.27 1.83 0.54 0.18 1.60 

 * Asterisk indicates a significant odds ratio. These ratios are also shown in bold. 

Table 24 displays the odds ratio calculations for engaging in any simple tertiary task across “All” 

and “Vehicle 1 At-Fault” events for each lighting level. The results in Table 24 suggest that 

engaging in any of the simple tertiary tasks significantly increased the risk of a driver being 

involved in a safety-critical event in two of the five lighting conditions. When “All” events were 

considered, drivers were 1.4 times more likely to be involved in a safety-critical event (compared 

to a baseline epoch) when driving in the dark. When “Vehicle 1 At-Fault” events were 

54 
considered, drivers were 1.4 times more likely to be involved in a safety-critical event (compared 

to a baseline epoch) when driving in daylight and 1.6 times more likely when driving in the dark. 

Table 24. Odds Ratios and 95% Confidence Intervals for the Interaction of Simple Tertiary Tasks 

by Lighting Level across “All” and “Vehicle 1 At-Fault” (V1) Events 

Lighting 

Levels 

ALL 

Odds Ratio 

ALL 

LCL 

ALL 

UCL 

V1 

Odds Ratio 

V1 

LCL 

V1 

UCL 

Daylight 1.16 0.99 1.36 1.37* 1.15 1.62 

Dark 1.42* 1.09 1.85 1.60* 1.19 2.15 

Dark but lighted 1.28 0.59 2.81 1.38 0.54 3.49 

Dawn 0.13 0.02 1.04 0.15 0.02 1.21 

Dusk 0.96 0.32 2.84 0.83 0.25 2.71 

 * Asterisk indicates a significant odds ratio. These ratios are also shown in bold. 

Table 25 displays the odds ratio calculations for engaging in any secondary task across “All” and 

“Vehicle 1 At-Fault” events for each lighting level. The results in Table 25 suggest that engaging 

in any of the secondary tasks significantly increased the risk of a driver being involved in a 

safety-critical event in two of the five lighting conditions. When “All” events were considered, 

drivers were 1.2 times more likely to be involved in a safety-critical event (compared to a 

baseline epoch) when driving in daylight and 2.0 times more likely when driving in dark, but 

lighted, conditions. When “Vehicle 1 At-Fault” events were considered, drivers were 1.2 times 

more likely to be involved in a safety-critical event (compared to a baseline epoch) when driving 

in daylight and 2.2 times more likely when driving dark, but lighted, conditions. 

Table 25. Odds Ratios and 95% Confidence Intervals for the Interaction of Secondary Tasks by 

Lighting Level across “All” and “Vehicle 1 At-Fault” (V1) Events 

Lighting 

Levels 

ALL 

Odds Ratio 

ALL 

LCL 

ALL 

UCL 

V1 

Odds Ratio 

V1 

LCL 

V1 

UCL 

Daylight 1.23* 1.09 1.39 1.23* 1.07 1.41 

Dark 0.81 0.63 1.06 0.87 0.65 1.18 

Dark but lighted 1.97* 1.16 3.34 2.17* 1.15 4.08 

Dawn 0.63 0.26 1.53 0.72 0.29 1.80 

Dusk 1.39 0.65 3.01 1.16 0.50 2.65 

 * Asterisk indicates a significant odds ratio. These ratios are also shown in bold. 

5.4.2 Weather Conditions 

Weather conditions indicate the atmospheric weather conditions at the time of the safety-critical 

events or baseline epoch. Data analysts were instructed to use the video data to assist in 

determining the appropriate weather condition. During data reduction, data analysts selected one 

of eight weather conditions: 

 No adverse conditions. 

 Rain. 

 Sleet. 

 Snow. 

55 
56 

nd fog. 

oke, sand/dust, crosswind, hail). 

sks during safety-critical events and 

baseline epochs across “All” and “Vehicle 1 At-Fault” events for six weather conditions. Those 

 Fog. 

 Rain a

 Sleet and fog. 

 Other (smog, sm

Table 26 shows the frequency of tertiary and secondary ta

conditions that were not observed are not included. As can be seen, most of the data were 

collected in “No adverse conditions” and “Rain.” 

.
Table 26. The Frequency of Secondary and Tertiary Tasks during Safety-Critical Events and Baseline 

across “All” and “Vehicle 1 At-Fault” (V1) Events for each Weather Condition 

Weather 

Conditions 

ALL 

Frequency 

of Tertiary 

Task SafetyCritical 

Events 

ALL 

Frequency of 

Secondary 

Task SafetyCritical 

Events 

ALL 

Frequency 

of Tertiary 

Task 

Baselines 

ALL 

Frequency 

of 

Secondary 

Task 

Baselines 

V1 

Frequency 

of Tertiary 

Task SafetyCritical 

Events 

V1 

Frequency of 

Secondary 

Task SafetyCritical 

Events 

V

Frequ

of Te

Ta

Base

No adverse conditions 1,860 902 6,564 3,693 1,658 659 6,5Rain 85 54 557 339 75 41 5Sleet 0 1 1 1 0 1 Snow 9 1 17 14 8 0 Fog 8 5 15 15 8 5 Rain and fog 0 1 7 4 0 1 

57 
Table 27–Table 32 present the results of the odds ratio calculations for six weather conditions. 

Table 27 displays the odds ratio calculations for engaging in any secondary and/or tertiary task 

across “All” and “Vehicle 1 At-Fault” events for each weather condition. The results in Table 27

suggest that engaging in any secondary or tertiary task significantly increased the risk of a driver 

being involved in a safety-critical event (compared to a baseline epoch) in two weather 

conditions. When “All” events were considered, drivers were 1.3 times more likely to be 

involved in a safety-critical event (compared to a baseline epoch) when driving in no adverse 

weather conditions and 1.4 times more likely when driving in the rain. When “Vehicle 1 AtFault” events were considered, drivers were 1.5 times more likely to be involved in a safetycritical event (compared to a baseline epoch) when driving in no adverse weather conditions and 

2.1 times more likely when driving in the rain. 

Table 27. Odds Ratios and 95% Confidence Intervals for the Interaction of Secondary and/or 

Tertiary Tasks by Weather Condition across “All” and “Vehicle 1 At-Fault” (V1) Events 

Weather 

Conditions 

ALL 

Odds Ratio 

ALL 

LCL 

ALL 

UCL 

V1 

Odds Ratio 

V1 

LCL 

V1 

UCL 

No adverse conditions 1.31* 1.21 1.43 1.48* 1.34 1.63 

Rain 1.44* 1.01 2.06 2.10* 1.34 3.27 

Sleet 1.00 0.03 29.81 1.00 0.03 29.81 

Snow 1.33 0.33 5.42 1.71 0.34 8.74 

Fog 2.56 0.52 12.51 2.39 0.49 11.74 

Rain and fog 0.25 0.01 4.92 – – –

* Asterisk indicates a significant odds ratio. These ratios are also shown in bold. 

Table 28 displays the odds ratio calculations for engaging in any tertiary task across “All” and 

“Vehicle 1 At-Fault” events for four weather conditions. The results in Table 28 suggest that 

engaging in any tertiary task significantly increased the risk of a driver being involved in a 

safety-critical event (compared to a baseline epoch) in two weather conditions. When “All” 

events were considered, drivers were 1.5 times more likely to be involved in a safety-critical 

event (compared to a baseline epoch) when driving in no adverse weather conditions, and 1.5 

times more likely when driving in the rain. When “Vehicle 1 At-Fault” events were considered, 

drivers were 1.8 times more likely to be involved in a safety-critical event (compared to a 

baseline epoch) when driving in no adverse weather conditions, and 2.2 times more likely when 

driving in the rain. 

Table 28. Odds Ratios and 95% Confidence Intervals for the Interaction of Tertiary Tasks by 

Weather Condition across “All” and “Vehicle 1 At-Fault” (V1) Events 

Weather 

Conditions 

ALL 

Odds Ratio 

ALL 

LCL 

ALL 

UCL 

V1 

Odds Ratio 

V1 

LCL 

V1 

UCL 

No adverse conditions 1.52* 1.39 1.67 1.84* 1.66 2.04 

Rain 1.48* 1.00 2.19 2.23* 1.38 3.59 

Snow 2.12 0.47 9.50 2.82 0.51 15.72 

Fog 4.00 0.73 22.04 4.00 0.73 22.04 

* Asterisk indicates a significant odds ratio. These ratios are also shown in bold. 

58 
Table 29 displays the odds ratio calculations for engaging in any complex tertiary task across 

“All” and “Vehicle 1 At-Fault” events for three weather conditions. The results in Table 29

suggest that engaging in any complex tertiary task significantly increased the risk of a driver 

being involved in a safety-critical event (compared to a baseline epoch) in two weather 

conditions. When “All” events were considered, drivers were 10.5 times more likely to be 

involved in a safety-critical event (compared to a baseline epoch) when driving in no adverse 

weather conditions, and 6.7 more likely when driving in the rain. When “Vehicle 1 At-Fault” 

events were considered, drivers were 13.9 times more likely to be involved in a safety-critical 

event (compared to a baseline epoch) when driving in no adverse weather conditions, and 11.5 

times more likely when driving in the rain. 

Table 29. Odds Ratios and 95% Confidence Intervals for the Interaction of Complex Tertiary Tasks 

by Weather Condition across “All” and “Vehicle 1 At-Fault” (V1) Events 

Weather 

Conditions 

ALL 

Odds Ratio 

ALL 

LCL 

ALL 

UCL 

V1 

Odds Ratio 

V1 

LCL 

V1 

UCL 

No adverse conditions 10.45* 8.59 12.71 13.92* 11.39 17.02 

Rain 6.70* 2.70 16.63 11.45* 4.45 29.45 

Fog 3.75 0.22 62.76 3.75 0.22 62.76 

* Asterisk indicates a significant odds ratio. These ratios are also shown in bold. 

Table 30 displays the odds ratio calculations for engaging in any moderate tertiary task across 

“All” and “Vehicle 1 At-Fault” events for four weather conditions. The results in Table 30

suggest that engaging in any moderate tertiary task significantly increased the risk of a driver 

being involved in a safety-critical event (compared to a baseline epoch) in three weather 

conditions. When “All” events were considered, drivers were 1.3 times more likely to be 

involved in a safety-critical event (compared to a baseline epoch) when driving in no adverse 

weather conditions, and 7.5 times more likely when driving in the fog. When “Vehicle 1 AtFault” events were considered, drivers were 1.5 times more likely to be involved in a safetycritical event (compared to a baseline epoch) when driving in no adverse weather conditions, 

2.3 times more likely when driving in the rain, and 7.5 times more likely when driving in the fog. 

Table 30. Odds Ratios and 95% Confidence Intervals for the Interaction of Moderate Tertiary Tasks 

by Weather Condition across “All” and “Vehicle 1 At-Fault” (V1) Events 

Weather 

Conditions 

ALL 

Odds Ratio 

ALL 

LCL 

ALL 

UCL 

V1 

Odds Ratio 

V1 

LCL 

V1 

UCL 

No adverse conditions 1.27* 1.14 1.41 1.49* 1.33 1.68 

Rain 1.51 0.96 2.39 2.29* 1.34 3.90 

Snow 1.82 0.35 9.45 2.73 0.44 17.05 

Fog 7.50* 1.17 48.15 7.50* 1.17 48.15 

* Asterisk indicates a significant odds ratio. These ratios are also shown in bold. 

Table 31 displays the odds ratio calculations for engaging in any simple tertiary task across “All” 

and “Vehicle 1 At-Fault” events for three weather conditions. The results in Table 31 suggest 

that engaging in any simple tertiary task significantly increased the risk of a driver being 

involved in a safety-critical event (compared to a baseline epoch) in one weather condition. 

When “All” events were considered, drivers were 1.2 times more likely to be involved in a 

59 
safety-critical event (compared to a baseline epoch) when driving in no adverse weather 

conditions. When “Vehicle 1 At-Fault” events were considered, drivers were 1.4 times more 

likely to be involved in a safety-critical event (compared to a baseline epoch) when driving in no 

adverse weather conditions. 

Table 31. Odds Ratios and 95% Confidence Intervals for the Interaction of Simple Tertiary Tasks 

by Weather Condition across “All” and “Vehicle 1 At-Fault” (V1) Events 

Weather 

Conditions 

ALL 

Odds Ratio 

ALL 

LCL 

ALL 

UCL 

V1 

Odds Ratio 

V1 

LCL 

V1 

UCL 

No adverse conditions 1.23* 1.07 1.41 1.41* 1.22 1.63 

Rain 1.12 0.63 1.98 1.60 0.83 3.10 

Snow 2.00 0.24 16.61 1.50 0.11 21.31 

* Asterisk indicates a significant odds ratio. These ratios are also shown in bold. 

Table 32 displays the odds ratio calculations for engaging in any secondary task across “All” and 

“Vehicle 1 At-Fault” events for six weather conditions. The results in Table 32 suggest that 

engaging in any secondary task significantly increased the risk of a driver being involved in a 

safety-critical event (compared to a baseline epoch) in two weather conditions. When “All” 

events were considered, drivers were 1.3 times more likely to be involved in a safety-critical 

event (compared to a baseline epoch) when driving in no adverse weather conditions, and 1.5 

times more likely when driving in the rain. When “Vehicle 1 At-Fault” events were considered, 

drivers were 1.3 times more likely to be involved in a safety-critical event (compared to a 

baseline epoch) when driving in no adverse weather conditions, and 2.0 times more likely when 

driving in the rain. 

Table 32. Odds Ratios and 95% Confidence Intervals for the Interaction of Secondary Tasks by 

Weather Condition across “All” and “Vehicle 1 At-Fault” (V1) Events 

Weather 

Conditions 

ALL 

Odds Ratio 

ALL 

LCL 

ALL 

UCL 

V1 

Odds Ratio 

V1 

LCL 

V1 

UCL 

No adverse conditions 1.31 1.18 1.46 1.30* 1.15 1.47 

Rain 1.54 1.00 2.37 2.00* 1.18 3.38 

Sleet 2.00 0.05 78.25 2.00 0.05 78.25 

Snow 0.29 0.03 3.12 

Fog 2.50 0.42 14.96 2.50 0.42 14.96 

Rain and fog 1.00 0.05 22.18 – – –

* Asterisk indicates a significant odds ratio. These ratios are also shown in bold. 

5.4.3 Roadway Surface Conditions 

The roadway surface condition indicates the condition of the road during the safety-critical event 

or baseline epoch. The roadway surface conditions at the time of the safety-critical event or 

baseline epoch were assessed. Data analysts were instructed to use the video data to assist in 

determining the appropriate roadway surface condition. During data reduction, data analysts 

selected one of six roadway surface conditions: 

60 
61 

 Dry. 

 Wet. 

 Snow or slush. 

 Ice. 

 Sand, oil, dirt. 

 Other. 

Table 33 shows the frequency of secondary and tertiary task during safety-critical events and 

baselines epochs across “All” and “Vehicle 1 At-Fault” events for four roadway surface 

conditions. Those conditions that were not observed are not included. As shown in Table 33, 

most of the events occurred on either dry or wet roads. 
Table 33. The Frequency of Secondary and Tertiary Task during Safety-Critical Events and Baseline Epochs acros

At-Fault” (V1) Event for each Roadway Surface Condition 

Roadway 

Surface 

Conditions 

Frequency 

of ALL 

Tertiary 

Task SafetyCritical 

Events 

Frequency 

of ALL 

Secondary 

Task SafetyCritical 

Events 

Frequency 

ALL of 

Tertiary 

Task 

Baselines 

Frequency 

of ALL 

Secondary 

Task 

Baselines 

Frequency 

of V1 

Tertiary 

Task SafetyCritical 

Events 

Frequency 

of V1 

Secondary 

Task SafetyCritical 

Events 

Freq

of 

Ter

Ta

Base

Dry 1,849 896 6,466 3,638 1,652 655 6,4Wet 104 66 677 413 89 51 6Snow/Slush 9 2 18 15 8 1 Sand, oil, dirt 0 1 0 0 0 1 

62 
Table 34–Table 39 present the results of the odds ratio calculations for each roadway surface 

condition. Table 34 displays the odds ratio calculations for engaging in any secondary and/or 

tertiary task across “All” and “Vehicle 1 At-Fault” events for three roadway surface conditions. 

The results in Table 34 suggest that engaging in any secondary or tertiary task significantly 

increased the risk of a driver being involved in a safety-critical event (compared to a baseline 

epoch) in two roadway surface conditions. When “All” events were considered, drivers were 1.3 

times more likely to be involved in a safety-critical event (compared to a baseline epoch) when 

driving on a dry roadway surface. When “Vehicle 1 At-Fault” events were considered, drivers 

were 1.5 times more likely to be involved in a safety-critical event (compared to a baseline 

epoch) when driving on a dry roadway surface, and 1.7 times more likely when driving on a wet 

roadway surface. 

Table 34. Odds Ratios and 95% Confidence Intervals for the Interaction of Secondary and/or 

Tertiary Tasks by Roadway Surface Condition across “All” and “Vehicle 1 At-Fault” (V1) Events 

Roadway Surface 

Conditions 

ALL 

Odds Ratio 

ALL 

LCL 

ALL 

UCL 

V1 

Odds Ratio 

V1 

LCL 

V1 

UCL 

Dry 1.33* 1.22 1.45 1.50* 1.36 1.65 

Wet 1.23 0.91 1.67 1.69* 1.16 2.46 

Snow/Slush 1.14 0.36 3.61 1.54 0.39 6.12 

 * Asterisk indicates a significant odds ratio. These ratios are also shown in bold. 

Table 35 displays the odds ratio calculations for engaging in any tertiary task across “All” and 

“Vehicle 1 At-Fault” events for three roadway surface conditions. The results in Table 35

suggest that engaging in any tertiary task significantly increased the risk of a driver being 

involved in a safety-critical event (compared to a baseline epoch) in two roadway surface 

conditions. When “All” events were considered, drivers were 1.6 times more likely to be 

involved in a safety-critical event (compared to a baseline epoch) when driving on a dry roadway 

surface. When “Vehicle 1 At-Fault” events were considered, drivers were 1.9 times more likely 

to be involved in a safety-critical event (compared to a baseline epoch) when driving on a dry 

roadway surface, and 1.8 times more likely when driving on a wet roadway surface. 

Table 35. Odds Ratios and 95% Confidence Intervals for the Interaction of Tertiary Tasks by 

Roadway Surface Condition across “All” and “Vehicle 1 At-Fault” (V1) Events 

Roadway Surface 

Conditions 

ALL 

Odds Ratio 

ALL 

LCL 

ALL 

UCL 

V1 

Odds Ratio 

V1 

LCL 

V1 

UCL 

Dry 1.55* 1.41 1.70 1.87* 1.69 2.07 

Wet 1.24 0.88 1.75 1.77* 1.17 2.66 

Snow/Slush 1.60 0.44 5.78 2.37 0.54 10.50 

 * Asterisk indicates a significant odds ratio. These ratios are also shown in bold. 

Table 36 displays the odds ratio calculations for engaging in any complex tertiary task across 

“All” and “Vehicle 1 At-Fault” events for two roadway surface conditions. The results in Table 

36 suggest that engaging in any complex tertiary task significantly increased the risk of a driver 

being involved in a safety-critical event (compared to a baseline epoch) in two roadway surface 

conditions. When “All” events were considered, drivers were 10.6 times more likely to be 

involved in a safety-critical event (compared to a baseline epoch) when driving on a dry roadway 

63 
surface, and 5.6 times more likely when driving on a wet roadway surface. When “Vehicle 1 AtFault” events were considered, drivers were 14.0 times more likely to be involved in a safetycritical event (compared to a baseline epoch) when driving on a dry roadway surface, and 8.4 

times more likely when driving on a wet roadway surface. 

Table 36. Odds Ratios and 95% Confidence Intervals for the Interaction of Complex Tertiary Tasks 

by Roadway Surface Condition across “All” and “Vehicle 1 At-Fault” (V1) Events 

Roadway Surface 

Conditions 

ALL 

Odds Ratio 

ALL 

LCL 

ALL 

UCL 

V1 

Odds Ratio 

V1 

LCL 

V1 

UCL 

Dry 10.56* 8.67 12.85 14.06* 11.49 17.20 

Wet 5.57* 2.47 12.58 8.39* 3.55 19.86 

 * Asterisk indicates a significant odds ratio. These ratios are also shown in bold. 

Table 37 displays the odds ratio calculations for engaging in any moderate tertiary task across 

“All” and “Vehicle 1 At-Fault” events for three roadway surface conditions. The results in Table 

37 suggest that engaging in any moderate tertiary task significantly increased the risk of a driver 

being involved in a safety-critical event (compared to a baseline epoch) in two roadway surface 

conditions. When “All” events were considered, drivers were 1.3 times more likely to be 

involved in a safety-critical event (compared to a baseline epoch) when driving on a dry roadway 

surface. When “Vehicle 1 At-Fault” events were considered, drivers were 1.5 times more likely 

to be involved in a safety-critical event (compared to a baseline epoch) when driving on a dry 

roadway surface, and 1.9 times more likely when driving on a wet roadway surface. 

Table 37. Odds Ratios and 95% Confidence Intervals for the Interaction of Moderate Tertiary Tasks 

by Roadway Surface Condition across “All” and “Vehicle 1 At-Fault” (V1) Events 

Roadway Surface 

Conditions 

ALL 

Odds Ratio 

ALL 

LCL 

ALL 

UCL 

V1 

Odds Ratio 

V1 

LCL 

V1 

UCL 

Dry 1.29* 1.16 1.44 1.52* 1.35 1.71 

Wet 1.32 0.89 1.96 1.87* 1.18 2.97 

Snow/Slush 1.45 0.34 6.25 2.42 0.48 12.30 

 * Asterisk indicates a significant odds ratio. These ratios are also shown in bold. 

Table 38 displays the odds ratio calculations for engaging in any simple tertiary task across “All”

and “Vehicle 1 At-Fault” events for three roadway surface conditions. The results in Table 38

suggest that engaging in any simple tertiary task significantly increased the risk of a driver being 

involved in a safety-critical event (compared to a baseline epoch) in one roadway surface 

condition. When “All” events were considered, drivers were 1.3 times more likely to be involved 

in a safety-critical event (compared to a baseline epoch) when driving on a dry roadway surface. 

When “Vehicle 1 At-Fault” events were considered, drivers were 1.4 times more likely to be 

involved in a safety-critical event (compared to a baseline epoch) when driving on a dry roadway 

surface. 

64 
Table 38. Odds Ratios and 95% Confidence Intervals for the Interaction of Simple Tertiary Tasks 

by Roadway Surface Condition across “All” and “Vehicle 1 At-Fault” (V1) Events 

Roadway Surface 

Conditions 

ALL 

Odds Ratio 

ALL 

LCL 

ALL 

UCL 

V1 

Odds Ratio 

V1 

LCL 

V1 

UCL 

Dry 1.25* 1.09 1.43 1.43* 1.23 1.65 

Wet 0.86 0.51 1.45 1.22 0.68 2.21 

Snow/Slush 1.60 0.22 11.50 1.33 0.11 16.48 

 * Asterisk indicates a significant odds ratio. These ratios are also shown in bold. 

Table 39 displays the odds ratio calculations for engaging in any secondary task across “All” and 

“Vehicle 1 At-Fault” events for three roadway surface conditions. The results in Table 39

suggest that engaging in any secondary task significantly increased the risk of a driver being 

involved in a safety-critical event (compared to a baseline epoch) in two roadway surface 

conditions. When “All” events were considered, drivers were 1.3 times more likely to be 

involved in a safety-critical event (compared to a baseline epoch) when driving on a dry roadway 

surface. When “Vehicle 1 At-Fault” events were considered, drivers were 1.3 times more likely 

to be involved in a safety-critical event (compared to a baseline epoch) when driving on a dry 

roadway surface, and 1.7 times more likely when driving on a wet roadway surface. 

Table 39. Odds Ratios and 95% Confidence Intervals for the Interaction of Secondary Tasks by 

Roadway Surface Condition across “All” and “Vehicle 1 At-Fault” (V1) Events 

Roadway Surface 

Conditions 

ALL 

Odds Ratio 

ALL 

LCL 

ALL 

UCL 

V1 

Odds Ratio 

V1 

LCL 

V1 

UCL 

Dry 1.33* 1.20 1.48 1.32* 1.17 1.49 

Wet 1.30 0.89 1.89 1.66* 1.06 2.60 

Snow/Slush 0.43 0.07 2.54 0.36 0.03 3.80 

 * Asterisk indicates a significant odds ratio. These ratios are also shown in bold. 

5.4.4 Relation to Junction 

The relation to junction indicates an intersection or the connection between a driveway access 

and a roadway other than a driveway access during the safety-critical event or baseline epoch. 

The relation to junction at the time of the safety-critical event or baseline epoch was assessed. 

Data analysts were instructed to use the video data to assist in determining the appropriate 

relation to junction. During data reduction, data analysts selected one of the ten relation to 

junction options: 

 Non-junction. 

 Intersection. 

 Intersection-related. 

 Driveway, alley access, etc. 

 Parking lot. 

 Entrance/exit ramp. 

 Rail grade crossing. 

65 
66 

 Bridge. 

 Crossover-related. 

 Other. 

Table 40 shows the frequency of secondary and tertiary tasks during safety-critical events and 

baseline epochs across “All” and “Vehicle 1 At-Fault” events for each relation to junction. Those 

conditions that were not observed are not included. As can be seen in Table 40, most of the 

events occurred on non-junctions. 
Table 40. The Frequency of Secondary and Tertiary Tasks during Safety-Critical Events and Baseline 

across “All” and “Vehicle 1 At-Fault” (V1) for each Relation to Junction 

Relation to Junction 

Frequency 

of ALL 

Tertiary 

Task 

SafetyCritical 

Events 

Frequency 

of ALL 

Secondary 

Task 

SafetyCritical 

Events 

Frequency 

of ALL 

Tertiary 

Task 

Baselines 

Frequency 

of ALL 

Secondary 

Task 

Baselines 

Frequency 

of V1 

Tertiary 

Task 

SafetyCritical 

Events 

Frequency 

of V1 

Secondary 

Task 

SafetyCritical 

Events 

Fre

T

Ba

Non-junction 1,712 614 6,825 3,852 1,576 504 6Intersection 20 42 39 18 13 19 Intersection-related 142 175 66 30 102 114 Driveway, alley access, etc. 3 1 3 0 2 1 Parking lot 3 20 12 3 2 9 Entrance/exit ramp 57 94 94 95 33 49 Rail grade crossing 3 1 0 1 2 0 Bridge 16 2 113 56 15 2 Crossover-related 2 1 0 2 1 1 Other 4 14 9 9 3 8 

67 
Table 41–Table 46 present the results of the odds ratio calculations for each relation to junction. 

Table 41 displays the odds ratio calculations for engaging in any secondary and/or tertiary task 

across “All” and “Vehicle 1 At-Fault” events for nine relation to junction options. The results in 

Table 41 suggest that engaging in any secondary or tertiary task significantly increased the risk 

of a driver being involved in a safety-critical event (compared to a baseline epoch) in one 

relation to junction option. Further, Table 41 illustrates that engaging in any task significantly 

decreased the risk of a driver being involved in a safety-critical event (compared to a baseline 

epoch) in one relation to junction option. When “All” events were considered, drivers were 1.4 

times more likely to be involved in a safety-critical event (compared to a baseline epoch) when 

driving on a non-junction. When “Vehicle 1 At-Fault” events were considered, drivers were 1.7 

times more likely to be involved in a safety-critical event (compared to a baseline epoch) when 

driving on a non-junction. and 0.6 times less likely when driving on a entrance/exit ramp. 

Table 41. Odds Ratios and 95% Confidence Intervals for the Interaction of Secondary and/or 

Tertiary Tasks by Relation to Junction across “All” and “Vehicle 1 At-Fault” (V1) Events 

Relation to Junction ALL 

Odds Ratio

ALL 

LCL 

ALL 

UCL 

V1 

Odds Ratio 

V1 

LCL 

V1 

UCL 

Non-junction 1.44* 1.31 1.58 1.70* 1.53 1.89 

Intersection 0.77 0.38 1.54 1.00 0.40 2.51 

Intersection-related 0.96 0.64 1.45 0.86 0.56 1.32 

Parking lot 2.57 0.43 15.41 2.48 0.25 24.65 

Entrance/exit ramp 0.72 0.48 1.07 0.62* 0.39 0.98 

Bridge 1.53 0.57 4.13 1.42 0.52 3.86 

Crossover-related 1.00 0.05 18.91 1.50 0.06 40.63 

Other 0.84 0.26 2.71 2.07 0.37 11.49 

Driveway, alley access, etc. 1.00 0.45 22.18 0.75 0.03 17.51 

* Asterisk indicates a significant odds ratio. These ratios are also shown in bold. 

Table 42 suggests that engaging in any tertiary task significantly increased the risk of a driver 

being involved in a safety-critical event (compared to a baseline epoch) in one relation to 

junction option. Further, Table 42 illustrates that engaging in any tertiary task significantly 

decreased the risk of a driver being involved in a safety-critical event (compared to a baseline 

epoch) in two relation to junction options. When “All” events were considered, drivers were 1.9 

times more likely to be involved in a safety-critical event (compared to a baseline epoch) when 

driving on a non-junction, 0.4 times less likely when driving in an intersection, and 0.5 times less 

likely when driving on an entrance/exit ramp. When “Vehicle 1 At-Fault” events were 

considered, drivers were 2.3 times more likely to be involved in a safety-critical event (compared 

to a baseline epoch) when driving on a non-junction and 0.5 times less likely when driving on a 

entrance/exit ramp. Note that the low frequencies in many of the junction categories make it 

difficult to read too much into these findings. 

68 
Table 42. Odds Ratios and 95% Confidence Intervals for the Interaction of Tertiary Tasks by 

Relation to Junction across “All” and “Vehicle 1 At-Fault” (V1) Events 

Relation to Junction ALL 

Odds Ratio 

ALL 

LCL 

ALL 

UCL 

V1 

Odds Ratio 

V1 

LCL 

V1 

UCL 

Non-junction 1.85* 1.67 2.04 2.25* 2.02 2.52 

Intersection 0.42* 0.18 0.96 0.75 0.26 2.13 

Intersection-related 0.72 0.46 1.15 0.69 0.42 1.11 

Driveway, alley access, etc. 1.00 0.04 24.55 0.67 0.02 18.06 

Parking lot 0.50 0.06 4.15 0.67 0.05 9.47 

Entrance/exit ramp 0.51* 0.03 0.84 0.47* 0.27 0.83 

Bridge 1.73 0.60 4.94 1.62 0.56 4.67 

Other 0.39 0.08 1.84 1.17 0.15 9.01 

* Asterisk indicates a significant odds ratio. These ratios are also shown in bold. 

Table 43 displays the odds ratio calculations for engaging in any complex tertiary task across 

“All” and “Vehicle 1 At-Fault” events for four relation to junction options. The results in Table 

43 suggest that engaging in any complex tertiary task significantly increased the risk of a driver 

being involved in a safety-critical event (compared to a baseline epoch) in two relation to 

junction options. When “All” events were considered, drivers were 13.8 times more likely to be 

involved in a safety-critical event (compared to a baseline epoch) when driving on a nonjunction, and 9.2 more likely when driving on a bridge. When “Vehicle 1 At-Fault” events were 

considered, drivers were 18.1 times more likely to be involved in a safety-critical event 

(compared to a baseline epoch) when driving on a non-junction, and 9.2 times more likely when 

driving on a bridge. Note that the low frequencies in many junction categories make it difficult to 

read too much into these findings. 

Table 43. Odds Ratios and 95% Confidence Intervals for the Interaction of Complex Tertiary Tasks 

by Relation to Junction across “All” and “Vehicle 1 At-Fault” (V1) Events 

Relation to Junction ALL 

Odds Ratio 

ALL 

LCL 

ALL 

UCL 

V1 

Odds Ratio 

V1 

LCL 

V1 

UCL 

Non-junction 13.77 11.30 16.78 18.05* 14.74 22.11 

Intersection-related 1.68 0.19 14.82 1.33 0.13 13.21 

Entrance/exit ramp 0.42 0.04 4.79 0.67 0.06 7.65 

Bridge 9.15 1.59 52.80 9.15* 1.59 52.80 

* Asterisk indicates a significant odds ratio. These ratios are also shown in bold. 

Table 44 displays the odds ratio calculations for engaging in any moderate tertiary task across 

“All” and “Vehicle 1 At-Fault” events for seven relation to junction options. The results in Table 

44 suggest that engaging in any moderate tertiary task significantly increased the risk of a driver 

being involved in a safety-critical event (compared to a baseline epoch) in one relation to 

junction option. Further, Table 44 illustrates that engaging in any moderate tertiary task 

significantly decreased the risk of a driver being involved in a safety-critical event (compared to 

a baseline epoch) in three relation to junction options. When “All” events were considered, 

drivers were 1.6 times more likely to be involved in a safety-critical event (compared to a 

baseline epoch) when driving on a non-junction, 0.3 times less likely when driving in an 

intersection, 0.5 times less likely when driving in an intersection-related junction, and 0.4 times 

69 
less likely when driving on an entrance/exit ramp. When “Vehicle 1 At-Fault” events were 

considered, drivers were 1.9 times more likely to be involved in a safety-critical event (compared 

to a baseline epoch) when driving on a non-junction, 0.3 times less likely when driving in an 

intersection, 0.6 times less likely when driving in an intersection-related junction, and 0.3 times 

less likely when driving on an entrance/exit ramp. Note that the low frequencies in many of the 

junction categories make it difficult to read too much into these findings. 

Table 44. Odds Ratios and 95% Confidence Intervals for the Interaction of Moderate Tertiary 

Tasks by Relation to Junction across “All” and “Vehicle 1 At-Fault” (V1) Events 

Relation to 

Junction 

ALL 

Odds Ratio 

ALL 

LCL 

ALL 

UCL 

V1 

Odds Ratio 

V1 

LCL 

V1 

UCL 

Non-junction 1.55* 1.38 1.74 1.86* 1.64 2.12 

Intersection 0.25* 0.10 0.67 0.31* 0.08 1.18 

Intersection-related 0.54* 0.32 0.92 0.55* 0.32 0.96 

Parking lot 0.86 0.10 7.51 1.14 0.08 16.95 

Entrance/exit ramp 0.43* 0.25 0.75 0.34* 0.17 0.67 

Bridge 1.48 0.46 4.77 1.29 0.39 4.29 

Other 0.35 0.05 2.41 0.70 0.05 10.01 

 * Asterisk indicates a significant odds ratio. These ratios are also shown in bold. 

Table 45 displays the odds ratio calculations for engaging in any simple tertiary task across “All”

and “Vehicle 1 At-Fault” events for five relation to junction options. The results in Table 45

suggest that engaging in any simple tertiary task significantly increased the risk of a driver being 

involved in a safety-critical event (compared to a baseline epoch) in one relation to junction 

option. When “All” events were considered, drivers were 1.4 times more likely to be involved in 

a safety-critical event (compared to a baseline epoch) when driving on a non-junction. When 

“Vehicle 1 At-Fault” events were considered, drivers were 1.6 times more likely to be involved 

in a safety-critical event (compared to a baseline epoch) when driving on a non-junction. Note 

that the low frequencies in many of the junction categories make it difficult to read too much into 

these findings. 

Table 45. Odds Ratios and 95% Confidence Intervals for the Interaction of Simple Tertiary Tasks 

by Relation to Junction across “All” and “Vehicle 1 At-Fault” (V1) Events 

Relation to 

Junction 

ALL 

Odds Ratio 

ALL 

LCL 

ALL 

UCL 

V1 

Odds Ratio 

V1 

LCL 

V1 

UCL 

Non-junction 1.38* 1.19 1.59 1.59* 1.36 1.85 

Intersection 1.23 0.30 5.03 2.81 0.59 13.34 

Intersection-related 1.24 0.61 2.52 1.16 0.55 2.44 

Entrance/exit ramp 0.62 0.29 1.36 0.64 0.26 1.55 

Bridge 0.98 0.18 5.37 0.98 0.18 5.37 

 * Asterisk indicates a significant odds ratio. These ratios are also shown in bold. 

Table 46 displays the odds ratio calculations for engaging in any secondary task across “All” and 

“Vehicle 1 At-Fault” events for eight relation to junction options. The results in Table 46 suggest 

that engaging in any secondary task significantly increased the risk of a driver being involved in 

a safety-critical event (compared to a baseline epoch) in three relation to junction options. When 

70 
71 

“All” events were considered, drivers were 1.2 times more likely to be involved in a safetycritical event (compared to a baseline epoch) when driving on a non-junction, 2.0 times more 

likely when driving on an intersection-related junction, and 13.3 times more likely when driving 

in a parking lot. When “Vehicle 1 At-Fault” events were considered, drivers were 1.3 times more 

likely to be involved in a safety-critical event (compared to a baseline epoch) when driving on a 

non-junction. Note that the low frequencies in many of the junction categories make it difficult to 

read too much into these findings. 

Table 46. Odds Ratios and 95% Confidence Intervals for the Interaction of Secondary Tasks 

by Relation to Junction across “All” and “Vehicle 1 At-Fault” (V1) Events 

Relation to 

Junction 

ALL 

Odds Ratio 

ALL 

LCL 

ALL 

UCL 

V1 

Odds Ratio 

V1 

LCL 

V1 

UCL 

Non-junction 1.17* 1.04 1.32 1.28* 1.12 1.46 

Intersection 1.91 0.83 4.39 2.38 0.83 6.81 

Intersection-related 1.96* 1.16 3.32 1.69 0.98 2.92 

Parking lot 13.33* 1.65 107.43 12.00 0.94 153.89 

Entrance/exit ramp 0.84 0.53 1.32 0.69 0.41 1.18 

Bridge 0.44 0.08 2.34 0.44 0.08 2.34 

Crossover-related 0.25 0.01 7.45 0.50 0.01 19.56 

Other 1.36 0.37 5.07 3.11 0.50 19.54 

 * Asterisk indicates a significant odds ratio. These ratios are also shown in bold. 

5.4.5 Trafficway Flow 

The trafficway flow indicates whether the safety-critical event or baseline epoch occurred on a 

trafficway that was not physically divided or was divided with a median strip (with or without a 

traffic barrier), and whether it served one-way or two-way traffic. The trafficway flow at the time 

of the safety-critical event or baseline epoch was assessed. Data analysts were instructed to use 

the video data to assist in determining the appropriate relation to junction. During data reduction, 

data analysts selected one of the four trafficway flow options: 

 Not physically divided (center 2-way left turn lane). 

 Not physically divided (2-way trafficway). 

 Divided (median strip or barrier). 

 One-way trafficway. 

Table 47 shows the frequency of secondary and tertiary tasks during safety-critical events and 

baseline epochs across “All” and “Vehicle 1 At-Fault” events for each trafficway flow. Those 

conditions that were not observed are not included. As shown in Table 47, most data were 

collected on divided roadways.
Table 47. The Frequency of Secondary and Tertiary Tasks during Safety-Critical Events and Baseline Epochs 

“Vehicle 1 At-Fault” (V1) Events for each Trafficway Flow Condition 

Trafficway Flow 

Frequency 

of ALL 

Tertiary 

Task SafetyCritical 

Events 

Frequency 

of ALL 

Secondary 

Task SafetyCritical 

Events 

Frequency 

of ALL 

Tertiary 

Task 

Baselines 

Frequency 

of ALL 

Secondary 

Task 

Baselines 

Frequency 

of V1 

Tertiary 

Task SafetyCritical 

Events 

Frequency 

of V1 

Secondary 

Task SafetyCritical 

Events 

Not physically divided (center 2-way turn lane) 41 33 110 43 32 23 Not physically divided (2-way trafficway) 253 204 590 315 185 118 Divided (median strip or barrier) 1,654 706 6,368 3,634 1,519 550 One-way trafficway 14 21 93 74 13 16 

72 
Table 48–Table 53 present the results of the odds ratio calculations for each trafficway flow. 

Table 48 displays the odds ratio calculations for engaging in any secondary and/or tertiary task 

across “All” and “Vehicle 1 At-Fault” events for each trafficway flow option. The results in 

Table 48 suggest that engaging in any secondary or tertiary task significantly increased the risk 

of a driver being involved in a safety-critical event (compared to a baseline epoch) in one 

trafficway flow option. When “All” events were considered, drivers were 1.5 times more likely 

to be involved in a safety-critical event (compared to a baseline epoch) when driving on a 

divided (median strip or barrier) trafficway. When “Vehicle 1 At-Fault” events were considered, 

drivers were 1.7 times more likely to be involved in a safety-critical event (compared to a 

baseline epoch) when driving on a divided (median strip or barrier) trafficway. 

Table 48. Odds Ratios and 95% Confidence Intervals for the Interaction of Secondary and/or 

Tertiary Tasks by Trafficway Flow across “All” and “Vehicle 1 At-Fault” (V1) Events 

Trafficway Flow ALL 

Odds Ratio 

ALL 

LCL 

ALL 

UCL 

V1 

Odds Ratio 

V1 

LCL 

V1 

UCL 

Not physically divided 

(center 2-way turn lane) 

0.79 0.45 1.38 0.71 0.39 1.30 

Not physically divided 

(2-way trafficway) 

1.04 0.85 1.27 1.00 0.79 1.27 

Divided (median strip or barrier) 1.45* 1.32 1.59 1.69* 1.52 1.88 

One-way trafficway 1.22 0.54 2.75 1.21 0.48 3.03 

* Asterisk indicates a significant odds ratio. These ratios are also shown in bold. 

Table 49 displays the odds ratio calculations for engaging in any tertiary task across “All” and 

“Vehicle 1 At-Fault” events for each trafficway flow option. The results in Table 49 suggest that 

engaging in any tertiary task significantly increased the risk of a driver being involved in a 

safety-critical event (compared to a baseline epoch) in one trafficway flow option. When “All” 

events were considered, drivers were 1.7 times more likely to be involved in a safety-critical 

event (compared to a baseline epoch) when driving on a divided (median strip or barrier) 

trafficway. When “Vehicle 1 At-Fault” events were considered, drivers were 2.1 times more 

likely to be involved in a safety-critical event (compared to a baseline epoch) when driving on a 

divided (median strip or barrier) trafficway. 

Table 49. Odds Ratios and 95% Confidence Intervals for the Interaction of Tertiary Tasks by 

Trafficway Flow across “All” and “Vehicle 1 At-Fault” (V1) Events 

Trafficway Flow ALL 

Odds Ratio 

ALL 

LCL 

ALL 

UCL 

V1 

Odds Ratio 

V1 

LCL 

V1 

UCL 

Not physically divided 

(center 2-way turn lane) 

0.75 0.40 1.38 0.70 0.36 1.37

Not physically divided 

(2-way trafficway) 

1.00 0.79 1.25 1.09 0.84 1.41

Divided (median strip or barrier) 1.74* 1.57 1.92 2.14* 1.91 2.39

One-way trafficway 0.94 0.37 2.39 1.16 0.42 3.25

* Asterisk indicates a significant odds ratio. These ratios are also shown in bold. 

73 
Table 50 displays the odds ratio calculations for engaging in any complex tertiary task across 

“All” and “Vehicle 1 At-Fault” events for each trafficway flow. The results in Table 50 suggest 

that engaging in any complex tertiary task significantly increased the risk of a driver being 

involved in a safety-critical event (compared to a baseline epoch) in two trafficway flow options. 

When “All” events were considered, drivers were 12.9 times more likely to be involved in a 

safety-critical event (compared to a baseline epoch) when driving on a divided (median strip or 

barrier) trafficway and 3.6 times more likely when driving on a not physically divided (2-way) 

trafficway. When “Vehicle 1 At-Fault” events were considered, drivers were 17.2 times more 

likely to be involved in a safety-critical event (compared to a baseline epoch) when driving on a 

divided (median strip or barrier) trafficway and 4.8 times more likely when driving on a not 

physically divided (2-way) trafficway. 

Table 50. Odds Ratios and 95% Confidence Intervals for the Interaction of Complex Tertiary Tasks 

by Trafficway Flow across “All” and “Vehicle 1 At-Fault” (V1) Events 

Trafficway Flow ALL 

Odds Ratio 

ALL 

LCL 

ALL 

UCL 

V1 

Odds Ratio 

V1 

LCL 

V1 

UCL 

Not physically divided 

(center 2-way turn lane) 

2.00 0.53 7.61 1.94 0.47 8.01 

Not physically divided 

(2-way trafficway) 

3.63 1.89 6.95 4.77* 2.43 9.37 

Divided (median strip or barrier) 12.89* 10.51 15.80 17.21* 13.96 21.22 

One-way trafficway 2.08 0.19 22.58 2.78 0.25 31.13 

* Asterisk indicates a significant odds ratio. These ratios are also shown in bold. 

Table 51 displays the odds ratio calculations for engaging in any moderate tertiary task across 

“All” and “Vehicle 1 At-Fault” events for each trafficway flow. The results in Table 51 suggest 

that engaging in any moderate tertiary task significantly increased the risk of a driver being 

involved in a safety-critical event (compared to a baseline epoch) in one trafficway flow option. 

When “All” events were considered, drivers were 1.4 times more likely to be involved in a 

safety-critical event (compared to a baseline epoch) when driving on a divided (median strip or 

barrier) trafficway. When “Vehicle 1 At-Fault” events were considered, drivers were 1.7 times 

more likely to be involved in a safety-critical event (compared to a baseline epoch) when driving 

on a divided (median strip or barrier) trafficway. 

Table 51. Odds Ratios and 95% Confidence Intervals for the Interaction of Moderate Tertiary Tasks 

by Trafficway Flow across “All” and “Vehicle 1 At-Fault” (V1) Events 

ALL ALL ALL V1 V1 V1 Trafficway Flow Odds Ratio LCL UCL Odds Ratio LCL UCL 

Not physically divided 

(center 2-way turn lane) 

0.54 0.26 1.14 0.53 0.24 1.19 

Not physically divided 

(2-way trafficway) 

0.97 0.74 1.27 1.04 0.77 1.43 

Divided (median strip or barrier) 1.44* 1.28 1.62 1.74* 1.53 1.97 

One-way trafficway 1.08 0.40 2.94 1.44 0.49 4.23 

* Asterisk indicates a significant odds ratio. These ratios are also shown in bold. 

74 
Table 52 displays the odds ratio calculations for engaging in any simple tertiary task across “All”

and “Vehicle 1 At-Fault” events for each trafficway flow. The results in Table 52 suggest that 

engaging in any simple tertiary task significantly increased the risk of a driver being involved in 

a safety-critical event (compared to a baseline epoch) in one trafficway flow option. When “All” 

events were considered, drivers were 1.3 times more likely to be involved in a safety-critical 

event (compared to a baseline epoch) when driving on a divided (median strip or barrier) 

trafficway. When “Vehicle 1 At-Fault” events were considered, drivers were 1.5 times more 

likely to be involved in a safety-critical event (compared to a baseline epoch) when driving on a 

divided (median strip or barrier) trafficway. 

Table 52. Odds Ratios and 95% Confidence Intervals for the Interaction of Simple Tertiary Tasks 

by Trafficway Flow across “All” and “Vehicle 1 At-Fault” (V1) Events 

Trafficway Flow ALL 

Odds Ratio 

ALL 

LCL 

ALL 

UCL 

V1 

Odds Ratio 

V1 

LCL 

V1 

UCL 

Not physically divided 

(center 2-way turn lane) 1.27 0.55 2.94 1.32 0.55 3.19 

Not physically divided 

(2-way trafficway) 0.96 0.69 1.35 1.08 0.74 1.59 

Divided (median strip or barrier) 1.31* 1.13 1.52 1.52* 1.30 1.78 

One-way trafficway 0.60 0.12 3.04 0.40 0.05 3.50 

* Asterisk indicates a significant odds ratio. These ratios are also shown in bold. 

Table 53 displays the odds ratio calculations for engaging in any secondary task across “All” and 

“Vehicle 1 At-Fault” events for each trafficway flow. The results in Table 53 suggest that 

engaging in any secondary task significantly increased the risk of a driver being involved in a 

safety-critical event (compared to a baseline epoch) in two trafficway flow options. When “All” 

events were considered, drivers were 1.3 times more likely to be involved in a safety-critical 

event (compared to a baseline epoch) when driving on a divided (median strip or barrier) 

trafficway, and 1.5 times more likely to be driving on a not physically divided (2-way) 

trafficway. When “Vehicle 1 At-Fault” events were considered, drivers were 1.4 times more 

likely to be involved in a safety-critical event (compared to a baseline epoch) when driving on a 

divided (median strip or barrier) trafficway. 

Table 53. Odds Ratios and 95% Confidence Intervals for the Interaction of Secondary Tasks by 

Trafficway Flow across “All” and “Vehicle 1 At-Fault” (V1) Events 

Trafficway Flow ALL 

Odds Ratio 

ALL 

LCL 

ALL 

UCL 

V1 

Odds Ratio 

V1 

LCL 

V1 

UCL 

Not physically divided 

(center 2-way turn lane) 1.53 0.78 3.02 1.30 0.62 2.70 

Not physically divided 

(2-way trafficway) 1.50 1.17 1.93 1.30 0.97 1.74 

Divided (median strip or barrier) 1.30* 1.15 1.46 1.36* 1.19 1.55 

One-way trafficway 1.77 0.73 4.32 1.80 0.66 4.92 

* Asterisk indicates a significant odds ratio. These ratios are also shown in bold. 

75 
76 

5.4.6 Roadway Alignment 

The roadway alignment condition indicates the alignment of the road during the safety-critical 

event or baseline epoch. The roadway alignment the time of the safety-critical event or baseline 

epoch was assessed. Data analysts were instructed to use the video data to assist in determining 

the appropriate roadway alignment. During data reduction, data analysts selected one of the four 

roadway alignments options: 

 Straight. 

 Curve right. 

 Curve left. 

 Unknown. 

Table 54 shows the frequency of secondary and tertiary tasks during safety-critical events and 

baseline epochs across “All” and “Vehicle 1 At-Fault” events for three roadway alignment 

options. Those conditions that were not observed are not included. As can be seen in Table 54, 

most data were collected on straight roads. 
Table 54. The Frequency of Secondary and Tertiary Tasks during Safety-Critical Events and Baseline Epochs acro

1 At-Fault” (V1) Events for each Roadway Alignment 

Roadway 

Alignment 

Frequency of 

ALL Tertiary Task SafetyCritical 

Events 

Frequency of 

ALL 

Secondary 

Task SafetyCritical Events 

Frequency 

of ALL 

Tertiary 

Task 

Baselines 

Frequency 

of ALL 

Secondary 

Task 

Baselines 

Frequency of 

V1 Tertiary 

Task SafetyCritical 

Events 

Frequency of 

V1 Secondary 

Task SafetyCritical 

Events 

Fre

T

Ba

Straight 1,763 890 6,510 3,637 1,568 647 Curve right 111 31 383 213 102 29 Curve left 88 43 268 216 79 31 

77 
Table 55–Table 60 present the results of the odds ratio calculations for each roadway alignment 

option. Table 55 displays the odds ratio calculations for engaging in any secondary and/or 

tertiary task across “All” and “Vehicle 1 At-Fault” events for three roadway alignment options. 

The results in Table 55 suggest that engaging in any secondary or tertiary task significantly 

increased the risk of a driver being involved in a safety-critical event (compared to a baseline 

epoch) in one roadway alignment option. When “All” events were considered, drivers were 1.4 

times more likely to be involved in a safety-critical event (compared to a baseline epoch) when 

driving on a straight roadway alignment. When “Vehicle 1 At-Fault” events were considered, 

drivers were 1.6 times more likely to be involved in a safety-critical event (compared to a 

baseline epoch) when driving on a straight roadway alignment. 

Table 55. Odds Ratios and 95% Confidence Intervals for the Interaction of Secondary and/or 

Tertiary Tasks by Roadway Alignment across “All” and “Vehicle 1 At-Fault” (V1) Events 

Roadway 

Alignment 

ALL 

Odds Ratio 

ALL 

LCL 

ALL 

UCL 

V1 

Odds Ratio 

V1 

LCL 

V1 

UCL 

Straight 1.37* 1.25 1.49 1.55* 1.40 1.71 

Curve right 0.88 0.64 1.21 1.17 0.81 1.68 

Curve left 1.29 0.88 1.89 1.32 0.87 1.99 

 * Asterisk indicates a significant odds ratio. These ratios are also shown in bold. 

Table 56 displays the odds ratio calculations for engaging in any tertiary task across “All” and 

“Vehicle 1 At-Fault” events for three roadway alignment options. The results in Table 56 suggest 

that engaging in any tertiary task significantly increased the risk of a driver being involved in a 

safety-critical event (compared to a baseline epoch) in three roadway alignment options. When 

“All” events were considered, drivers were 1.6 times more likely to be involved in a safetycritical event (compared to a baseline epoch) when driving on a straight roadway alignment, and 

1.8 times more likely when driving on a curve left roadway alignment. When “Vehicle 1 AtFault” events were considered, drivers were 1.9 times more likely to be involved in a safetycritical event (compared to a baseline epoch) when driving on a straight roadway alignment, 1.7 

times more likely when driving on a curve right roadway alignment, and 1.9 times more likely 

when driving on a curve left roadway alignment. 

Table 56. Odds Ratios and 95% Confidence Intervals for the Interaction of Tertiary Tasks by 

Roadway Alignment across “All” and “Vehicle 1 At-Fault” (V1) Events 

Roadway 

Alignment 

ALL 

Odds Ratio 

ALL 

LCL 

ALL 

UCL 

V1 

Odds Ratio 

V1 

LCL 

V1 

UCL 

Straight 1.55* 1.41 1.70 1.89* 1.70 2.10 

Curve right 1.21 0.86 1.71 1.67* 1.14 2.46 

Curve left 1.78* 1.18 2.70 1.94* 1.24 3.02 

 * Asterisk indicates a significant odds ratio. These ratios are also shown in bold. 

Table 57 displays the odds ratio calculations for engaging in any complex tertiary task across 

“All” and “Vehicle 1 At-Fault” events for three roadway alignment options. The results in 

78 
Table 57 suggest that engaging in any complex tertiary task significantly increased the risk of a 

driver being involved in a safety-critical event (compared to a baseline epoch) in all of the 

roadway alignment options. When “All” events were considered, drivers were 10.4 times more 

likely to be involved in a safety-critical event (compared to a baseline epoch) when driving on a 

straight roadway alignment, 13.2 times more likely when driving on a curve right roadway 

alignment, and 10.0 times more likely when driving on a curve left roadway alignment. When 

“Vehicle 1 At-Fault” events were considered, drivers were 14.0 times more likely to be involved 

in a safety-critical event (compared to a baseline epoch) when driving on a straight roadway 

alignment, 19.9 times more likely when driving on a curve right roadway alignment, and 12.1 

times more likely when driving on a curve left roadway alignment. 

Table 57. Odds Ratios and 95% Confidence Intervals for the Interaction of Complex Tertiary Tasks 

by Roadway Alignment across “All” and “Vehicle 1 At-Fault” (V1) Events 

Roadway 

Alignment 

ALL 

Odds Ratio 

ALL 

LCL 

ALL 

UCL 

V1 

Odds Ratio 

V1 

LCL 

V1 

UCL 

Straight 10.35* 8.49 12.61 13.91* 11.35 17.04 

Curve right 13.24* 5.09 34.46 19.86* 7.52 52.47 

Curve left 9.95* 3.48 28.43 12.06* 4.18 34.80 

 * Asterisk indicates a significant odds ratio. These ratios are also shown in bold. 

Table 58 displays the odds ratio calculations for engaging in any moderate tertiary task across 

“All” and “Vehicle 1 At-Fault” events for three roadway alignment options. The results in Table 

58 suggest that engaging in any moderate tertiary task significantly increased the risk of a driver 

being involved in a safety-critical event (compared to a baseline epoch) in two roadway 

alignment options. When “All” events were considered, drivers were 1.3 times more likely to be 

involved in a safety-critical event (compared to a baseline epoch) when driving on a straight 

roadway alignment. When “Vehicle 1 At-Fault” events were considered, drivers were 1.6 times 

more likely to be involved in a safety-critical event (compared to a baseline epoch) when driving 

on a straight roadway alignment, and 1.5 times more likely when driving on a curve right 

roadway alignment. 

Table 58. Odds Ratios and 95% Confidence Intervals for the Interaction of Moderate Tertiary Tasks 

by Roadway Alignment across “All” and “Vehicle 1 At-Fault” (V1) Events 

Roadway 

Alignment 

Straight 1.30* 1.17 1.46 1.55* 1.37 1.76 

Curve right 1.17 0.78 1.74 1.53* 0.98 2.39 

Curve left 1.47 0.90 2.41 1.55 0.91 2.63 

ALL ALL ALL V1 V1 V1 

Odds Ratio LCL UCL Odds Ratio LCL UCL 

 * Asterisk indicates a significant odds ratio. These ratios are also shown in bold. 

Table 59 displays the odds ratio calculations for engaging in any simple tertiary task across “All” 

and “Vehicle 1 At-Fault” events for three roadway alignment options. The results in Table 59

suggest that engaging in any simple tertiary task significantly increased the risk of a driver being 

involved in a safety-critical event (compared to a baseline epoch) in one roadway alignment 

79 
option. When “All” events were considered, drivers were 1.2 times more likely to be involved in 

a safety-critical event (compared to a baseline epoch) when driving on a straight roadway 

alignment. When “Vehicle 1 At-Fault” events were considered, drivers were 1.4 times more 

likely to be involved in a safety-critical event (compared to a baseline epoch) when driving on a 

straight roadway alignment. 

Table 59. Odds Ratios and 95% Confidence Intervals for the Interaction of Simple Tertiary Tasks 

by Roadway Alignment across “All” and “Vehicle 1 At-Fault” (V1) Events 

Roadway ALL ALL 

LCL 

Straight 1.24* 1.08 1.43 1.43* 1.23 1.66

Curve right 0.89 0.52 1.52 1.28 0.73 2.25

Curve left 1.34 0.72 2.48 1.26 0.64 2.49

ALL V1 V1 V1 

Alignment Odds Ratio UCL Odds Ratio LCL UCL 

 * Asterisk indicates a significant odds ratio. These ratios are also shown in bold. 

Table 60 displays the odds ratio calculations for engaging in any secondary task across “All” and 

“Vehicle 1 At-Fault” events for three roadway alignment options. The results in Table 60 suggest 

that engaging in any secondary task significantly increased the risk of a driver being involved in 

a safety-critical event (compared to a baseline epoch) in one roadway alignment option. Further, 

Table 60 shows that engaging in any secondary task significantly decreased the risk of a driver 

being involved in a safety-critical event (compared to a baseline epoch) in one roadway 

alignment option. When “All” events were considered, drivers were 1.4 times more likely to be 

involved in a safety-critical event (compared to a baseline epoch) when driving on a straight 

roadway alignment and 0.6 times less likely when driving on a curve right roadway alignment. 

When “Vehicle 1 At-Fault” events were considered, drivers were 1.4 times more likely to be 

involved in a safety-critical event (compared to a baseline epoch) when driving on a straight 

roadway alignment. 

Table 60. Odds Ratios and 95% Confidence Intervals for the Interaction of Secondary Tasks by 

Roadway Alignment across “All” and “Vehicle 1 At-Fault” (V1) Events 

Roadway ALL ALL ALL 

UCL 

Straight 1.40* 1.26 1.56 1.39* 1.23 1.58 

Curve right 0.61* 0.38 0.97 0.85 0.52 1.41 

Curve left 1.08 0.68 1.73 0.94 0.56 1.60 

V1 V1 V1 

Alignment Odds Ratio LCL Odds Ratio LCL UCL 

 * Asterisk indicates a significant odds ratio. These ratios are also shown in bold. 

5.4.7 Road Profile 

The road profile condition indicates the profile of the road during the safety-critical event or 

baseline epoch. The road profile at the time of the safety-critical event or baseline epoch was 

assessed. Data analysts were instructed to use the video data to assist in determining the 

appropriate road profile. During data reduction, data analysts selected one of the five road profile 

options: 

80 
81 

 Level. 

 Grade up. 

 Grade down. 

 Hillcrest. 

 Sag. 

Table 61 shows the frequency of secondary and tertiary tasks during safety-critical events and 

baseline epochs across “All” and “Vehicle 1 At-Fault” events for each road profile. Those 

conditions that were not observed are not included. As shown in Table 61, most of the data were 

collected on level roads. 
Table 61. The Frequency of Secondary and Tertiary Tasks during Safety-Critical Events and Baseline Epochs acro

1 At-Fault” (V1) Events for each Road Profile 

Road Profile 

Frequency 

of ALL 

Tertiary 

Task SafetyCritical 

Events 

Frequency 

of ALL 

Secondary 

Task SafetyCritical 

Events 

Frequency 

of ALL 

Tertiary 

Task 

Baselines 

Frequency 

of ALL 

Secondary 

Task 

Baselines 

Frequency 

of V1 

Tertiary 

Task SafetyCritical 

Events 

Frequency 

of V1 

Secondary 

Task SafetyCritical 

Events 

Freq

o

Te

T

Bas

Level 1,856 913 7,049 3,982 1,649 664 Grade up 94 44 72 50 91 39 Grade down 12 7 34 31 9 4 Hillcrest 0 0 4 2 0 0 Sag 0 0 2 1 0 0 

82 
Table 62–Table 64 present the results of the odds ratio calculations for each road profile. Table 

62 displays the odds ratio calculations for engaging in any secondary and/or tertiary task across 

“All” and “Vehicle 1 At-Fault” events for three road profile options. The results in Table 62

suggest that engaging in any secondary or tertiary task significantly increased the risk of a driver 

being involved in a safety-critical event (compared to a baseline epoch) in one road profile 

option. Further, Table 62 shows that engaging in any task significantly decreased the risk of a 

driver being involved in a safety-critical event (compared to a baseline epoch) in one road profile 

option. When “All” events were considered, drivers were 1.4 times more likely to be involved in 

a safety-critical event (compared to a baseline epoch) when driving on a level road profile, and 

0.3 times less likely while driving on a grade down road profile. When “Vehicle 1 At-Fault” 

events were considered, drivers were 1.5 times more likely to be involved in a safety-critical 

event (compared to a baseline epoch) when driving on a level road profile, and 0.3 times less 

likely while driving on a grade down road profile. Note that the low frequencies in many of the 

road profile categories make it difficult to read too much into these findings. 

Table 62. Odds Ratios and 95% Confidence Intervals for the Interaction of Secondary and/or 

Tertiary Tasks by Road Profile across “All” and “Vehicle 1 At-Fault” (V1) Events 

Road Profile ALL 

Odds Ratio 

ALL 

LCL 

ALL 

UCL 

V1 

Odds Ratio 

V1 

LCL 

V1 

UCL 

Level 1.35* 1.24 1.47 1.53* 1.39 1.68 

Grade up 1.05 0.66 1.68 1.33 0.81 2.20 

Grade down 0.34* 0.14 0.83 0.31* 0.11 0.86 

 * Asterisk indicates a significant odds ratio. These ratios are also shown in bold. 

Table 63 displays the odds ratio calculations for engaging in any tertiary task across “All” and 

“Vehicle 1 At-Fault” events for three road profiles. The results in Table 63 suggest that engaging 

in any tertiary task significantly increased the risk of a driver being involved in a safety-critical 

event (compared to a baseline epoch) in two road profile options. When “All” events were 

considered, drivers were 1.6 times more likely to be involved in a safety-critical event (compared 

to a baseline epoch) when driving on a level road profile. When “Vehicle 1 At-Fault” events 

were considered, drivers were 1.9 times more likely to be involved in a safety-critical event 

(compared to a baseline epoch) when driving on a level road profile, and 2.0 times more likely 

while driving on a grade up road profile. Note that the low frequencies in many of the road 

profile categories make it difficult to read too much into these findings. 

Table 63. Odds Ratios and 95% Confidence Intervals for the Interaction of Tertiary Tasks by Road 

Profile across “All” and “Vehicle 1 At-Fault” (V1) Events 

Road Profile ALL 

Odds Ratio 

ALL 

LCL 

ALL 

UCL 

V1 

Odds Ratio 

V1 

LCL 

V1 

UCL 

Level 1.55* 1.41 1.70 1.88* 1.70 2.09 

Grade up 1.53 0.91 2.56 2.02* 1.17 3.51 

Grade down 0.55 0.20 1.49 0.56 0.18 1.72 

 * Asterisk indicates a significant odds ratio. These ratios are also shown in bold. 

83 
Table 64 displays the odds ratio calculations for engaging in any complex tertiary task across 

“All” and “Vehicle 1 At-Fault” events for two road profiles. The results in Table 64 suggest that 

engaging in any complex tertiary task significantly increased the risk of a driver being involved 

in a safety-critical event (compared to a baseline epoch) in two road profile options. When “All” 

events were considered, drivers were 10.3 times more likely to be involved in a safety-critical 

event (compared to a baseline epoch) when driving on a level road profile, and 24.6 times more 

likely while driving on a grade up road profile. When “Vehicle 1 At-Fault” events were 

considered, drivers were 13.9 times more likely to be involved in a safety-critical event 

(compared to a baseline epoch) when driving on a level road profile, and 33.6 times more likely 

while driving on a grade up road profile. Note that the low frequencies in many of the road 

profile categories make it difficult to read too much into these findings. 

Table 64. Odds Ratios and 95% Confidence Intervals for the Interaction of Complex Tertiary Tasks 

by Road Profile across “All” and “Vehicle 1 At-Fault” (V1) Events 

Road Profile ALL 

Odds Ratio 

ALL 

LCL 

ALL 

UCL 

V1 

Odds Ratio 

V1 

LCL 

V1 

UCL 

Level 10.34* 8.53 12.54 13.92* 11.41 16.97 

Grade up 24.59* 3.17 190.77 33.60* 4.29 262.91 

 * Asterisk indicates a significant odds ratio. These ratios are also shown in bold. 

Table 64 displays the odds ratio calculations for engaging in any moderate tertiary task across 

“All” and “Vehicle 1 At-Fault” events for three road profiles. The results in Table 64 suggest 

that engaging in any moderate tertiary task significantly increased the risk of a driver being 

involved in a safety-critical event (compared to a baseline epoch) in one road profile option. 

When “All” events were considered, drivers were 1.3 times more likely to be involved in a 

safety-critical event (compared to a baseline epoch) when driving on a level road profile. When 

“Vehicle 1 At-Fault” events were considered, drivers were 1.6 times more likely to be involved 

in a safety-critical event (compared to a baseline epoch) when driving on a level road profile. 

Note that the low frequencies in many of the road profile categories make it difficult to read too 

much into these findings. 

Table 65. Odds Ratios and 95% Confidence Intervals for the Interaction of Moderate Tertiary Tasks 

by Road Profile across “All” and “Vehicle 1 At-Fault” (V1) Events 

Road Profile ALL 

Odds Ratio 

ALL 

LCL 

ALL 

UCL 

V1 

Odds Ratio 

V1 

LCL 

V1 

UCL 

Level 1.32* 1.19 1.47 1.57* 1.40 1.77 

Grade up 1.05 0.57 1.95 1.35 0.71 2.59 

Grade down 0.52 0.15 1.82 0.43 0.10 1.90 

 * Asterisk indicates a significant odds ratio. These ratios are also shown in bold. 

Table 66 displays the odds ratio calculations for engaging in any simple tertiary task across “All” 

and “Vehicle 1 At-Fault” events for three road profiles. The results in Table 66 suggest that 

engaging in any simple tertiary task significantly increased the risk of a driver being involved in 

a safety-critical event (compared to a baseline epoch) in one road profile option. When “All” 

84 
events were considered, drivers were 1.2 times more likely to be involved in a safety-critical 

event (compared to a baseline epoch) when driving on a level road profile. When “Vehicle 1 AtFault” events were considered, drivers were 1.4 times more likely to be involved in a safetycritical event (compared to a baseline epoch) when driving on a level road profile. Note that the 

low frequencies in many of the road profile categories make it difficult to read too much into 

these findings. 

Table 66. Odds Ratios and 95% Confidence Intervals for the Interaction of Simple Tertiary Tasks 

by Road Profile across “All” and “Vehicle 1 At-Fault” (V1) Events 

Road Profile ALL 

Odds Ratio 

ALL 

LCL 

ALL 

UCL 

V1 

Odds Ratio 

V1 

LCL 

V1 

UCL 

Level 1.22* 1.07 1.39 1.40* 1.21 1.62 

Grade up 1.17 0.58 2.36 1.53 0.74 3.19 

Grade down 0.48 0.12 1.84 0.49 0.11 2.22 

 * Asterisk indicates a significant odds ratio. These ratios are also shown in bold. 

Table 67 displays the odds ratio calculations for engaging in any secondary task across “All” and 

“Vehicle 1 At-Fault” events for three road profile options. The results in Table 67 suggest that 

engaging in any secondary task significantly increased the risk of a driver being involved in a 

safety-critical event (compared to a baseline epoch) in one road profile option. When “All” 

events were considered, drivers were 1.4 times more likely to be involved in a safety-critical 

event (compared to a baseline epoch) when driving on a level road profile. When “Vehicle 1 AtFault” events were considered, drivers were 1.3 times more likely to be involved in a safetycritical event (compared to a baseline epoch) when driving on a level road profile. Note that the 

low frequencies in many of the road profile categories make it difficult to read too much into 

these findings. 

Table 67. Odds Ratios and 95% Confidence Intervals for the Interaction of Secondary Tasks by 

Road Profile across “All” and “Vehicle 1 At-Fault” (V1) Events 

Road Profile ALL 

Odds Ratio 

ALL 

LCL 

ALL 

UCL 

V1 

Odds Ratio 

V1 

LCL 

V1 

UCL 

Level 1.35* 1.21 1.50 1.34* 1.19 1.51 

Grade up 1.03 0.58 1.84 1.25 0.67 2.32 

Grade down 0.35 0.11 1.07 0.27 0.07 1.05 

 * Asterisk indicates a significant odds ratio. These ratios are also shown in bold. 

5.4.8 Traffic Density 

Traffic density is listed in increasing order from level of service (LOS) A to LOS F. LOS A is 

described as conditions where traffic flows at or above the posted speed limit and all motorists 

have complete mobility between lanes. LOS B is slightly more congested, with some 

impingement of maneuverability; two motorists might be forced to drive side by side, limiting 

lane changes. LOS C has more congestion than B, where ability to pass or change lanes is not 

always assured. In LOS D speeds are somewhat reduced, and motorists are hemmed in by other 

85 
86 

cars and trucks. LOS E is a marginal service state; flow becomes irregular and speed varies 

rapidly, but rarely reaches the posted limit. LOS F is the lowest measurement of efficiency for a 

road’s performance. Flow is forced; every vehicle moves in lockstep with the vehicle in front of 

it, with frequent drops in speed to nearly 0 mi/h (Mannering, Kilareski, & Washburn, 2004). The 

LOS at the time of the safety-critical event or baseline epoch was assessed. Data analysts were 

instructed to use the video data to assist in determining the appropriate LOS. During data 

reduction, data analysts selected one of the six LOS options: 

 LOS A: Free flow. 

 LOS B: Flow with some restrictions. 

 LOS C: Stable flow, maneuverability and speed are more restricted. 

 LOS D: Unstable flow: temporary restrictions substantially slow driver. 

 LOS E: Flow is unstable; vehicles are unable to pass, temporary stoppages, etc. 

 LOS F: Forced traffic flow condition with low speeds and traffic volumes that are below 

capacity; queues forming in particular locations. 

Table 68 shows the frequency of secondary and tertiary tasks during safety-critical events and 

baseline epochs across “All” and “Vehicle 1 At-Fault” events for each LOS. As can be seen in 

Table 68, most data were collected in LOS A and B. 
Table 68. The Frequency of Secondary and Tertiary Tasks during Safety-Critical Events and Baseline 

across “All” and “Vehicle 1 At-Fault” (V1) Events for each LOS 

Traffic Density 

Frequency 

of ALL 

Tertiary 

Task SafetyCritical 

Events 

Frequency 

of ALL 

Secondary 

Task SafetyCritical 

Events 

Frequency 

of ALL 

Tertiary 

Task 

Baselines 

Frequency 

of ALL 

Secondary 

Task 

Baselines 

Frequency 

of V1 

Tertiary 

Task SafetyCritical 

Events 

Frequency 

of V1 

Secondary 

Task SafetyCritical 

Events 

Freq

o

Te

T

Bas

LOS A 1,356 411 5,539 3,060 1,249 309 5LOS B 460 347 1,567 963 397 260 1LOS C 114 134 50 40 86 90 LOS D 14 39 5 3 7 27 LOS E 13 23 0 0 9 15 LOS F 4 10 0 0 3 6 

87 
Table 69–Table 74 present the results of the odds ratio calculations for each LOS. Table 69

displays the odds ratio calculations for engaging in any secondary and/or tertiary task across 

“All” and “Vehicle 1 At-Fault” events for four LOS options. The results in Table 69 suggest that 

engaging in any secondary or tertiary task significantly increased the risk of a driver being 

involved in a safety-critical event (compared to a baseline epoch) in three traffic density options. 

When “All” events were considered, drivers were 1.4 times more likely to be involved in a 

safety-critical event (compared to a baseline epoch) when driving in LOS A traffic density, 1.3 

times more likely when driving in LOS B traffic density, and 1.7 times more likely when driving 

in LOS C traffic density. When “Vehicle 1 At-Fault” events were considered, drivers were 1.5 

times more likely to be involved in a safety-critical event (compared to a baseline epoch) when 

driving in LOS A traffic density, 1.6 times more likely when driving in LOS B traffic density, 

and 2.0 times more likely when driving in LOS C traffic density. Note that the low frequencies in 

many of the LOS categories make it difficult to read too much into these findings. 

Table 69. Odds Ratios and 95% Confidence Intervals for the Interaction of Secondary and/or 

Tertiary Tasks by Traffic Density across “All” and “Vehicle 1 At-Fault” (V1) Events 

Traffic Density ALL 

Odds Ratio 

ALL 

LCL 

ALL 

UCL 

V1 

Odds Ratio 

V1 

LCL 

V1 

UCL 

LOS A 1.40* 1.26 1.56 1.53* 1.36 1.72 

LOS B 1.30* 1.11 1.53 1.60* 1.32 1.93 

LOS C 1.65* 1.10 2.49 2.03* 1.28 3.21 

LOS D 0.46 0.12 1.71 0.51 0.13 2.00 

 * Asterisk indicates a significant odds ratio. These ratios are also shown in bold. 

Table 70 displays the odds ratio calculations for engaging in any tertiary task across “All” and 

“Vehicle 1 At-Fault” events for four LOS options. The results in Table 70 suggest that engaging 

in any tertiary task significantly increased the risk of a driver being involved in a safety-critical 

event (compared to a baseline epoch) in three LOS options. When “All” events were considered, 

drivers were 1.9 times more likely to be involved in a safety-critical event (compared to a 

baseline epoch) when driving in LOS A traffic density and 1.3 times more likely when driving in 

LOS B traffic density. When “Vehicle 1 At-Fault” events were considered, drivers were 2.1 

times more likely to be involved in a safety-critical event (compared to a baseline epoch) when 

driving in LOS A traffic density, 1.7 times more likely when driving in LOS B traffic density, 

and 1.9 times more likely when driving in LOS C traffic density. Note that the low frequencies in 

many of the LOS categories make it difficult to read too much into these findings. 

88 
Table 70. Odds Ratios and 95% Confidence Intervals for the Interaction of Tertiary Tasks by Traffic 

Density across “All” and “Vehicle 1 At-Fault” (V1) Events 

Traffic Density ALL 

Odds Ratio 

ALL 

LCL 

ALL 

UCL 

V1 

Odds Ratio 

V1 

LCL 

V1 

UCL 

LOS A 1.85* 1.65 2.08 2.14* 1.89 2.42 

LOS B 1.31* 1.10 1.57 1.71* 1.40 2.10 

LOS C 1.41 0.87 2.28 1.87* 1.10 3.18 

LOS D 0.28 0.06 1.34 0.23 0.04 1.25 

 * Asterisk indicates a significant odds ratio. These ratios are also shown in bold. 

Table 71 displays the odds ratio calculations for engaging in any complex tertiary task across 

“All” and “Vehicle 1 At-Fault” events for three LOS options. The results in Table 71 suggest 

that engaging in any complex tertiary task significantly increased the risk of a driver being 

involved in a safety-critical event (compared to a baseline epoch) in three LOS options. When 

“All” events were considered, drivers were 13.6 times more likely to be involved in a safetycritical event (compared to a baseline epoch) when driving in LOS A traffic density and 7.9 

times more likely when driving in LOS B traffic density. When “Vehicle 1 At-Fault” events were 

considered, drivers were 16.7 times more likely to be involved in a safety-critical event 

(compared to a baseline epoch) when driving in LOS A traffic density, 11.8 times more likely 

when driving in LOS B traffic density, and 5.8 times more likely when driving in LOS C traffic 

density. Note that the low frequencies in many of the LOS categories make it difficult to read too 

much into these findings. 

Table 71. Odds Ratios and 95% Confidence Intervals for the Interaction of Complex Tertiary Tasks 

by Traffic Density across “All” and “Vehicle 1 At-Fault” (V1) Events 

Traffic Density ALL 

Odds Ratio 

ALL 

LCL 

ALL 

UCL 

V1 

Odds Ratio 

V1 

LCL 

V1 

UCL 

LOS A 13.54* 10.83 16.94 16.69* 13.28 20.98 

LOS B 7.94* 5.34 11.81 11.83* 7.86 17.81 

LOS C 3.29 0.91 11.87 5.80* 1.59 21.20 

 * Asterisk indicates a significant odds ratio. These ratios are also shown in bold. 

Table 72 displays the odds ratio calculations for engaging in any moderate tertiary task across 

“All” and “Vehicle 1 At-Fault” events for four LOS options. The results in Table 72 suggest that 

engaging in any moderate tertiary task significantly increased the risk of a driver being involved 

in a safety-critical event (compared to a baseline epoch) in three LOS options. When “All” 

events were considered, drivers were 1.6 times more likely to be involved in a safety-critical 

event (compared to a baseline epoch) when driving in LOS A traffic density. When “Vehicle 1 

At-Fault” events were considered, drivers were 1.8 times more likely to be involved in a safetycritical event (compared to a baseline epoch) when driving in LOS A traffic density, 1.4 times 

more likely when driving in LOS B traffic density, and 2.0 times more likely when driving in 

LOS C traffic density. Note that the low frequencies in many of the LOS categories make it 

difficult to read too much into these findings. 

89 
Table 72. Odds Ratios and 95% Confidence Intervals for the Interaction of Moderate Tertiary Tasks 

by Traffic Density across “All” and “Vehicle 1 At-Fault” (V1) Events 

Traffic Density ALL 

Odds Ratio 

ALL 

LCL 

ALL 

UCL 

V1 

Odds Ratio 

V1 

LCL 

V1 

UCL 

LOS A 1.56* 1.37 1.77 1.76* 1.52 2.02 

LOS B 1.05 0.85 1.30 1.35* 1.07 1.71 

LOS C 1.44 0.80 2.61 1.99* 1.05 3.77 

LOS D 0.37 0.06 2.10 0.28 0.04 1.82 

 * Asterisk indicates a significant odds ratio. These ratios are also shown in bold. 

Table 73 displays the odds ratio calculations for engaging in any simple tertiary task across “All”

and “Vehicle 1 At-Fault” events for four LOS options. The results in Table 73 suggest that 

engaging in any simple tertiary task significantly increased the risk of a driver being involved in 

a safety-critical event (compared to a baseline epoch) in one LOS option. When “All” events 

were considered, drivers were 1.5 times more likely to be involved in a safety-critical event 

(compared to a baseline epoch) when driving in LOS A traffic density. When “Vehicle 1 AtFault” events were considered, drivers were 1.6 times more likely to be involved in a safetycritical event (compared to a baseline epoch) when driving in LOS A traffic density. Note that 

the low frequencies in many of the LOS categories make it difficult to read too much into these 

findings. 

Table 73. Odds Ratios and 95% Confidence Intervals for the Interaction of Simple Tertiary Tasks 

by Traffic Density across “All” and “Vehicle 1 At-Fault” (V1) Events 

Traffic Density ALL 

Odds Ratio 

ALL 

LCL 

ALL 

UCL 

V1 

Odds Ratio 

V1 

LCL 

V1 

UCL 

LOS A 1.46* 1.24 1.70 1.62* 1.37 1.92 

LOS B 1.15 0.87 1.53 1.35 0.98 1.86 

LOS C 1.10 0.52 2.32 1.09 0.47 2.52 

LOS D 0.30 0.02 3.86 0.33 0.02 4.93 

 * Asterisk indicates a significant odds ratio. These ratios are also shown in bold. 

Table 74 displays the odds ratio calculations for engaging in any secondary task across “All” and 

“Vehicle 1 At-Fault” events for four LOS options. The results in Table 74 suggest that engaging 

in any secondary task significantly increased the risk of a driver being involved in a safetycritical event (compared to a baseline epoch) in two LOS options. When “All” events were 

considered, drivers were 1.6 times more likely to be involved in a safety-critical event (compared 

to a baseline epoch) when driving in LOS B traffic density, and 2.1 times more likely when 

driving in LOS C traffic density. When “Vehicle 1 At-Fault” events were considered, drivers 

were 1.8 times more likely to be involved in a safety-critical event (compared to a baseline 

epoch) when driving in LOS B traffic density, and 2.5 times more likely when driving in LOS C 

traffic density. Note that the low frequencies in many of the LOS categories make it difficult to 

read too much into these findings. 

90 
Table 74. Odds Ratios and 95% Confidence Intervals for the Interaction of Secondary Tasks by 

Traffic Density across “All” and “Vehicle 1 At-Fault” (V1) Events 

Traffic Density ALL 

Odds Ratio 

ALL 

LCL 

ALL 

UCL 

V1 

Odds Ratio 

V1 

LCL 

V1 

UCL 

LOS A 1.02 0.88 1.17 0.96 0.82 1.12 

LOS B 1.61* 1.33 1.95 1.83* 1.46 2.28 

LOS C 2.07* 1.26 3.41 2.45* 1.42 4.23 

LOS D 1.30 0.24 6.90 1.50 0.27 8.28 

 * Asterisk indicates a significant odds ratio. These ratios are also shown in bold. 

5.4.9 Summary 

The exploration of the various environmental conditions provided some interesting findings; 

however, the authors concede there were not many obvious conclusions from this set of analyses. 

If anything, the results support the conclusions from Research Question 1; that is, complex 

tertiary tasks were associated with the highest risk, and this was not abated when the data were 

parsed as a function of different environmental condition. Because many of the environmental 

categories (or sub-categories) had few data points, significant findings tended to occur when the 

data set for an environmental category was sufficiently large (i.e., sufficient statistical power was 

obtained). As such, it is difficult to read too much into these results. The results may resonate 

with, or be of interest to, other researchers looking at relationships between tasks and the 

different environmental conditions. 

5.5 RESEARCH QUESTION 3: WHAT ARE THE ODDS RATIOS OF EYES-OFFFORWARD-ROADWAY? DOES EYES-OFF-FORWARD-ROADWAY 

SIGNIFICANTLY AFFECT SAFETY AND/OR DRIVING PERFORMANCE? 

The third research question was directed at measuring visual distraction using eye glance 

analysis. To answer this research question, all safety-critical events (n = 3,565) and baseline 

epochs (n = 19,056) with valid eye glance data were included. Valid eye glance data meant that it 

was possible to conduct eye glance analysis on the entire 6 s (i.e., no shadows, camera 

malfunctions, or other issues blocking the view of the driver’s eyes). Following the method 

outlined in Klauer et al. (2006), eye glance locations were determined for 5 s prior to the event 

onset (i.e., the initiating behavior such as a lead vehicle braking) and for 1 s after the event onset 

for all safety-critical events. The entire 6-s epoch was analyzed for all baseline epochs. 

5.5.1 Eyes off Forward Roadway 

Eyes off forward roadway was operationally defined as any time the driver was not looking 

forward, regardless of where he/she was looking. All non-forward glances (i.e., all non-forward 

eye glance locations) were combined to determine the total eyes off forward roadway time for 

each 6-s interval (i.e., this time duration could be made up of a single long glance, or multiple 

shorter glances). Total eyes off forward roadway time was grouped into five different time bins: 

91 
 Less than or equal to 0.5 s. 

 Greater than 0.5 s but less than or equal to 1.0 s. 

 Greater than 1.0 s but less than or equal to 1.5 s. 

 Greater than 1.5 s but less than or equal to 2.0 s. 

 Greater than 2.0 s. 

To approximate whether there was an increased risk of being involved in a safety-critical event 

while looking away from the forward roadway, compared to a baseline epoch, odds ratios were 

calculated. The odds ratio for this analysis used the frequency of safety-critical events and 

baseline epochs where drivers’ eyes were off the forward roadway and frequency of safetycritical events and baseline epochs where drivers’ eyes were on the forward roadway. Table 75

illustrates the 2 × 2 contingency table used to calculate the odds ratios for the eyes off forward 

roadway time analysis. 

Table 75. Contingency Tables Used to Calculate Eyes off Forward Roadway Odds Ratios 

Event Type Eyes Forward Eyes off Forward 

Roadway Total 

Baseline Epoch n11 (A) n12 (B) n1.

Safety-Critical 

Event n21 (C) n22 (D) n2.

n.1 n.2 n..

Where: 

A = frequency of baseline epochs where the driver’s eyes were not off the forward 

roadway. 

B = frequency of baseline epochs where the drivers’ eyes were off the forward roadway. 

C = frequency of safety-critical events where the driver’s eyes were not off the forward 

roadway. 

D = frequency of at-fault events where safety-critical events where the driver’s eyes were 

off the forward roadway 

Table 76 displays the results of the odds ratio calculations for each of the five eyes off forward 

roadway time bins across “All” events and “Vehicle 1 At-Fault” events. As shown in Table 76, 

the results indicate that three of the time periods for eyes off forward roadway, across “All” 

safety-critical events, had a significant odds ratio. More specifically, when “All” events were 

considered, drivers were 1.4 times more likely to be involved in a safety-critical event (compared 

to a baseline epoch) when eyes off forward roadway was less than or equal to 0.5 s, 1.3 times 

more likely when eyes off forward roadway was greater than 1.5 s but less than or equal to 2.0 s, 

and 2.9 times more likely when eyes off forward roadway was greater than 2.0 s. 

92 
93 

Table 76 also shows the odds ratios for each of the five eyes off forward roadway time groupings 

across all “Vehicle 1 At-Fault” safety-critical events. When “Vehicle 1 At-Fault” events were 

considered, drivers were 1.3 times more likely to be involved in a safety-critical event (compared 

to a baseline epoch) when eyes off forward roadway was less than or equal to 0.5 s, 1.2 times 

more likely when eyes off forward roadway was greater than or equal to 1.0 s and less than or 

equal to 1.5 s, 1.5 times more likely when eyes off forward roadway was greater than or equal to 

1.5 s and less than or equal to 2.0 s, and 3.9 times more likely when eyes off forward roadway 

was greater than 2.0 s. 
Table 76. Odds Ratios and 95% Confidence Intervals to Assess Likelihood of a Safety-Critical Event While Eyes O

across “All” and “Vehicle 1 At-Fault” (V1) Events 

Total Eyes Off Forward 

Roadway 

ALL 

Odds 

Ratio 

ALL LCL 

ALL 

UCL 

Frequency of 

ALL SafetyCritical 

Events 

Frequency 

of ALL 

Baseline 

epochs 

V1 

Odds 

Ratio 

V1 

LCL 

V1 

UCL 

Freq

V1 

Critic

Less than or equal to 0.5 s 1.36* 1.16 1.58 268 1,537 1.28* 1.06 1.53 Greater than 0.5 s but less than or equal to 1.0 s 0.91 0.80 1.03 434 3,712 0.94 0.81 1.09 Greater than 1.0 s but less than or equal to 1.5 s 1.07 0.94 1.23 343 2,483 1.18* 1.01 1.38 Greater than 1.5 s but less than or equal to 2.0 s 1.29* 1.12 1.49 317 1,903 1.52* 1.30 1.79 Greater than 2.0 s 2.93* 2.65 3.23 1,504 3,989 3.85* 3.44 4.30 1,* Asterisk indicates a significant odds ratio. These ratios are also shown in bold. 

94 
Based on findings from light-vehicle drivers, all but one of these significant findings was 

consistent with the 100-Car Study (Klauer et al., 2006). For example, Klauer et al. found that 

longer glance times away from the forward roadway were associated with an increased 

likelihood of being involved in a crash or near-crash. The findings in the current study support 

the results reported by Klauer et al.; longer glances away from the forward roadway are 

inherently riskier. The current study illustrates that longer glances away from forward roadway 

(more than 2.0 s) were associated with a higher risk ratio. Note that the “Vehicle 1 At-Fault” 

analysis shows a linear increase in the odds ratio as the time bins increase from eyes off forward 

roadway greater than 1.0 s to greater than 2.0 s. However, the finding that eyes off forward 

roadway time less than or equal to 0.5 s was associated with an increase in involvement in a 

safety-critical event was not shown by Klauer et al. 

How can this finding be explained? Keep in mind that the 100-Car Study was conducted with 

light-vehicle drivers. Driving a CMV driving imposes different challenges on a driver, 

particularly with regard to glance patterns. For example, CMV drivers are taught the importance 

of situation awareness and scanning the environment and mirrors (Smith System, 2009). Based 

on an ideal CMV driver eye scanning technique while driving, one possible explanation for the 

significant finding for glances under 0.5 s is that some drivers may have spent too much time 

looking forward and were not performing the necessary environmental scans (i.e., “gaze 

concentration”; Reagan et al., 2009). Follow-up analyses would be required to conduct a more 

extensive eye glance analysis with drivers who rarely scanned the environment and mirrors (i.e., 

primarily focused on the forward roadway) to investigate the risk implications of such behavior, 

but this serves as a potential hypothesis for this novel finding. 

5.5.2 Duration of Eyes off Forward Roadway 

Duration of eyes off forward roadway was operationally defined as the total length of time 

(either a single glance or multiple glances) the driver was not looking at the forward roadway 

during the 6-s interval during the safety-critical event or baseline epoch. The analyses in this 

section were grouped by event type (i.e., crash, near-crash, crash-relevant conflict, unintentional 

lane deviation, and baseline/routine events) across “All” and “Vehicle 1 At-Fault” events. These 

results include the following analyses: 

 All tasks. 

 All tertiary tasks. 

– Complex tertiary tasks. 

– Moderate tertiary tasks. 

– Simple tertiary tasks. 

 Secondary tasks. 

5.5.2.1 All Tasks 

Figure 5 shows the mean duration of eyes off forward roadway for each event type across “All” 

and “Vehicle 1 At-Fault” events for any task. A one-way analysis of variance (ANOVA) found a 

significant difference in the mean duration of eyes off forward roadway between the five event 

types across “All” events (F(4, 22616) = 451.02, p < 0.0001). 

95 
96 

Figure 5. Graph. Mean Duration of Eyes off Forward Roadway by Event Type for All Tasks 

2.1

1.7

1.6

2.8

1.2

3.5

2.1 1.8

2.8

1.2

0

1

2

3

4

5

Crash Near‐Crash Crash‐Relevant

Conflict

Unintentional

Lane Deviation

Mean

Baseline

Duration of Eyes off Forward Roadway

(seconds)

All Events Vehicle 1 At‐Fault Events

2.1

1.7

1.6

2.8

1.2

3.5

2.1 1.8

2.8

1.2

0

1

2

3

4

5

Crash Near‐Crash Crash‐Relevant

Conflict

Unintentional

Lane Deviation

Mean

Baseline

Duration of Eyes off Forward Roadway

(seconds)

All Events Vehicle 1 At‐Fault Events

2.1

1.7

1.6

2.8

1.2

3.5

2.1 1.8

2.8

1.2

0

1

2

3

4

5

Crash Near‐Crash Crash‐Relevant

Conflict

Unintentional

Lane Deviation

Mean

Baseline

Duration of Eyes off Forward Roadway

(seconds)

All Events Vehicle 1 At‐Fault Events

As the ANOVA was significant, post hoc Tukey t tests were conducted on all pair-wise 

combinations of event types to determine simple effects. The results of all these pair-wise t tests 

can be found in appendix D. Simple effects tests indicated six significant combinations across 

“All” events. More specifically, Tukey t tests indicated that the mean duration of eyes off 

forward roadway during unintentional lane deviations (2.8 s) was significantly longer than nearcrashes (1.7 s; t(22616) = 9.95, p < 0.0001), crash-relevant conflicts (1.6 s; t(22616) = 23.81, p < 

0.0001), and baseline epochs (1.2 s; t(22616) = 39.30, p < 0.0001). Crashes (2.1 s; t(22616) = 3.36, p 

= 0.007), near-crashes (1.7 s; t(22616) = 5.76, p = 0.001) and crash-relevant conflicts (1.6 s; t(22616)

= 18.08, p < 0.0001) had a significantly longer mean duration of eyes off forward roadway than 

baseline epochs (1.2 s). 

Figure 5 also shows the mean duration of eyes off forward roadway for each event type across 

“Vehicle 1 At-Fault” events. A one-way ANOVA also found a significant difference in the mean 

duration of eyes off forward roadway between the five event types across “Vehicle 1 At-Fault” 

events (F(4, 21913) = 502.75, p < 0.0001). Tukey t tests indicated that the mean duration of eyes off 

forward roadway during unintentional lane deviations (2.8 s) was significantly longer than nearcrashes (2.1 s; t(21913) = 5.25, p < 0.0001), crash-relevant conflicts (1.8 s; t(21913) = 18.95, p < 

0.0001), and baseline epochs (1.2 s; t(21913) = 39.51, p < 0.0001). Crashes (3.5 s; t(21913) = 5.61, p 

< 0.0001), near-crashes (2.1 s; t(21913) = 6.99, p < 0.0001) and crash-relevant conflicts (1.8 s; 

t(21913) = 22.15, p < 0.0001) had a significantly longer mean duration of eyes off forward roadway 

than baseline epochs (1.2 s). Crashes (3.5 s; t(21913) = 3.96, p = 0.001) had significantly longer 

mean duration of eyes off forward roadway than crash-relevant conflicts (1.8 s). 
5.5.2.2 All Tertiary Tasks 

Figure 6 shows the mean duration of eyes off forward roadway for each event type across “All” 

events and “Vehicle 1 At-Fault” events for any tertiary task (i.e., any safety-critical event or 

baseline epoch with a complex, moderate, or a simple tertiary task). A one-way ANOVA found a 

significant difference in the mean duration of eyes off forward roadway between the five event 

types across “All” events (F(4, 8277) = 372.78, p < 0.0001). Tukey t tests indicated that the mean 

duration of eyes off forward roadway during unintentional lane deviations (3.1 s) was 

significantly longer than crashes (1.1 s; t(8277) = 5.05, p < 0.0001), near-crashes (1.8 s; t(8277) = 

5.70, p < 0.0001), crash-relevant conflicts (2.1 s; t(8277) = 14.81, p < 0.0001), and baseline 

epochs (1.2 s; t(8277) = 34.97, p < 0.0001). Both near-crashes (1.8 s; t(8277) = 3.10, p = 0.017) and 

crash-relevant conflicts (2.1 s; t(8277) = 19.5, p < 0.0001) had a significantly longer mean duration 

of eyes off forward roadway than baseline epochs (1.2 s). 

Figure 6. Graph. Mean Duration of Eyes off Forward Roadway by Event Type for All Tertiary Tasks 

1.1

1.8 2.1

3.1

1.2

3.4

2.6 2.4

3.1

1.2

0

1

2

3

4

5

Crash Near‐Crash Crash‐Relevant

Conflict

Unintentional

Lane Deviation

Mean

Baseline

Duration of Eyes off Forward Roadway

(seconds)

All Events Vehicle 1 At‐Fault Events

1.1

1.8 2.1

3.1

1.2

3.4

2.6 2.4

3.1

1.2

0

1

2

3

4

5

Crash Near‐Crash Crash‐Relevant

Conflict

Unintentional

Lane Deviation

Mean

Baseline

Duration of Eyes off Forward Roadway

(seconds)

All Events Vehicle 1 At‐Fault Events

Figure 6 also shows the mean duration of eyes off forward roadway for each event type across 

“Vehicle 1 At-Fault” events. A one-way ANOVA found a significant difference in the mean 

duration of eyes off forward roadway between the five event types across “Vehicle 1 At-Fault” 

events (F(4, 8098) = 418.43, p < 0.0001). Tukey t tests indicated that the mean duration of eyes off 

forward roadway during unintentional lane deviations (3.1 s) was significantly longer than crashrelevant conflicts (2.4 s; t(8098) = 10.46, p < 0.0001), and baseline epochs (1.2 s; t(8098) = 35.19, p 

< 0.0001). Both near-crashes (2.6 s; t(8098) = 5.11, p = < 0.0001) and crash-relevant conflicts (2.4 

s; t(8098) = 23.31, p < 0.0001) had a significantly longer mean duration of eyes off forward 

roadway time than baseline epochs (1.2 s). 

97 
5.5.2.3 Complex Tertiary Tasks 

Figure 7 shows the mean duration of eyes off forward roadway for four event types across “All” 

and “Vehicle 1 At-Fault” events for any complex tertiary tasks (i.e., any safety-critical event or 

baseline epoch with only a complex tertiary task). A one-way ANOVA found a significant 

difference in the mean duration of eyes off forward roadway between four event types across 

“All” events (F(3, 492) = 5.74, p = 0.0007). Tukey t tests indicated that the mean duration of eyes 

off forward roadway during unintentional lane deviations (4.4 s) was significantly longer than 

baseline epochs (4.0 s; t(492) = 4.14, p = 0.0002). 

Figure 7. Graph. Mean Duration of Eyes off Forward Roadway by Event Type 

for Complex Tertiary Tasks 

4.3 4.2

4.4

4.0

4.3 4.3 4.4

4.0

0

1

2

3

4

5

Crash Near‐Crash Crash‐Relevant

Conflict

Unintentional

Lane Deviation

Mean

Baseline

Duration of Eyesoff Forward Roadway

(seconds)

All Events Vehicle 1 At‐Fault Events

4.3 4.2

4.4

4.0

4.3 4.3 4.4

4.0

0

1

2

3

4

5

Crash Near‐Crash Crash‐Relevant

Conflict

Unintentional

Lane Deviation

Mean

Baseline

Duration of Eyesoff Forward Roadway

(seconds)

All Events Vehicle 1 At‐Fault Events

Figure 7 also shows the mean duration of eyes off forward roadway for four event types across 

“Vehicle 1 At-Fault” events. A one-way ANOVA found a significant difference in the mean 

duration of eyes off forward roadway between the four event types across “Vehicle 1 At-Fault” 

events (F(3, 487) = 6.16, p = 0.0004). Tukey t tests indicated that the mean duration of eyes off 

forward roadway during unintentional lane deviations (4.4 s) was significantly longer than 

baseline epochs (4.0 s; t(487) = 4.23, p =0.0002). 

5.5.2.4 Complex Tertiary Task Breakout Analyses 

Additional ANOVAs were calculated on the eight specific complex tertiary tasks that were 

shown to be significant in Table 17. In conducting this analysis, the mean duration of eyes off 

forward roadway was calculated for four groupings: 

 Safety-critical events with distraction of interest. 

 Baseline epochs with distraction of interest. 

98 
 Safety-critical events without distraction of interest. 

 Baseline epochs without distraction of interest. 

Because of the small sample size for many of the complex tertiary tasks, any safety-critical event 

or baseline epoch with the complex tertiary task of interest was used. Therefore, it was possible 

that the safety-critical event or baseline epoch contained additional tasks (e.g., if the distraction 

of interest was talking on a hands-free phone, the driver may have also been taking a drink at the 

same time). 

What is the reason for these four groupings? First, by comparing safety-critical events with the 

distraction of interest to baseline epochs with the distraction of interest, assessments can be made 

to determine the eye glance differences for distractions as a function of being involved, or not, in 

a safety-critical event. For example, consider safety-critical events that occurred when the driver 

was performing a cell phone dialing task. In this comparison, those safety-critical events were 

compared to baseline epochs (uneventful driving) where the driver was also dialing a cell phone. 

Second, by comparing safety-critical events with the distraction of interest with safety-critical 

events without distraction of interest, assessments can be made to determine eye glance 

differences, across safety-critical events, for the distraction of interest. For example, consider 

safety-critical events that occurred when the driver was performing a cell phone dialing task. In 

this comparison, those safety-critical events are compared to other safety-critical events where 

the driver was not dialing a cell phone. 

Third, by comparing safety-critical events with the distraction of interest with baseline epochs 

without distraction of interest, the comparison is made against uneventful driving that does not 

involve the distraction of interest. For example, consider safety-critical events that occurred 

when the driver was performing a cell phone dialing task. In this comparison, those safetycritical events were compared to baseline epochs where the driver was not dialing a cell phone. 

Note that rather than one comparison with a baseline condition, it was decided to include 

multiple comparisons to provide a more complete examination of all pertinent eye glance 

comparisons. 

Text Message on Cell Phone: Figure 8 shows the mean duration of eyes off forward roadway 

across “All” and “Vehicle 1 At-Fault” events for each of the four groupings. A one-way 

ANOVA found a significant difference in the mean duration of eyes off forward roadway 

between the four groupings across “All” events (F(3, 22617) = 455.45, p < 0.0001). Tukey t tests 

indicated that the mean duration of eyes off forward roadway during events with text messaging 

(4.6 s) was significantly longer than events without text messaging (1.9 s; t(22617) = 11.29, p < 

0.0001) and baselines without text messaging (1.2 s; t(22617) = 14.65, p < 0.0001). Baselines with 

text messaging (4.0 s) had a significantly longer mean duration of eyes off forward roadway than 

events without text messaging (1.9 s; t(22617) = 3.95, p = 0.001) and baselines without text 

messaging (1.2 s; t(22617) = 5.47, p < 0.0001). Events without text messaging (1.9 s; t(22617) = 

33.75, p < 0.0001) had a significantly longer mean duration of eyes off forward roadway than 

baselines without text messaging (1.2 s). 

99 
Figure 8. Graph. Mean Duration of Eyes off Forward Roadway for 

Text Message on Cell Phone 

4.6

4.0

1.9

1.2

4.7

4.0

2.1

1.2

0

1

2

3

4

5

Event with Text

Messaging

Baseline with Text

Messaging

Event without Text

Messaging

Baseline without

TextMessaging

MeanDuration of EyesOff Forward Roadway

(seconds)

All Events Vehicle 1 At‐Fault Events

4.6

4.0

1.9

1.2

4.7

4.0

2.1

1.2

0

1

2

3

4

5

Event with Text

Messaging

Baseline with Text

Messaging

Event without Text

Messaging

Baseline without

TextMessaging

MeanDuration of EyesOff Forward Roadway

(seconds)

All Events Vehicle 1 At‐Fault Events

Figure 8 also shows the mean duration of eyes off forward roadway “Vehicle 1 At-Fault” events 

for each of the four groupings. A one-way ANOVA found a significant difference in the mean 

duration of eyes off forward roadway between the four groupings across “Vehicle 1 At-Fault” 

events (F(3, 21914) = 590.78, p < 0.0001). Tukey t tests indicated that the mean duration of eyes off 

forward roadway during events with text messaging (4.7 s) was significantly longer than events 

without text messaging (2.1 s; t(21914) = 10.81, p < 0.0001) and baselines without text messaging 

(1.2 s; t(21914) = 15.03, p < 0.0001). Baselines with text messaging (4.0 s) had a significantly 

longer mean duration of eyes off forward roadway than events without text messaging (2.1 s; 

t(21914) = 3.58, p = 0.002) and baselines without text messaging (1.2 s; t(21914) = 5.52, p < 0.0001). 

Events without text messaging (2.1 s; t(21914) = 39.17, p < 0.0001) had a significantly longer mean 

duration of eyes off forward roadway than baselines without text messaging (1.2 s). 

Other—Complex Tertiary Tasks: Figure 9 shows the mean duration of eyes off forward 

roadway across “All” and “Vehicle 1 At-Fault” events for each of the four groupings. A one–

way ANOVA found a significant difference in the mean duration of eyes off forward roadway 

between the four groupings across “All” events (F(3, 22617) = 418.47, p < 0.0001). Tukey t tests 

indicated that the mean duration of eyes off forward roadway during events with other—complex 

tertiary task (4.4 s) was significantly longer than events without other—complex tertiary task 

(2.0 s; t(22617) = 5.46, p < 0.0001) and baselines without other—complex tertiary task (1.2 s; 

t(22617) = 7.24, p < 0.0001). Baselines with other—complex tertiary task (4.1 s) had a significantly 

longer mean duration of eyes off forward roadway than events without other—complex tertiary 

task (2.0 s; t(22617) = 3.45, p = 0.003) and baselines without other—complex tertiary task (1.2 s; 

t(22617) = 4.70, p < 0.0001). Events without other—complex tertiary task (2.0 s; t(22617) = 34.45, p 

100 
< 0.0001) had a significantly longer mean duration of eyes off forward roadway than baselines 

without other—complex tertiary task (1.2 s). 

Figure 9. Graph. Mean Duration of Eyes off Forward Roadway for 

Other—Complex Tertiary Task 

4.4

4.1

2.0

1.2

4.4

4.1

2.2

1.2

0

1

2

3

4

5

Event with Other ‐

Complex Tertiary

Task

Baseline with Other ‐

Complex Tertiary

Task

Event without Other

‐ Complex Tertiary

Task

Baseline without

Other ‐ Complex

Tertiary Task

MeanDuration of EyesOff Forward

Roadway (seconds)

All Events Vehicle 1 At‐Fault Events

4.4

4.1

2.0

1.2

4.4

4.1

2.2

1.2

0

1

2

3

4

5

Event with Other ‐

Complex Tertiary

Task

Baseline with Other ‐

Complex Tertiary

Task

Event without Other

‐ Complex Tertiary

Task

Baseline without

Other ‐ Complex

Tertiary Task

MeanDuration of EyesOff Forward

Roadway (seconds)

All Events Vehicle 1 At‐Fault Events

Figure 9 also shows the mean duration of eyes off forward roadway across “Vehicle 1 At-Fault” 

events for each of the four groupings. A one-way ANOVA found a significant difference 

between the four groupings across “Vehicle 1 At-Fault” events (F(3, 21914) = 555.18, p < 0.0001). 

Tukey t tests indicated that the mean duration of eyes off forward roadway during events with 

other—complex tertiary task (4.4 s) was significantly longer than events without other—complex 

tertiary task (2.2 s; t(21914) = 5.04, p < 0.0001), and baselines without other—complex tertiary 

task (1.2 s; t(21914) = 7.31, p < 0.0001). Baselines with other—complex tertiary task (4.1 s) had a 

significantly longer mean duration of eyes off forward roadway than events without other—

complex tertiary task (2.0 s; t(21914) = 3.14, p = 0.009) and baselines without other—complex 

tertiary task (1.2 s; t(21914) = 4.75, p < 0.0001). Events without other—complex tertiary task (2.2 

s; t(21914) = 39.95, p < 0.0001) had a significantly longer mean duration of eyes off forward 

roadway than baselines without other—complex tertiary task (1.2 s). 

Interact with Dispatching Device: Figure 10 shows the mean duration of eyes off forward 

roadway across “All” and “Vehicle 1 At-Fault” events for each of the four groupings. A one-way 

ANOVA found a significant difference in the mean duration of eyes off forward roadway 

between the four groupings across “All” events (F(3, 22617) = 641.57, p < 0.0001). Tukey t tests 

indicated that the mean duration of eyes off forward roadway during events with interact with 

dispatching device (4.1 s) was significantly longer than events without interact with dispatching 

device (1.9 s; t(22617) = 20.59, p < 0.0001) and baselines without interact with dispatching device 

(1.2 s; t(22617) = 27.00, p < 0.0001). Baselines with interact with dispatching device (3.7 s) had a 

significantly longer mean duration of eyes off forward roadway than events without interact with 

101 
dispatching device (1.9 s; t(22617) = 27.64, p < 0.0001) and baselines without interact with 

dispatching device (1.2 s; t(22617) = 16.23, p < 0.0001). Events without interact with dispatching 

device (1.9 s; t(22617) = 31.28, p < 0.0001) had a significantly longer mean duration of eyes off 

forward roadway than baselines without interact with dispatching device (1.2 s). 

Figure 10. Graph. Mean Duration of Eyes off Forward Roadway for 

Interact with Dispatching Device 

4.1

3.7

1.9

1.2

4.2

3.7

2.1

1.2

0

1

2

3

4

5

Event with Interact

with Dispatching

Device

Baseline with

Interact with

Dispatching Device

Event without

Interact with

Dispatching Device

Baseline without

Interact with

Dispatching Device

MeanDuration of EyesOff Forward Roadway

(seconds)

All Events Vehicle 1 At‐Fault Events

4.1

3.7

1.9

1.2

4.2

3.7

2.1

1.2

0

1

2

3

4

5

Event with Interact

with Dispatching

Device

Baseline with

Interact with

Dispatching Device

Event without

Interact with

Dispatching Device

Baseline without

Interact with

Dispatching Device

MeanDuration of EyesOff Forward Roadway

(seconds)

All Events Vehicle 1 At‐Fault Events

Figure 10 also shows the mean duration of eyes off forward roadway across “Vehicle 1 At-Fault” 

events for each of the four groupings. A one-way ANOVA found a significant difference in the 

mean duration of eyes off forward roadway between the four groupings across “Vehicle 1 AtFault” events (F(3, 21914) = 764.25, p < 0.0001). Tukey t tests indicated that the mean duration of 

eyes off forward roadway during events with interact with dispatching device (4.2 s) was 

significantly longer than baselines with interact with dispatching device (3.7 s; t(21914) = 2.75, p = 

0.030), events without interact with dispatching device (2.1 s; t(21914) = 19.08, p < 0.0001) and 

baselines without interact with dispatching device (1.2 s; t(21914) = 27.97, p < 0.0001). Baselines 

with interact with dispatching device (3.7 s) had a significantly longer mean duration of eyes off 

forward roadway than events without interact with dispatching device (2.1 s; t(21914) = 10.27, p = 

0.002) and baselines without interact with dispatching device (1.2 s; t(21914) = 16.37, p < 0.0001). 

Events without interact with dispatching device (2.1 s; t(21914) = 36.44, p < 0.0001) had a 

significantly longer mean duration of eyes off forward roadway than baselines without interact 

with dispatching device (1.2 s). 

Write on pad, notepad, etc: Figure 11 shows the mean duration of eyes off forward roadway 

across “All” and “Vehicle 1 At-Fault” events for each of the four groupings. A one-way 

ANOVA found a significant difference in the mean duration of eyes off forward roadway 

between the four groupings across “All” events (F(3, 22617) = 445.93, p < 0.0001). Tukey t tests 

102 
indicated that the mean duration of eyes off forward roadway during events with writing (4.2 s) 

was significantly longer than events without writing (1.9 s; t(22617) = 9.07, p < 0.0001) and 

baselines without writing (1.2 s; t(22617) = 12.24, p < 0.0001). Baselines with writing (3.5 s) had a 

significantly longer mean duration of eyes off forward roadway than events without writing (1.9 

s; t(22617) = 4.62, p < 0.0001) and baselines without writing (1.2 s; t(22617) = 6.96, p < 0.0001). 

Events without writing (1.9 s; t(22617) = 34.01, p < 0.0001) had a significantly longer mean 

duration of eyes off forward roadway than baselines without writing (1.2 s). 

Figure 11. Graph. Mean Duration of Eyes Off Forward Roadway for 

Write on Pad, Notepad, etc. 

4.2

3.5

1.9

1.2

4.2

3.5

2.1

1.2

0

1

2

3

4

5

Event with Writing Baseline with Writing Event without

Writing

Baseline without

Writing

MeanDuration of EyesOff Forward Roadway

(seconds)

All Events Vehicle 1 At‐Fault Events

4.2

3.5

1.9

1.2

4.2

3.5

2.1

1.2

0

1

2

3

4

5

Event with Writing Baseline with Writing Event without

Writing

Baseline without

Writing

MeanDuration of EyesOff Forward Roadway

(seconds)

All Events Vehicle 1 At‐Fault Events

Figure 11 also shows the mean duration of eyes off forward roadway across “Vehicle 1 At-Fault” 

events for each of the four groupings. A one-way ANOVA found a significant difference in the 

mean duration of eyes off forward roadway between the four event types across “Vehicle 1 AtFault” events (F(3, 21914) = 580.20, p < 0.0001). Tukey t tests indicated that the mean duration of 

eyes off forward roadway during events with writing (4.2 s) was significantly longer than events 

without writing (2.1 s; t(21914) = 8.28, p < 0.0001) and baselines without writing (1.2 s; t(21914) = 

12.35, p < 0.0001). Baselines with writing (3.5 s) had a significantly longer mean duration of 

eyes off forward roadway than events without writing (2.1 s; t(21914) = 4.05, p = 0.0003) and 

baselines without writing (1.2 s; t(21914) = 7.03, p < 0.0001). Events without writing (2.1 s; t(21914)

= 39.45, p < 0.0001) had a significantly longer mean duration of eyes off forward roadway than 

baselines without writing (1.2 s). 

Use Calculator: Figure 12 shows the mean duration of eyes off forward roadway across “All” 

and “Vehicle 1 At-Fault” events for each of the four groupings. A one-way ANOVA found a 

significant difference in the mean duration of eyes off forward roadway between the four 

groupings across “All” events (F(3, 22617) = 417.33, p < 0.0001). Tukey t tests indicated that the 

103 
mean duration of eyes off forward roadway during events with use calculator (4.4 s) was 

significantly longer than events without use calculator (2.0 s; t(22617) = 6.11, p < 0.0001) and 

baselines without use calculator (1.2 s; t(22617) = 8.10, p < 0.0001). Baselines with use calculator 

(3.1 s) had a significantly longer mean duration of eyes off forward roadway than baselines 

without use calculator (1.2 s; t(22617) = 3.37, p = 0.004). Events without use calculator (2.0 s; 

t(22617) = 34.37, p < 0.0001) had a significantly longer mean duration of eyes off forward roadway 

than baselines without use calculator (1.2 s). 

Figure 12. Graph. Mean Duration of Eyes off Forward Roadway for Use Calculator 

4.4

3.1

2.0

1.2

4.4

3.1

2.2

1.2

0

1

2

3

4

5

Event with Use

Calculator

Baseline with Use

Calculator

Event without Use

Calculator

Baseline without Use

Calculator

MeanDuration of EyesOff Forward Roadway

(seconds)

All Events Vehicle 1 At‐Fault Events

4.4

3.1

2.0

1.2

4.4

3.1

2.2

1.2

0

1

2

3

4

5

Event with Use

Calculator

Baseline with Use

Calculator

Event without Use

Calculator

Baseline without Use

Calculator

MeanDuration of EyesOff Forward Roadway

(seconds)

All Events Vehicle 1 At‐Fault Events

Figure 12 also shows the mean duration of eyes off forward roadway across “Vehicle 1 At-Fault” 

events for each of the four groupings. A one-way ANOVA found a significant difference in the 

mean duration of eyes off forward roadway between the four groupings across “Vehicle 1 AtFault” events (F(3, 21914) = 553.55, p < 0.0001). Tukey t tests indicated that the mean duration of 

eyes off forward roadway during events with use calculator (4.2 s) was significantly longer than 

events without use calculator (2.2 s; t(21914) = 5.64, p < 0.0001) and baselines without use 

calculator (1.2 s; t(21914) = 8.18, p < 0.0001). Baselines with use calculator (3.1 s) had a 

significantly longer mean duration of eyes off forward roadway than events without use 

calculator (2.2 s; t(21914) = 1.61, p < 0.0001) and baselines without use calculator (1.2 s; t(21914) = 

3.40, p < 0.0001). Events without use calculator (2.2 s; t(21914) = 39.86, p < 0.0001) had a 

significantly longer mean duration of eyes off forward roadway than baselines without use 

calculator (1.2 s). 

Look at Map: Figure 13 shows the mean duration of eyes off forward roadway across “All” and 

“Vehicle 1 At-Fault” events for each of the four groupings. A one-way ANOVA found a 

significant difference in the mean duration of eyes off forward roadway between the four 

groupings across “All” events (F(3, 22617) = 479.07, p < 0.0001). Tukey t tests indicated that the 

104 
mean duration of eyes off forward roadway during events with look at map (3.9s) was 

significantly longer than events without look at map (1.9 s; t(22617) = 10.02, p < 0.0001) and 

baselines without look at map (1.2 s; t(22617) = 14.13, p < 0.0001). Baselines with look at map (3.6 

s) had a significantly longer mean duration of eyes off forward roadway than events without look 

at map (1.9 s; t(22617) = 7.55, p < 0.0001) and baselines without look at map (1.2 s; t(22617) = 11.14,

p < 0.0001). Events without look at map (1.9 s; t(22617) = 33.18, p < 0.0001) had a significantly 

longer mean duration of eyes off forward roadway than baselines without look at map (1.2 s). 

Figure 13. Graph. Mean Duration of Eyes off Forward Roadway for Look at Map 

3.9

3.6

1.9

1.2

3.9

3.6

2.1

1.2

0

1

2

3

4

5

Event with Look at

Map

Baseline with Look at

Map

Event without Look

at Map

Baseline without

Look at Map

MeanDuration of EyesOff Forward Roadway

(seconds)

All Events Vehicle 1 At‐Fault Events

3.9

3.6

1.9

1.2

3.9

3.6

2.1

1.2

0

1

2

3

4

5

Event with Look at

Map

Baseline with Look at

Map

Event without Look

at Map

Baseline without

Look at Map

MeanDuration of EyesOff Forward Roadway

(seconds)

All Events Vehicle 1 At‐Fault Events

Figure 13 also shows the mean duration of eyes off forward roadway across “Vehicle 1 At-Fault” 

events for each of the four groupings. A one-way ANOVA found a significant difference in the 

mean duration of eyes off forward roadway between the four groupings across “Vehicle 1 AtFault” events (F(3, 21914) = 612.42, p < 0.0001). Tukey t tests indicated that the mean duration of 

eyes off forward roadway during events with look at map (3.9 s) was significantly longer than 

events without look at map (2.1 s; t(21914) = 9.02, p < 0.0001) and baselines without look at map 

(1.2 s; t(21914) = 14.26, p < 0.0001). Baselines with look at map (3.6 s) had a significantly longer 

mean duration of eyes off forward roadway than events without look at map (2.1 s; t(21914) = 6.67,

p = 0.002) and baselines without look at map (1.2 s; t(21914) = 11.25, p < 0.0001). Events without 

look at map (2.1 s; t(21914) = 39.23, p < 0.0001) had a significantly longer mean duration of eyes 

off forward roadway than baselines without look at map (1.2 s). 

Dial Cell Phone: Figure 14 shows the mean duration of eyes off forward roadway across “All” 

and “Vehicle 1 At-Fault” event for each of the four groupings. A one-way ANOVA found a 

significant difference in the mean duration of eyes off forward roadway between the four 

groupings types across “All” events (F(3, 22617) = 587.76, p < 0.0001). Tukey t tests indicated that 

the mean duration of eyes off forward roadway during events with dial cell phone (3.8 s) was 

105 
significantly longer than baselines with dial cell phone (3.2 s; t(22617) = 3.43, p = 0.003), events 

without dial cell phone (1.9 s; t(22617) = 16.28, p < 0.0001) and baselines without dial cell phone 

(1.2 s; t(22617) = 22.98, p < 0.0001). Baselines with dial cell phone (3.2 s) had a significantly 

longer mean duration of eyes off forward roadway than events without dial cell phone (1.9 s; 

t(22617) = 10.36, p < 0.0001) and baselines without dial cell phone (1.2 s; t(22617) = 16.41, p < 

0.0001). Events without dial cell phone (1.9 s; t(22617) = 32.29, p < 0.0001) had a significantly 

longer mean duration of eyes off forward roadway than baselines without dial cell phone (1.2 s). 

Figure 14. Graph. Mean Duration of Eyes off Forward Roadway for Dial Cell Phone 

3.8

3.2

1.9

1.2

3.8

3.2

2.1

1.2

0

1

2

3

4

5

Event with Dial Cell

Phone

Baseline with Dial

Cell Phone

Event without Dial

Cell Phone

Baseline without Dial

Cell Phone

MeanDuration of EyesOff Forward Roadway

(seconds)

All Events Vehicle 1 At‐Fault Events

3.8

3.2

1.9

1.2

3.8

3.2

2.1

1.2

0

1

2

3

4

5

Event with Dial Cell

Phone

Baseline with Dial

Cell Phone

Event without Dial

Cell Phone

Baseline without Dial

Cell Phone

MeanDuration of EyesOff Forward Roadway

(seconds)

All Events Vehicle 1 At‐Fault Events

Figure 14 also shows the mean duration of eyes off forward roadway across “Vehicle 1 At-Fault” 

events for each of the four groupings. A one-way ANOVA found a significant difference in the 

mean duration of eyes off forward roadway between the four grouping across “Vehicle 1 AtFault” events (F(3, 21914) = 714.63, p < 0.0001). Tukey t tests indicated that the mean duration of 

eyes off forward roadway during events with dial cell phone (3.8 s) was significantly longer than 

baselines with dial cell phone (3.2 s; t(21914) = 3.74, p = 0.001), events without dial cell phone 

(2.1 s; t(21914) = 14.81, p < 0.0001) and baselines without dial cell phone (1.2 s; t(21914) = 23.23, p 

< 0.0001). Baselines with dial cell phone (3.2 s) had a significantly longer mean duration of eyes 

off forward roadway than events without dial cell phone (2.1 s; t(21914) = 8.82, p < 0.0001) and 

baselines without dial cell phone (1.2 s; t(21914) = 16.56, p < 0.0001). Events without dial cell 

phone (2.1 s; t(21914) = 37.56, p < 0.0001) had a significantly longer mean duration of eyes off 

forward roadway than baselines without dial cell phone (1.2 s). 

Read book, newspaper, paperwork, etc: Figure 15 shows the mean duration of eyes off 

forward roadway across “All” and “Vehicle 1 At-Fault” events for each of the four groupings. A 

one-way ANOVA found a significant difference in the mean duration of eyes off forward 

roadway between the four grouping across “All” events (F(3, 22617) = 667.68, p < 0.0001). Tukey t

106 
tests indicated that the mean duration of eyes off forward roadway during events with reading 

(4.3 s) was significantly longer than events without reading (1.9 s; t(22617) = 17.37, p < 0.0001) 

and baselines without reading (1.1 s; t(22617) = 23.11, p < 0.0001). Baselines with reading (3.8 s) 

had a significantly longer mean duration of eyes off forward roadway than events without 

reading (1.9 s; t(22617) = 15.23, p < 0.0001) and baselines without reading (1.1 s; t(22617) = 21.45, p 

< 0.0001). Events without reading (1.9 s; t(22617) = 33.02, p < 0.0001) had a significantly longer 

mean duration of eyes off forward roadway than baselines without reading (1.1 s). 

Figure 15. Graph. Mean Duration of Eyes off Forward Roadway for 

Read Book, Newspaper, Paperwork, etc. 

4.3

3.8

1.9

1.1

4.3

3.8

2.1

1.1

0

1

2

3

4

5

Event with Reading Baseline with

Reading

Event without

Reading

Baseline without

Reading

MeanDuration of EyesOff Forward Roadway

(seconds)

All Events Vehicle 1 At‐Fault Events

4.3

3.8

1.9

1.1

4.3

3.8

2.1

1.1

0

1

2

3

4

5

Event with Reading Baseline with

Reading

Event without

Reading

Baseline without

Reading

MeanDuration of EyesOff Forward Roadway

(seconds)

All Events Vehicle 1 At‐Fault Events

Figure 15 also shows the mean duration of eyes off forward roadway across “Vehicle 1 At-Fault” 

events for each of the four groupings. A one-way ANOVA found a significant difference in the 

mean duration of eyes off forward roadway between the four groupings across “Vehicle 1 AtFault” events (F(3, 21914) = 797.25, p < 0.0001). Tukey t tests indicated that the mean duration of 

eyes off forward roadway during events with reading (4.3 s) was significantly longer than events 

without reading (2.1 s; t(21914) = 15.98, p < 0.0001) and baselines without reading (1.1 s; t(21914) = 

23.26, p < 0.0001). Baselines with reading (3.8 s) had a significantly longer mean duration of 

eyes off forward roadway than events without reading (2.1 s; t(21914) = 13.72, p < 0.0001) and 

baselines without reading (1.2 s; t(21914) = 23.65, p < 0.0001). Events without reading (2.1 s; 

t(21914) = 38.32, p < 0.0001) had a significantly longer mean duration of eyes off forward roadway 

than baselines without reading (1.1 s). 

5.5.2.5 Moderate Tertiary Tasks 

Figure 16 shows the mean duration of eyes off forward roadway for each event type across “All” 

and “Vehicle 1 At-Fault” events for any moderate tertiary task (i.e., any safety-critical event or 

107 
baseline with only a moderate tertiary task). A one-way ANOVA found a significant difference 

in the mean duration of eyes off forward roadway between the five event types across “All” 

events (F(4, 4218) = 97.22, p < 0.0001). Tukey t tests indicated that the mean duration of eyes off 

forward roadway during unintentional lane deviations (2.6 s) was significantly longer than 

crashes (1.5 s; t(4218) = 2.78, p = 0.044), near-crashes (1.6 s; t(4218) = 3.72, p = 0.002), crashrelevant conflicts (1.7 s; t(4218) = 8.68, p < 0.0001) and baseline epochs (1.1 s; t(4218) = 17.51, p < 

0.0001). Crash-relevant conflicts (1.7 s; t(4218) = 10.15, p < 0.0001) had a significantly longer 

mean duration of eyes off forward roadway time than baseline epochs (1.1 s). 

Figure 16. Graph. Mean Duration of Eyes off Forward Roadway by Event Type for 

Moderate Tertiary Tasks 

1.5 1.6

1.7

2.6

1.1

3.4

2.2 1.9

2.6

1.1

0

1

2

3

4

5

Crash Near‐Crash Crash‐Relevant

Conflict

Unintentional

Lane Deviation

Mean

Baseline

Duration of Eyes off Forward Roadway

(seconds)

All Events Vehicle 1 At‐Fault Events

1.5 1.6

1.7

2.6

1.1

3.4

2.2 1.9

2.6

1.1

0

1

2

3

4

5

Crash Near‐Crash Crash‐Relevant

Conflict

Unintentional

Lane Deviation

Mean

Baseline

Duration of Eyes off Forward Roadway

(seconds)

All Events Vehicle 1 At‐Fault Events

Figure 16 also shows the mean duration of eyes off forward roadway for each event type across 

“Vehicle 1 At-Fault” events. A one-way ANOVA found a significant difference in the mean 

duration of eyes off forward roadway between the five event types across “Vehicle 1 At-Fault” 

events (F(4, 4123) = 111.95, p < 0.0001). Tukey t tests indicated that the mean duration of eyes off 

forward roadway during unintentional lane deviations (2.6 s) was significantly longer than crashrelevant conflicts (1.7 s; t(4123) = 6.42, p < 0.0001) and baseline epochs (1.1 s; t(4123) = 17.57, p < 

0.0001). Crashes (3.4 s; t(4123) = 2.83, p = 0.038), near-crashes (2.2 s; t(4123) = 3.37, p = 0.007) and 

crash-relevant conflicts (1.9 s; t(4123) = 12.19, p < 0.0001) had a significantly longer mean 

duration of eyes off forward roadway time than baseline epochs (1.1 s). 

5.5.2.6 Moderate Tertiary Task Breakout Analyses 

Additional ANOVAs were calculated on the nine specific moderate tertiary tasks that were 

shown to be significant in Table 17. In conducting this analysis, the mean duration of eyes off 

forward roadway was calculated for the following four groupings: 

108 
 Safety-critical events with distraction of interest. 

 Baseline epochs with distraction of interest. 

 Safety-critical events without distraction of interest. 

 Baseline epochs without distraction of interest. 

Because of the small sample size for many of the moderate tertiary tasks, any safety-critical 

event or baseline epoch with the moderate tertiary task of interest was used. Therefore, it was 

possible that the safety-critical event or baseline epoch contained additional tasks (e.g., if the 

distraction of interest were talking on a hands-free phone, the driver may have also been taking a 

drink at the same time). 

Use/Reach for Other Electronic Device: Figure 17 shows the mean duration of eyes off 

forward roadway across “All” and “Vehicle 1 At-Fault” events for each of the four groupings. A 

one-way ANOVA found a significant difference in the mean duration of eyes off forward 

roadway between the four groupings across “All” events (F(3, 22617) = 411.9, p < 0.0001). Tukey t

tests indicated that the mean duration of eyes off forward roadway during events with use/reach 

for other device (4.1 s) was significantly longer than baselines with use/reach for other device 

(1.2 s; t(22617) = 5.07, p < 0.0001), events without use/reach for other device (2.0 s; t(22617) = 5.76,

p < 0.0001) and baselines without use/reach for other device (1.2 s; t(22617) = 7.85, p < 0.0001). 

Events without use/reach for other device (2.0 s; t(22617) = 34.34, p < 0.0001) had a significantly 

longer mean duration of eyes off forward roadway than baselines without use/reach for other 

device (1.2 s). 

Figure 17. Graph. Mean Duration of Eyes off Forward Roadway for Use/Reach for 

Other Electronic Device 

4.1

1.2

2.0

1.2

4.1

1.2

2.2

1.2

0

1

2

3

4

5

Event with

Use/Reach for Other

Device

Baseline with

Use/Reach for Other

Device

Event without

Use/Reach for Other

Device

Baseline without

Use/Reach for Other

Device

MeanDuration of EyesOff Forward Roadway

(seconds)

All Events Vehicle 1 At‐Fault Events

4.1

1.2

2.0

1.2

4.1

1.2

2.2

1.2

0

1

2

3

4

5

Event with

Use/Reach for Other

Device

Baseline with

Use/Reach for Other

Device

Event without

Use/Reach for Other

Device

Baseline without

Use/Reach for Other

Device

MeanDuration of EyesOff Forward Roadway

(seconds)

All Events Vehicle 1 At‐Fault Events

109 
Figure 17 also shows the mean duration of eyes off forward roadway across “Vehicle 1 At-Fault” 

events for each of the four groupings. A one-way ANOVA found a significant difference in the 

mean duration of eyes off forward roadway between the four groupings across “Vehicle 1 AtFault” events (F(3, 21914) = 546.90, p < 0.0001). Tukey t tests indicated that the mean duration of 

eyes off forward roadway during events with use/reach for other device (4.1 s) was significantly 

longer than baselines with use/reach for other device (1.2 s; t(21914) = 4.99, p < 0.0001), events 

without use/reach for other device (2.2 s; t(21914) = 4.98, p < 0.0001) and baselines without 

use/reach for other device (1.2 s; t(21914) = 7.52, p < 0.0001). Events without use/reach for other 

device (2.2 s; t(21914) = 39.86, p < 0.0001) had a significantly longer mean duration of eyes off 

forward roadway than baselines without use/reach for other device (1.2 s). 

Other—Moderate Tertiary Tasks: Figure 18 shows the mean duration of eyes off forward 

roadway across “All” and “Vehicle 1 At-Fault” events for each of the four groupings. A one-way 

ANOVA found a significant difference in the mean duration of eyes off forward roadway 

difference between the four groupings across “All” events (F(3, 22617) = 407.56, p < 0.0001). 

Tukey t tests indicated that the mean duration of eyes off forward roadway during events with 

other—moderate tertiary task (3.3 s) was significantly longer than baselines with other—

moderate tertiary task (1.2 s; t(22617) = 2.72, p = 0.033), events without other—moderate tertiary 

task (2.0 s; t(22617) = 4.19, p = 0.0002) and baselines without other—moderate tertiary task (1.2 s; 

t(22617) = 6.70, p < 0.0001). Events without other—moderate tertiary task (2.0 s; t(22617) = 34.36, p 

< 0.0001) had a significantly longer mean duration of eyes off forward roadway than baselines 

without other—moderate tertiary task (1.2 s). 

Figure 18. Graph. Mean Duration of Eyes off Forward Roadway for 

Other—Moderate Tertiary Task 

3.3

1.9 2.0

1.2

3.3

1.9

2.2

1.2

0

1

2

3

4

5

Event with Other ‐

Moderate Tertiary

Task

Baseline with Other ‐

Moderate Tertiary

Task

Event without Other ‐

Moderate Tertiary

Task

Baseline without

Other ‐ Moderate

Tertiary Task

MeanDuration of EyesOff Forward

Roadway (seconds)

All Events Vehicle 1 At‐Fault Events

3.3

1.9 2.0

1.2

3.3

1.9

2.2

1.2

0

1

2

3

4

5

Event with Other ‐

Moderate Tertiary

Task

Baseline with Other ‐

Moderate Tertiary

Task

Event without Other ‐

Moderate Tertiary

Task

Baseline without

Other ‐ Moderate

Tertiary Task

MeanDuration of EyesOff Forward

Roadway (seconds)

All Events Vehicle 1 At‐Fault Events

Figure 18 also shows the mean duration of eyes off forward roadway across “Vehicle 1 At-Fault” 

events for each of the four groupings. A one-way ANOVA found a significant difference in the 

110 
mean duration of eyes off forward roadway between the four groupings across “Vehicle 1 AtFault” events (F(3, 21914) = 543.77, p < 0.0001). Tukey t tests indicated that the mean duration of 

eyes off forward roadway during events with other—moderate tertiary task (3.3 s) was 

significantly longer than baselines with other—moderate tertiary task (1.2 s; t(21914) = 2.75, p = 

0.030), events without other—moderate tertiary task (2.2 s; t(21914) = 3.56, p = 0.002) and 

baselines without other—moderate tertiary task (1.2 s; t(21914) = 6.76, p < 0.0001). Events without 

other—moderate tertiary task (2.2 s; t(21914) = 39.86, p < 0.0001) had a significantly longer mean 

duration of eyes off forward roadway than baselines without other—moderate tertiary task 

(1.2 s). 

Personal Grooming: Figure 19 shows the mean duration of eyes off forward roadway across 

“All” and “Vehicle 1 At-Fault” events for each of the four groupings. A one-way ANOVA found 

a significant difference in the mean duration of eyes off forward roadway between the four 

groupings across “All” events (F(3, 22617) = 408.53, p < 0.0001). Tukey t tests indicated that the 

mean duration of eyes off forward roadway during events with personal grooming (3.7 s) was 

significantly longer than baselines with personal grooming (1.2 s; t(22617) = 5.66, p < 0.0001), 

events without personal grooming (2.0 s; t(22617) = 4.71, p < 0.0001), and baselines without 

personal grooming (1.2 s; t(22617) = 6.80, p < 0.0001). Baselines with personal grooming (0.7 s) 

had a significantly longer mean duration of eyes off forward roadway than events without 

personal grooming (1.9 s; t(22617) = 3.29, p = 0.006). Events without personal grooming (1.9 s; 

t(22617) = 34.37, p < 0.0001) had a significantly longer mean duration of eyes off forward roadway 

than baselines without personal grooming (1.2 s). 

Figure 19. Graph. Mean Duration of Eyes off Forward Roadway for 

Use/Reach for Personal Grooming 

3.7

0.7

2.0

1.2

3.7

0.7

2.2

1.2

0

1

2

3

4

5

Event with Personal

Grooming

Baseline with

Personal Grooming

Event without

PersonalGrooming

Baseline without

Personal Grooming

MeanDuration of EyesOff Forward Roadway

(seconds)

All Events Vehicle 1 At‐Fault Events

3.7

0.7

2.0

1.2

3.7

0.7

2.2

1.2

0

1

2

3

4

5

Event with Personal

Grooming

Baseline with

Personal Grooming

Event without

PersonalGrooming

Baseline without

Personal Grooming

MeanDuration of EyesOff Forward Roadway

(seconds)

All Events Vehicle 1 At‐Fault Events

111 
Figure 19 also shows the mean duration of eyes off forward roadway across “Vehicle 1 At-Fault” 

events for each of the four groupings. A one-way ANOVA found a significant difference in the 

mean duration of eyes off forward roadway between the four groupings across Vehicle 1 at-fault’ 

events (F(3, 21914) = 544.87, p < 0.0001). Tukey t tests indicated that the mean duration of eyes off 

forward roadway during events with personal grooming (3.7 s) was significantly longer than 

baselines with personal grooming (1.2 s; t(21914) = 5.72, p < 0.0001), events without personal 

grooming (2.2 s; t(21914) = 4.20, p = 0.0002), and baselines without personal grooming (1.2 s; 

t(21914) = 3.87, p = 0.001). Events without personal grooming (1.9 s; t(21914) = 39.84, p < 0.0001) 

had a significantly longer mean duration of eyes off forward roadway than baselines without 

personal grooming (1.2 s). 

Reach for Object in Vehicle: Figure 20 shows the mean duration of eyes off forward roadway 

across “All” and “Vehicle 1 At-Fault” events for each of the four groupings. A one-way 

ANOVA found a significant difference in the mean duration of eyes off forward roadway 

between the four groupings across “All” events (F(3, 22617) = 522.95, p < 0.0001). Tukey t tests 

indicated that the mean duration of eyes off forward roadway during events with reach for object 

(2.9 s) was significantly longer than baselines with reach for object (1.9 s; t(22617) = 11.41, p < 

0.0001), events without reach for object (1.9 s; t(22617) = 12.21, p < 0.0001), and baselines without 

reach for object (1.1 s; t(22617) = 22.28, p < 0.0001). Baselines with reach for object (1.8 s) had a 

significantly longer mean duration of eyes off forward roadway than baselines without reach for 

object (1.1 s; t(22617) = 14.16, p < 0.0001). Events without reach for object (1.9 s; t(22617) = 31.68,

p < 0.0001) had a significantly longer mean duration of eyes off forward roadway than baselines 

without reach for object (1.1 s). 

Figure 20. Graph. Mean Duration of Eyes off Forward Roadway for 

Use/Reach for Reach for Object in Vehicle 

2.9

1.8 1.9

1.1

3.0

1.8

2.1

1.1

0

1

2

3

4

5

Event with Reach for

Object

Baseline with Reach

for Object

Event without Reach

for Object

Baseline without

Reach for Object

MeanDuration of Eyes off Forward Roadway

(seconds)

All Events Vehicle 1 At‐Fault Events

2.9

1.8 1.9

1.1

3.0

1.8

2.1

1.1

0

1

2

3

4

5

Event with Reach for

Object

Baseline with Reach

for Object

Event without Reach

for Object

Baseline without

Reach for Object

MeanDuration of Eyes off Forward Roadway

(seconds)

All Events Vehicle 1 At‐Fault Events

112 
Figure 20 also shows the mean duration of eyes off forward roadway across “Vehicle 1 At-Fault” 

events for each of the four groupings. A one-way ANOVA found a significant difference in the 

mean duration of eyes off forward roadway between the four groupings across Vehicle 1 at-fault’ 

events (F(3, 21914) = 654.32, p < 0.0001). Tukey t tests indicated that the mean duration of eyes off 

forward roadway during events with reach for object (3.0 s) was significantly longer than 

baselines with reach for object (3.0 s; t(21914) = 12.68, p < 0.0001), events without reach for object 

(2.1 s; t(21914) = 10.99, p < 0.0001), and baselines without reach for object (1.2 s; t(21914) = 23.41, p 

< 0.0001). Baselines with reach for object (1.8 s) had a significantly longer mean duration of 

eyes off forward roadway than events without reach for object (2.1 s; t(21914) = 4.89, p < 0.0001) 

and baselines without reach for object (1.8 s; t(21914) = 14.29, p < 0.0001). Events without reach 

for object (2.1 s; t(21914) = 36.75, p < 0.0001) had a significantly longer mean duration of eyes off 

forward roadway than baselines without reach for object (1.1 s). 

Look Back in Sleeper Berth: Figure 21 shows the mean duration of eyes off forward roadway 

across “All” and “Vehicle 1 At-Fault” events for each of the four groupings. A one-way 

ANOVA found a significant difference in the mean duration of eyes off forward roadway 

between the four groupings across “All” events (F(3, 22617) = 414.46, p < 0.0001). Tukey t tests 

indicated that the mean duration of eyes off forward roadway during events with look back in 

sleeper berth (3.4 s) was significantly longer than baselines with look back in sleeper berth (2.4 

s; t(22617) = 11.41, p < 0.0001), events without look back in sleeper berth (2.0 s; t(22617) = 12.21, p 

< 0.0001) and baselines without look back in sleeper berth (1.2 s; t(22617) = 22.28, p < 0.0001). 

Baselines with look back in sleeper berth (3.4 s) had a significantly longer mean duration of eyes 

off forward roadway than baselines without look back in sleeper berth (1.2 s; t(22617) = 14.16, p < 

0.0001). Events without look back in sleeper berth (2.0 s; t(22617) = 31.68, p < 0.0001) had a 

significantly longer mean duration of eyes off forward roadway than baselines without look back 

in sleeper berth (1.2 s). 

113 
Figure 21. Graph. Mean Duration of Eyes off Forward Roadway for 

Use/Reach for Look Back in Sleeper Berth 

3.4

2.4

2.0

1.2

3.7

2.4

2.2

1.2

0

1

2

3

4

5

Event with Look Back

in Sleeper Berth

Baseline with Look

Back in Sleeper Berth

Event without Look

Back in Sleeper Berth

Baseline without

Look Back in Sleeper

Berth

MeanDuration of EyesOff Forward Roadway

(seconds)

All Events Vehicle 1 At‐Fault Events

3.4

2.4

2.0

1.2

3.7

2.4

2.2

1.2

0

1

2

3

4

5

Event with Look Back

in Sleeper Berth

Baseline with Look

Back in Sleeper Berth

Event without Look

Back in Sleeper Berth

Baseline without

Look Back in Sleeper

Berth

MeanDuration of EyesOff Forward Roadway

(seconds)

All Events Vehicle 1 At‐Fault Events

Figure 21 also shows the mean duration of eyes off forward roadway across “Vehicle 1 At-Fault” 

events for each of the four groupings. A one-way ANOVA found a significant difference in the 

mean duration of eyes off forward roadway between the four groupings across “All” events (F(3, 

21914) = 552.80, p < 0.0001). Tukey t tests indicated that the mean duration of eyes off forward 

roadway during events with look back in sleeper berth (3.7 s) was significantly longer than 

baselines with look back in sleeper berth (2.4 s; t(21914) = 3.12, p = 0.010), events without look 

back in sleeper berth (2.2 s; t(21914) = 4.41, p < 0.0001) and baselines without look back in sleeper 

berth (1.2 s; t(21914) = 7.31, p < 0.0001). Baselines with look back in sleeper berth (3.7 s) had a 

significantly longer mean duration of eyes off forward roadway than baselines without look back 

in sleeper berth (1.2 s; t(21914) = 4.67, p < 0.0001). Events without look back in sleeper berth (2.2 

s; t(21914) = 39.91, p < 0.0001) had a significantly longer mean duration of eyes off forward 

roadway than baselines without look back in sleeper berth (1.2 s). 

Smoking-Related—Lighting: Figure 22 shows the mean duration of eyes off forward roadway 

across “All” and “Vehicle 1 At-Fault” events for each of the four groupings. A one-way 

ANOVA found a significant difference in the mean duration of eyes off forward roadway 

between the four groupings across “All” events (F(3, 22617) = 409.63, p < 0.0001). Tukey t tests 

indicated that the mean duration of eyes off forward roadway during events with smokingrelated—lighting (1.6 s) was significantly longer than baselines without smoking-related—

lighting (1.2 s; t(22617) = 2.92, p = 0.019). Baselines with smoking-related—lighting (1.0 s) had a 

significantly shorter mean duration of eyes off forward roadway than events without smokingrelated—lighting (2.0 s; t(22617) = 3.70, p = 0.001) and baselines without smoking-related—

114 
lighting (1.2 s; t(22617) = 5.17, p < 0.0001). Events without smoking-related—lighting (2.0 s; 

t(22617) = 34.77, p < 0.0001) had a significantly longer mean duration of eyes off forward roadway 

than baselines without smoking-related—lighting (1.2 s). 

Figure 22. Graph. Mean Duration of Eyes off Forward Roadway for 

Smoking-Related—Lighting 

1.6

1.0

2.0

1.2

1.8

1.0

2.2

1.2

0

1

2

3

4

5

Event with Smoking  ‐

Lighting

Baseline with

Smoking ‐ Lighting

Event without

Smoking ‐ Lighting

Baseline without

Smoking ‐ Lighting

MeanDuration of EyesOff Forward Roadway

(seconds)

All Events Vehicle 1 At‐Fault Events

1.6

1.0

2.0

1.2

1.8

1.0

2.2

1.2

0

1

2

3

4

5

Event with Smoking  ‐

Lighting

Baseline with

Smoking ‐ Lighting

Event without

Smoking ‐ Lighting

Baseline without

Smoking ‐ Lighting

MeanDuration of EyesOff Forward Roadway

(seconds)

All Events Vehicle 1 At‐Fault Events

Figure 22 also shows the mean duration of eyes off forward roadway across “Vehicle 1 At-Fault” 

events for each of the four groupings. A one-way ANOVA found a significant difference in the 

mean duration of eyes off forward roadway between the four groupings across “Vehicle 1 AtFault” events (F(3, 21914) = 547.88, p < 0.0001). Tukey t tests indicated that the mean duration of 

eyes off forward roadway during events with smoking—lighting (1.8 s) was significantly longer 

than baselines without smoking—lighting (1.2 s; t(21914) = 3.67, p = 0.001). Baselines with 

smoking—lighting (1.0 s) had a significantly shorter mean duration of eyes off forward roadway 

than events without smoking—lighting (2.2 s; t(21914) = 5.99, p = 0.001) and baselines without 

smoking—lighting (1.2 s; t(21914) = 5.22, p < 0.0001). Events without smoking—lighting (2.2 s; 

t(21914) = 40.24, p < 0.0001) had a significantly longer mean duration of eyes off forward roadway 

than baselines without smoking—lighting (1.2 s). 

Talk/Listen to CB Radio: Figure 23 shows the mean duration of eyes off forward roadway 

across “All” and “Vehicle 1 At-Fault” events for each of the four groupings. A one-way 

ANOVA found a significant difference in the mean duration of eyes off forward roadway 

between the four groupings across “All” events (F(3, 22617) = 409.31, p < 0.0001). Tukey t tests 

indicated that the mean duration of eyes off forward roadway during events with talk/listen to 

CB (1.3 s) was significantly shorter than events without talk/listen to CB (2.0 s; t(22617) = 3.46, p 

= 0.003). Baselines with talk/listen to CB (0.9 s) had a significantly shorter mean duration of 

eyes off forward roadway than events without talk/listen to CB (2.0 s; t(22617) = 15.17, p < 

115 
0.0001) and baselines without talk/listen to CB (1.2 s; t(22617) = 3.73, p = 0.001). Events without 

talk/listen to CB (2.0 s; t(22617) = 34.58, p < 0.0001) had a significantly longer mean duration of 

eyes off forward roadway than baselines without talk/listen to CB (1.2 s). 

Figure 23. Graph. Mean Duration of Eyes off Forward Roadway for 

Talk/Listen to CB Radio 

1.3

0.9

2.0

1.2 1.3

0.9

2.2

1.2

0

1

2

3

4

5

Event with

Talk/Listen to CB

Baseline with

Talk/Listen to CB

Event without

Talk/Listen to CB

Baseline without

Talk/Listen to CB

MeanDuration of EyesOff Forward Roadway

(seconds)

All Data Vehicle 1 At‐Fault

1.3

0.9

2.0

1.2 1.3

0.9

2.2

1.2

0

1

2

3

4

5

Event with

Talk/Listen to CB

Baseline with

Talk/Listen to CB

Event without

Talk/Listen to CB

Baseline without

Talk/Listen to CB

MeanDuration of EyesOff Forward Roadway

(seconds)

All Data Vehicle 1 At‐Fault

Figure 23 also shows the mean duration of eyes off forward roadway across “Vehicle 1 At-Fault” 

events for each of the four groupings. A one-way ANOVA found a significant difference in the 

mean duration of eyes off forward roadway between the four groupings across “Vehicle 1 AtFault” events (F(3, 21914) = 547.38, p < 0.0001). Tukey t tests indicated that the mean duration of 

eyes off forward roadway during events with talk/listen to CB (1.3 s) was significantly shorter 

than events without talk/listen to CB (2.2 s; t(21914) = 3.46, p = 0.003). Baselines with talk/listen 

to CB (0.9 s) had a significantly shorter mean duration of eyes off forward roadway than events 

without talk/listen to CB (2.2 s; t(21914) = 18.16, p < 0.0001) and baselines without talk/listen to 

CB (1.2 s; t(21914) = 3.76, p = 0.001). Events without talk/listen to CB (2.2 s; t(21914) = 40.11, p < 

0.0001) had a significantly longer mean duration of eyes off forward roadway than baselines 

without talk/listen to CB (1.2 s). 

Look Outside Vehicle: Figure 24 shows the mean duration of eyes off forward roadway across 

“All” and “Vehicle 1 At-Fault” events for each of the four groupings. A one-way ANOVA found 

a significant difference in the mean duration of eyes off forward roadway between the four 

groupings across “All” events (F(3, 22617) = 764.49, p < 0.0001). Tukey t tests indicated that the 

mean duration of eyes off forward roadway during events with external distraction (2.0 s) was 

significantly longer than baselines with external distraction (1.7 s; t(22617) = 6.19, p < 0.0001) and 

baselines without external distraction (1.0 s; t(22617) = 19.04, p < 0.0001). Baselines with external 

distraction (1.7 s) had a significantly shorter mean duration of eyes off forward roadway than 

116 
events without external distraction (1.9 s; t(22617) = 9.05, p < 0.0001) and a significantly longer 

mean duration of eyes off forward roadway than baselines without external distraction (1.0 s; 

t(22617) = 32.18, p < 0.0001). Events without external distraction (1.9 s; t(22617) = 38.28, p < 

0.0001) had a significantly longer mean duration of eyes off forward roadway than baselines 

without external distraction (1.0 s). 

Figure 24. Graph. Mean Duration of Eyes off Forward Roadway for External Distraction 

2.0

1.7 1.9

1.0

2.2

1.7

2.2

1.0

0

1

2

3

4

5

Event with External

Distraction

Baseline with

External Distraction

Event without

ExternalDistraction

Baseline without

External Distraction

MeanDuration of EyesOff Forward Roadway

(seconds)

All Data Vehicle 1 At‐Fault

2.0

1.7 1.9

1.0

2.2

1.7

2.2

1.0

0

1

2

3

4

5

Event with External

Distraction

Baseline with

External Distraction

Event without

ExternalDistraction

Baseline without

External Distraction

MeanDuration of EyesOff Forward Roadway

(seconds)

All Data Vehicle 1 At‐Fault

Figure 24 also shows the mean duration of eyes off forward roadway across “Vehicle 1 At-Fault” 

events for each of the four groupings. A one-way ANOVA found a significant difference in the 

mean duration of eyes off forward roadway between the four groupings across “Vehicle 1 AtFault” events (F(3, 21914) = 916.66, p < 0.0001). Tukey t tests indicated that the mean duration of 

eyes off forward roadway during events with external distraction (2.2 s) was significantly longer 

than baselines with external distraction (1.7 s; t(21914) = 7.74, p < 0.0001) and baselines without 

external distraction (1.0 s; t(21914) = 19.16, p < 0.0001). Baselines with external distraction (1.7 s) 

had a significantly shorter mean duration of eyes off forward roadway than events without 

external distraction (2.2 s; t(21914) = 15.77, p < 0.0001) and a significantly longer mean duration 

of eyes off forward roadway than baselines without external distraction (1.0 s; t(22617) = 32.53, p 

< 0.0001). Events without external distraction (2.2 s; t(21914) = 43.7, p < 0.0001) had a 

significantly longer mean duration of eyes off forward roadway than baselines without external 

distraction (1.0 s). 

Talk/Listen to Hands-Free Phone: Figure 25 shows the mean duration of eyes off forward 

roadway across “All” and “Vehicle 1 At-Fault” events for each of the four groupings. A one-way 

ANOVA found a significant difference in the mean duration of eyes off forward roadway 

between the four groupings across “All” events (F(3, 22617) = 404.72, p < 0.0001). Tukey t tests 

indicated that the mean duration of eyes off forward roadway during events with talk/listen to 

117 
hands-free phone (1.6 s) was significantly longer than baselines with talk/listen to hands-free 

phone (1.0 s; t(22617) = 4.01, p = 0.0004) and baselines without talk/listen to hands-free phone (1.2 

s; t(22617) = 3.33, p = 0.005). Baselines with talk/listen to hands-free phone (1.0 s) had a 

significantly shorter mean duration of eyes off forward roadway than events without talk/listen to 

hands-free phone (2.0 s; t(22617) = 19.32, p < 0.0001) and baselines without talk/listen to handsfree phone (1.2 s; t(22617) = 2.76, p = 0.030). Events without talk/listen to hands-free phone (2.0 s; 

t(22617) = 34.28, p < 0.0001) had a significantly longer mean duration of eyes off forward roadway 

than baselines without talk/listen to hands-free phone (1.2 s). 

Figure 25. Graph. Mean Duration of Eyes off Forward Roadway for 

Talk/Listen to Hands-Free Phone 

1.6

1.0

2.0

1.2

1.8

1.0

2.2

1.2

0

1

2

3

4

5

Event with

Talk/Listen to Hands‐

Free Phone

Baseline with

Talk/Listen to Hands‐

Free Phone

Event without

Talk/Listen to Hands‐

Free Phone

Baseline without

Talk/Listen to Hands‐

Free Phone

MeanDuration of EyesOff Forward Roadway

(seconds)

All Data Vehicle 1 At‐Fault

1.6

1.0

2.0

1.2

1.8

1.0

2.2

1.2

0

1

2

3

4

5

Event with

Talk/Listen to Hands‐

Free Phone

Baseline with

Talk/Listen to Hands‐

Free Phone

Event without

Talk/Listen to Hands‐

Free Phone

Baseline without

Talk/Listen to Hands‐

Free Phone

MeanDuration of EyesOff Forward Roadway

(seconds)

All Data Vehicle 1 At‐Fault

Figure 25 also shows the mean duration of eyes off forward roadway across “Vehicle 1 At-Fault” 

events for each of the four groupings. A one-way ANOVA found a significant difference in the 

mean duration of eyes off forward roadway between the four groupings across “Vehicle 1 AtFault” events (F(3, 21914) = 543.03, p < 0.0001). More specifically, Tukey t tests indicated that the 

mean duration of eyes off forward roadway during events with talk/listen to hands-free phone 

(1.8 s) was significantly longer than baselines with talk/listen to hands-free phone (1.0 s; t(21914) = 

4.12, p = 0.0002) and baselines without talk/listen to hands-free phone (1.2 s; t(21914) = 3.52, p = 

0.002). Baselines with talk/listen to hands-free phone (1.0 s) had a significantly shorter mean 

duration of eyes off forward roadway than events without talk/listen to hands-free phone (2.2 s; 

t(21914) = 23.39, p < 0.0001) and baselines without talk/listen to hands-free phone (1.2 s; t(21914) = 

2.79, p = 0.027). Events without talk/listen to hands-free phone (2.2 s; t(21914) = 39.83, p < 

0.0001) had a significantly longer mean duration of eyes off forward roadway than baselines 

without talk/listen to hands-free phone (1.2 s). 

118 
5.5.2.7 Simple Tertiary Tasks 

Figure 26 shows the mean duration of eyes off forward roadway for each event type across “All” 

and “Vehicle 1 At-Fault” events for any simple tertiary tasks (i.e., any safety-critical event or 

baseline epoch with only a simple tertiary task). A one-way ANOVA found a significant 

difference in the mean duration of eyes off forward roadway between the five event types across 

“All” events (F(4, 2063) = 60.04, p < 0.0001). Tukey t tests indicated that the mean duration of eyes 

off forward roadway during unintentional lane deviations (2.2 s) was significantly longer than 

crashes (0.1 s; t(2063) = 3.29, p = 0.009), crash-relevant conflicts (1.2 s; t(2063) = 7.64, p < 0.0001) 

and baseline epochs (0.7 s; t(2063) = 14.35, p < 0.0001). Crash-relevant conflicts (1.2 s; t(2063) = 

6.79, p < 0.0001) had a significantly longer mean duration of eyes off forward roadway than 

baseline epochs (0.7 s). 

Figure 26. Graph. Mean Duration of Eyes off Forward Roadway by Event Type for 

Simple Tertiary Tasks 

0.1

1.1 1.2

2.2

0.7

3.1

1.5

2.2

0.7

0

1

2

3

4

5

Crash Near‐Crash Crash‐Relevant

Conflict

Unintentional

Lane Deviation

Mean

Baseline

Duration of Eyesoff Forward Roadway

(seconds)

All Events Vehicle 1 At‐Fault Events

0.1

1.1 1.2

2.2

0.7

3.1

1.5

2.2

0.7

0

1

2

3

4

5

Crash Near‐Crash Crash‐Relevant

Conflict

Unintentional

Lane Deviation

Mean

Baseline

Duration of Eyesoff Forward Roadway

(seconds)

All Events Vehicle 1 At‐Fault Events

Figure 26 also shows the mean duration of eyes off forward roadway for each event type across 

“Vehicle 1 At-Fault” events. A one-way ANOVA found a significant difference in the mean 

duration of eyes off forward roadway between the five event types across “Vehicle 1 At-Fault” 

events (F(3, 2009) = 92.04, p < 0.0001). Tukey t tests indicated that the mean duration of eyes off 

forward roadway during unintentional lane deviations (2.2 s) was significantly longer than crashrelevant conflicts (1.5 s; t(2009) = 5.39, p < 0.0001) and baseline epochs (0.7 s; t(2009) = 14.37, p < 

0.0001). Near-crashes (3.1 s; t(2009) = 3.10, p = 0.011) and crash-relevant conflicts (1.5 s; t(2009) = 

8.79, p < 0.0001) had a significantly longer mean duration of eyes off forward roadway time than 

baseline epochs (0.7 s). 

119 
5.5.2.8 Simple Tertiary Task Breakout Analyses 

Additional ANOVAs were calculated on the five specific simple tertiary tasks that were shown 

to be significant in Table 17. In conducting this analysis, the mean duration of eyes off forward 

roadway was calculated for the following four groupings: 

 Safety-critical events with distraction of interest. 

 Baseline epochs with distraction of interest. 

 Safety-critical events without distraction of interest. 

 Baseline epochs without distraction of interest. 

Because of the small sample size for many of the simple tertiary tasks, any safety-critical event 

or baseline epoch with the moderate tertiary task of interest was used. Therefore, it was possible 

that the safety-critical event or baseline epoch contained additional tasks (e.g., if the distraction 

of interest was talking on a hands-free phone, the driver may have also been taking a drink at the 

same time). 

Put on/remove/adjust Sunglasses or Glasses: Figure 27 shows the mean duration of eyes off 

forward roadway across “All” and “Vehicle 1 At-Fault” events for each of the four groupings. A 

one-way ANOVA found a significant difference in the mean duration of eyes off forward 

roadway between the four groupings across “All” events (F(3, 22617) = 401.01, p < 0.0001). Tukey 

t tests indicated that the mean duration of eyes off forward roadway during events with put 

on/remove/adjust sunglasses or glasses (2.3 s) was significantly longer than baselines with put 

on/remove/adjust sunglasses or glasses (1.3 s; t(22617) = 3.24, p = 0.007) and baselines without put 

on/remove/adjust sunglasses or glasses (1.2 s; t(22617) = 4.59, p < 0.0001). Baselines with put 

on/remove/adjust sunglasses or glasses (1.3 s) had a significantly shorter mean duration of eyes 

off forward roadway than events without put on/remove/adjust sunglasses or glasses (2.0 s; 

t(22617) = 3.46, p = 0.003). Events without put on/remove/adjust sunglasses or glasses (2.0 s; 

t(22617) = 34.44, p < 0.0001) had a significantly longer mean duration of eyes off forward roadway 

than baselines without put on/remove/adjust sunglasses or glasses (1.2 s). 

120 
Figure 27. Graph. Mean Duration of Eyes off Forward Roadway for 

Put on/remove/adjust Sunglasses or Glasses 

2.3

1.3

2.0

1.2

2.6

1.3

2.2

1.2

0

1

2

3

4

5

Event with Put

on/remove/adjust

sunglasses or glasses

Baseline with Put

on/remove/adjust

sunglasses or glasses

Event without Put

on/remove/adjust

sunglasses or glasses

Baseline without Put

on/remove/adjust

sunglasses or glasses

MeanDuration of EyesOff Forward Roadway

(seconds)

All Events Vehicle 1 At‐Fault Events

2.3

1.3

2.0

1.2

2.6

1.3

2.2

1.2

0

1

2

3

4

5

Event with Put

on/remove/adjust

sunglasses or glasses

Baseline with Put

on/remove/adjust

sunglasses or glasses

Event without Put

on/remove/adjust

sunglasses or glasses

Baseline without Put

on/remove/adjust

sunglasses or glasses

MeanDuration of EyesOff Forward Roadway

(seconds)

All Events Vehicle 1 At‐Fault Events

Figure 27 also shows the mean duration of eyes off forward roadway across “Vehicle 1 At-Fault” 

events for each of the four groupings. A one-way ANOVA found a significant difference in the 

mean duration of eyes off forward roadway between the four groupings across Vehicle 1 at-fault’ 

events (F(3, 21914) = 538.94, p < 0.0001). Tukey t tests indicated that the mean duration of eyes off 

forward roadway during events with put on/remove/adjust sunglasses or glasses (2.6 s) was 

significantly longer than baselines with put on/remove/adjust sunglasses or glasses (1.3 s; t(21914)

= 3.95, p = 0.001) and baselines without put on/remove/adjust sunglasses or glasses (1.2 s; t(21914)

= 5.35, p < 0.0001). Baselines with put on/remove/adjust sunglasses or glasses (1.3 s) had a 

significantly shorter mean duration of eyes off forward roadway than events without put 

on/remove/adjust sunglasses or glasses (2.2 s; t(21914) = 4.55, p < 0.0001). Events without put 

on/remove/adjust sunglasses or glasses (2.2 s; t(21914) = 39.92, p < 0.0001) had a significantly 

longer mean duration of eyes off forward roadway than baselines without put on/remove/adjust 

sunglasses or glasses (1.2 s). 

Adjust Instrument Panel: Figure 28 shows the mean duration of eyes off forward roadway 

across “All” and “Vehicle 1 At-Fault” events for each of the four groupings. A one-way 

ANOVA found a significant difference in the mean duration of eyes off forward roadway 

between the four groupings across “All” events (F(3, 22617) = 531.85, p < 0.0001). Tukey t tests 

indicated that the mean duration of eyes off forward roadway during events with adjust 

instrument panel (2.6 s) was significantly longer than baselines with adjust instrument panel (2.0 

s; t(22617) = 5.31, p < 0.0001), events without adjust instrument panel (1.9 s; t(22617) = 6.78, p < 

0.0001) and baselines without adjust instrument panel (1.1 s; t(22617) = 14.61, p < 0.0001). 

Baselines with adjust instrument panel (2.0 s) had a significantly longer mean duration of eyes 

off forward roadway than baselines without adjust instrument panel (1.1 s; t(22617) = 18.14, p < 

121 
0.0001). Events without adjust instrument panel (1.9 s; t(22617) = 34.29, p < 0.0001) had a 

significantly longer mean duration of eyes off forward roadway than baselines without adjust 

instrument panel (1.1 s). 

Figure 28. Graph. Mean Duration of Eyes off Forward Roadway for Adjust Instrument Panel 

2.6

2.0 1.9

1.1

2.8

2.0 2.1

1.1

0

1

2

3

4

5

Event with Adjust

Instrument Panel

Baseline with Adjust

Instrument Panel

Event without Adjust

Instrument Panel

Baseline without

Adjust Instrument

Panel

MeanDuration of EyesOff Forward Roadway

(seconds)

All Data Vehicle 1 At‐Fault Events

2.6

2.0 1.9

1.1

2.8

2.0 2.1

1.1

0

1

2

3

4

5

Event with Adjust

Instrument Panel

Baseline with Adjust

Instrument Panel

Event without Adjust

Instrument Panel

Baseline without

Adjust Instrument

Panel

MeanDuration of EyesOff Forward Roadway

(seconds)

All Data Vehicle 1 At‐Fault Events

Figure 28 also shows the mean duration of eyes off forward roadway across “Vehicle 1 At-Fault” 

events for each of the four groupings. A one-way ANOVA found a significant difference in the 

mean duration of eyes off forward roadway between the four groupings across “Vehicle 1 AtFault” events (F(3, 21914) = 672.75, p < 0.0001). Tukey t tests indicated that the mean duration of 

eyes off forward roadway during events with adjust instrument panel (2.8 s) was significantly 

longer than baselines with adjust instrument panel (2.0 s; t(21914) = 6.80, p < 0.0001), events 

without adjust instrument panel (2.1 s; t(21914) = 6.39, p = 0.0002) and baselines without adjust 

instrument panel (1.1 s; t(21914) = 15.74, p < 0.0001). Baselines with adjust instrument panel (2.0 

s) had a significantly longer mean duration of eyes off forward roadway than baselines without 

adjust instrument panel (1.1 s; t(21914) = 18.32, p < 0.0001). Events without adjust instrument 

panel (2.1 s; t(21914) = 39.56, p < 0.0001) had a significantly longer mean duration of eyes off 

forward roadway than baselines without adjust instrument panel (1.1 s). 

Other Personal Hygiene: Figure 29 shows the mean duration of eyes off forward roadway 

across “All” and “Vehicle 1 At-Fault” events for each of the four groupings. A one-way 

ANOVA found a significant difference in the mean duration of eyes off forward roadway 

between the four groupings across “All” events (F(3, 22617) = 411.11, p < 0.0001). Tukey t tests 

indicated that the mean duration of eyes off forward roadway during events with other personal 

hygiene (1.6 s) was significantly longer than baselines with other personal hygiene (1.1 s; t(22617)

= 5.84, p < 0.0001) and baselines without other personal hygiene (1.2 s; t(22617) = 5.45, p < 

0.0001) and was significantly shorter than events without other personal hygiene (2.0 s; t(22617) = 

122 
5.24, p < 0.0001). Baselines with other personal hygiene (1.1 s) had a significantly shorter mean 

duration of eyes off forward roadway than events without other personal hygiene (2.2 s; t(22617) = 

25.06, p < 0.0001). Events without other personal hygiene (2.0 s; t(22617) = 34.28, p < 0.0001) had 

a significantly longer mean duration of eyes off forward roadway than baselines without other 

personal hygiene (1.2 s). 

Figure 29. Graph. Mean Duration of Eyes off Forward Roadway for Other Personal Hygiene 

1.6

1.1

2.0

1.2

1.7

1.1

2.2

1.2

0

1

2

3

4

5

Event with Other

Personal Hygiene

Baseline with Other

PersonalHygiene

Event without Other

Personal Hygiene

Baseline without

Other Personal

Hygiene

MeanDuration of EyesOff Forward Roadway

(seconds)

All Data Vehicle 1 At‐Fault

1.6

1.1

2.0

1.2

1.7

1.1

2.2

1.2

0

1

2

3

4

5

Event with Other

Personal Hygiene

Baseline with Other

PersonalHygiene

Event without Other

Personal Hygiene

Baseline without

Other Personal

Hygiene

MeanDuration of EyesOff Forward Roadway

(seconds)

All Data Vehicle 1 At‐Fault

Figure 29 also shows the mean duration of eyes off forward roadway across “Vehicle 1 At-Fault” 

events for each of the four groupings. A one-way ANOVA found a significant difference in the 

mean duration of eyes off forward roadway between the four groupings across “Vehicle 1 AtFault” events (F(3, 21914) = 553.95, p < 0.0001). Tukey t tests indicated that the mean duration of 

eyes off forward roadway during events with other personal hygiene (1.7 s) was significantly 

longer than baselines with other personal hygiene (1.1 s; t(21914) = 6.72, p < 0.0001) and baselines 

without other personal hygiene (1.2 s; t(21914) = 6.38, p < 0.0001) and was significantly shorter 

than events without other personal hygiene (2.2 s; t(21914) = 6.40, p < 0.0001). Baselines with 

other personal hygiene (1.1 s) had a significantly shorter mean duration of eyes off forward 

roadway than events without other personal hygiene (2.2 s; t(21914) = 30.23, p < 0.0001). Events 

without other personal hygiene (2.2 s; t(21914) = 39.89, p < 0.0001) had a significantly longer 

mean duration of eyes off forward roadway than baselines without other personal hygiene (1.2 s). 

Bite Nails: Figure 30 shows the mean duration of eyes off forward roadway across “All” and 

“Vehicle 1 At-Fault” events for each of the four groupings. A one-way ANOVA found a 

significant difference in the mean duration of eyes off forward roadway between the four 

groupings across “All” events (F(3, 22617) = 408.55, p < 0.0001). Tukey t tests indicated that the 

mean duration of eyes off forward roadway during events with bite nails (1.1 s) was significantly 

longer than events without bite nails (2.0 s; t(22617) = 2.77, p = 0.029). Baselines with bite nails 

123 
(0.8 s) had a significantly shorter mean duration of eyes off forward roadway than events without 

bite nails (2.0 s; t(22617) = 12.01, p < 0.0001) and baselines without bite nails (1.2 s; t(22617) = 4.00,

p < 0.0001). Events without bite nails (0.8 s; t(22617) = 34.6, p = 0.0004) had a significantly 

shorter mean duration of eyes off forward roadway than baselines without bite nails (1.2 s). 

Figure 30. Graph. Mean Duration of Eyes off Forward Roadway for Bite Nails 

1.1

0.8

2.0

1.2 1.2

0.8

2.2

1.2

0

1

2

3

4

5

Event with Bite Nails Baseline with Bite

Nails

Event without Bite

Nails

Baseline without

Bite Nails

MeanDuration of EyesOff Forward Roadway

(seconds)

All Data Vehicle 1 At‐Fault

1.1

0.8

2.0

1.2 1.2

0.8

2.2

1.2

0

1

2

3

4

5

Event with Bite Nails Baseline with Bite

Nails

Event without Bite

Nails

Baseline without

Bite Nails

MeanDuration of EyesOff Forward Roadway

(seconds)

All Data Vehicle 1 At‐Fault

Figure 30 also shows the mean duration of eyes off forward roadway across “Vehicle 1 At-Fault” 

events for each of the four groupings. A one-way ANOVA found a significant difference in the 

mean duration of eyes off forward roadway between the four groupings across “Vehicle 1 AtFault” events (F(3, 21914) = 546.38, p < 0.0001). Tukey t tests indicated that the mean duration of 

eyes off forward roadway during events with bite nails (1.2 s) was significantly shorter than 

events without bite nails (2.2 s; t(21914) = 2.65, p = 0.041). Baselines with bite nails (0.8 s) had a 

significantly shorter mean duration of eyes off forward roadway than events without bite nails 

(2.2 s; t(21914) = 14.18, p < 0.0001) and baselines without bite nails (1.2 s; t(21914) = 4.04, p < 

0.0001). Events without bite nails (0.8 s; t(21914) = 40.12, p < 0.0001) had a significantly shorter 

mean duration of eyes off forward roadway than baselines without bite nails (1.2 s). 

Interact with Other Occupant(s): Figure 31 shows the mean duration of eyes off forward 

roadway across “All” and “Vehicle 1 At-Fault” events for each of the four groupings. A one-way 

ANOVA found a significant difference in the mean duration of eyes off forward roadway 

between the four groupings across “All” events (F(3, 22617) = 408.93, p < 0.0001). Tukey t tests 

indicated that the mean duration of eyes off forward roadway during events with interact with 

other occupant (2.0 s) was significantly longer than baselines without interact with other 

occupant (1.2 s; t(22617) = 2.73, p = 0.032). Baselines without interact with other occupant (1.2 s) 

had a significantly shorter mean duration of eyes off forward roadway than events without 

interact with other occupant (2.0 s; t(22617) = 34.79, p < 0.0001) and baselines with interact with 

124 
other occupant (1.6 s; t(22617) = 4.98, p < 0.0001). Events without interact with other occupant 

(2.0 s; t(22617) = 34.79, p < 0.0001) had a significantly longer mean duration of eyes off forward 

roadway than baselines without interact with other occupant (1.2 s). 

Figure 31. Graph. Mean Duration of Eyes off Forward Roadway for Interact with Other Occupant(s) 

2.0

1.6

2.0

1.2

2.1

1.6

2.2

1.2

0

1

2

3

4

5

Event with Interact

with Occupant

Baseline with

Interact with

Occupant

Event without

Interact with

Occupant

Baseline without

Interact with

Occupant

MeanDuration of EyesOff Forward Roadway

(seconds)

All Data Vehicle 1 At‐Fault

2.0

1.6

2.0

1.2

2.1

1.6

2.2

1.2

0

1

2

3

4

5

Event with Interact

with Occupant

Baseline with

Interact with

Occupant

Event without

Interact with

Occupant

Baseline without

Interact with

Occupant

MeanDuration of EyesOff Forward Roadway

(seconds)

All Data Vehicle 1 At‐Fault

Figure 31 also shows the mean duration of eyes off forward roadway across “Vehicle 1 At-Fault” 

events for each of the four groupings. A one-way ANOVA found a significant difference in the 

mean duration of eyes off forward roadway between the four groupings across “Vehicle 1 AtFault” events (F(3, 21914) = 547.11, p < 0.0001). Tukey t tests indicated that the mean duration of 

eyes off forward roadway during events with interact with other occupant (2.1 s) was 

significantly shorter than baselines without interact with other occupant (2.2 s; t(21914) = 2.78, p = 

0.028). Baselines without interact with other occupant (1.2 s) had a significantly shorter mean 

duration of eyes off forward roadway than events without interact with other occupant (2.2 s; 

t(21914) = 40.30, p < 0.0001) and baselines with interact with other occupant (1.6 s; t(21914) = 5.03,

p < 0.0001). Events without interact with other occupant (2.2 s; t(21914) = 40.30, p < 0.0001) had a 

significantly longer mean duration of eyes off forward roadway than baselines without interact 

with other occupant (1.2 s). 

5.5.2.9 Secondary Tasks 

Figure 32 shows the mean duration of eyes off forward roadway for each event type across “All” 

and “Vehicle 1 At-Fault” events for any secondary task (i.e., any safety-critical event or baseline 

epoch with only a secondary task). A one-way ANOVA found a significant difference in the 

mean duration of eyes off forward roadway between the five event types across “All” events (F(4, 

4704) = 37.40, p < 0.0001). Tukey t tests indicated that the mean duration of eyes off forward 

roadway during unintentional lane deviations (2.4 s) was significantly longer crash-relevant 

125 
conflicts (1.8 s; t(4704) = 5.72, p < 0.0001) and baseline epochs (1.5 s; t(4704) = 8.73, p < 0.0001). 

Crashes (3.4 s; t(4704) = 4.41, p = 0.0001), near-crashes (2.3 s; t(4704) = 5.29, p < 0.0001), and 

crash-relevant conflicts (1.8 s; t(4704) = 6.10, p < 0.0001 had a significantly longer mean duration 

of eyes off forward roadway than baseline epochs (1.5 s). Crashes (3.4 s; t(4704) = 3.76, p = 

0.0002) and near-crashes (2.3 s; t(4704) = 3.29, p = 0.009) had a significantly longer mean duration 

of eyes off forward roadway than crash-relevant conflicts (1.8 s). 

Figure 32. Graph. Mean Duration of Eyes off Forward Roadway by Event Type for 

Secondary Tasks 

3.4

2.3

1.8

2.4

1.5

3.4

2.4

1.8

2.4

1.5

0

1

2

3

4

5

Crash Near‐Crash Crash‐Relevant

Conflict

Unintentional

Lane Deviation

Mean

Baseline

Duration of Eyes off Forward Roadway

(seconds)

All Events Vehicle 1 At‐Fault Events

3.4

2.3

1.8

2.4

1.5

3.4

2.4

1.8

2.4

1.5

0

1

2

3

4

5

Crash Near‐Crash Crash‐Relevant

Conflict

Unintentional

Lane Deviation

Mean

Baseline

Duration of Eyes off Forward Roadway

(seconds)

All Events Vehicle 1 At‐Fault Events

Figure 32 also shows the mean duration of eyes off forward roadway for each event type across 

“Vehicle 1 At-Fault” events. A one-way ANOVA found a significant difference in the mean 

duration of eyes off forward roadway between the five event types across “Vehicle 1 At-Fault” 

events (F(4, 4495) = 36.65, p < 0.0001). Tukey t tests indicated that the mean duration of eyes off 

forward roadway during unintentional lane deviations (2.4 s) was significantly longer crashrelevant conflicts (1.8 s; t(4495) = 5.47, p < 0.0001) and baseline epochs (1.5 s; t(4495) = 8.89, p < 

0.0001). Crashes (3.4 s; t(4495) = 4.49, p < 0.0001), near-crashes (2.4 s; t(4495) = 4.82, p < 0.0001), 

and crash-relevant conflicts (1.8 s; t(4495) = 5.73, p < 0.0001 had a significantly longer mean 

duration of eyes off forward roadway than baseline epochs (1.5 s). Crashes (3.4 s; t(4495) = 3.77, p 

= 0.0002) and near-crashes (2.4 s; t(4495) = 3.10, p = 0.017) had a significantly longer mean 

duration of eyes off forward roadway than crash-relevant conflicts (1.8 s). 

5.5.2.10 Secondary Task Breakout Analyses 

Additional ANOVAs were calculated on the three specific secondary tasks that were shown to be 

significant in Table 17. In conducting this analysis, the mean duration of eyes off forward 

roadway was calculated for the following four groupings: 

126 
 Safety-critical events with distraction of interest. 

 Baseline epochs with distraction of interest. 

 Safety-critical events without distraction of interest. 

 Baseline epochs without distraction of interest. 

Look at Left-side Mirror/Out Left Window: Figure 33 shows the mean duration of eyes off 

forward roadway across “All” and “Vehicle 1 At-Fault” events for each of the four groupings. A 

one-way ANOVA found a significant difference in the mean duration of eyes off forward 

roadway between the four groupings across “All” events (F(3, 22617) = 883.63, p < 0.0001). Tukey 

t tests indicated that the mean duration of eyes off forward roadway during events with left-side 

mirror/window (2.1 s) was significantly longer than baselines with left-side mirror/window (1.7 

s; t(22617) = 8.58, p < 0.0001), events without left-side mirror/window (1.9 s; t(22617) = 3.90, p = 

0.0006) and baselines without left-side mirror/window (1.0 s; t(22617) = 27.67, p < 0.0001). 

Baselines with left-side mirror/window (1.7 s) had a significantly shorter mean duration of eyes 

off forward roadway than events without left-side mirror/window (1.9 s; t(22617) = 6.33, p < 

0.0001) and a significantly longer mean duration of eyes off forward roadway than baselines 

without left-side mirror/window (1.0 s; t(22617) = 36.90, p < 0.0001). Events without left-side 

mirror/window (1.9 s; t(22617) = 35.89, p < 0.0001) had a significantly longer mean duration of 

eyes off forward roadway than baselines without left-side mirror/window (1.0 s). 

Figure 33. Graph. Mean Duration of Eyes off Forward Roadway for 

Look at Left-side Mirror/Out Left Window 

2.1

1.7 1.9

1.0

2.2

1.7

2.2

1.0

0

1

2

3

4

5

Event with Left

Mirror/Window

Baseline with Left

Mirror/Window

Event without Left

Mirror/Window

Baseline without

LeftMirror/Window

MeanDuration of EyesOff Forward Roadway

(seconds)

All Data Vehicle 1 At‐Fault

2.1

1.7 1.9

1.0

2.2

1.7

2.2

1.0

0

1

2

3

4

5

Event with Left

Mirror/Window

Baseline with Left

Mirror/Window

Event without Left

Mirror/Window

Baseline without

LeftMirror/Window

MeanDuration of EyesOff Forward Roadway

(seconds)

All Data Vehicle 1 At‐Fault

Figure 33 also shows the mean duration of eyes off forward roadway across “Vehicle 1 At-Fault” 

events for each of the four groupings. A one-way ANOVA found a significant difference in the 

mean duration of eyes off forward roadway between the four groupings across “Vehicle 1 At127 
Fault” events (F(3, 21914) = 1036.10, p < 0.0001). Tukey t tests indicated that the mean duration 

eyes off forward roadway during events with left-side mirror/window (2.2 s) was significantly 

longer than baselines with left-side mirror/window (1.7 s; t(21914) = 9.21, p < 0.0001) and 

baselines without left-side mirror/window (1.2 s; t(21914) = 26.30, p < 0.0001). Baselines w

side mirror/window (1.7 s) had a significantly shorter mean duration of eyes off forward 

roadway than events without left-side mirror/window (2.2 s; t(21914) = 14.25, p < 0.0001) a

significantly longer mean duration of eyes off forward roadway than baselines without left-side

mirror/window (1.2 s; t(21914) = 37.30, p = 0.001). Events without left-side mirror/window (2.2 s; 

t(21914) = 42.45, p < 0.0001) had a significantly longer mean duration of eyes off forward roadway 

than baselines without left-side mirror/window (1.2 s). 

of 

ith leftnd a 

Look at Right-Side Mirror/Out Right Window: Figure 34 shows the mean duration of eyes 

off forward roadway across “All” and “Vehicle 1 At-Fault” events for each of the four 

groupings. A one-way ANOVA found a significant difference in the mean duration of eyes off 

forward roadway between the four groupings across “All” events (F(3, 22617) = 741.57, p < 

0.0001). Tukey t tests indicated that the mean duration of eyes off forward roadway during 

events with right-side mirror/window (2.2 s) was significantly longer than baselines without 

right-side mirror/window (1.1 s; t(22617) = 4.64, p < 0.0001), events without right-side 

mirror/window (1.9 s; t(22617) = 4.81, p < 0.0001) and baselines without right-side mirror/window 

(1.1 s; t(22617) = 18.93, p < 0.0001). Baselines with right-side mirror/window (1.9 s) had a 

significantly longer mean duration of eyes off forward roadway than baselines without right-side 

mirror/window (1.2 s; t(22617) = 30.81, p < 0.0001). Events without right-side mirror/window (1.9 

s; t(22617) = 35.97, p < 0.0001) had a significantly longer mean duration of eyes off forward 

roadway than baselines without right-side mirror/window (1.1 s). 

Figure 34. Graph. Mean Duration of Eyes off Forward Roadway for 

Look at Right-side Mirror/Out Right Window 

2.2

1.9 1.9

1.1

2.3

1.9

2.2

1.1

0

1

2

3

4

5

Event with Right

Mirror/Window

Baseline with Right

Mirror/Window

Event without Right

Mirror/Window

Baseline without

Right

Mirror/Window

MeanDuration of EyesOff Forward Roadway

(seconds)

AllData Vehicle 1 At‐Fault

2.2

1.9 1.9

1.1

2.3

1.9

2.2

1.1

0

1

2

3

4

5

Event with Right

Mirror/Window

Baseline with Right

Mirror/Window

Event without Right

Mirror/Window

Baseline without

Right

Mirror/Window

MeanDuration of EyesOff Forward Roadway

(seconds)

AllData Vehicle 1 At‐Fault

128 
Figure 34 also s cle 1 At-Fault” 

events for each of the four ificant difference in the 

d 

. 

hows the mean duration of eyes off forward roadway across “Vehi

 groupings. A one-way ANOVA found a sign

mean duration of eyes off forward roadway between the four groupings across “Vehicle 1 AtFault” events (F(3, 21914) = 885.3, p < 0.0001). Tukey t tests indicated that the mean duration of 

eyes off forward roadway during events with right-side mirror/window (2.3 s) was significantly 

longer than baselines with right-side mirror/window (1.1 s; t(21914) = 4.14, p < 0.0002) and 

baselines without right-side mirror/window (1.1 s; t(21914) = 15.68, p < 0.0001). Baselines with 

right-side mirror/window (1.9 s) had a significantly longer mean duration of eyes off forward 

roadway than events without right-side mirror/window (1.9 s; t(21914) = 6.53, p < 0.0001) and 

baselines without right-side mirror/window (1.1 s; t(21914) = 31.13, p < 0.0001). Events without 

right-side mirror/window (1.9 s; t(21914) = 42.57, p < 0.0001) had a significantly longer mean 

duration of eyes off forward roadway than baselines without right-side mirror/window (1.1 s). 

Check Speedometer: Figure 35 shows the mean duration of eyes off forward roadway across 

“All” and “Vehicle 1 At-Fault” events for each of the four groupings. A one-way ANOVA foun

a significant difference in the mean duration of eyes off forward roadway between the four 

groupings across “All” events (F(3, 22617) = 523.43, p < 0.0001). Tukey t tests indicated that the 

mean duration of eyes off forward roadway during events with check speedometer (1.9 s) was 

significantly longer than baselines without check speedometer (1.1 s; t(22617) = 7.26, p < 0.0001)

Events without check speedometer (2.0 s) had a significantly longer mean duration of eyes off 

forward roadway than baselines with check speedometer (1.6 s; t(22617) = 9.10, p < 0.0001) and 

baselines without check speedometer (1.1 s; t(22617) = 36.71, p < 0.0001). Baselines with check 

speedometer (1.6 s; t(22617) = 18.71, p < 0.0001) had a significantly longer mean duration of eyes

off forward roadway than baselines without check speedometer (1.1 s). 

Figure 35. Graph. Mean Duration of Eyes off Forward Roadway for Check Speedometer 

1.9

1.6

2.0

1.1

2.0

1.6

2.2

1.1

0

1

2

3

4

5

Event with Check

Speedometer

Baseline with Check

Speedometer

Event without Check

Speedometer

Baseline without

Check Speedometer

MeanDuration of EyesOff Forward Roadway

(seconds)

All Data Vehicle Vehicle 1 At‐Fault

1.9

1.6

2.0

1.1

2.0

1.6

2.2

1.1

0

1

2

3

4

5

Event with Check

Speedometer

Baseline with Check

Speedometer

Event without Check

Speedometer

Baseline without

Check Speedometer

MeanDuration of EyesOff Forward Roadway

(seconds)

129 
Figur ault” 

defined as the number of 

-

-

ped by event type (i.e., crash, near-

 tasks. 

ry tasks. 

e mean number of glances away from forward roadway for each event type 

across “All” and “Vehicle 1 At-Fault” events for any task. A one-way ANOVA found a 

ted that 

). 

e 35 also shows the mean duration of eyes off forward roadway across “Vehicle 1 At-F

events for each of the four groupings. A one-way ANOVA found a significant difference in the 

mean duration of eyes off forward roadway between the four groupings across “Vehicle 1 AtFault” events (F(3, 21914) = 667.02, p < 0.0001). Tukey t tests indicated that the mean duration of 

eyes off forward roadway during events with check speedometer (2.1 s) was significantly longer

than baselines with check speedometer (1.6 s; t(21914) = 2.78, p = 0.028) and baselines without 

check speedometer (1.1 s; t(21914) = 7.68, p < 0.0001). Events without check speedometer (2.2 s) 

had a significantly longer mean duration of eyes off forward roadway than baselines with check 

speedometer (1.6 s; t(21914) = 14.71, p < 0.0001) and baselines without check speedometer (1.1 s; 

t(21914) = 42.12, p < 0.0001). Baselines with check speedometer (1.6 s; t(21914) = 18.90, p < 0.0001)

had a significantly longer mean duration of eyes off forward roadway than baselines without 

check speedometer (1.1 s). 

5.5.3 Number of Glances Away From Forward Roadway 

Number of glances away from forward roadway was operationally 

glances away from the forward roadway during the 6-s interval or epoch period. This may 

include partial glances at either the beginning or end of the 6-s interval. A glance was 

operationally defined as any time the driver took his/her eyes off of the forward roadway, 

regardless of where he/she looked. For example, if the driver looked forward-right window

forward, that was considered one glance. In addition, if the driver looked forward-cell phone

right window-forward, that was also considered one glance. 

As in section 3.2 above, the analyses in this section were grou

crash, crash-relevant conflict, etc.) across “All” and “Vehicle 1 At-Fault” events. These results 

are presented in Tables 36–41 and include the following analyses: 

 All tasks. 

 All tertiary

– Complex tertia

– Moderate tertiary tasks. 

– Simple tertiary tasks. 

 Secondary tasks. 

 All Tasks. 

Figure 36 shows th

significant difference in the mean number of glances away from forward roadway between the 

five event types across “All” events (F(4, 22616) = 186.61, p < 0.0001). Tukey t tests indica

the mean number of glances away from forward roadway during unintentional lane deviations 

(2.3) was significantly higher than near-crashes (1.5; t(22616) = 7.58, p < 0.0001), crash-relevant 

conflicts (1.6; t(22616) = 14.82, p < 0.0001), and baseline epochs (1.3; t(22616) = 25.16, p < 0.0001

Crash-relevant conflicts (1.5) had a significantly higher number of glances away from forward 

roadway than baseline epochs (1.3; t(22616) = 12.31, p < 0.0001). 

130 
Figure 36. Graph. Mean Number of Glances Away from Forward Roadway by Event Type 

for All Tasks 

1.7 1.5 1.6

2.3

1.3

2.2

1.8 1.8

2.3

1.3

0

1

2

3

4

Crash Near‐Crash Crash‐Relevant

Conflict

Unintentional

Lane Deviation

Baseline

MeanNumberof GlancesAway from

ForwardRoadway

All Events Vehicle 1 At‐Fault Events

1.7 1.5 1.6

2.3

1.3

2.2

1.8 1.8

2.3

1.3

0

1

2

3

4

Crash Near‐Crash Crash‐Relevant

Conflict

Unintentional

Lane Deviation

Baseline

MeanNumberof GlancesAway from

ForwardRoadway

All Events Vehicle 1 At‐Fault Events

Figure 36 also shows the mean number of glances away from forward roadway for each event 

type across “Vehicle 1 At-Fault” events. A one-way ANOVA found a significant difference in 

the mean number of glances away from forward roadway between the five event types across 

“Vehicle 1 At-Fault” events (F(4, 21996) = 209.90, p < 0.0001). Tukey t tests indicated that the 

mean number of glances away from forward roadway during unintentional lane deviations (2.3) 

was significantly higher than near-crashes (1.8; t(21996) = 3.80, p = 0.001), crash-relevant 

conflicts (1.8; t(21996) = 11.30, p < 0.0001), and baseline epochs (1.3; t(21996) = 25.19, p < 0.0001). 

Both near-crashes (1.8 s; t(21996) = 3.98, p = 0.001) and crash-relevant conflicts (1.8; t(21996) = 

15.38, p < 0.0001) had a significantly higher number of glances away from forward roadway 

than baseline epochs (1.3). 

All Tertiary Tasks 

Figure 37 shows the mean number of glances away from forward roadway for each event type 

across “All” and “Vehicle 1 At-Fault” events for any tertiary task (i.e., any safety-critical event 

or baseline epoch with a complex, moderate, or simple tertiary task). A one-way ANOVA found 

a significant difference in the mean number of glances away from forward roadway between the 

five event types across “All” events (F(4, 8722) = 188.54, p < 0.0001). Tukey t tests indicated that 

the mean number of glances away from forward roadway during unintentional lane deviations 

(2.5) was significantly higher than crashes (1.0; t(8722) = 4.26, p = 0.0002), near-crashes (1.7; 

t(8722) = 4.09, p = 0.0004), crash-relevant conflicts (1.9; t(8722) = 10.11, p < 0.0001), and baseline 

epochs (1.3; t(8722) = 24.66, p < 0.0001). Crash-relevant conflicts (1.9; t(8722) = 16.92, p < 0.0001) 

had a significantly higher number of glances away from forward roadway than baseline epochs 

(1.3). 

131 
Figure 37. Graph. Mean Number of Glances Away from Forward Roadway by Event Type 

for All Tertiary Tasks 

1.0

1.7

1.9

2.5

1.3

2.0 2.1 2.1

2.5

1.3

0

1

2

3

4

Crash Near‐Crash Crash‐Relevant

Conflict

Unintentional

Lane Deviation

Baseline

MeanNumberof GlancesAway from

ForwardRoadway

All Events Vehicle 1 At‐Fault Events

1.0

1.7

1.9

2.5

1.3

2.0 2.1 2.1

2.5

1.3

0

1

2

3

4

Crash Near‐Crash Crash‐Relevant

Conflict

Unintentional

Lane Deviation

Baseline

MeanNumberof GlancesAway from

ForwardRoadway

All Events Vehicle 1 At‐Fault Events

Figure 37 also shows the mean number of glances away from forward roadway for each event 

type across “Vehicle 1 At-Fault” events. A one-way ANOVA found a significant difference in 

the mean number of glances away from forward roadway between the five event types across 

“Vehicle 1 At-Fault” events (F(4, 8098) = 209.37, p < 0.0001). Tukey t tests indicated that the mean 

number of glances away from forward roadway during unintentional lane deviations (2.5) was 

significantly higher than crash-relevant conflicts (2.1; t(8098) = 6.94, p < 0.0001) and baseline 

epochs (1.3; t(8098) = 24.71, p < 0.0001). Crash-relevant conflicts (2.1; t(8098) = 16.92, p < 0.0001) 

had a significantly higher mean number of glances away from forward roadway than baseline 

epochs (1.3). 

5.5.3.1 Complex Tertiary Tasks 

Figure 38 shows the mean number of glances away from forward roadway for each event type 

across “All” and “Vehicle 1 At-Fault” events for any complex tertiary tasks (i.e., any safetycritical event or baseline epoch with only a complex tertiary task). A one-way ANOVA did not 

find a significant difference in the mean number of glances away from forward roadway between 

the four event types across “All” events (F(3, 492) = 2.14, p = 0.0948). 

132 
Figure 38. Graph. Mean Number of Glances Away from Forward Roadway by Event Type 

for Complex Tertiary Tasks 

2.7

3.1 3.2

3.3

2.7

3.1 3.2

3.3

0

1

2

3

4

Crash Near‐Crash Crash‐Relevant

Conflict

Unintentional

Lane Deviation

Baseline

MeanNumberof GlancesAway from

ForwardRoadway

All Events Vehicle 1 At‐Fault Events

2.7

3.1 3.2

3.3

2.7

3.1 3.2

3.3

0

1

2

3

4

Crash Near‐Crash Crash‐Relevant

Conflict

Unintentional

Lane Deviation

Baseline

MeanNumberof GlancesAway from

ForwardRoadway

All Events Vehicle 1 At‐Fault Events

Figure 38 also shows the mean number of glances away from forward roadway for each event 

type across “Vehicle 1 At-Fault” events. A one-way ANOVA did not find a significant 

difference in the mean number of glances away from forward roadway between the four event 

types across “Vehicle 1 At-Fault” events (F(3, 487) = 1.84, p = 0.1388). 

5.5.3.2 Moderate Tertiary Tasks 

Figure 39 shows the mean number of glances away from forward roadway for each event type 

across “All” and “Vehicle 1 At-Fault” events for any moderate tertiary tasks (i.e., any safetycritical event or baseline epoch with only a moderate tertiary task). A one-way ANOVA found a 

significant difference in the mean number of glances away from forward roadway between the 

five event types across “All” events (F(4, 4218) = 48.08, p < 0.0001). Tukey t tests indicated that 

the mean number of glances away from forward roadway during unintentional lane deviations 

(2.3) was significantly higher than crash-relevant conflicts (1.7; t(4218) = 6.61, p < 0.0001) and 

baseline epochs (1.3 s; t(4218) = 12.56, p < 0.0001). Crash-relevant conflicts (1.7; t(4218) = 6.64, p < 

0.0001) had a significantly higher mean number of glances away from forward roadway than 

baseline epochs (1.3). 

133 
Figure 39. Graph. Mean Number of Glances Away from Forward Roadway by Event Type 

for Moderate Tertiary Tasks 

1.3

1.6 1.7

2.3

1.3

2.0 1.8 1.8

2.3

1.3

0

1

2

3

4

Crash Near‐Crash Crash‐Relevant

Conflict

Unintentional

Lane Deviation

Baseline

MeanNumberof GlancesAway from

ForwardRoadway

All Events Vehicle 1 At‐Fault Events

1.3

1.6 1.7

2.3

1.3

2.0 1.8 1.8

2.3

1.3

0

1

2

3

4

Crash Near‐Crash Crash‐Relevant

Conflict

Unintentional

Lane Deviation

Baseline

MeanNumberof GlancesAway from

ForwardRoadway

All Events Vehicle 1 At‐Fault Events

Figure 39 also shows the mean number of glances away from forward roadway for each event 

type across “Vehicle 1 At-Fault” events. A one-way ANOVA found a significant difference 

between the five event types across “Vehicle 1 At-Fault” events (F(3, 4123) = 52.70, p < 0.0001). 

Tukey t tests indicated that the mean number of glances away from forward roadway during 

unintentional lane deviations (2.3) was significantly higher than crash-relevant conflicts (1.8; 

t(4123) = 5.15, p < 0.0001) and baseline epochs (1.2; t(4123) = 12.56, p < 0.0001). Crash-relevant 

conflicts (1.8; t(4123) = 7.83, p < 0.0001) had a significantly higher mean number of glances away 

from forward roadway than baseline epochs (1.3). 

5.5.3.3 Simple Tertiary Tasks 

Figure 40 shows the mean number of glances away from forward roadway for each event type 

across “All” and “Vehicle 1 At-Fault” events for any simple tertiary tasks (i.e., any safety-critical 

event or baseline epoch with only a simple tertiary task). A one-way ANOVA found a significant 

difference in the mean number of glances away from forward roadway between the five event 

types across “All” events (F(4, 2063) = 38.10, p < 0.0001). Tukey t tests indicated that the mean 

number of glances away from forward roadway during unintentional lane deviations (2.0) was 

significantly higher than crash-relevant conflicts (1.3; t(2063) = 6.61, p < 0.0001) and baseline 

epochs (0.8; t(2063) = 12.56, p < 0.0001). Crash-relevant conflicts (1.3 s; t(2063) = 6.64, p < 0.0001) 

had a significantly higher mean number of glances away from forward roadway than baseline 

epochs (0.8). 

134 
Figure 40. Graph. Mean Number of Glances Away from Forward Roadway by Event Type 

for Simple Tertiary Tasks 

0.3

1.3 1.3

2.0

0.8

3.0

1.5

2.0

0.8

0

1

2

3

4

Crash Near‐Crash Crash‐Relevant

Conflict

Unintentional

Lane Deviation

Baseline

MeanNumberof GlancesAway from

ForwardRoadway

All Events Vehicle 1 At‐Fault Events

0.3

1.3 1.3

2.0

0.8

3.0

1.5

2.0

0.8

0

1

2

3

4

Crash Near‐Crash Crash‐Relevant

Conflict

Unintentional

Lane Deviation

Baseline

MeanNumberof GlancesAway from

ForwardRoadway

All Events Vehicle 1 At‐Fault Events

Figure 40 also shows the mean number of glances away from forward roadway for each event 

type across “Vehicle 1 At-Fault” events. A one-way ANOVA found a significant difference in 

the mean number of glances away from forward roadway between the four event types across 

“Vehicle 1 At-Fault” events (F(3, 2009) = 59.50, p < 0.0001). Tukey t tests indicated that the mean 

number of glances away from forward roadway during unintentional lane deviations (2.0) was 

significantly higher than crash-relevant conflicts (1.5; t(2009) = 5.15, p < 0.0001) and baseline 

epochs (0.8; t(2009) = 12.56, p < 0.0001). Crash-relevant conflicts (1.5 s; t(2009) = 7.83, p < 0.0001) 

had a significantly higher mean number of glances away from forward roadway than baseline 

epochs (0.8). 

5.5.3.4 Secondary Tasks 

Figure 41 shows the mean number of glances away from forward roadway for each event type 

across “All” and “Vehicle 1 At-Fault” events for any secondary tasks (i.e., any safety-critical 

event or baseline epochs with only a secondary task). A one-way ANOVA found a significant 

difference in the mean number of glances away from forward roadway between the five event 

types across “All” events (F(4, 4704) = 8.77, p < 0.0001). Tukey t tests indicated that the mean 

number of glances away from forward roadway during unintentional lane deviations 

(2.1; t(4704) = 4.38, p = 0.001) and crash-relevant conflicts (1.9 s; t(4704) = 4.07, p = 0.001) was 

significantly higher than baseline epochs (1.7). 

135 
Figure 41. Graph. Mean Number of Glances Away from Forward Roadway by Event Type 

for Secondary Tasks 

1.8 1.9 1.9

2.1

1.8 1.7 2.0 1.9

2.1

1.7

0

1

2

3

4

Crash Near‐Crash Crash‐Relevant

Conflict

Unintentional

Lane Deviation

Baseline

MeanNumberof GlancesAway from

ForwardRoadway

All Events Vehicle 1 At‐Fault Events

1.8 1.9 1.9

2.1

1.8 1.7 2.0 1.9

2.1

1.7

0

1

2

3

4

Crash Near‐Crash Crash‐Relevant

Conflict

Unintentional

Lane Deviation

Baseline

MeanNumberof GlancesAway from

ForwardRoadway

All Events Vehicle 1 At‐Fault Events

Figure 41 also shows the mean number of glances away from forward roadway for each event 

type across “Vehicle 1 At-Fault” events. A one-way ANOVA found a significant difference in 

the mean number of glances away from forward roadway between the five event types across 

“Vehicle 1 At-Fault” events (F(4, 4495) = 9.13, p < 0.0001). Tukey t tests indicated that the mean 

number of glances away from forward roadway during unintentional lane deviations 

(2.1; t(4495) = 4.39, p = 0.0001) and crash-relevant conflicts (1.9 s; t(4495) = 4.13, p = 0.0004) was 

significantly higher than baseline epochs (1.7). 

5.5.4 Length of Longest Glance Away From Forward Roadway 

Length of longest glance away from forward roadway was operationally defined as the longest 

single glance (defined in section 3.3) where the driver was not looking forward during the 6-s 

safety-critical event or baseline epoch. As in the previous analysis, this may include partial 

glances at either the beginning or end of the 6-s interval. The analyses in this section were 

grouped by event type (i.e., crash, near-crash, crash-relevant conflict, etc.) across “All” and 

“Vehicle 1 At-Fault” events. These results are presented in Figures 42–47 and include the 

following analyses: 

 All tasks. 

 All tertiary tasks. 

– Complex tertiary tasks. 

– Moderate tertiary tasks. 

– Simple tertiary tasks. 

 Secondary task. 

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5.5.4.1 All Tasks 

Figure 42 shows the mean length of longest glance away from forward roadway for each event 

type across “All” and “Vehicle 1 At-Fault” events for any task. A one-way ANOVA found a 

significant difference in the mean length of longest glance away from forward roadway between 

the five event types across “All” events (F(4, 22616) = 314.37, p < 0.0001). Tukey t tests indicated 

that the mean length of longest glance away from forward roadway during unintentional lane 

deviations (1.5 s) was significantly longer than near-crashes (1.1 s; t(22616) = 6.51, p < 0.0001), 

crash-relevant conflicts (1.0 s; t(22616) = 19.57, p < 0.0001), and baselines (0.8 s; t(22616) = 32.36, p 

< 0.0001). Crashes (1.6 s) were significantly longer than crash-relevant conflicts (1.0 s; t(22616) = 

3.58, p = 0.003) and baselines (1.6 s; t(22616) = 5.00, p < 0.0001). Both near-crashes (1.1 s; t(22616)

= 6.56, p = 0.001) and crash-relevant conflicts (1.0 s; t(22616) = 14.93, p < 0.0001) had a 

significantly longer mean length of longest glance away from forward roadway than baselines 

(0.8 s). 

Figure 42. Graph. Mean Length of Longest Glance Away from Forward Roadway 

by Event Type for All Tasks 

1.6

1.1 1.0

1.5

0.8

2.6

1.3

1.1

1.5

0.8

0

1

2

3

4

5

Crash Near‐Crash Crash‐Relevant

Conflict

Unintentional

Lane Deviation

Baseline

MeanLengthof Longest Glance Away

fromForward Roadway (seconds)

All Events Vehicle 1 At‐Fault Events

1.6

1.1 1.0

1.5

0.8

2.6

1.3

1.1

1.5

0.8

0

1

2

3

4

5

Crash Near‐Crash Crash‐Relevant

Conflict

Unintentional

Lane Deviation

Baseline

MeanLengthof Longest Glance Away

fromForward Roadway (seconds)

All Events Vehicle 1 At‐Fault Events

Figure 42 also shows the mean length of longest glance away from forward roadway for each 

event type across “Vehicle 1 At-Fault” events. A one-way ANOVA found a significant 

difference between the five event types across “Vehicle 1 At-Fault” events (F(4, 21913) = 355.35, p 

< 0.0001). Tukey t tests indicated that the mean length of longest glance away from forward 

roadway during unintentional lane deviations (1.5 s) was significantly longer than near-crashes 

(1.3 s; t(21913) = 3.36, p = 0.007), crash-relevant conflicts (1.1 s; t(21913) = 15.90, p < 0.0001), and 

baselines (0.8 s; t(21913) = 32.76, p < 0.0001). Crashes (2.6 s; t(21913) = 7.75, p < 0.0001), nearcrashes (1.3 s; t(21913) = 6.83, p < 0.0001) and crash-relevant conflicts (1.1 s; t(21913) = 18.07, p < 

0.0001) had a significantly longer mean length of longest glance away from forward roadway 

than baselines (0.8 s). Crashes (2.6 s) had a significantly longer mean length of longest glance 

away from forward roadway than near-crashes (1.3 s; t(21913) = 5.36, p < 0.0001), crash-relevant 

137 
conflicts (1.0 s; t(21913) = 3.43, p = 0.0006), and unintentional lane deviations (1.5 s; t(21913) = 4.49,

p < 0.0001). 

5.5.4.2 All Tertiary Tasks 

Figure 43 shows the mean length of longest glance away from forward roadway for each event 

type across “All” and “Vehicle 1 At-Fault” events for any tertiary task (i.e., any safety-critical 

event or baseline epoch with a complex, moderate, or simple tertiary task). A one-way ANOVA 

found a significant difference in the mean length of longest glance away from forward roadway 

between the five event types across “All” events (F(4, 8722) = 232.83, p < 0.0001). Tukey t tests 

indicated that the mean length of longest glance away from forward roadway during 

unintentional lane deviations (1.6 s) was significantly longer than crashes (0.9 s; t(8722) = 3.20, p 

= 0.012), near-crashes (1.1 s; t(8722) = 4.03, p = 0.001), crash-relevant conflicts (1.2 s; t(8722) = 

11.75, p < 0.0001), and baselines (0.8 s; t(8722) = 27.64, p < 0.0001). Both near-crashes (1.2 s; 

t(8722) = 2.93, p = 0.028) and crash-relevant conflicts (1.2 s; t(8722) = 15.35, p < 0.0001) had a 

significantly longer mean length of longest glance away from forward roadway than baselines 

(0.8 s). 

Figure 43. Graph. Mean Length of Longest Glance Away from Forward Roadway 

by Event Type for All Tertiary Tasks 

0.9

1.1 1.2

1.6

0.8

2.6

1.5 1.3

1.6

0.8

0

1

2

3

4

5

Crash Near‐Crash Crash‐Relevant

Conflict

Unintentional

Lane Deviation

Baseline

MeanLengthof Longest Glance Away

fromForward Roadway (seconds)

All Events Vehicle 1 At‐Fault Events

0.9

1.1 1.2

1.6

0.8

2.6

1.5 1.3

1.6

0.8

0

1

2

3

4

5

Crash Near‐Crash Crash‐Relevant

Conflict

Unintentional

Lane Deviation

Baseline

MeanLengthof Longest Glance Away

fromForward Roadway (seconds)

All Events Vehicle 1 At‐Fault Events

Figure 43 also shows the mean length of longest glance away from forward roadway for each 

event type across “Vehicle 1 At-Fault” events. A one-way ANOVA found a significant 

difference in the mean length of longest glance away from forward roadway between the five 

event types across “Vehicle 1 At-Fault” events (F(4, 8098) = 264.63, p < 0.0001). Tukey t tests 

indicated that the mean length of longest glance away from forward roadway during 

unintentional lane deviations (1.6 s) was significantly longer than crash-relevant conflicts (1.3 s; 

t(8098) = 8.19, p < 0.0001), and baselines (0.8 s; t(8098) = 27.76, p < 0.0001). Crashes (2.6 s; t(8098) = 

3.50, p = 0.004), near-crashes (1.5 s; t(8098) = 4.87, p = < 0.0001) and crash-relevant conflicts (1.3 

138 
s; t(8098) = 18.48, p < 0.0001) had a significantly longer mean length of longest glance away from 

forward roadway than baselines (0.8 s). 

5.5.4.3 Complex Tertiary Tasks 

Figure 44 shows the mean length of longest glance away from forward roadway for each event 

type across “All” and “Vehicle 1 At-Fault” events for any complex tertiary task (i.e., any safetycritical event or baseline epoch with only a complex tertiary task). A one-way ANOVA found a 

significant difference in the mean length of longest glance away from forward roadway between 

the four event types across “All” events (F(3, 492) = 8.40, p < 0.0001). Tukey t tests indicated that 

the mean length of longest glance away from forward roadway during unintentional lane 

deviations (2.2 s; t(492) = 4.57, p < 0.0001) and crash-relevant conflicts (2.1 s; t(492) = 3.16, p = 

0.009) was significantly longer than baselines (1.8 s). 

Figure 44. Graph. Mean Length of Longest Glance Away from Forward Roadway 

by Event Type for Complex Tertiary Tasks 

2.8

2.1 2.2

1.8

2.8

2.1 2.2

1.8

0

1

2

3

4

5

Crash Near‐Crash Crash‐Relevant

Conflict

Unintentional

Lane Deviation

Baseline

MeanLengthof Longest Glance Away

fromForward Roadway (seconds)

All Events Vehicle 1 At‐Fault Events

2.8

2.1 2.2

1.8

2.8

2.1 2.2

1.8

0

1

2

3

4

5

Crash Near‐Crash Crash‐Relevant

Conflict

Unintentional

Lane Deviation

Baseline

MeanLengthof Longest Glance Away

fromForward Roadway (seconds)

All Events Vehicle 1 At‐Fault Events

Figure 44 also shows the mean length of longest glance away from forward roadway for each 

event type across “Vehicle 1 At-Fault” events. A one-way ANOVA found a significant 

difference in the mean length of longest glance away from forward roadway between the four 

event types across “Vehicle 1 At-Fault” events (F(3, 487) = 8.95, p < 0.0001). Tukey t tests 

indicated that the mean length of longest glance away from forward roadway during 

unintentional lane deviations (2.2 s; t(487) = 4.59, p < 0.0001) and crash-relevant conflicts (2.1 s; 

t(487) = 3.60, p = 0.002) was significantly longer than baselines (1.8 s). 

5.5.4.4 Moderate Tertiary Tasks 

Figure 45 shows the mean length of longest glance away from forward roadway for each event 

type across “All” and “Vehicle 1 At-Fault” events for any moderate tertiary task (i.e., any safetycritical event or baseline epoch with only a moderate tertiary task). A one-way ANOVA found a 

139 
significant difference in the mean length of longest glance away from forward roadway between 

the five event types across “All” events (F(4, 4218) = 55.58, p < 0.0001). Tukey t tests indicated 

that the mean length of longest glance away from forward roadway during unintentional lane 

deviations (1.5 s) was significantly longer than near-crashes (1.0 s; t(4218) = 2.75, p = 0.048), 

crash-relevant conflicts (1.1 s; t(4218) = 5.88, p < 0.0001), and baselines (0.8 s; t(4218) = 12.83, p < 

0.0001). Crash-relevant conflicts (1.1 s) had a significantly longer mean length of longest glance 

away from forward roadway than baselines (0.8 s; t(4218) = 8.24, p < 0.0001). 

Figure 45. Graph. Mean Length of Longest Glance Away from Forward Roadway 

by Event Type for Moderate Tertiary Tasks 

1.2

1.0 1.1

1.5

0.8

2.6

1.3 1.2

1.5

0.8

0

1

2

3

4

5

Crash Near‐Crash Crash‐Relevant

Conflict

Unintentional

Lane Deviation

Baseline

MeanLengthof Longest Glance Away

fromForward Roadway (seconds)

All Events Vehicle 1 At‐Fault Events

1.2

1.0 1.1

1.5

0.8

2.6

1.3 1.2

1.5

0.8

0

1

2

3

4

5

Crash Near‐Crash Crash‐Relevant

Conflict

Unintentional

Lane Deviation

Baseline

MeanLengthof Longest Glance Away

fromForward Roadway (seconds)

All Events Vehicle 1 At‐Fault Events

Figure 45 also shows the mean length of longest glance away from forward roadway for each 

event type across “Vehicle 1 At-Fault” events. A one-way ANOVA found a significant 

difference in the mean length of longest glance away from forward roadway between the five 

event types across “Vehicle 1 At-Fault” events (F(3, 4123) = 66.87, p < 0.0001). Tukey t tests 

indicated that the mean length of longest glance away from forward roadway during 

unintentional lane deviations (1.5 s) was significantly longer than crash-relevant conflicts (1.2 s; 

t(4183) = 4.07, p = 0.001) and baselines (0.8 s; t(4123) = 12.86, p < 0.0001). Crashes (2.6 s) were 

significantly long than crash-relevant conflicts (1.2 s; t(4123) = 2.86, p = 0.035) and baselines (0.8 

s; t(4123) = 3.78, p = 0.002). Near-crashes (1.3 s; t(4123) = 2.77, p = 0.044) and crash-relevant 

conflicts (1.2 s; t(4123) = 9.92, p < 0.0001) had a significantly longer mean length of longest 

glance away from forward roadway than baselines (0.8 s). 

5.5.4.5 Simple Tertiary Tasks 

Figure 46 shows the mean length of longest glance away from forward roadway for each event 

type across “All” and “Vehicle 1 At-Fault” events for any simple tertiary task (i.e., any safetycritical event or baseline epoch with only a simple tertiary task). A one-way ANOVA found a 

significant difference in the mean length of longest glance away from forward roadway between 

140 
the five event types across “All” events (F(4, 2063) = 48.84, p < 0.0001). Tukey t tests indicated 

that the mean length of longest glance away from forward roadway during unintentional lane 

deviations (1.3 s) was significantly longer than crashes (0.1 s; t(2063) = 3.29, p = 0.009), crashrelevant conflicts (0.7 s; t(2063) = 7.64, p < 0.0001) and baselines (0.5 s; t(2063) = 14.35, p < 

0.0001). Crash-relevant conflicts (0.7 s) were significantly longer than baselines (0.5 s; t(2063) = 

6.79, p < 0.0001). 

Figure 46. Graph. Mean Length of Longest Glance Away from Forward Roadway 

by Event Type for Simple Tertiary Tasks 

0.1

0.7 0.7

1.3

0.5

1.7

0.8

1.3

0.5

0

1

2

3

4

5

Crash Near‐Crash Crash‐Relevant

Conflict

Unintentional

Lane Deviation

Baseline

MeanLengthof Longest Glance Away

fromForward Roadway (seconds)

All Events Vehicle 1 At‐Fault Events

0.1

0.7 0.7

1.3

0.5

1.7

0.8

1.3

0.5

0

1

2

3

4

5

Crash Near‐Crash Crash‐Relevant

Conflict

Unintentional

Lane Deviation

Baseline

MeanLengthof Longest Glance Away

fromForward Roadway (seconds)

All Events Vehicle 1 At‐Fault Events

Figure 46 also shows the mean length of longest glance away from forward roadway for each 

event type across “Vehicle 1 At-Fault” events. A one-way ANOVA found a significant 

difference in the mean length of longest glance away from forward roadway between the five 

event types across “Vehicle 1 At-Fault” events (F(3, 2009) = 72.62, p < 0.0001). Tukey t tests 

indicated that the mean length of longest glance away from forward roadway during 

unintentional lane deviations (1.3 s) was significantly longer than crash-relevant conflicts (0.8 s; 

t(2174) = 5.86, p < 0.0001) and baselines (0.5 s; t(2174) = 13.29, p < 0.0001). Near-crashes (1.7 s; 

t(2174) = 2.59, p = 0.048) and crash-relevant conflicts (0.8 s; t(2174) = 6.83, p < 0.0001) had a 

significantly longer mean length of longest glance away from forward roadway than baselines 

(0.5). 

5.5.4.6 Secondary Tasks 

Figure 47 shows the mean length of longest glance away from forward roadway for each event 

type across “All” and “Vehicle 1 At-Fault” events for any secondary task (i.e., any safety-critical 

event or baseline epochs with only a driving-related inattention task). A one-way ANOVA found 

a significant difference in the mean length of longest glance away from forward roadway 

between the five event types across “All” events (F(4, 4704) = 46.15, p < 0.0001). Tukey t tests 

indicated that the mean length of longest glance away from forward roadway during 

141 
unintentional lane deviations (1.5 s) was significantly longer than crash-relevant conflicts (1.1 s; 

t(4704) = 5.98, p < 0.0001) and baselines (1.0 s; t(4704) = 9.64, p < 0.0001). Crashes (2.8 s) had a 

significantly longer mean length of longest glance away from forward roadway than near-crashes 

(1.5 s; t(4704) = 5.17, p < 0.0001), crash-relevant conflicts (1.1 s; t(4,704) = 6.94, p < 0.0001), 

unintentional lane deviations (1.5 s; t(4704) = 5.27, p < 0.0001), and baselines (1.0 s; t(4704) = 7.52,

p < 0.0001). Near-crashes (1.5 s) were significantly longer than crash-relevant conflicts (1.1 s; 

t(4704) = 4.34, p = 0.0001) and baselines (1.0 s; t(4704) = 6.12, p < 0.0001). Crash-relevant conflicts 

(1.1 s) were significantly longer than baselines (1.0 s; t(4704) = 5.24, p < 0.0001). 

Figure 47. Graph. Mean Length of Longest Glance Away from Forward Roadway 

by Event Type for Secondary Tasks 

2.8

1.5

1.1

1.5

1.0

2.8

1.6

1.1

1.5

1.0

0

1

2

3

4

5

Crash Near‐Crash Crash‐Relevant

Conflict

Unintentional

Lane Deviation

Baseline

MeanLengthof Longest Glance Away

fromForward Roadway (seconds)

All Events Vehicle 1 At‐Fault Events

2.8

1.5

1.1

1.5

1.0

2.8

1.6

1.1

1.5

1.0

0

1

2

3

4

5

Crash Near‐Crash Crash‐Relevant

Conflict

Unintentional

Lane Deviation

Baseline

MeanLengthof Longest Glance Away

fromForward Roadway (seconds)

All Events Vehicle 1 At‐Fault Events

Figure 47 also shows the mean length of longest glance away from forward roadway for each 

event type across “Vehicle 1 At-Fault” events. A one-way ANOVA found a significant 

difference in the mean length of longest glance away from forward roadway between the five 

event types across “Vehicle 1 At-Fault” events (F(4, 4495) = 46.80, p < 0.0001). Tukey t tests 

indicated that the mean length of longest glance away from forward roadway during 

unintentional lane deviations (1.0 s) was significantly longer than near-crashes (1.3 s; t(3742) = 

2.79, p < 0.043) crash-relevant conflicts (1.1 s; t(3742) = 8.48, p < 0.0001) and baseline epochs 

(1.0 s; t(3742) = 9.85, p < 0.0001). Crashes (1.8 s) had a significantly longer mean length of 

longest glance away from forward roadway than crash-relevant conflicts (1.1 s; t(3742) = 2.79, p = 

0.043) and baseline epochs (1.0 s; t(3742) = 2.98, p = 0.024). 

5.5.5 Summary 

There were several interesting results from the eye glance analysis; however, the primary finding 

was being involved in a safety-critical event was associated with longer and more frequent 

glances away from the forward roadway. For example, crashes had a mean duration of eyes off 

forward roadway of 2.1 s and 3.5 s for “All” and “Vehicle 1 At-Fault” events, respectively, 

142 
compared to a mean duration of eyes off forward roadway of 1.2 s for baseline (normal) driving. 

This finding clearly shows the importance of drivers maintaining their eyes on the forward 

roadway. However, the results also indicated that drivers with a short mean duration of eyes off 

forward roadway (less than or equal to 0.5 s over a 6-s interval) may not be sufficiently scanning 

the environment. Thus, fixating on the forward roadway increased the risk of being involved in a 

safety-critical event; however, not looking at the forward roadway for longer periods of time 

(i.e., over 1.5 s) was also risky (with very long glances away from the forward roadway, over 2.0 

s, being the most dangerous). One conclusion from this set of findings was that CMV driver 

tasks must not draw the driver’s eyes away from the forward roadway and/or impede the driver’s 

environmental scanning patterns (or situational awareness). 

Several specific tasks stand out as being particularly dangerous. Texting while driving was the 

riskiest task that drivers engaged in during the study. The eye glance results indicate that drivers 

spent 77 percent of their eye glance time looking away from the forward roadway while engaged 

in texting during safety-critical events. More specifically, drivers spent 4.6 s (out of a 6-s 

interval) not looking at the forward roadway when texting while driving during a safety-critical 

event. Drivers spent almost 4 times longer not looking at the forward roadway while texting 

during a safety-critical event, compared to baseline epochs when not texting. This presents a 

significant risk and is an activity that drivers should avoid while driving. To highlight just how 

risky this is, consider that if the truck is traveling at 55 mi/h, and the driver is not looking at the 

forward roadway for 4.6 s (out of the 6-s interval), the truck would travel approximately 370 ft. 

This is equivalent to the truck traveling, essentially “blind,” the length of a football field. It is the 

view of the authors that this activity, in no uncertain terms, should be prevented. 

Texting while driving is not an essential task for CMV drivers and was not prevalent when the 

data was collected. However, manually interacting with a dispatching device is a common task 

CVM drivers engaged in while driving. Study findings suggest this task resulted in eye glance 

results that were particularly dangerous. The analyses for dispatching devices indicated that 

safety-critical events when drivers were manually interacting with dispatching devices 

significantly drew the drivers’ eyes away from the forward roadway. More specifically, drivers 

spent an average of 4.1 s and 4.2 s not looking forward when interacting with a dispatching 

device during “All” and “Vehicle 1 At-Fault” events, respectively. Even during baseline epochs, 

while the driver was interacting with a dispatching device, the mean duration of eyes off forward 

roadway was 3.7 s. While texting may be a relatively novel behavior that is not performed by the 

greater population of CMV drivers, interacting with dispatching devices is a common CMV 

driver activity. 

Other complex tertiary tasks had a dangerously high mean duration of eyes off forward roadway, 

including writing on a notepad, using a calculator, reading a book or newspaper, and looking at a 

map. These complex tertiary tasks had a mean duration of eyes off forward roadway of 

approximately 4 s or more (in a 6-s interval). Dialing a cell phone also resulted in a dangerously 

high mean duration of eyes off forward roadway. More specifically, drivers’ mean duration of 

eyes off forward roadway was 3.8 s while dialing a cell phone compared to 1.2 s for baseline 

driving while not dialing a cell phone. The authors recommend that drivers avoid these tasks 

while driving. 

The analyses from several of the moderate tertiary tasks also resulted in dangerously high mean 

durations of eyes off forward roadway, including personal grooming (3.7 s), reaching for an 

143 
144 

object (2.9 s), and looking back in the sleeper berth (3.4 s). When compared to the mean duration 

of eyes off forward roadway for baseline driving (1 s), each of these tasks or activities involved 

looking away from the forward roadway approximately 3–4 times longer. Another particularly 

interesting finding was the eye glance analysis for CB radio use. CB radio use during baseline 

driving resulted in a mean duration of eyes off forward roadway of 0.9 s, compared to the mean 

duration of eyes off forward roadway during baseline driving without CB radio use of 1.2 s. The 

mean duration of eyes off forward roadway during safety-critical events with CB radio use (1.3 

s) did not significantly differ from baseline epochs where the driver was not using a CB radio 

(1.2 s). Therefore, these results suggest that CB radio use does not significantly draw the driver’s 

eyes away from the primary driving task. 

Eye glance analysis with the simple tertiary tasks indicated that seemingly routine activities, such 

as putting on sunglasses or adjusting the instrument panel, can significantly draw the drivers’ 

eyes away from the forward roadway. It is important to stress to drivers how that activity can be 

risky. However, instrument panel interaction, which was shown to be risky, may be addressed 

through human factors design. Events when the driver was interacting with the instrument panel 

resulted in the driver’s eyes off the forward roadway for 2.6 s and 2.8 s for “All” and “Vehicle 1 

At-Fault” events, respectively, compared to eyes off the forward roadway of 1.1 s for baseline 

driving when the driver was not adjusting the instrument panel. Drivers’ eyes were not looking at 

the forward roadway for 2.0 s during baseline epochs while adjusting the instrument panel. As 

such, it seems that drivers, for whatever reason, were spending too much time adjusting the 

instrument panel during safety-critical events (approximately 0.7 s longer). Perhaps with an 

improved instrument panel design drivers could more quickly make necessary adjustments 

without additional, or substantial, eye draw away from the forward roadway. 

The secondary tasks analysis did not show as many significant results as did the tertiary tasks. 

One reason for this was that there were many more tertiary tasks than secondary tasks. A second 

reason for this was that through video review, it was difficult to distinguish between drivers 

checking mirrors or looking out the side window at a passing object. While data analysts were 

trained to assess if the driver’s behavior was driving-related (e.g., checking the side mirror 

before a lane change) or not driving-related (e.g., observing oncoming traffic across the median), 

in some cases it was difficult to tell exactly where the driver was looking because of the camera 

placement in the truck. This is a limitation with the naturalistic method used to generate the data 

used in this study. 

Analyses focusing on mean number of glances away from the forward roadway and mean length 

of glances away from the forward roadway were consistent with the previous results. Safetycritical events, when compared to baseline driving, tended to have more glances away from 

forward and these glances were generally longer glances away from forward. As shown in Figure 

36, there was a decreasing trend in the mean number of glances away from the forward roadway 

as event severity decreases. Similarly, there was a decreasing trend in the mean length of longest 

glance away from forward roadway as events severity decreased (see Figure 42). Intuitively, this 

makes sense as near-crashes and crash-relevant conflicts may have required an evasive 

maneuver, while there was no evasive maneuver (or at least an ineffective evasive maneuver) 

during a crash. Therefore, the driver was more likely to have been looking forward in nearcrashes and crash-relevant conflicts compared to crashes. 
6. SUMMARY AND CONCLUSIONS 

Research is clear that “driver error” is the predominant contributing factor in crashes. This is true 

for both light vehicles and CMVs. Estimates as to the prominence of driver error in crashes find 

human factors to be noted in as many as 93 percent of light-vehicle crashes (Treat et al., 1977). 

Driver distraction is one type of driver error that is known to be an important contributing factor 

in crashes. Though estimates vary widely depending on the research cited, perhaps the most 

reliable source comes from a naturalistic driving study with light-vehicle drivers that found 

“driver distraction” to be involved in 78 percent of light-vehicle crashes (Klauer et al., 2006). 

The scientific community now has a solid understanding of the negative consequences of 

distraction through a plethora of research directed at driver distraction, so much so that specific 

distracters, such as cell phone dialing, are now illegal in many states. However, despite the 

knowledge base that has grown with regard to light-vehicle driver distraction, relatively few 

research studies have been directed at assessing driver distraction in CMVs. Filling this 

knowledge gap was the primary goal of the current study. 

As “driver distraction” was the focus of the current study, three research questions were raised 

and addressed. Briefly, these questions asked: 

 What types of distraction tasks (or behaviors) do CMV drivers engage in? And, are these 

tasks risky leading to involvement of safety-critical events? 

 Do environmental driving conditions impact the engagement of tasks? 

 What is the impact of distraction tasks on drawing the driver’s eyes away from the 

forward roadway? 

Previous sections of this report detailed the data preparation and analyses that were conducted to 

answer these questions and summaries were presented at the end of each research question to 

highlight key findings. The remainder of this chapter will provide a summary and conclusions 

from this study and offer recommendations for moving forward. 

6.1 RISK ASSOCIATED WITH DISTRACTING TASKS 

Odds ratios were calculated to identify tasks that were high risk; that is, tasks that were 

associated with increased likelihood of being involved in a safety-critical event compared to 

baseline or uneventful driving. Odds of occurrence were defined as the probability of event 

occurrence (safety-critical event) divided by the probability of non-occurrence (baseline epoch). 

These probability estimates were conditioned on the presence/absence of the behavior of interest 

and then compared via ratios. For a given task, an odds ratio of 1.0 indicated the outcome was 

equally likely to occur given the condition (i.e., equally likely to occur in the safety-critical event 

data as in the baseline, uneventful driving data). An odds ratio greater than 1.0 indicated the 

outcome was more likely to occur given the condition, and odds ratios of less than 1.0 indicated 

the outcome was less likely to occur (Pedhazur, 1997). When considering odds ratios, it was also 

important to look at calculated CLs. Along with an odds ratio statistic, LCLs and UCLs were 

calculated. To interpret the odds ratio, the range of the LCL and UCL must be considered and 

ranges that did not include 1.0 were considered statistically significant (a 95 percent confidence 

interval was used in the current study). 

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Table 77 shows the results from the analyses that included “All” events. As detailed in the body 

of the report, tasks were analyzed individually, but grouped based on level of complexity (Klauer 

et al., 2006). Odds ratios, along with LCLs and UCLs, are shown in Table 77. Odds ratios greater 

than 1.0 that have an LCL and UCL range that does not include 1.0 indicate the task is risky 

(compared to baseline epochs). As shown in Table 77, the most risky behavior identified was 

“text message on cell phone,” with a significant odds ratio of 23.2 (as the LCL and UCL range 

does not include 1.0). This means that drivers who text message while driving were 23.2 times 

more likely to be involved in a safety-critical event, compared to a baseline epoch, than if they

were not text messaging while driving. 

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Table 77. Odds Ratios and 95% Confidence Intervals to Assess Likelihood of a Safety-Critical 

Event While Engaging In Tasks for “All” Events 

Task Odds 

Ratio LCL UCL 

Complex Tertiary Task 

Text message on cell phone 23.24* 9.69 55.73 

Other—Complex 

(e.g., cleaning side mirror, rummaging through a grocery bag) 10.07* 3.10 32.71 

Interact with/look at dispatching device 9.93* 7.49 13.16 

Write on pad, notebook, etc. 8.98* 4.73 17.08 

Use calculator 8.21* 3.03 22.21 

Look at map 7.02* 4.62 10.69 

Dial cell phone 5.93* 4.57 7.69 

Read book, newspaper, paperwork, etc. 3.97* 3.02 5.22 

Moderate Tertiary Task 

Use/reach for other electronic device 6.72* 2.74 16.44 

Other—Moderate 

(e.g., opening a pill bottle to take medicine, exercising in the cab) 5.86* 2.84 12.07 

Personal grooming 4.48* 2.01 9.97 

Reach for object in vehicle 3.09* 2.75 3.48 

Look back in sleeper berth 2.30* 1.30 4.07 

Talk or listen to hand-held phone 1.04 0.89 1.22 

Eating 1.01 0.83 1.21 

Smoking-related behavior—reaching, lighting, extinguishing 0.60* 0.40 0.89 

Talk or listen to CB radio 0.55* 0.41 0.75 

Look at outside vehicle, animal, person, object, or undetermined 0.54* 0.50 0.60 

Talk or listen to hands-free phone 0.44* 0.35 0.55 

Simple Tertiary Task 

Put on/remove/adjust sunglasses or reading glasses 3.63* 2.37 5.58 

Adjust instrument panel 1.25* 1.06 1.47 

Remove/adjust jewelry 1.68 0.44 6.32 

Other—Simple 

(e.g., opening and closing driver’s door) 2.23 0.41 12.20 

Put on/remove/adjust hat 1.31 0.69 2.49 

Use chewing tobacco 1.02 0.51 2.02 

Put on/remove/adjust seat belt 1.26 0.60 2.64 

Talk/sing/dance with no indication of passenger 1.05 0.90 1.22 

Smoking-related behavior—cigarette in hand or mouth 0.97 0.82 1.14 

Drink from a container 0.97 0.72 1.30 

Other personal hygiene 0.67* 0.59 0.75 

Bite nails/cuticles 0.45* 0.28 0.73 

Interact with or look at other occupant(s) 0.35* 0.22 0.55 

Secondary Task 

Look at left-side mirror/out left window 1.09* 1.01 1.17 

Look at right-side mirror/out right window 0.95 0.86 1.05 

Check speedometer 0.32* 0.28 0.38 

 * Asterisk indicates a significant odds ratio. These ratios are also shown in bold. 

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Along with texting, several other tasks had significantly high odds ratios. Interacting with a 

dispatching device (OR = 9.9) and dialing a cell phone (OR = 5.9) were two noteworthy complex 

tasks associated with substantially elevated risk in being involved in a safety-critical event. 

Reaching for objects—both electronic devices such as video cameras (OR = 6.72) or other 

objects (OR = 3.1)—was also noteworthy because of their common occurrence as found in the 

PAR analysis. 

One noteworthy finding from the analyses was the result for cell phone use. As indicated above, 

reaching for or dialing a cell phone were associated as high-risk tasks. However, talking or 

listening on a hand-held phone was found to have an odds ratio that was not significantly 

different than 1.0 (thus, it did not elevate the likelihood of being involved in a safety-critical 

event); this finding was consistent with Klauer et al. (2006). Furthermore, talking or listening on 

a hands-free phone provided a significant protective effect (OR = 0.4). A similar significant 

protective effect was found for using a CB radio (OR = 0.6). One hypothesis for these results is 

that reaching for a phone and dialing a phone, like texting, requires manual manipulation (i.e., 

hand off wheel) and substantial visual attention to complete the task. This visual attention is 

directed away from the forward roadway (as found in eye glance analysis above) such that the 

driver is not effectively, or safely, operating the CMV. Listening and talking, on the other hand, 

allows drivers to maintain their eyes on the road; however this hypothesis does not consider 

“gaze concentration” (Reagan et al., 2009) and “cognitive distraction” which, as noted 

previously, has been associated with driving performance decrement (Redelmeier & Tibshirani, 

1997; Goodman et al., 1999; Strayer & Johnston, 2001; Harbluk et al., 2002; Strayer et al., 2003; 

Patten et al., 2004); though it is important to note that these were not naturalistic studies. In 

addition, it could be that other performance decrements not assessed in this study (e.g., speed 

variability) may be affected by talking, though results from a recent naturalistic study with lightvehicle drivers suggests that such performance decrements would not be found (Sayer et al., 

2007). The bottom line is that for safety-critical events, as defined and recorded in the current 

study, talking on devices (including cell phones, both hand-held and hands-free, and CB radios) 

did not increase the risk of being involved in a safety-critical event. 

6.2 POPULATION RISK FOR DISTRACTING TASKS 

Odds ratios and CLs only inform part of the story; that is, which tasks are shown to increase the 

likelihood of involvement in a safety-critical event. The other part of the story considers the 

frequency of occurrence of each task (i.e., which task, if removed, would provide the largest 

reduction in safety-critical events). For example, tasks that are rare occurrences, even though 

they might be risky, may not have a significant impact on the population. 

Table 78 shows the results from the PAR analysis for the tertiary and secondary tasks with an 

odds ratio greater than 1.0. As shown in Table 78, tasks are ordered from largest PAR percentage 

to smallest PAR percentage. Specific tasks with the largest PAR percentage included: reaching 

for an object (PAR = 7.6), interacting with a dispatching device (PAR = 3.1), and dialing a cell 

phone (PAR = 2.5). Why were the PAR percentages for these tasks greater than the other tasks? 

The reason was that they were commonly performed tasks. Text messaging, on the other hand, 

though it had a very high odds ratio, was a task performed infrequently by drivers in the current 

study, thus it does not have a high PAR percentage. However, this does not mean that it should 

148 
be ignored. On the contrary, it suggests that as more drivers text message while driving, the 

frequency of safety-critical events is likely to increase. 

Table 78. Population Attributable Risk and 95% Confidence Intervals 

for Driver Tasks Across All Events 

TASK PAR 

Percentage LCL UCL 

Complex Tertiary Task 27.46 27.24 27.67 

Interact with/look at dispatching device 3.13 2.84 3.42 

Dial cell phone 2.46 2.02 2.91 

Read book, newspaper, paperwork, etc. 1.65 0.96 2.34 

Look at map 1.08 0.48 1.68 

Text message on cell phone 0.67 0.29 1.04 

Write on pad, notebook, etc. 0.56 -0.16 1.28 

Use calculator 0.22 -1.00 1.43 

Other—Complex 

(e.g., cleaning side mirror, rummaging through a 

grocery bag) 

0.18 -0.99 1.35 

Moderate Tertiary Task 11.77 11.32 12.23 

Reach for object in vehicle 7.64 7.27 8.02 

Other—Moderate 

(e.g., opening a pill bottle to take medicine, 

exercising in the cab) 

0.32 -0.92 1.55 

Use/reach for other electronic device 0.23 -1.10 1.56 

Personal grooming 0.21 -1.58 2.00 

Look back in sleeper berth 0.23 -2.24 2.70 

Talk or listen to hand-held phone 0.18 -1.29 1.64 

Eating 0.02 -1.80 1.83 

Simple Tertiary Task 5.96 5.20 6.73 

Adjust instrument panel 0.82 -0.47 2.11 

Put on/remove/adjust sunglasses or reading 

glasses 

0.62 -0.56 1.80 

Talk/sing/dance with no indication of passenger 0.23 -1.12 1.59 

Put on/remove/adjust hat 0.06 -4.85 4.98 

Use chewing tobacco 0.00 -6.75 6.76 

Put on/remove/adjust seat belt 0.04 -5.84 5.92 

Remove/adjust jewelry 0.03 -7.89 7.95 

Other—Simple 

(e.g., opening and closing driver’s door) 

0.02 -7.57 7.62 

Secondary Task 11.71 11.29 12.13 

Look at left-side mirror/out left window 2.25 1.77 2.75 

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A driver interacting with a dispatching device was a commonly performed task in the current 

study. However, as indicated in the high odds ratio (OR = 9.9) and PAR percentage (PAR = 3.1), 

this is an issue that the authors recommend be addressed. The authors recommend that drivers 

not use these dispatching devices while driving, fleet safety managers stress to drivers the 

dangers of using this device while driving, and designers consider redesigning these devices for 

ease of use. An in-vehicle inventory (Llaneras & Singer, 2002) found that most technologies did 

not offer a “lockout” feature to prevent the driver from using the device while the vehicle was in 

motion. Instead, they found that limiting the number of available menu options and the use of 

auditory output may help to prevent the driver from taking his/her eyes off the forward roadway 

as frequently. 

Reaching for an object was another task with a high odds ratio (OR = 3.1) and PAR percentage 

(PAR = 7.6). Again, the authors recommend fleet managers inform drivers that this common task 

should be avoided. Dialing a cell phone also had a high odds ratio (OR = 5.9) and PAR 

percentage (PAR = 2.5). 

6.3 VISUAL DEMAND FOR DISTRACTING TASKS 

The eye glance analyses that were conducted on the various tasks provided the “why” for the 

findings in the odds ratio analysis. Repeatedly, the eye glance analyses indicated that tasks that 

draw the driver’s eyes away from the forward roadway were those with highest odds ratios for 

risk. For example, texting while driving, which had the highest odds ratio of 23.2, also had the 

longest duration of eyes off road (4.6 s over a 6-s interval). As noted above, this equates to a 

driver traveling the length of an entire football field, at 55 mi/h, without looking at the roadway 

during the 6-s interval. Other high visual attention tasks that reduced attention to the forward 

roadway included those tasks that involved the driver interacting with some type of technological 

device, such as: dispatching device (4.1 s), cell phone dialing (3.8 s), and calculator (4.4 s). 

Technology-related tasks were not the only tasks with high visual demands away from the 

forward roadway. Non-technology, commonly performed daily activities with high visual 

demands included: writing (4.2 s), reading a book/newspaper/other (4.3 s), looking at a map (3.9 

s), and reaching for an object (2.9 s). The authors recommend that fleet safety managers be 

aware of these tasks and educate drivers on their associated risks. 

6.4 LENGTH OF GLANCES 

CMV drivers’ total eyes off forward roadway time was 2.1 s prior to the onset of a crash, 1.7 s 

prior to the onset of a near-crash, 1.6 s prior to the onset of a crash-relevant conflict, 1.2 s during 

the baseline epoch. In comparison, Klauer et al. (2006) reported that light-vehicle drivers’ total 

mean eyes off forward roadway time was 1.8 s prior to the onset of a crash, 1.3 s prior to the 

onset of a near-crash, 1.1 s prior to the onset of a crash-relevant conflict, 0.9 s during the baseline 

epoch. 

One of the analyses calculated the odds ratios of the total eyes off forward roadway time for five 

different time durations. Table 79 illustrates the odds ratios across “All” events in each of the 

five different time durations: the total eyes off forward roadway time was measured over a 6-s 

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interval for events and epochs. Not surprising, longer glances over 1.5 s were associated with 

high risk (OR = 1.3) and very long glances over 2 s had the highest risk (OR = 2.9). These 

findings (i.e., that long eye glance durations away from the forward roadway increase risk) were 

consistent with previous light-vehicle results. For example, Klauer et al. (2006) reported that 

light-vehicle drivers were 2.2 times more likely to be involved in a crash/near-crash (compared 

to a baseline epoch) when total time eyes off forward roadway was greater than 2 s. 

Table 79. Odds Ratios and 95% Confidence Intervals for All Events to Assess Likelihood of a 

Safety-Critical Event While Eyes Off Forward Roadway 

Total Eyes Off Forward Roadway Odds 

Ratio LCL UCL 

Frequency 

of SafetyCritical 

Events 

Frequency 

of 

Baseline 

Epochs 

Less than or equal to 0.5 s 1.36* 1.16 1.58 268 1,537 

Greater than 0.5s but less than or equal to 1.0 s 0.91 0.80 1.03 434 3,712 

Greater than 1.0s but less than or equal to 1.5 s 1.07 0.94 1.23 343 2,483 

Greater than 1.5s but less than or equal to 2.0 s 1.29* 1.12 1.49 317 1,903 

Greater than 2.0 s 2.93* 2.65 3.23 1,504 3,989 

* Asterisk indicates a significant odds ratio. These ratios are also shown in bold. 

An additional significant result was found for very short eyes off forward roadway time (less 

than or equal to 0.5 s). Klauer et al. (2006) found a similar trend with light-vehicle drivers in the 

100-Car Study; however, the odds ratio was not significant. As shown in table 79, a significant 

odds ratio was found when the total eyes off forward roadway time was less than or equal to 0.5 

s (OR = 1.4). One possible explanation was that the scanning behavior performed by CMV 

drivers was likely to be different than the scanning behavior of light-vehicle drivers. More 

specifically, CMV drivers are taught to monitor their environment continually and regularly scan 

their mirrors. Moreover, large trucks have many blind spots and it can be difficult for CMV 

drivers to locate other vehicles in their mirrors. It is possible that these mirror-checking 

behaviors lasted longer than 0.5 s in the current study and more complex tasks required many 

short duration glances. There is some support for this contention in the eye glance analyses 

results as the mean length of longest glance for secondary tasks (e.g., checking mirrors) was 

greater than 0.5s: 2.8 s for crashes, 1.5 s for near-crashes, 1.1 s for crash-relevant conflicts and 

1.5 s for unintentional lane deviations (see Figure 47). Also, the mean number of glances away 

from the forward roadway was 2.7 for near-crashes, 3.1 for crash-relevant conflicts, and 3.2 for 

unintentional lane deviations when complex tertiary tasks were considered compared to 1.3 for 

crashes, 1.6 for near-crashes, 1.7 for crash-relevant conflicts and 2.3 for unintentional lane 

deviations when moderate tertiary tasks were considered (see Figure 38 and Figure 39). It is also 

possible that the significant finding for glances under 0.5 s was because drivers may have been in 

high-load situations, such as following closely behind a lead vehicle, which would require longer 

and more frequent glances to monitor the forward roadway. This situation would likely result in 

more safety-critical events and may help explain the significant odds ratio. Further analysis 

would be required to test these hypotheses by conducting a more detailed eye glance analysis 

with drivers who rarely scanned the driving environment and/or mirrors (i.e., primarily focused 

on the forward roadway). Such an analysis could investigate the risk implications of not 

151 
regularly scanning the driving environment. At this point, it is an interesting finding that invites 

further exploration. 

6.5 FINAL CONSIDERATIONS 

One of the objectives of this study was to compare results between this CMV study and the 

Klauer et al. (2006) light-vehicle study. Though a few result comparisons have been described, 

perhaps the most important finding, common across both studies, is that driver distraction is 

prevalent in both light vehicles and CMV operations. It is difficult to make clear comparisons 

across studies because of the caveats noted previously, including: 

 Mirror check as a distraction type and the expected mirror use differences between lightvehicle and CMV drivers. 

 Different data collection time frames. 

 Different distraction types. 

 Small number of crashes in the CMV study. 

Nonetheless, a key take-away when a side-by-side study comparison is made is that driver 

distraction is an important contributing factor in safety-critical events for both light-vehicle 

drivers and CMV drivers. 

The current study resulted in a number of important findings related to driver distraction and 

CMV driver safety. Because this is one of the first studies to focus on CMV driver distraction, it 

will be important to conduct follow-on research to assess the robustness of these findings. As 

outlined, many of the results were consistent with previous distraction studies with light-vehicle 

drivers. However, some results (e.g., the high risk associated with short glances) may be novel to 

CMV operations. 

6.6 STUDY LIMITATIONS AND NEXT STEPS 

Before listing recommendations that are based on the results of this study, some study limitations 

should be discussed. It is important to keep these limitations in mind when considering the 

results. First, because the data used in this study was collected naturalistically, and not in a 

controlled environment, the “cognitive distraction” effects of driver behaviors could not be easily 

determined. Past research has found that cognitive demands impact the driver’s ability to focus 

on the driving task while talking on a cell phone (Redelmeier & Tibshirani, 1997; Goodman et 

al., 1999; Strayer & Johnston, 2001; Harbluk et al., 2002; Strayer et al., 2003; Patten et al., 

2004), though this has not been shown in naturalistic driving research. In the current study, given 

the video camera placement, “visual distraction” and whether the driver was looking forward or 

not during task performance was more readily measurable. It may be possible to investigate 

cognitive distraction in a follow-up data mining effort with this naturalistic data set by looking at 

changes in eye scanning behavior as a function of task performance. A reduction in normal 

scanning patterns may indicate “cognitive distraction.” Also, vehicle speed (e.g., speed variation 

from the posted speed limit) while performing the task could be evaluated. However, based on 

152 
research by Sayer et al. (2007), it should not be expected that findings from controlled studies 

will always be replicated in real-driving environments. For example, unlike the driving simulator 

studies referenced above, Sayer et al. also found benign cell phone effects in a naturalistic study 

with light-vehicle drivers. 

A second limitation in the current study was the lack of continuous audio data. While the results 

found that manual dialing was the riskiest part of using a cell phone (talking on a hand-held 

phone was not significantly risky and talking on a hands-free phone decreased the risk of being 

involved in a safety-critical event), it was not possible to analyze dialing a hands-free cell phone 

as audio data was not available to hear the driver use a voice-activated phone feature. 

A third limitation of the current study is the small sample size of some of the individual 

distractions. While approximately 200 drivers participated over 3 million miles of driving, some 

distraction types did not occur frequently. Due to small sample sizes of some distractions, there 

were no statistical approaches that could be used to examine interactions (e.g., text messaging 

and rain). It is believed that as future CMV naturalistic studies are conducted and the naturalistic 

data set increases, larger samples of distractions may enable the investigation of interaction 

effects. While the current study resulted in many interesting findings, it is important that the 

reader keep these study limitations in mind when interpreting the following recommendations. 

Based on these study limitations, additional follow-on efforts and analyses could be conducted 

with these combined naturalistic CMV data sets including, as noted, investigating the effects of 

cognitive distraction on cell phone conversations and other secondary/tertiary tasks. For 

example, changes in eye glance scanning and vehicle speed changes (or difference from the 

posted speed limit) could be evaluated to assess cognitive distraction. Additionally, future 

research could explore in more detail the impact of texting on the driving risk. For example, 

measures including task completion time, eyes-off-road time, and hands-off-wheel-time (for the 

entire task rather than for the 6-s interval used in the current study) could be analyzed to provide 

a more complete picture of texting while driving. A similar evaluation of dispatching device and 

other tertiary tasks could also be conducted that focuses on the task itself (e.g., task duration) 

rather than limiting the evaluation to a window of time preceding a safety-critical event. 

Finally, it is important to highlight that some results of the current study and other recent 

naturalistic driving studies (e.g., Klauer et al., 2006; Sayer et al., 2007) are at odds with results 

obtained from simulator studies (e.g., Beede & Kass, 2006; Strayer et al., 2003) and future 

research should be conducted to explore the reasons why such study results often differ from 

studies conducted in actual driving conditions (i.e., the full context of the driving environment). 

It may be, as Sayer et al. note; that controlled investigations cannot account for driver choice 

behavior and risk perception as it actually occurs in real-world driving. If this assessment is 

accurate, the generalizability of simulator findings, at least in some cases, may be greatly limited 

outside of the simulated environment. 

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6.7 SUMMARY FINDINGS AND RECOMMENDATIONS 

The following findings and recommendations by the authors to address driver distraction in 

CMV operations were formulated through a review of this study. These findings and 

recommendations provide a summarized list of critical issues and are ordered from general 

recommendations (e.g., maintain eyes on forward roadway) to more specific recommendations 

(e.g., no texting). These recommendations focus on improving CMV safety by reducing driver 

distraction and are intended to provide key take-aways for fleet-safety managers on how they 

might improve safety by applying the findings from the current study. The authors found and 

recommended that: 

 Fleet safety managers engage and educate their drivers, and discuss the importance of 

being attentive and not engaging in distracting tasks or behaviors. Even routine types of 

behaviors (e.g., reaching for an object, putting on sunglasses, or adjusting the instrument 

panel) can distract and may lead to a safety-critical event. 

 Fleet safety managers develop policies to minimize or eliminate the use of in-vehicle 

devices while driving. The authors also urge fleet safety managers to be cognizant of 

devices that drivers may bring in the truck cab and use while driving. These may seem 

innocuous (e.g., calculator), but may increase crash risk, if used while driving. 

 Drivers not use dispatching devices while driving and that fleet safety managers educate 

drivers on the danger of interacting with these devices while driving. Similar to manually 

dialing a cell phone, if drivers must interact with a dispatching device, the authors 

recommend that drivers do so only when the truck is stopped. 

 Drivers not text while driving. This is a relatively new phenomenon, but data from the 

current study clearly show an increased risk when drivers text while driving. 

 Drivers not manually dial cell phones while driving. If a call must be made, the authors 

suggest that drivers pull off the road to a safe area, and then dial to make the phone call. 

Another option, requiring further study, is the use of voice-activated, hands-free dialing, 

which would allow the driver to maintain eyes on the forward roadway. However, this 

approach may have implications for “cognitive distraction” (though visual distraction 

would be expected to be reduced). 

 Drivers not read, write, or look at maps while driving. What may seem like quick, 

commonly performed tasks, such as reading, writing, and looking at maps, were found to 

significantly draw visual attention away from the forward roadway. These activities, 

which may be integral to the driver’s job, are not integral to operating the vehicle and the 

authors recommend that such tasks never be performed while the vehicle is on and in 

motion. 

 Drivers not be prohibited from talking on a cell phone or CB radio as this was not found 

to increase risk. Regarding cell phones, the findings from the current study clearly 

indicated that manual device interaction, and the associated high eyes off forward road 

time, was the key factor to increased risk. Though “visual distraction” is foremost in 

manual device interaction, potential “cognitive distraction” of talking/listening was not 

measured in the current study. However, based on the analysis of safety-critical events 

from the current study, talking or listening were not risk factors. 

154 
155 

 Designers of dispatching devices consider the increased risk associated with using their 

devices and work to develop more user-friendly interfaces that do not draw the driver’s 

eyes from the forward roadway. Possible solutions include a hands-free interface and/or 

blocking manual use while the vehicle is in motion. 

 Designers of instrument panels consider the increased risk of adjusting panel controls. 

The authors suggest that designs be intuitive, user-friendly, and not require long glances 

away from the forward roadway. 

 Further research be undertaken into the protective effects of performing certain tasks. 

Identifying the characteristics of tasks that had protective effects may lead to safety 

countermeasures. 

APPENDIX A. DATA CODING DIRECTORY 

EVENT VARIABLES 

Event Classification 

Note: Categories adapted from Dingus et al. (2006) and Hickman et al. (in press). Crash: Tire 

Strike category only used in Hickman et al. This variable is extremely subjective and is 

determined using the best judgment of an analyst. 

 01 = Crash. Any contact with an object, either moving or fixed, at any speed. Includes 

other vehicles, roadside barriers, objects on or off of the roadway, pedestrians, cyclists, or 

animals. 

 02 = Crash: Tire Strike. Any contact with an object, either moving or fixed, at any speed 

in which kinetic energy is measurably transferred or dissipated where the contact occurs 

on the truck’s tire only. No damage occurs during these events (e.g., a truck is making a 

right turn at an intersection and runs over the sidewalk/curb with a tire). 

 03 = Near-Crash. Any circumstance that requires a rapid, evasive maneuver (e.g., hard 

braking, steering) by the subject vehicle or any other vehicle, pedestrian, cyclist, or 

animal, in order to avoid a crash. 

 04 = Crash-Relevant Conflict. Any circumstance that requires a crash-avoidance 

response on the part of the subject vehicle, any other vehicle, pedestrian, cyclist, or 

animal that was less severe than a rapid evasive maneuver (as defined above), but greater 

in severity than a normal maneuver. A crash-avoidance response can include braking, 

steering, accelerating, or any combination of control inputs. 

Date 

Note: Will be automatically obtained through GPS data. 

Time

Note: Will be automatically obtained through GPS data. 

Day of Week 

Note: Using a calendar, please enter the day of week for which the event occurred. 

Vehicles/Non-Motorists Involved 

Note: For some events (e.g., those involving transient encroachment into an oncoming lane), it 

will be difficult to decide whether the event should be considered a one or two vehicle event. 

Consider the event a two-vehicle event if the crash resulting from the incident would likely have 

involved two vehicles, and/or if either driver’s maneuvers were influenced by the presence of the 

other vehicle (e.g., if DV1 maneuvered to avoid V2). Consider the event a one-vehicle event if 

the presence of other vehicles presented no immediate threat and had no effect on DV1’s 

maneuvers or behaviors. 

157 
 00 = Not applicable (baseline epoch). 

 01 = 1 vehicle (Subject vehicle only or subject vehicle + object). 

 02 = 2 vehicles. 

 03 = 3 vehicles. 

 04 = 4 or more vehicles. 

 05 = Subject vehicle + pedestrian. 

 06 = Subject vehicle + pedal cyclist. 

 07 = Subject vehicle + animal. 

 08 = Other. 

Vehicle/Non-Motorist 2 Type 

Note: Highly abridged version of GES V5, Body Type; above codes do not match GES codes. 

Examples of heavy vehicles are shown in the tables below. 

 00a = Not applicable (baseline epoch). 

 00b = Not applicable (single vehicle event—no object). 

 01 = Automobile. 

 02 = Van (minivan or standard van). 

 03 = Pickup truck. 

 03a = SUV (includes Jeep). 

ach) 

traight truck (includes panel truck, U-Haul truck). 

oped. 

lice, fire, EMS = in service). 

nimal. 

vehicle event with relevant object). 

 04 = Bus (transit or motor co

 05 = School bus. 

 06 = Single-unit s

 07 = Tractor-trailer. 

 08 = Motorcycle or m

 09 = Emergency vehicle (po

 10 = Vehicle pulling trailer (other than tractor-trailer). 

 11 = Other vehicle type. 

 12 = Pedestrian. 

 13 = Pedacyclist.

 14 = Deer. 

 15 = Other a

 16 = Object (single 

 99 = Unknown vehicle type. 

158 
Relevant Object 

Note: Please choo

constituted a crash

se the most relevant object; (i.e., one that was struck in a crash or which 

 threat) for near-crashes and crash-relevant conflicts. 

nt but no relevant object; e.g., shoulder only). 

pact attenuator/crash cushion. 

cture (e.g., abutment). 

gitudinal barrier (e.g., “Jersey Barrier”). 

or support (e.g., sign, light). 

rant. 

ery or bush. 

hang—see below]. 

quipment (e.g., fork lift, pallets). 

ck by top of truck/trailer): 

nch. 

ss. 

 00a = Not applicable (baseline epoch). 

 00b = Not applicable (single vehicle eve

 00c = Not applicable (two vehicle (or more) event, pedestrian, animal, etc.). 

 01 = Parked motor vehicle. 

Fixed objects: 

 02 = Building. 

 03 = Im

 04 = Bridge stru

 05 = Guardrail. 

 06 = Concrete traffic barrier or other lon

 07 = Post, pole, 

 08 = Culvert or ditch. 

 09 = Curb. 

 10 = Embankment. 

 11 = Fence.

 12 = Wall. 

 13 = Fire hyd

 14 = Shrubb

 15 = Tree [not over

 16 = Boulder. 

 17 = Loading dock. 

 18 = Loading e

 19 = Cargo. 

Overhanging objects (only if struck or potentially stru

 20 = Tree bra

 21 = Overhanging part of sign or post. 

 22 = Bridge/overpa

 23 = Building. 

 24 = Telephone wires. 

159 
Non-fixed objects: 

 25 = Vehicle parts, including tire parts. 

. 

ons in italics are not A06 codes. 

 26 = Spilled cargo. 

 27 = Dead animal in roadway. 

 28 = Broken tree limbs or other tree/shrub parts. 

 29 = Trash/debris. 

 30 = Construction barrel. 

 31 = Construction cone. 

Other: 

 98 = Other. 

 99 = Unknown object hit

Note: GES A06, First Harmful Event. Opti

Vehicle/Non-Motorist 2 Position (in Relation to V1) 

Note: The vehicle in Figure 48 represents the subject vehicle (V1, the truck). The relative 

position of Vehicle 2 (in relation to Vehicle 1) is coded for the time in which the Critical Event 

occurs (i.e., the event creating the crash risk). Vehicles in adjacent left lane are coded J, I, H, or 

G depending on position. Vehicles in adjacent right lane are coded B, C, D or E depending on 

position. 

Please also code the position of animals, pedestrians, pedacyclists and objects. 

 00a = Not applicable (baseline epoch) 

 00b = Not applicable (single vehicle event—no object) 

 K = Top of vehicle 

Figure 48. Vehicle/Non-Motorist 2 Position 

160 
Vehicle 1 Pre-Event Movement 

Note: LTCCS Variable 4 with expanded choices for 8 and 9. The Pre-Event Movement is 

considered to be outside of the Critical Crash Envelope (Figure 49). For Baseline epochs, the 

primary movement of the vehicle during the epoch is coded. 

 01 = Going straight (and not known to be engaged in movements listed below). 

 02 = Decelerating in traffic lane. 

 03 = Accelerating in traffic lane. 

 04 = Starting in traffic lane. 

 05 = Stopped in traffic lane. 

 06 = Passing or overtaking another vehicle. 

 07 = Disabled or parked in travel lane. 

 08a = Leaving a parking position, moving forward. 

 08b = Leaving a parking position, backing. 

 09a = Entering a parking position, moving forward. 

 09b = Entering a parking position, backing. 

 10 = Turning right. 

 11 = Turning left. 

 12 = Making a u-turn. 

 13 = Backing up (other than parking). 

 14 = Negotiating a curve. 

 15 = Changing lanes. 

 16 = Merging. 

 17 = Successful avoidance maneuver to a previous critical event. 

 98 = Other. 

 99 = Unknown. 

Figure 49. Critical Crash Envelope 

161 
Vehicle 2 Pre-Event Movement 

Note: LTCCS Variable 4 with expanded choices for 8 and 9. The Pre-Event Movement is 

considered to be outside of the Critical Crash Envelope (Figure 49). 

 00a = Not applicable (baseline epoch). 

 00b = Not applicable (single vehicle event). 

 01 = Going straight (and not known to be engaged in movements listed below). 

 02 = Decelerating in traffic lane. 

 03 = Accelerating in traffic lane. 

 04 = Starting in traffic lane. 

 05 = Stopped in traffic lane. 

 06 = Passing or overtaking another vehicle. 

 07 = Disabled or parked in travel lane. 

 08a = Leaving a parking position, moving forward. 

 08b = Leaving a parking position, backing. 

 09a = Entering a parking position, moving forward. 

 09b = Entering a parking position, backing. 

 10 = Turning right. 

 11 = Turning left. 

 12 = Making a u-turn. 

 13 = Backing up (other than parking). 

 14 = Negotiating a curve. 

 15 = Changing lanes. 

 16 = Merging. 

 17 = Successful avoidance maneuver to a previous critical event. 

 98 = Other. 

 99 = Unknown. 

Vehicle 1 Critical Pre-Crash Event 

Note: This variable is coded for both vehicles in a two-vehicle incident. However, the Critical 

Reason (see above), is coded for only one vehicle. For consistency with the Accident Type 

variable (20), lane edges between travel lanes and non-travel lanes (e.g., shoulders) are 

considered road edges. Unlike the Accident Type variable, however, you should code the actual 

precipitating event and should not project or extrapolate the event. LTCCS Variable 5. 

 00 = Not applicable (baseline epoch). 

162 
THIS VEHICLE (V1) LOSS OF CONTROL DUE TO: 

 01 = Blow out or flat tire. 

 02 = Stalled engine. 

 03 = Disabling vehicle failure (e.g., wheel fell off). 

 04 = Non-disabling vehicle problem (e.g., hood flew up). 

 05 = Poor road conditions (wet road, puddle, pot hole, ice, etc.). 

 06 = Traveling too fast for conditions. 

 07 = Jackknife event. 

 08 = Cargo shift. 

 09 = Braking. 

 10 = Steering. 

 18 = Other cause of control loss. 

 19 = Unknown cause of control loss. 

THIS VEHICLE (V1) TRAVELING 

 20 = Toward or over the lane line on left side of travel lane. 

 21 = Toward or over the lane line on right side of travel lane. 

 22 = Toward or off the edge of the road on the left side. 

 23 = Toward or off the edge of the road on the right side. 

 24 = End departure. 

 25 = Turning left at intersection. 

 26 = Turning right at intersection. 

 27 = Crossing over (passing through) intersection. 

 27a = This vehicle stopped. 

 28 = This vehicle decelerating. 

 28a = This vehicle accelerating. 

 29 = Unknown travel direction. 

OTHER MOTOR VEHICLE (V2) IN LANE 

 50 = Other vehicle stopped. 

 51 = Traveling in same direction with lower steady speed. 

 52 = Traveling in same direction while decelerating. 

 53 = Traveling in same direction with higher speed. 

163 
 54 = Traveling in opposite direction. 

 55 = In crossover. 

 56 = Backing. 

 59 = Unknown travel direction of other motor vehicle in lane. 

OTHER MOTOR VEHICLE (V2) ENCROACHING INTO LANE 

 60 = From adjacent lane (same direction)—toward or over left lane line. 

 61 = From adjacent lane (same direction)—toward or over right lane line. 

 62 = From opposite direction - toward or over left lane line. 

 63 = From opposite direction - toward or over right lane line. 

 64 = From parking lane. 

 65 = From crossing street, turning into same direction. 

 66 = From crossing street, across path. 

 67 = From crossing street, turning into opposite direction. 

 68 = From crossing street, intended path not known. 

 70 = From driveway, turning into same direction. 

 71 = From driveway, across path. 

 72 = From driveway, turning into opposite direction. 

 73 = From driveway, intended path not known. 

 74 = From entrance to limited access highway. 

 78 = Encroachment by other vehicle - details unknown. 

PEDESTRIAN, PEDALCYCLIST, OR OTHER NONMOTORIST 

 80 = Pedestrian in roadway. 

 81 = Pedestrian approaching roadway. 

 82 = Pedestrian - unknown location. 

 83 = Pedalcyclist or other nonmotorist in roadway. 

 84 = Pedalcyclist or other nonmotorist approaching roadway. 

 85 = Pedalcyclist or other nonmotorist - unknown location. 

OBJECT OR ANIMAL 

 87 = Animal in roadway. 

 88 = Animal approaching roadway. 

 89 = Animal - unknown location. 

164 
 90 = Object in roadway. 

 91 = Object approaching roadway. 

 92 = Object - unknown location. 

OTHER 

 98 = Other critical pre-crash event. 

 99 = Unknown. 

Vehicle 2 Critical Pre-Crash Event 

Note: This variable is coded for both vehicles in a two-vehicle incident. However, the Critical 

Reason (see below), is coded for only one vehicle. LTCCS Variable 5. 

 00a = Not applicable (baseline epoch). 

 00b = Not applicable (single vehicle event). 

THIS VEHICLE (V2) LOSS OF CONTROL DUE TO: 

 01 = Blow out or flat tire. 

 02 = Stalled engine. 

 03 = Disabling vehicle failure (e.g., wheel fell off). 

 04 = Non-disabling vehicle problem (e.g., hood flew up). 

 05 = Poor road conditions (wet road, puddle, pot hole, ice, etc.). 

 06 = Traveling too fast for conditions. 

 07 = Jackknife event. 

 08 = Cargo shift. 

 09 = Braking. 

 10 = Steering. 

 18 = Other cause of control loss. 

 19 = Unknown cause of control loss. 

THIS VEHICLE (V2) TRAVELING 

 20 = Toward or over the lane line on left side of travel lane. 

 21 = Toward or over the lane line on right side of travel lane. 

 22 = Toward or off the edge of the road on the left side. 

 23 = Toward or off the edge of the road on the right side. 

 24 = End departure. 

165 
 25 = Turning left at intersection. 

 26 = Turning right at intersection. 

 27 = Crossing over (passing through) intersection. 

 27a = This vehicle stopped. 

 28 = This vehicle decelerating. 

 28a = This vehicle accelerating. 

 29 = Unknown travel direction. 

OTHER MOTOR VEHICLE (V1) IN LANE 

 50 = Other vehicle stopped. 

 51 = Traveling in same direction with lower steady speed. 

 52 = Traveling in same direction while decelerating. 

 53 = Traveling in same direction with higher speed. 

 54 = Traveling in opposite direction. 

 55 = In crossover. 

 56 = Backing. 

 59 = Unknown travel direction of other motor vehicle in lane. 

OTHER MOTOR VEHICLE (V1) ENCROACHING INTO LANE 

 60 = From adjacent lane (same direction)—toward or over left lane line. 

 61 = From adjacent lane (same direction)—toward or over right lane line. 

 62 = From opposite direction - toward or over left lane line. 

 63 = From opposite direction - toward or over right lane line. 

 64 = From parking lane. 

 65 = From crossing street, turning into same direction. 

 66 = From crossing street, across path. 

 67 = From crossing street, turning into opposite direction. 

 68 = From crossing street, intended path not known. 

 70 = From driveway, turning into same direction. 

 71 = From driveway, across path. 

 72 = From driveway, turning into opposite direction. 

 73 = From driveway, intended path not known. 

 74 = From entrance to limited access highway. 

 78 = Encroachment by other vehicle - details unknown. 

166 
PEDESTRIAN, PEDALCYCLIST, OR OTHER NONMOTORIST 

 80 = Pedestrian in roadway. 

 81 = Pedestrian approaching roadway. 

 82 = Pedestrian - unknown location. 

 83 = Pedalcyclist or other nonmotorist in roadway. 

 84 = Pedalcyclist or other nonmotorist approaching roadway. 

 85 = Pedalcyclist or other nonmotorist - unknown location. 

OBJECT OR ANIMAL 

 87 = Animal in roadway. 

 88 = Animal approaching roadway. 

 89 = Animal - unknown location. 

 90 = Object in roadway. 

 91 = Object approaching roadway. 

 92 = Object - unknown location. 

OTHER 

 98 = Other critical pre-crash event. 

 99 = Unknown. 

Vehicle 1 Critical Reason for the Critical Event 

Note: “This vehicle” will always be used for the vehicle being coded. Note that vehicle-related 

factors will rarely be apparent to analysts. Analysts will choose one critical reason that appears to 

be the main critical reason. LTCCS Variable 6 with revisions in italics. 

 000a = Not applicable (baseline epoch). 

 000b = Critical reason not coded to this vehicle. 

DRIVER RELATED FACTOR: Critical Non-Performance Errors 

 100 = Sleep, that is, actually asleep. 

 101 = Heart attack or other physical impairment of the ability to act. 

 107 = Drowsiness, fatigue, or other reduced alertness (not asleep). 

 108 = Other critical non-performance. 

 109 = Unknown critical non-performance. 

167 
DRIVER RELATED FACTOR: Recognition Errors 

 110 = Inattention (i.e., daydreaming). 

 111 = Internal distraction. 

 112 = External distraction. 

., failed to look, looked but did not see). 

cision Errors 

users). 

. 

peed. 

ected actions (close proximity for 2 or 

n on head lamps. 

te evasive action (e.g., braking only not braking and steering; release 

 128a = Aggressive driving behavior: Intimidation: any behavior emitted by a driver while 

rm to another person. 

vior: excessive 

eaving through 

anging, and 

 error. 

 which). 

rrors 

 113 = Inadequate surveillance (e.g

 118 = Other recognition error. 

 119 = Unknown recognition error. 

DRIVER RELATED FACTOR: De

 120 = Too fast for conditions (e.g., for safe vehicle control or to be able to respond to 

unexpected actions of other road 

 121 = Too slow for traffic stream

 122 = Misjudgment of gap or other’s s

 123 = Following too closely to respond to unexp

more seconds). 

 124 = False assumption of other road user’s actions. 

 125a = Apparently intentional sign/signal violation. 

 125b = Illegal U-turn. 

 125c = Other illegal maneuver. 

 126 = Failure to tur

 127 = Inadequa

accelerator only instead of braking). 

driving that is intended to cause physical or psychological ha

 128b = Aggressive driving behavior: Wanton, neglectful or reckless beha

risky driving behaviors performed without intent to harm others, such as w

traffic, maneuvering without signaling, running red lights, frequent lane ch

tailgating. 

 138 = Other decision error. 

 139 = Unknown decision

 140 = Apparent recognition or decision error (unknown

DRIVER RELATED FACTOR: Performance E

 141 = Panic/Freezing. 

 142 = Overcompensation. 

168 
 143 = Poor directional control (e.g., failing to control vehicle with skill ordinarily 

expected). 

 148 = Other performance error. 

 149 = Unknown performance error. 

 199 = Type of driver error unknown. 

VEHICLE RELATED FACTOR 

 200 = Tires/wheels failed. 

 201 = Brakes failed. 

 202 = Steering failed. 

 203 = Cargo shifted. 

 204 = Trailer attachment failed. 

 205 = Suspension failed. 

 206 = Lights failed. 

 207 = Vehicle related vision obstructions. 

 208 = Body, doors, hood failed. 

 209 = Jackknifed. 

 298 = Other vehicle failure. 

 299 = Unknown vehicle failure. 

ENVIRONMENT RELATED FACTOR: Highway Related 

 500 = Signs/signals missing. 

 501 = Signs/signals erroneous/defective. 

 503 = View obstructions by roadway design. 

 504 = View obstructed by other vehicles crash circumstance. 

 505 = Road design - roadway geometry (e.g., ramp curvature). 

 506 = Road design - sight distance. 

 507 = Road design - other. 

 508 = Maintenance problems (potholes, deteriorated road edges, etc.). 

 509 = Slick roads (low friction road surface due to ice, loose debris, any other cause). 

 518 = Other highway-related condition. 

ENVIRONMENT RELATED FACTOR: Weather Related 

 521 = Rain, snow [Note: code loss-of-control as 509]. 

169 
 522 = Fog. 

 523 = Wind gust. 

 528 = Other weather-related condition. 

ENVIRONMENT RELATED FACTOR: Other 

 530 = Glare. 

 531 = Blowing debris. 

 532 = Animal in roadway (no driver error). 

 533 = Pedestrian or pedalcyclist in roadway (no driver error). 

 534 = Object in roadway (no driver error). 

 538 = Other sudden change in ambience. 

 999 = Unknown reason for critical event. 

Vehicle 2 Critical Reason for the Critical Event 

Note: The remaining elements for DV2 are either maneuvers or conditions visible from outside 

the vehicle (e.g., most of the decision error choices) or reasonable general inferences. Analysts 

will choose one critical reason that appears to be the main critical reason. 

 000a = Not applicable (baseline epoch). 

 000b = Not applicable (single vehicle event). 

 000c = Critical reason not coded to this vehicle. 

DRIVER RELATED FACTOR 

 109 = Apparent critical non-performance [includes any apparent driver impairment]. 

DRIVER RELATED FACTOR: Recognition Errors 

 119 = Apparent recognition error. 

DRIVER RELATED FACTOR: Decision Errors 

 120 = Too fast for conditions (e.g., for safe vehicle control or to be able to respond to 

unexpected actions of other road users). 

 121 = Too slow for traffic stream. 

 123 = Following too closely to respond to unexpected actions (close proximity for 2 or 

more seconds). 

 124 = False assumption of other road user’s actions. 

 125a = Apparently intentional sign/signal violation. 

 125b = Illegal U-turn. 

170 
 125c = Other illegal maneuver. 

ps. 

nd steering; release 

accelerator only instead of braking). 

on: any behavior emitted by a driver while 

 to cause physical or psychological harm to another person. 

vior: Wanton, neglectful or reckless behavior: excessive 

formed without intent to harm others, such as weaving through 

and 

rror. 

 Errors 

e error. 

nknown. 

OR 

ed. 

ure. 

ilure. 

D FACTOR: Highway Related 

ehicles crash circumstance. 

etry (e.g., ramp curvature). 

eteriorated road edges, etc.). 

e to ice, loose debris, any other cause). 

 126 = Failure to turn on head lam

 127 = Inadequate evasive action (e.g., braking only not braking a

 128a = Aggressive driving behavior: Intimidati

driving that is intended

 128b = Aggressive driving beha

risky driving behaviors per

traffic, maneuvering without signaling, running red lights, frequent lane changing, 

tailgating. 

 138 = Other decision error. 

 139 = Apparent, unknown decision e

DRIVER RELATED FACTOR: Performance

 149 = Apparent performanc

 199 = Type of driver error u

VEHICLE RELATED FACT

 200 = Tires/wheels fail

 201 = Brakes failed. 

 298 = Apparent other vehicle fail

 299 = Unknown vehicle fa

ENVIRONMENT RELATE

 500 = Signs/signals missing. 

 501 = Signs/signals erroneous/defective. 

 503 = View obstructions by roadway design. 

 504 = View obstructed by other v

 505 = Road design - roadway geom

 506 = Road design - sight distance. 

 507 = Road design - other . 

 508 = Maintenance problems (potholes, d

 509 = Slick roads (low friction road surface du

 518 = Other highway-related condition. 

171 
ENVIRONMENT RELATED FACTOR: Weather Related 

 521 = Rain, snow [Note: code loss-of-control as 509]. 

 522 = Fog. 

 523 = Wind gust. 

 528 = Other weather-related condition. 

ENVIRONMENT RELATED FACTOR: Other 

 530 = Glare. 

 531 = Blowing debris. 

 538 = Other sudden change in ambience. 

 999 = Unknown reason for critical event. 

Which vehicle is considered to be at fault? 

Note: The “at fault” vehicle is defined as the vehicle with the assigned Critical Reason with a 

few exceptions: 

 Animal/pedestrian/pedal cyclist in roadway is considered to be “Vehicle 2” at fault. 

 Object in roadway is considered to be “No fault.” 

 All environmental factors are considered to be “No fault.” 

 00 = Not applicable (baseline epoch). 

 01 = Vehicle 1 (subject vehicle). 

 02 = Vehicle 2 (other vehicle, animal, pedestrian, pedacyclist). 

 03 = No fault (object, environmental). 

 04 = Unknown. 

Vehicle 1 Attempted Avoidance Maneuver 

Note: LTCCS Variable 7 and also GES V27, Corrective Action Attempted. “Released gas pedal” 

elements added because this evasive maneuver by subject drivers is sometimes observed. 

 00a = Not applicable (baseline epoch) 

 01 = No avoidance maneuver 

 02 = Braking (no lockup or lockup unknown) 

 03 = Braking (lockup) 

 05 = Releasing brakes 

 06 = Steered to left 

 07 = Steered to right 

172 
 08a = Braked and steered to left (no lockup or lockup unknown) 

 08b = Braked and steered to left (lockup) 

 09a = Braked and steered to right (no lockup or lockup unknown) 

 09b = Braked and steered to right (lockup) 

 10 = Accelerated 

 11 = Accelerated and steered to left 

 12 = Accelerated and steered to right 

 13 = Released gas pedal without braking 

 14 = Released gas pedal (without braking) and steered to left 

 15 = Released gas pedal (without braking) and steered to left 

 98 = Other actions 

 99 = Unknown if driver attempted any corrective action 

Vehicle 2 Attempted Avoidance Maneuver 

Note: LTCCS Variable 7 and also GES V27, Corrective Action Attempted. “Released gas pedal” 

elements added because this evasive maneuver by subject drivers is sometimes observed. 

 00a = Not applicable (baseline epoch) 

 00b = Not applicable (single vehicle event) 

 01 = No avoidance maneuver 

 02 = Braking (no lockup or lockup unknown) 

 03 = Braking (lockup) 

 05 = Releasing brakes 

 06 = Steered to left 

 07 = Steered to right 

 08a = Braked and steered to left (no lockup or lockup unknown) 

 08b = Braked and steered to left (lockup) 

 09a = Braked and steered to right (no lockup or lockup unknown) 

 09b = Braked and steered to right (lockup) 

 10 = Accelerated 

 11 = Accelerated and steered to left 

 12 = Accelerated and steered to right 

 13 = Released gas pedal without braking 

 14 = Released gas pedal (without braking) and steered to left 

173 
 15 = Released gas pedal (without braking) and steered to left 

 98 = Other actions 

 99 = Unknown if driver attempted any corrective action 

Vehicle 1 Accident Type 

Note: Since this variable “includes intent,” analysts should project likely scenario roles for 

incidents where outcomes are not definite. In other words, if the trigger-related event had 

resulted in a crash, what would the crash scenario be? When specific scenarios cannot be 

projected, use the “Specifics Unknown” choices (e.g., 5, 10, 16, 33, etc.). 

 888 = Not applicable (baseline epoch) 

Additional clarifications: 

– Drive off road codes (e.g., 01 and 06) are used when a vehicle has crossed, or is 

projected to cross, a roadside delineation such as a lane edge line (going onto the 

shoulder or median), curb, or the edge of the pavement. This includes scenarios 

involving parked vehicles and stationary objects if those objects are outside of the 

roadway delineation (e.g., on an unpaved shoulder). 

– Forward impact codes (e.g., 11, 12) are used when the objects are in the travel lane or 

when there is no lane edge delineation as described above. Thus, a scenario involving 

a parked vehicle on the pavement where there is no lane edge delineation is coded 12. 

– If Single Driver codes (01-16) are used for V1, the V2 Accident Type code is 00b. 

– For left-side lane departures into the oncoming traffic lane, code 64/65 if the lateral 

encroachment is less than a few feet. Code 50/51 only if the lateral encroachment was 

sufficient to create a significant risk of a head-on crash. 

Vehicle 2 Accident Type 

 888 = Not applicable (baseline epoch) 

 777 = Not applicable (single vehicle event) 

Please note, 999 and 00 which are used to denote single vehicle event in other variables, 

are actual accident types, please use 777 for single vehicle event. 

Note: Since this variable “includes intent,” analysts should project likely scenario roles for 

incidents where outcomes are not definite. In other words, if the trigger-related event had 

resulted in a crash, what would the crash scenario be? When specific scenarios cannot be 

projected, use the “Specifics Unknown” choices (e.g., 5, 10, 16, 33, etc.). Figure 50 illustrates 

the Accident Types. 

Additional clarifications: 

– Single Driver codes (01-16) are not applicable to V2. If a Single Driver code (01-16) 

was used for V1, the V2 Accident Type code is 00b. 

– For left-side lane departures into the oncoming traffic lane, code 64/65 if the lateral 

encroachment is less than a few feet. Code 50/51 only if the lateral encroachment was 

sufficient to create a significant risk of a head-on crash. 

174 
Figure 50. Description of the Accident Types 

Source: Thieriez, Radja, and Toth (2002) 

175 
Vehicle 1 Incident Type 

Numbers listed below are shown in Table 80. 

 888 = Not applicable (baseline epoch) 

 01/02 = Aborted lane change 

 05/06/07/08 = Backing in roadway 

 09/10 = Clear path for emergency vehicle 

 68/69 = Close proximity to turning vehicle 

 11/12 = Conflict between merging and existing traffic 

 65 = Conflict with animal/pedestrian/pedacyclist/object in roadway 

 66 = Conflict with animal/pedestrian/pedacyclist/object on side of road 

 13/14 = Conflict with oncoming traffic 

 72/73 = Conflict with through traffic 

 15/16 = Exit then re-entrance onto roadway 

 17/18 = Following too closely 

 19/20 = Improper lane change 

 21/22/23 = Improper passing 

 24/25 = Improper u-turn 

 26/27 = Lane change without sufficient gap 

 28/29 = Lane drift 

 30/31 = Late braking for stopped/stopping traffic 

 32/33 = Lateral deviation of through vehicle 

 34/35 = Left turn without clearance 

 36/37 = Merge out of turn (before lead vehicle) 

 38/39/40 = Merge without sufficient gap 

 41/42 = Obstruction in roadway 

 43/44 = Proceeding through red traffic signal 

 45/46 = Roadway entrance without clearance 

 47/48 = Slow speed 

 49/50 = Slow upon passing 

 51/52/53 = Sudden braking in roadway 

 54/55 = Through traffic does not allow lane change 

 56/57/58 = Through traffic does not allow merge 

 59/60 = Turn without sufficient warning 

176 
 61/62 = Turn/exit from incorrect lane 

 63/64 = Wide turn into adjacent lane 

 67 = Other single vehicle event 

 70/71 = Vehicle passes through intersection without clearance 

 99 = Unknown 

Vehicle 2 Incident Type 

 888 = Not applicable (baseline epoch) 

 00 = Not applicable (single vehicle event) 

 01/02 = Aborted lane change 

 05/06/07/08 = Backing in roadway 

 09/10 = Clear path for emergency vehicle 

 68/69 = Close proximity to turning vehicle 

 11/12 = Conflict between merging and existing traffic 

 13/14 = Conflict with oncoming traffic 

 72/73 = Conflict with through traffic 

 15/16 = Exit then re-entrance onto roadway 

 17/18 = Following too closely 

 19/20 = Improper lane change 

 21/22/23 = Improper passing 

 24/25 = Improper u-turn 

 26/27 = Lane change without sufficient gap 

 28/29 = Lane drift 

 30/31 = Late braking for stopped/stopping traffic 

 32/33 = Lateral deviation of through vehicle 

 34/35 = Left turn without clearance 

 36/37 = Merge out of turn (before lead vehicle) 

 38/39/40 = Merge without sufficient gap 

 41/42 = Obstruction in roadway 

 43/44 = Proceeding through red traffic signal 

 45/46 = Roadway entrance without clearance 

 47/48 = Slow speed 

 49/50 = Slow upon passing 

177 
 51/52/53 = Sudden braking in roadway 

 54/55 = Through traffic does not allow lane change 

 56/57/58 = Through traffic does not allow merge 

 59/60 = Turn without sufficient warning 

 61/62 = Turn/exit from incorrect lane 

 63/64 = Wide turn into adjacent lane 

 70/71 = Vehicle passes through intersection without clearance 

 99 = Unknown 

178 
Table 80. Descriptions and Diagrams of Incident Types 

Incident Type Description Illustration 

Aborted Lane ge Chan A driver tries to make a lane change into 

 already a vehicle 

ehicle). The driver 

ve back into the 

a lane where there is

(driver doesn’t see v

has to brake and mo 1 2

original lane. 

Backing i dway A driver backs the vehicle while on a 

roadway in order to maneuver around an 

n Roa Obsta

obstacle ahead on the roadway. 

Obstacle

5

6

7

8

Clear Path fo Emergency r A driver is traveling ahead of an 

 fire 

ehicle pass. 

Vehicle emergency vehicle (e.g., ambulance,

truck) and has to move to the side of the 

road to let the emergency v

Emergency 

Vehicle

9

10

9

10

Close Proxim y to Turning 

Vehicle 

The lead vehicle is making a right/left 

turn or changing lanes to the right/left,

it

and the following vehicle comes close to 

the rear of the lead vehicle as they pass. 

68

69

179 
Incident Type Description Illustration 

Conflict Betw en Merging e Drivers entering and/or exiting a 

and/or Exiting Traffic roadway, causing a conflict. 

11 12

Conflict With 

Animal/Pedestrian/Pedal 

cyclist/Obj n Roadway roadway (on the pavement) and 

makes contact with it, or perform

ect i

A vehicle approaches an animal/ 

pedestrian/pedal cyclist/object in the 

either 

s an 

evasive maneuver in order to avoid it.

Object/ 

Animal

65

Conflict With A vehicle approaches an animal/ 

Animal/Pede dal strian/Pe ng 

cyclist /Object on Side of 

pedestrian/pedal cyclist/object (includi

a guardrail) on the side of the road and 

Roadway either makes contact with it, or performs 

an evasive maneuver in order to avoid it. 

Object/ 

Animal

66

Conflict With Oncoming 

Traffic 

A driver is approaching on

(e.g., on an undivided

coming traffic 

 road, at an 

to maneuver back 

id an 

. 

13

14

13

intersection) and has 

into the correct lane to avo

oncoming vehicle

13

14

13

Conflict with hrough T A vehicle starts to turn (right or left) at an 

 a 

e 

Traffic intersection, but has to brake to avoid

conflict with traffic passing through th

72

73

73

intersection. 

180 
Incide t Type n Description Illustration 

Exit Then Re ntrance -E A driver ex

ay

its a roadway then crosses a 

Onto Roadw solid white line to re-enter. 

15

16

Following To sely A driver does not allow adequ

spacing between their vehicle

o Clo ate 

 and the 

lead vehicle (e.g., tailgating). 17

18

Improper Lane Change A driver makes an improper lane change 

with regard to another vehicle (e.g., does 

not use blinker, changes lanes behind 

another vehicle then does not let vehicle 

change lanes, changes lanes across 

multiple lanes, etc.) 

19

20

Improper Passing A driver passes another vehicle when it 

is illegal or unsafe (e.g., passing across 

a double yellow line or without clearance 

from oncoming traffic). 22

21

23

Improper U-turn A driver makes a u-turn in the middle of 

the road (over the double yellow line) 

and blocks traffic in the opposite 

direction. 

24

25

181 
Incident Type Description Illustration 

Lane Change Without A driver enters ane without 

Sufficient Gap 

 an adjacent l

allowing adequate space between the 

driver’s vehicle and the vehicle 

ahead/behind it. 

26

27

Lane Drift A driver drifts into an adjacent lane 

without intention to make a lane change.

28 29

Late Braking (and/or 

steering) for Stopped/ 

Stopping Traffic 

A driver fails to slow in advance for 

stopped or stopping traffic and must 

brake and/or steer abruptly. 

Stationary/ 

Slowing

Late Braking

30

31

Stationary/ 

Slowing

Late Braking

30

31

Lateral Deviation of 

Through Vehicle 

A driver has substantial lateral deviation 

of a through vehicle. Vehicle may or may 

not deviate from the lane. 32

33

Left Turn Without 

Clearance 

A driver turns left without adequate 

clearance from either oncoming through 

traffic or cross traffic from the left. The 

driver crosses another driver’s path while 

entering an intersecting roadway. 

34

35

182 
Incident Type Description Illustration 

Merge Out of Turn (Before 

Lead Vehicle) 

A driver merges onto a roadway b

the lead vehicle. The lead ve

efore 

hicle must 

wait for the merged vehicle to pass 

before it is safe to enter the main 

highway. 

36

37

Merge Without Sufficient A driver merges into traffic without a 

Gap sufficient gap to either the front or back 

of one or more vehicles. 38

39

40

Obstruction in Roadway A stationary object blocks through traffic, 

such as traffic that is backed up or an 

animal in the roadway. 

41

42

Proceeding Through Red A driver fails to respond to a red traffic 

Traffic Signal signal, conflicting with a vehicle 

proceeding through the intersection 

legally. 43

44

Roadway Entrance A driver turns onto a roadway without 

Without Clearance adequate clearance from through traffic. 

45 46

183 
Incident Type Description Illustration 

Slow Speed A driver is traveling at a much slower 

speed than the rest of the traffic, causing 

following traffic to pass the slow vehicle 

to avoid a conflict. 

Slow

Spee

er 

d

47

48

Slow

Spee

er 

d

47

48

Slow Upon Passing A driver moves in front of another 

then slows, causing the second (passed

vehicle to slow as well, or to go around 

the first vehicle. 

vehicle 

) 

49

50

Sudden Braking in A driver is traveling ahead of another 

Roadway vehicle and brakes suddenly and 

improperly in the roadway for traffic, a 

traffic light, etc., causing the following 

vehicle to come close to their vehicle or 

to also brake suddenly. 

Sudden 

Braking

51

52

53

51

52

53

Through Traffic Does Not A driver is trying to make a lane change 

Allow Lane Change (with their turn signal on) but traffic in the

adjacent lane will not allow the lane 

change to be completed. 

Tu

Si

rn 

gnal On 54

55

Tu

Si

rn 

gnal On 54

55

Through Traffic Does Not Through traffic obstructs (either 

r.

Allow Merge intentionally or unintentionally) a driver 

from entering the roadway or from 

performing any type of merging behavio

56

57

58

184 
Incident Type Description Illustration 

Turn Without Sufficient

Warning l 

A driver slows and turns without using a 

turn signal or without using a turn signa

in advance. 

59

60

Turn/Exit From Incorrect 

Lane 

right turn from the left lane instead of the 

right lane). 

A driver turns onto a side road from the 

incorrect lane (e.g., a driver makes a 

61

62

Wide Turn Into Adjacent 

Lane 

djacent 

lane when turning. Traffic in the adjacent 

lane may be moving in the same or 

opposite direction. 

A vehicle partially enters an a

63

64

Vehicle Passes Through 

Intersection Without 

Clearance 

A vehicle passes through an intersection 

(signal or non-signal) without adequate 

clearance from through traffic. 

70

71

185 
Incident Type Description Illustration 

Other Single Vehicle 

Event 

A vehicle is involved in a single vehicle 

event. For example runs off the side of 

the road without a threat of hitting a 

object. 

fixed 67 

Unable to Determine It is not possible to determine which 

vehicle is at fault, therefore, it is not 

possible to assign an incident type to the 

event. 

99 

Source: Hanowski, Wierwille, G

Hickman, et al. (in press) 

arness, and Dingus (2000); Hanowski, Olson, Hickman, and Dingus (2006); and 

186 
DRIVER/VEHICLE 1 VARIABLES 

Dr 1 (DV1) is alw (i

Driver Safety Belt Worn? 

 00 = No 

 01 = Yes 

 02 = Unknown 

Po er Rat

 00 = Yes 

 01 = No—wearing sun

 02 = No—not enough video 

 03 = No—cannot see driver’s eyes 

Driver 1 Vision Obscured b

No obstruction 

 01 = Rain, snow, smok

 02 = Reflected glare, s

 03 = Curve or hill 

 04 = Building, billboard, or other design features (includes sig

eg

ehicle (

 07 = Parked vehicle 

 08 = Splash or spray of passing vehicle [any other vehicle] 

 09 = Inadequate defrost or defog system 

 10 = Inadequate lighting system [includes vehicle/object in dark area] 

er

 12 = Mirrors 

 13 = Head restraints 

 14 = Broken or improperly cleaned windshield 

 15 = Fog 

 16 = Other vehicle or object in blind spot 

 97 = Vision obscured—no details 

 98 = Other obstruction 

 99 = Unknown whether vision was obstructed 

Note: GES Variable D4. Element 16 added because of relevance to large trucks. 

iver/Vehicle ays the study subject driver/vehicle .e., the truck or truck driver). 

ssible to do Observ ing of Drowsiness? 

glasses 

y 

e, sand, dust 

unlight, headlights 

 00 = 

ns, embankment) 

 05 = Trees, crops, v

 06 = Moving v

etation 

including load) 

 11 = Obstruction int ior to vehicle 

187 
Driver 1 Potentially Distracting Driver Behaviors 

No rs m .0

trig ere are more than in

Similar to GES Variable D7 (Driver Distracted By), with expansions of many elem

capture direct observations. 

Table 81. Potentially Distracting Driver Behavi

te: Up to four behavio

ger. If th

ay be coded for the 5.0 s prior to and 1

 four, select the ones occurring closest

 s after the event onset 

 time to the trigger. 

ents to 

ors 

Distraction Type and Behavior Description of Behavior 

Internal Distraction: 

Person or Object 

T cat

pa er 

 e

 t

ng 

ehavior or is whistling. 

alk/sing/dance with no indi

sseng

ion of Driver appears to be vocalizing

passenger, to self, or singing to

category are instances where th

b

ither to an unknown 

he radio. Also, in this 

e driver exhibits danci

Interact with or look at other 

occupant(s) 

Driver is talking to a passenger r’s seat 

or in the sleeper berth that can be id n 

encroaching into the came rly 

looking and talking to the passe

sitting in the passenge

entified by the perso

ra view or the driver is clea

nger. 

Reach for object in vehicle Driver may or may not remo

roadway to reach for an object inside the vehicle. Objects 

ited t

i if 

ve attention from the forward 

may include, but are not lim

drink, map, paperwork. This opt

the driver is not engaging in any

time. 

o, cell phone, CB, food, 

on should only be marked

other behavior at the same 

Look back in Sleeper Berth s body to look behind him/h

Berth. 

Driver turn er into the Sleeper 

Use calculator Driver uses hand-held calculator g 

at and may reach for object. 

. Assumes driver is lookin

Read book, newspaper, paperwork, 

etc. 

Driver reads a book, newspaper,

visible in the driver’s hands, on the driv

driver’s steering wheel, or on the passenger seat. Assumes 

c

 paperwork, etc, which is 

er’s lap, on the 

driver is looking at and may rea h for object. 

Lo isib

e driver’s 

passenger seat. Assumes driver i

for object. 

ok at map Driver reads a map which is v

the driver’s lap, on th

le in the driver’s hands, on 

steering wheel, or on the 

s looking at and may reach 

Write on pad, notebook, etc. Driver writes on some kind

driver’s hands, on the driver’s la

wheel, or on the passeng

at and may reach for object. 

 of paper which is visible in the 

p, on the driver’s steering 

er seat. Assumes driver is looking 

Put on/remove/adjust seat belt Driver puts on, removes, or adjusts his/her seat belt. 

Assumes driver is looking at and may reach for object. 

Put on/remove/adjust sunglasses or 

reading glasses 

Driver puts on, removes, or adjusts his/her sunglasses. 

Assumes driver is looking at and may reach for object. 

Put on/remove/adjust hat Driver puts on, removes, or adjusts his/her hat. Assumes 

driver is looking at and may reach for object. 

188 
Distraction Type and Behavior Description of Behavior 

Internal Distraction: 

Electronic Devices 

Dial cell phone ay also include answering 

the phone or hanging up the phone, if the driver presses a 

key during this time. Assumes driver is looking at and may 

Driver dials a cell phone. This m

reach for object. 

Talk or listen to hand-held phone Driver holds a hand-held phone to ear, appears to be talking 

and/or listening. 

Talk or listen to hands-free phone Driver talks or listens to a hands-free phone. This is apparent 

by an earpiece in the driver’s ear. 

Te e. 

Driver is focusing on the cell phone for an extended amount 

of time while continuously pressing keys. Assumes driver is 

looking at and may reach for object. 

xt message on cell phone Driver appears to be text messaging using a cell phon

Talk or listen to CB radio Driver talks or listens to a CB radio. Assumes driver is 

looking at and may reach for object. 

Interact with/look at dispatching device Driver interacts with or looks at a dispatching device. The 

driver usually keeps the device on the passenger seat or on 

the floor between the two seats and holds the device on 

his/her lap or steering wheel while in use. Assumes driver is 

looking at and may reach for object. 

Look at/handle DAS Driver looks at/handles data acquisition system (DAS). This 

may include a system box under the driver’s seat and, video 

cameras mounted on the windshield. Assumes driver is 

looking at and may reach for object. 

Use/reach for other device Driver reaches for or uses an alternate electronic device. 

Assumes driver is looking at and may reach for object. 

Internal Distraction: 

Dining 

Eating Driver eats with, or without, a utensil (i.e., fork or spoon). This 

also includes the driver opening a food bag or anything 

closely related to eating just prior to or after the trigger. 

Assumes driver is looking at and may reach for object. 

Drink from a container Driver drinks from a container. This also includes the driver 

opening/closing a drink container or anything closely related 

to drinking just prior to or after the trigger. Assumes driver is 

looking at and may reach for object. 

Internal Distraction: 

Smoking-related 

Smoking-related behavior—reaching, 

lighting, extinguishing 

Driver is reaching (ashing), lighting, or extinguishing a 

cigarette. May include behaviors such as driver reaching for 

a lighter or reaching for a pack of cigarettes. Assumes driver 

is looking at and may reach for object. 

Smoking-related behavior—cigarette in 

hand or mouth 

Driver has a cigarette in hand or mouth. 

Use chewing tobacco Driver is using chewing tobacco. This may include putting 

tobacco into mouth or spitting into container. Assumes driver 

is looking at and may reach for object. 

189 
Distraction Type and Behavior Description of Behavior 

Internal Distraction: 

Grooming 

Personal grooming Driver is grooming him/herself. This may include 

combing/fixing hair, applying make-up, shaving, and brushing 

teeth. Assumes driver is looking at and may reach for object. 

Bite nails/cuticles Driver is biting nails and/or cuticles. Assumes driver is 

looking at hands. 

Remove/adjust jewelry Driver is removing or adjusting jewelry. This may include, 

watch, bracelet, necklace or earrings. Assumes driver is 

looking at and may reach for object. 

Other personal hygiene Driver is conducting some kind of other personal hygiene. 

This may include rubbing eyes/face, scratching face/neck, or 

picking nose. 

Internal Distraction: 

Vehicle Related 

Adjust instrument panel Driver is adjusting something on the instrument panel. This 

may include, radio, climate controls, head lights, and other 

switches to the front and right of the driver. Assumes driver is 

reaching for and/or looking at the instrument panel while 

adjusting. 

Driver 1 Actions/Factors/Behaviors Relating to Event 

 four factors believed to have relevance to the occurrence of the 

actors). If there are more than four, select the four most 

ave been 

d in no order of importance. 

gardless of speed limit; does 

 limit) 

otional state 

tracted (mark only if related to event, not just because a potential 

g slowly; below speed limit or in relation to other traffic 

e) 

unsafe passing 

Note: You may code up to

incident; (e.g., as contributing f

important. This variable was used in Dingus et al. (2006) although some new elements h

added. Analysts code all that apply an

 00a = Not applicable (baseline epoch) 

 00b = None coded 

 01 = Apparent excessive speed for conditions or location (re

not include tailgating, unless above speed

 02 = Drowsy, sleepy, asleep, fatigued, other reduced alertness 

 03 = Angry 

 04 = Other em

 05 = Inattentive or dis

distraction is marked) 

 07 = Drivin

 08 = Illegal passing (i.e., across double lin

 09 = Passing on right 

 10 = Other improper or 

190 
 11a = Cutting in, too close in front of other vehicle 

 16 = Aggressive driving, specific, directed menacing actions 

 17 = Aggressive ed menacing actions 

t overtaking [include oncoming lane] 

too close 

tion

 Failed to signal, or improp

 21 = Improper turn: wide right t

 cut corner o

 23 = Other improper turning 

ing, did not 

 25 = Improper backing, other 

 26 = Improper start from parked

 27 = Disregarded officer or wat 

ntly did not see signal 

nal violation, intentiona

to be

31 = Stop sign violation, appare

 32 = Stop sign violation, intenti

top sign violation, “rollin

 34 = Other sign (e.g., Yield) vio

, Yield) vio

 36 = Other sign violation 

 37 = Non-signed crossing violat roadway) 

r in relat

r ve

t-of-way error in relat lure 

 vehicle prior 

 11b = Cutting in at safe distance but then decelerated, causing conflict 

 12 = Cutting in, too close behind other vehicle

 13 = Making turn from wrong lane (e.g., across lanes) 

 14 = Did not see other vehicle during lane change or merge 

 15 = Driving in other vehicle’s blind spot 

 driving, other; i.e., reckless driving without direct

 18 = Wrong side of road, no s partial or full drift into

 19 = Following 

 19a = Inadequate evasive ac

 20 = er signal 

urn 

 22 = Improper turn: n left turn 

 24 = Improper back see 

 position 

chman

 28 = Signal violation, appare

 29= Sig lly ran red light 

 30 = Signal violation, tried 



at signal change 

ntly did not see stop sign 

onally ran stop sign at speed 

 33 = S g stop” 

lation, apparently did not see sign 

 35 = Other sign (e.g. lation, intentionally disregarded 

ion (e.g., driveway entering

 38 = Right-of-way erro ion to other vehicle or person, apparent recognition 

failure (e.g., did not see othe

 39 = Righ

hicle) 

ion to other vehicle or person, apparent decision fai

(i.e., did see other to action but misjudged gap) 

191 
 40 = Right-of-way error in relation to other vehicle or person, other or unknown cause 

mproper stopping on roadway 

 improper or dangerous location; e.g., shoulder of Interstate 

 43 = Speeding or other unsafe a

 44 = Failure to dim headlights 

ts or in

 47 = Avoiding other vehicle 

 48 = Avoiding animal 

 48a = Avoiding object 

familiarity wit

it

 51 = Use of cruise control contr

cruise control system) 

 52 = Excessive braking/decelera

lippery

 54 = Loss of control on dry (or 

cle failure (e

DRIVER/VEHICLE 2 VARIABLES 

Driver Behavior: Driver 2 Actions/Fa

N to four factors 

incident; e.g., as contributing factors. If

00a = Not applicable (baseline epoch) 

able (single vehi le event) 

 excessive speed f

not include tailgating, unless ab

w /distracted driving 

teering, weaving, lane break, or

l-impaired drivin

 07 = Driving slowly; below spe

 08 = Illegal passing (i.e., across double line) 

 41 = Sudden or i

 42 = Parking in

ctions in work zone 

 45 = Driving without ligh sufficient lights 

 46 = Avoiding pedestrian

 49 = Apparent un h roadway 

 50 = Apparent unfamiliarity w h vehicle; e.g., displays and controls 

ibuted to late braking (does not imply malfunction of 

tion creating potential hazard 

 53 = Loss of control on s road surface 

unknown) surface 

 55 = Apparent vehi .g., brakes) 

 56 = Other 

ctors Relating to Event 

ote: Analyst codes up believed to have relevance to the occurrence of the 

 there are more than four, select the four most important.

 00b = Not applic c

 00 = None coded 

 01 = Apparent or conditions or location (regardless of speed limit; does

ove speed limit) 

 06a = Vehicle “drift” or “slow 

 06b = E

eave” consistent with possible drowsy

rratic s

possible alcoho

 other vehicle motion consistent with 

g. 

ed limit or in relation to other traffic 

192 
 09 = Passing on right 

oper or unsafe pa sing 

Cutting in, too close in front of other vehicle 

 11b = Cutting in at safe distance

 in, too close behin

 13 = Making turn from wrong lane (e.g., across lanes) 

e other vehicle d

 15 = Driving in other vehicle’s 

 16 = Aggressive driving, specifi

 17 = Aggressive driving, other; i.e., reckless driving without directed menacing actions 

e of road, not overtaking [includes partial or full drift into oncoming lane] 

oo close 

 19a = Inadequate evasive action

 20 = Failed to signal, or improp

 21 = Improper turn: wide right turn 

n 

an 

n violation, “rolling stop” 

 violation 

ng violation (e.g., driveway entering roadway) 

er or dangerous location; e.g., shoulder of Interstate 

 work zone 

 44 = Failure to dim headlights 

 10 = Other impr

 11a = 

s

 but then decelerated, causing conflict 

 12 = Cutting d other vehicle 

 14 = Did not se uring lane change or merge 

blind zone 

c, directed menacing actions 

 18 = Wrong sid

 19 = Following t

er signal 

 22 = Improper turn: cut corner on left turn 

 23 = Other improper turning 

 24 = Improper backing, [apparently] did not see 

 25 = Improper backing, other 

 26 = Improper start from parked positio

 27 = Disregarded officer or watchm

 28 = Signal violation 

 30 = Signal violation, tried to beat signal change 

 31 = Stop sign violation 

 33 = Stop sig

 34 = Other sign (e.g., Yield)

 36 = Other sign violation 

 37 = Non-signed crossi

 38 = Right-of-way error in relation to other vehicle or person 

 41 = Sudden or improper stopping on roadway

 42 = Parking in improp

 43 = Speeding or other unsafe actions in

193 
 45 = Driving without lights or insufficient lights 

 46 = Avoiding pedestrian 

 47 = Avoiding other vehicle 

 48 = Avoiding animal 

 48a = Avoiding object 

 52 = Excessive braking/deceleration creating potential hazard 

 53 = Loss of control on slippery road surface 

 54 = Loss of control on dry (or unknown) surface 

 55 = Apparent vehicle failure (e.g., brakes) 

 56 = Other 

 57 = Unknown 

ENVIRONMENTAL VARIABLES 

Environmental variables are coded at the time of the trigger for both events and baseline epochs. 

Light Condition 

Note: GES A19 

 01 = Daylight 

 02 = Dark 

 03 = Dark but lighted 

 04 = Dawn 

 05 = Dusk 

 09 = Unknown 

Weather 

Note: GES A20. 

 01 = No adverse conditions 

 02 = Rain 

 03 = Sleet 

 04 = Snow 

 05 = Fog 

 06 = Rain & fog 

 07 = Sleet & fog 

 08 = Other (smog, smoke, sand/dust, crosswind, hail) 

 09 = Unknown 

194 
Roadway Surface Condition 

Note: GES A15. 

 01 = Dry 

 02 = Wet 

 03 = Snow or slush 

 04 = Ice 

 05 = Sand, oil, dirt 

 08 = Other 

 09 = Unknown 

Relation to Junction 

Note: GES variable A09. 

 00 = Non-Junction 

 01 = Intersection 

 02 = Intersection-related 

 03 = Driveway, alley access, etc. 

 03a = Parking Lot 

 04 = Entrance/exit ramp 

 05 = Rail grade crossing 

 06 = On a bridge 

 07 = Crossover related 

 08 = Other 

 09 = Unknown 

Traffic-way Flow 

Note: GES variable V A11. Coded in relation to subject vehicle; baseline epoch coded at time of 

trigger. 

 00 = Not physically divided (center 2-way left turn lane) 

 01 = Not physically divided (2-way trafficway) 

 02 = Divided (median strip or barrier) 

 03 = One-way trafficway 

 09 = Unknown 

195 
Number of Travel Lanes 

Note: GES V A12. Per GES, if road is divided, only lanes in travel direction are counted. If 

undivided, all lanes are counted. Coded in relation to subject vehicle. Count all contiguous lanes 

at the time & location of the incident; e.g., include entrance or exit lanes if contiguous. Do not 

include lanes if blocked by cones or barrels. 

 01 = 1 

 02 = 2 

 03 = 3 

 04 = 4 

 05 = 5 

 06 = 6 

 07 = 7+ 

 09 = Unknown 

Roadway Alignment 

Note: GES V A13, with expansion of curve choices. Coded in relation to subject vehicle. 

 01 = Straight 

 02a = Curve right 

 02b = Curve left 

 09 = Unknown 

Roadway Profile 

Note: GES V A14, with expansion of grade choices. Coded in relation to subject vehicle. 

 01 = Level (or unknown) 

 02a = Grade up 

 02b = Grade down 

 03 = Hillcrest 

 04 = Sag 

Traffic Density 

Code the traffic density for the time prior to the pre-crash event. LOS: Level-of-Service. 

 01 = LOS A: Free flow—Individual users are virtually unaffected by the presence of 

others in the traffic stream. Freedom to select desired speeds and to maneuver within the 

traffic stream is extremely high. The general level of comfort and convenience provided 

to the motorist, passenger, or pedestrian is excellent. 

196 
 02 = LOS B: Flow with some restrictions—In the range of stable traffic flow, but the 

able. Freedom to select 

line in the freedom to 

, because the presence of others in the 

ore restricted—In the range of 

ge of flow in which the operation of 

cantly affected by the interactions with others in the 

ted by the presence of others, and 

bstantial vigilance on the part of the 

omfort and convenience declines noticeably at this level. 

emporary restrictions substantially slow driver—Represents 

 traffic flow. Speed and freedom to maneuver are severely 

r or pedestrian experiences a generally poor level of comfort and 

eases in traffic flow will generally cause operational problems at 

ary stoppages, etc.—

capacity level. All speeds are reduced to a 

euver within the traffic stream is 

plished by forcing a vehicle or pedestrian to 

. Comfort and convenience levels are 

or, and driver or pedestrian frustration is generally high. Operations at this 

table, because small increases in flow or minor perturbations within 

 will cause breakdowns. 

 condition with low speeds and traffic volumes that are 

g in particular locations—This condition exists whenever 

-go waves, and they are extremely unstable. Vehicles may progress at 

more, and then be required to stop in a 

ion. LOS F is used to describe the operating conditions within the queue, as 

t of the breakdown. It should be noted, however, that in many cases 

onditions of vehicles or pedestrians discharged from the queue may be quite 

rtheless, it is the point at which arrival flow exceeds discharge slow which 

m, and LOS F is an appropriate designation for such points. 

able to determine 

presence of other users in the traffic stream begins to be notice

desired speeds is relatively unaffected, but there is a slight dec

maneuver within the traffic stream from LOS A

traffic stream begins to affect individual behavior. 

 03 = LOS C: Stable flow, maneuverability and speed are m

stable traffic flow, but marks the beginning of the ran

individual uses becomes signifi

traffic stream. The selection of speed is now affec

maneuvering within the traffic stream requires su

user. The general level of c

 04 = LOS D: Unstable flow: t

high-density, but stable

restricted, and the drive

convenience. Small incr

this level. 

 05 = LOS E: Flow is unstable; vehicles are unable to pass, tempor

Represents operating conditions at or near the 

low, but relatively uniform value. Freedom to man

extremely difficult, and it is generally accom

“give way” to accommodate such maneuvers

extremely po

level are usually uns

the traffic stream

 06 = LOS F: Forced traffic flow

below capacity. Queues’ formin

the amount of traffic approaching a point exceeds the amount which can traverse the 

point. Queues form behind such locations. Operations within the queue are characterized 

by stop-and

reasonable speeds for several hundred feet or 

cyclic fash

well as the poin

operating c

good. Neve

causes the queue to for

 Unknown/un

Construction Zone Related 

Note: Any area with one or more traffic cones, barrels, etc. is considered to be a construction 

zone. 

 00 = Not construction zone-related (or unknown) 

 01 = Construction zone (occurred in zone) 

 02 = Construction zone-related (occurred in approach or otherwise related to zone) 

197 
Truck Pre-Event Speed 

Note: For events, coded for the period just prior to the occurrence of the critical event and/or just 

prior to any avoidance maneuver. For example, when braking is involved, the pre-event speed is 

the speed just prior to the beginning of braking. If there is no avoidance maneuver, enter the 

speed at the time of the trigger. 

 999 = Unknown 

GENERAL 

Event Comments 

Note: This text variable will permit analysts to provide any comments on the event, including 

information not captured by data variables, assumptions made about the event affecting coding, 

and coding issues that arose. Ordinarily this will not contain information that is captured by the 

coded variables. 

198 
APPENDIX B. BASELINE EPOCH DATA DIRECTORY 

Determine if trigger is valid. If any of the following conditions apply, then the trigger is invalid: 

 It is the wrong driver  mark as ‘wrong driver’ 

 Face camera is not available  mark as ‘no video’ 

 The front camera is out and you cannot get ALL variables from the side cameras  mark 

as ‘no video’ 

 Truck is traveling below 15 mi/h (24.14 kph) at any point during trigger  mark as 

‘speed below 15 mi/h’ 

 Driver is wearing sunglasses  mark as ‘wearing sunglasses’ 

 Trigger overlaps an existing event  mark as ‘overlaps event’ 

 You cannot see the drivers eyes clearly because the camera is misaligned or the video is 

too dark  mark as ‘cannot see drivers eyes’ 

VEHICLE 1 PRIMARY MOVEMENT 

Note: Code this variable for what the truck is doing the majority of the 6-s trigger. 

 Going straight 

 Decelerating in traffic lane 

 Accelerating in traffic lane 

 Starting in traffic lane 

 Stopped in traffic lane 

 Passing or overtaking another vehicle—the vehicle that the driver is passing has to be in 

the adjacent lane; does not count if the vehicle is slowing in a turn lane or on an exit ramp 

 Disabled or parked in travel lane 

 Leaving a parking position, moving forward 

 Leaving a parking position, backing 

 Entering a parking position, moving forward 

 Entering a parking position, backing 

 Turning right 

 Turning left 

 Making a u-turn 

 Backing up (other than parking) 

 Negotiating a curve 

 Changing lanes 

199 
 Merging 

 Successful avoidance maneuver to a previous critical event 

 Other 

 Unknown 

d 

unt) 

 conditions 

Note: For the remaining variables, enter what the conditions are at the end of the 6-s trigger. 

Driver Safety Belt Worn? 

 Yes 

 No 

 Unknown 

Light Condition 

 Daylight 

 Dark 

 Dark but lighted—lights have to be on drivers side of the road (if they’re on a divide

highway, and the lights are only on the opposite direction of travel, then it doesn’t co

 Dawn 

 Dusk 

 Unknown 

Weather 

 No adverse

 Rain—automatically code wet below 

 Sleet 

 Snow—automatically code snow or slush below 

 Fog 

 Rain and fog—automatically code wet below 

 Sleet and fog 

 Other (smog, smoke, sand/dust, crosswind, hail) 

 Unknown 

Roadway Surface Condition 

 Dry 

 Wet 

 Snow or slush 

200 
201 

Number of Travel Lanes 

Note: If road is divided, only lanes in travel direction are counted. If undivided, all lanes are 

counted. Coded in relation to subject vehicle; count all contiguous lanes at the time & location of 

the incident; e.g., include entrance or exit lanes if contiguous (including entrance/exit lanes that 

are separated by paint) 

 1 

 2 

 Ice 

 Sand, oil, dirt 

 Other 

 Unknown 

Relation to Junction 

 Non-junction 

 Intersection 

 Intersection-related 

 Driveway, alley access, etc.—the driver has to be completely on the driveway, alley 

access, etc. in order to code this variable 

 Parking lot—the driver has to be completely in the parking lot in order to code this 

variable; if the driver is turning into a parking lot, code as intersection 

 Entrance/exit ramp 

 Rail grade crossing 

 On a bridge 

 Crossover-related (A crossover is defined as a designated opening within a median used 

primarily for “U-turns”) 

 Other 

 Unknown 

Trafficway Flow 

Note: Coded in relation to subject vehicle. 

 Not physically divided (center 2-way left turn lane) 

 Not physically divided (2-way trafficway) 

 Divided (median strip or barrier) 

 One-way trafficway 

 Unknown 
 3 

 4 

 5 

 6 

 7 or more 

 Unknown 

Roadway Alignment 

Note: Coded in relation to subject vehicle. 

 Straight 

 Curve right 

 Curve left 

 Unknown 

Roadway Profile 

Note: Coded in relation to subject vehicle. 

 Level 

 Grade up 

 Grade down 

 Hillcrest 

 Sag (e.g., a valley) 

 Unknown 

Traffic Density 

 LOS A: Free flow—Individual users are virtually unaffected by the presence of others in 

the traffic stream. Freedom to select desired speeds and to maneuver within the traffic 

stream is extremely high. The general level of comfort and convenience provided to the 

motorist, passenger, or pedestrian is excellent. 

 LOS B: Flow with some restrictions—In the range of stable traffic flow, but the presence 

of other users in the traffic stream begins to be noticeable. Freedom to select desired 

speeds is relatively unaffected, but there is a slight decline in the freedom to maneuver 

within the traffic stream from level-of-Service A, because the presence of others in the 

traffic stream begins to affect individual behavior. 

 LOS C: Stable flow, maneuverability and speed are more restricted—In the range of 

stable traffic flow, but marks the beginning of the range of flow in which the operation of 

individual uses becomes significantly affected by the interactions with others in the 

traffic stream. The selection of speed is now affected by the presence of others, and 

202 
maneuvering within the traffic stream requires substantial vigilance on the part of th

user. The general level of comfort and convenience declines noticeably at this level. 

e 

eedom to maneuver are severely 

level of comfort and 

se operational problems at 

s unstable; vehicles are unable to pass, temporary stoppages, etc.—

to a 

erally accomplished by forcing a vehicle or pedestrian to 

venience levels are 

high. Operations at this 

inor perturbations within 

 speeds and traffic volumes that are below 

capacity. Queues’ forming in particular locations—This condition exists whenever the 

nt exceeds the amount which can traverse the point. 

Queues form behind such locations. Operations within the queue are characterized by 

 at reasonable 

speeds for several hundred feet or more, then be required to stop in a cyclic fashion. 

e F is used to describe the operating conditions within the queue, as well 

n. It should be noted, however, that in many cases operating 

estrians discharged from the queue may be quite good. 

at which arrival flow exceeds discharge slow which causes 

level-of-Service F is an appropriate designation for such points. 

ermine 

. 

ch or otherwise related to zone). 

 LOS D: Unstable flow: temporary restrictions substantially slow driver—Represents 

high-density, but stable traffic flow. Speed and fr

restricted, and the driver or pedestrian experiences a generally poor 

convenience. Small increases in traffic flow will generally cau

this level. 

 LOS E: Flow i

Represents operating conditions at or near the capacity level. All speeds are reduced 

low, but relatively uniform value. Freedom to maneuver within the traffic stream is 

extremely difficult, and it is gen

“give way” to accommodate such maneuvers. Comfort and con

extremely poor, and driver or pedestrian frustration is generally 

level are usually unstable, because small increases in flow or m

the traffic stream will cause breakdowns. 

 LOS F: Forced traffic flow condition with low

amount of traffic approaching a poi

stop-and-go waves, and they are extremely unstable. Vehicles may progress

Level-of-Servic

as the point of the breakdow

conditions of vehicles or ped

Nevertheless, it is the point 

the queue to form, and 

 Unknown/unable to det

Construction Zone 

Note: For the purposes of the coding, consider any area with multiple traffic cones, barrels, etc. 

to be a construction zone. 

 No construction zone. 

 Construction zone (occurred in zone)

 Construction zone-related (occurred in approa

 Unknown. 

Truck Speed 

Event Comments 

Note: This text variable will permit analysts to provide any comments on the event, including 

information not captured by data variables, assumptions made about the event affecting coding, 

and coding issues that arose. Ordinarily this will not contain information that is captured by the 

coded variables. 

203 

Task Definition Task 

Category 

Manual/Visual 

Complexity 

Dial cell phone Driver dials a cell phone. This may also include 

answering the phone or hanging up the phone, 

if the driver presses a key during this time. 

Assumes driver is looking at and may reach for 

object. 

Tertiary 

Task 

Complex 

Interact with/look at 

dispatching device 

Driver interacts with or looks at a dispatching 

device. The driver usually keeps the device on 

the passenger seat or on the floor between the 

two seats and holds the device on his/her lap 

or steering wheel while in use. Assumes driver 

is looking at and may reach for object. 

Tertiary 

Task 

Complex 

Look at map Driver reads a map which is visible in the 

driver’s hands, on the driver’s lap, on the 

driver’s steering wheel, or on the passenger 

seat. Assumes driver is looking at and may 

reach for object. 

Tertiary 

Task 

Complex 

Other—Complex Driver is engaging in an other complex task Tertiary 

Task 

Complex 

Read book, 

newspaper, 

paperwork, etc. 

Driver reads a book, newspaper, paperwork, 

etc, which is visible in the driver’s hands, on 

the driver’s lap, on the driver’s steering wheel, 

or on the passenger seat. Assumes driver is 

looking at and may reach for object. 

Tertiary 

Task 

Complex 

Text message on cell 

phone 

Driver appears to be text messaging using a 

cell phone. Driver is focusing on the cell phone 

for an extended amount of time while 

continuously pressing keys. Assumes driver is 

looking at and may reach for object. 

Tertiary 

Task 

Complex 

Use calculator Driver uses hand-held calculator. Assumes 

driver is looking at and may reach for object. 

Tertiary 

Task 

Complex 

Write on pad, 

notebook, etc. 

Driver writes on some kind of paper which is 

visible in the driver’s hands, on the driver’s lap, 

on the driver’s steering wheel, or on the 

passenger seat. Assumes driver is looking at 

and may reach for object. 

Tertiary 

Task 

Complex 

205 

APPENDIX C. DRIVER TASKS, TASK CATEGORIES AND 

MANUAL/VISUAL COMPLEXITY 

Table 82 lists all tasks and definitions, grouped into secondary and tertiary task categories that 

were identified in the DDWS and NTDS data sets. Note that these do not necessarily represent 

the universe of secondary and tertiary tasks, but only those that were observed in the video from 

the DDWS FOT and NTDS data sets. 

Table 82. Driver Tasks, Task Categories and Manual/Visual Complexity 
206 

Task Definition Task 

Category 

Manual/Visual 

Complexity 

Eating Driver eats with or without a utensil (i.e., fork or 

spoon). This also includes the driver opening a 

food bag or anything closely related to eating 

just prior to or after the trigger. Assumes driver 

is looking at and may reach for object. 

Tertiary 

Task 

Moderate 

Look at outside 

vehicle, animal, 

object 

Driver looks outside the vehicle to another 

vehicle. May be out the front windshield or side 

window. Must be apparent that driver is 

focused on outside vehicle. 

Tertiary 

Task 

Moderate 

Look back in Sleeper 

Berth 

Driver turns body to look behind him/her into 

the Sleeper Berth. 

Tertiary 

Task 

Moderate 

Other—moderate Driver is engaging in a other moderate task Tertiary 

Task 

Moderate 

Personal grooming Driver is grooming him/herself. This may 

include combing/fixing hair, applying make-up, 

shaving, and brushing teeth. Assumes driver is 

looking at and may reach for object. 

Tertiary 

Task 

Moderate 

Reach for object in 

vehicle 

Driver may or may not remove attention from 

the forward roadway to reach for an object 

inside the vehicle. Objects may include, but are 

not limited to, cell phone, CB, food, drink, map, 

paperwork. This option should only be marked 

if the driver is not engaging in any other 

behavior at the same time. 

Tertiary 

Task 

Moderate 

Smoking-related 

behavior—reaching, 

lighting, extinguishing 

Driver is reaching (ashing), lighting, or 

extinguishing a cigarette. May include 

behaviors such as driver reaching for a lighter 

or reaching for a pack of cigarettes. Assumes 

driver is looking at and may reach for object. 

Tertiary 

Task 

Moderate 

Talk or listen to CB 

radio 

Driver talks or listens to a CB radio. Assumes 

driver is looking at and may reach for object. 

Tertiary 

Task 

Moderate 

Talk or listen to handheld phone Driver holds a hand-held phone to ear, appears 

to be talking and/or listening. 

Tertiary 

Task 

Moderate 

Talk or listen to 

hands-free phone 

Driver talks or listens to a hands-free phone. 

This is apparent by an earpiece in the driver’s 

ear. 

Tertiary 

Task 

Moderate 

Use/reach for other 

device 

Driver reaches for or uses an alternate 

electronic device. Assumes driver is looking at 

and may reach for object. 

Tertiary 

Task 

Moderate 
207 

Task Definition Task 

Category 

Manual/Visual 

Complexity 

Adjust instrument 

panel 

Driver is adjusting something on the instrument 

panel. This may include, radio, climate 

controls, head lights, and other switches to the 

front and right of the driver. Assumes driver is 

reaching for and/or looking at the instrument 

panel while adjusting. 

Tertiary 

Task 

Simple 

Bite nails/cuticles Driver is biting nails and/or cuticles. Assumes 

driver is looking at hands. 

Tertiary 

Task 

Simple 

Drink from a 

container 

Driver drinks from a container. This also 

includes the driver opening/closing a drink 

container or anything closely related to drinking 

just prior to or after the trigger. Assumes driver 

is looking at and may reach for object. 

Tertiary 

Task 

Simple 

Interact with or look 

at other occupant(s) 

Driver is talking to a passenger sitting in the 

passenger’s seat or in the sleeper berth that 

can be identified by the person encroaching 

into the camera view or the driver is clearly 

looking and talking to the passenger. 

Tertiary 

Task 

Simple 

Other—simple Driver is engaging in a other simple task Tertiary 

Task 

Simple 

Other personal 

hygiene 

Driver is conducting some kind of other 

personal hygiene. This may include rubbing 

eyes/face, scratching face/neck, or picking 

nose. 

Tertiary 

Task 

Simple 

Put on/remove/adjust 

hat 

Driver puts on, removes, or adjusts his/her hat. 

Assumes driver is looking at and may reach for 

object. 

Tertiary 

Task 

Simple 

Put on/remove/adjust 

seat belt 

Driver puts on, removes, or adjusts his/her seat 

belt. Assumes driver is looking at and may 

reach for object. 

Tertiary 

Task 

Simple 

Put on/remove/adjust 

sunglasses or 

reading glasses 

Driver puts on, removes, or adjusts his/her 

sunglasses. Assumes driver is looking at and 

may reach for object. 

Tertiary 

Task 

Simple 

Remove/adjust 

jewelry 

Driver is removing or adjusting jewelry. This 

may include, watch, bracelet, necklace or 

earrings. Assumes driver is looking at and may 

reach for object. 

Tertiary 

Task 

Simple 

Smoking-related 

behavior—cigarette 

in hand or mouth 

Driver has a cigarette in hand or mouth. Tertiary 

Task 

Simple 
208 

Task Definition Task 

Category 

Manual/Visual 

Complexity 

Talk/sing/dance with 

no indication of 

passenger 

Driver appears to be vocalizing either to an 

unknown passenger, to self, or singing to the 

radio. Also, in this category are instances 

where the driver exhibits dancing behavior or is 

whistling. 

Tertiary 

Task 

Simple 

Use chewing tobacco Driver is using chewing tobacco. This may 

include putting tobacco into mouth or spitting 

into container. Assumes driver is looking at and 

may reach for object. 

Tertiary 

Task 

Simple 

Check speedometer Driver glances directly down to the 

speedometer. Must be apparent that the driver 

is looking at the speedometer and not in lap. 

Secondary 

Task 

N/A 

Look at left-side 

mirror/out left window 

Driver looks at the left-side mirror or out the left 

window for a driving-related reason (i.e., 

checking traffic before a lane change or turn). 

Secondary 

Task 

N/A 

Look at right-side 

mirror/out left window 

Driver looks at the right-side mirror or out the 

right window for a driving-related reason (i.e., 

checking traffic before a lane change or turn). 

Secondary 

Task 

N/A 
209 

APPENDIX D. T TEST TABLES 

Post hoc Tukey t tests were conducted on all pair-wise combinations of event types to determine 

simple effects. The results of all these pair-wise t tests can be found in the following tables. 

Table 83. Mean Duration of Eyes off Forward Roadway—Any Task— 

across All Events and Vehicle 1 At-Fault (V1) Events 

Event Type ALL: 

df 

ALL: 

t-value 

ALL: 

p-value 

V1: 

df 

V1: 

t-value 

V1: 

p-value 

Crash and Near-crash 22616 1.31 0.686 21913 3.26 0.010 

Crash and Crash-Relevant Conflict 22616 1.65 0.463 21913 3.96 0.001 

Crash and Unintentional Lane 

Deviation 22616 2.27 0.156 21913 1.69 0.439 

Crash and Baseline 22616 3.36 0.007 21913 5.61 < 0.0001 

Near-Crash and Crash-Relevant 

Conflict 22616 0.79 0.935 21913 1.72 0.422 

Near-Crash and Unintentional Lane 

Deviation 22616 9.95 < 0.0001 21913 5.25 < 0.0001 

Near-Crash and Baseline 22616 5.76 0.001 21913 6.99 < 0.0001 

Crash-Relevant Conflict and 

Unintentional Lane Deviation 22616 23.81 < 0.0001 21913 18.95 < 0.0001 

Crash-Relevant Conflict and Baseline 22616 18.05 < 0.0001 21913 22.15 < 0.0001 

Unintentional Lane Deviation and 

Baseline 22616 39.30 < 0.0001 21913 39.51 < 0.0001 
210 

Table 84. Mean Duration of Eyes off Forward Roadway—All Tertiary Tasks— 

across All Events and Vehicle 1 At-Fault (V1) Events 

Event Type ALL: 

df 

ALL: 

t-value 

ALL: 

p-value 

V1: 

df 

V1: 

t-value 

V1: 

p-value 

Crash and Near-crash 8277 1.62 0.485 8098 0.86 0.911

Crash and Crash-Relevant Conflict 8277 2.46 0.101 8098 1.14 0.788

Crash and Unintentional Lane 

Deviation 8277 5.05 < 0.0001 8098 0.31 0.998

Crash and Baseline 8277 0.13 1.000 8098 2.44 0.105

Near-Crash and Crash-Relevant 

Conflict 8277 1.11 0.804 8098 0.78 0.937

Near-Crash and Unintentional Lane 

Deviation 8277 5.70 < 0.0001 8098 1.91 0.314

Near-Crash and Baseline 8277 3.10 0.017 8098 5.11 < 0.0001

Crash-Relevant Conflict and 

Unintentional Lane Deviation 8277 14.81 < 0.0001 8098 10.46 < 0.0001

Crash-Relevant Conflict and Baseline 8277 19.50 < 0.0001 8098 23.31 < 0.0001

Unintentional Lane Deviation and 

Baseline 8277 34.97 < 0.0001 8098 35.19 < 0.0001

Table 85. Mean Duration of Eyes off Forward Roadway—Complex Tertiary Tasks— 

across All Events and Vehicle 1 At-Fault (V1) Events 

Event Type ALL: 

df 

ALL: 

t-value 

ALL: 

p-value 

V1: 

df 

V1: 

t-value 

V1: 

p-value 

Near-Crash and Crash-Relevant 

Conflict 492 0.23 0.996 487 0.10 1.000

Near-Crash and Unintentional Lane 

Deviation 492 0.16 0.999 487 0.16 0.999

Near-Crash and Baseline 492 0.59 0.934 487 0.61 0.930

Crash-Relevant Conflict and 

Unintentional Lane Deviation 492 1.98 0.196 487 1.29 0.568

Crash-Relevant Conflict and Baseline 492 1.85 0.252 487 2.58 0.050

Unintentional Lane Deviation and 

Baseline 492 4.14 0.0002 487 4.23 0.0002
211 

Table 86. Mean Duration of Eyes off Forward Roadway—Moderate Tertiary Tasks— 

across All Events and Vehicle 1 At-Fault (V1) Events 

Event Type ALL: 

df 

ALL: 

t-value 

ALL: 

p-value 

V1: 

df 

V1: 

t-value 

V1: 

p-value 

Crash and Near-crash 4218 0.30 0.998 4123 1.40 0.628

Crash and Crash-Relevant Conflict 4218 0.65 0.966 4123 1.80 0.371

Crash and Unintentional Lane 

Deviation 4218 2.78 0.044 4123 0.97 0.869

Crash and Baseline 4218 0.86 0.911 4123 2.83 0.038

Near-Crash and Crash-Relevant 

Conflict 4218 0.47 0.990 4123 0.77 0.940

Near-Crash and Unintentional Lane 

Deviation 4218 3.72 0.002 4123 1.30 0.688

Near-Crash and Baseline 4218 1.93 0.304 4123 3.37 0.007

Crash-Relevant Conflict and 

Unintentional Lane Deviation 4218 8.68 < 0.0001 4123 6.42 < 0.0001

Crash-Relevant Conflict and Baseline 4218 10.15 < 0.0001 4123 12.19 < 0.0001

Unintentional Lane Deviation and 

Baseline 4218 17.51 < 0.0001 4123 17.57 < 0.0001

Table 87. Mean Duration of Eyes off Forward Roadway—Simple Tertiary Tasks— 

across All Events and Vehicle 1 At-Fault (V1) Events 

Event Type ALL: 

df 

ALL: 

t-value 

ALL: 

p-value 

V1: 

df 

V1: 

t-value 

V1: 

p-value 

Crash and Near-crash 2063 1.34 0.665 – – –

Crash and Crash-Relevant Conflict 2063 1.74 0.407 – – –

Crash and Unintentional Lane 

Deviation 2063 3.29 0.009 – – –

Crash and Baseline 2063 0.87 0.906 – – –

Near-Crash and Crash-Relevant 

Conflict 2063 0.23 0.999 2009 2.03 0.177

Near-Crash and Unintentional Lane 

Deviation 2063 2.57 0.077 2009 1.07 0.706

Near-Crash and Baseline 2063 1.11 0.801 2009 3.10 0.011

Crash-Relevant Conflict and 

Unintentional Lane Deviation 2063 7.64 < 0.0001 2009 5.39 < 0.0001

Crash-Relevant Conflict and Baseline 2063 6.79 < 0.0001 2009 8.79 < 0.0001

Unintentional Lane Deviation and 

Baseline 2063 14.35 < 0.0001 2009 14.37 < 0.0001
212 

Table 88. Mean Duration of Eyes off Forward Roadway—Secondary Tasks— 

across All Events and Vehicle 1 At-Fault (V1) Events 

Event Type ALL: 

df 

ALL: 

t-value 

ALL: 

p-value 

V1: 

df 

V1: 

t-value 

V1: 

p-value 

Crash and Near-crash 4704 2.48 0.094 4495 2.20 0.179

Crash and Crash-Relevant Conflict 4704 3.76 0.002 4495 3.77 0.002

Crash and Unintentional Lane 

Deviation 4704 2.23 0.170 4495 2.27 0.156

Crash and Baseline 4704 4.41 0.0001 4495 4.49 < 0.0001

Near-Crash and Crash-Relevant 

Conflict 4704 3.29 0.009 4495 3.10 0.017

Near-Crash and Unintentional Lane 

Deviation 4704 0.81 0.929 4495 0.14 1.000

Near-Crash and Baseline 4704 5.29 < 0.0001 4495 4.82 < 0.0001

Crash-Relevant Conflict and 

Unintentional Lane Deviation 4704 5.72 < 0.0001 4495 5.47 < 0.0001

Crash-Relevant Conflict and Baseline 4704 6.10 < 0.0001 4495 5.73 0.031

Unintentional Lane Deviation and 

Baseline 4704 8.73 < 0.0001 4495 8.89 < 0.0001

Table 89. Mean Number of Glances Away from Forward Roadway—Any Task— 

across All Events and Vehicle 1 At-Fault (V1) Events 

Event Type ALL: 

df 

ALL: 

t-value 

ALL: 

p-value 

V1: 

df 

V1: 

t-value 

V1: 

p-value 

Crash and Near-crash 22616 0.48 0.989 21913 0.98 0.863

Crash and Crash-Relevant Conflict 22616 0.17 1.000 21913 1.13 0.788

Crash and Unintentional Lane 

Deviation 22616 2.27 0.156 21913 0.21 1.000

Crash and Baseline 22616 1.33 0.674 21913 2.28 0.153

Near-Crash and Crash-Relevant 

Conflict 22616 0.96 0.874 21913 0.34 0.997

Near-Crash and Unintentional Lane 

Deviation 22616 7.58 < 0.0001 21913 3.80 0.001

Near-Crash and Baseline 22616 2.39 0.117 21913 3.98 0.001

Crash-Relevant Conflict and 

Unintentional Lane Deviation 22616 14.82 < 0.0001 21913 11.30 < 0.0001

Crash-Relevant Conflict and Baseline 22616 12.31 < 0.0001 21913 15.38 < 0.0001

Unintentional Lane Deviation and 

Baseline 22616 25.16 < 0.0001 21913 25.19 < 0.0001
213 

Table 90. Mean Number of Glances Away from Forward Roadway—All Tertiary Tasks— 

across All Events and Vehicle 1 At-Fault (V1) Events 

Event Type ALL: 

df 

ALL: 

t-value 

ALL: 

p-value 

V1: 

df 

V1: 

t-value 

V1: 

p-value 

Crash and Near-crash 8277 1.73 0.417 8098 0.11 1.000

Crash and Crash-Relevant Conflict 8277 2.50 0.091 8098 0.08 1.000

Crash and Unintentional Lane 

Deviation 8277 4.26 0.0002 8098 0.62 0.972

Crash and Baseline 8277 0.80 0.930 8098 0.89 0.908

Near-Crash and Crash-Relevant 

Conflict 8277 0.95 0.875 8098 0.11 1.000

Near-Crash and Unintentional Lane 

Deviation 8277 4.09 0.0004 8098 1.67 0.454

Near-Crash and Baseline 8277 2.11 0.215 8098 0.87 0.908

Crash-Relevant Conflict and 

Unintentional Lane Deviation 8277 10.11 < 0.0001 8098 6.94 < 0.0001

Crash-Relevant Conflict and Baseline 8277 14.25 < 0.0001 8098 16.92 < 0.0001

Unintentional Lane Deviation and 

Baseline 8277 24.66 < 0.0001 8098 24.71 < 0.0001

Table 91. Mean Number of Glances Away from Forward Roadway—Complex Tertiary Tasks— 

across All Events and Vehicle 1 At-Fault (V1) Events 

Event Type ALL: 

df 

ALL: 

t-value 

ALL: 

p-value 

V1: 

df 

V1: 

t-value 

V1: 

p-value 

Near-Crash and Crash-Relevant 

Conflict 492 0.79 0.857 487 0.86 0.825

Near-Crash and Unintentional Lane 

Deviation 492 0.88 0.815 487 0.88 0.813

Near-Crash and Baseline 492 1.21 0.619 487 1.22 0.616

Crash-Relevant Conflict and 

Unintentional Lane Deviation 492 0.43 0.973 487 0.10 1.000

Crash-Relevant Conflict and Baseline 492 2.13 0.146 487 1.79 0.282

Unintentional Lane Deviation and 

Baseline 492 1.83 0.260 487 1.84 0.257
214 

Table 92. Mean Number of Glances Away from Forward Roadway—Moderate Tertiary Tasks— 

across All Events and Vehicle 1 At-Fault (V1) Events 

Event Type ALL: 

df 

ALL: 

t-value 

ALL: 

p-value 

V1: 

df 

V1: 

t-value 

V1: 

p-value 

Crash and Near-crash 4218 0.80 0.931 4123 0.19 1.000

Crash and Crash-Relevant Conflict 4218 1.09 0.812 4123 0.31 0.999

Crash and Unintentional Lane 

Deviation 4218 2.70 0.055 4123 0.36 0.996

Crash and Baseline 4218 0.10 1.000 4123 0.96 0.873

Near-Crash and Crash-Relevant 

Conflict 4218 0.24 0.999 4123 0.25 0.999

Near-Crash and Unintentional Lane 

Deviation 4218 2.72 0.051 4123 1.41 0.621

Near-Crash and Baseline 4218 1.33 0.674 4123 1.92 0.309

Crash-Relevant Conflict and 

Unintentional Lane Deviation 4218 6.61 < 0.0001 4123 5.15 < 0.0001

Crash-Relevant Conflict and Baseline 4218 6.64 < 0.0001 4123 7.83 < 0.0001

Unintentional Lane Deviation and 

Baseline 4218 12.56 < 0.0001 4123 12.56 < 0.0001

Table 93. Mean Number of Glances Away from Forward Roadway—Simple Tertiary Tasks— 

across All Events and Vehicle 1 At-Fault (V1) Events 

Event Type ALL: 

df 

ALL: 

t-value 

ALL: 

p-value 

V1: 

df 

V1: 

t-value 

V1: 

p-value 

Crash and Near-crash 2063 1.26 0.713 – – –

Crash and Crash-Relevant Conflict 2063 1.46 0.591 – – –

Crash and Unintentional Lane 

Deviation 2063 2.57 0.077 – – –

Crash and Baseline 2063 0.69 0.959 – – –

Near-Crash and Crash-Relevant 

Conflict 2063 0.07 1.000 2009 1.97 0.200

Near-Crash and Unintentional Lane 

Deviation 2063 1.61 0.489 2009 1.32 0.551

Near-Crash and Baseline 2063 1.26 0.719 2009 2.89 0.020

Crash-Relevant Conflict and 

Unintentional Lane Deviation 2063 5.48 < 0.0001 2009 3.65 0.002

Crash-Relevant Conflict and Baseline 2063 6.00 < 0.0001 2009 7.59 < 0.0001

Unintentional Lane Deviation and 

Baseline 2063 11.14 < 0.0001 2009 11.14 < 0.0001
215 

Table 94. Mean Number of Glances Away from Forward Roadway—Secondary Tasks— 

across All Events and Vehicle 1 At-Fault (V1) Events 

Event Type ALL: 

df 

ALL: 

t-value 

ALL: 

p-value 

V1: 

df 

V1: 

t-value 

V1: 

p-value 

Crash and Near-crash 4704 0.23 0.999 4495 0.41 0.994

Crash and Crash-Relevant Conflict 4704 0.20 1.000 4495 0.28 0.999

Crash and Unintentional Lane 

Deviation 4704 0.81 0.926 4495 0.82 0.925

Crash and Baseline 4704 0.23 0.999 4495 0.228 0.999

Near-Crash and Crash-248Relevant 

Conflict 4704 0.13 1.000 4495 0.38 0.996

Near-Crash and Unintentional Lane 

Deviation 4704 1.43 0.607 4495 0.79 0.934

Near-Crash and Baseline 4704 1.40 0.630 4495 1.56 0.523

Crash-Relevant Conflict and 

Unintentional Lane Deviation 4704 2.46 0.101 4495 2.10 0.219

Crash-Relevant Conflict and Baseline 4704 4.07 0.001 4495 4.13 0.0004

Unintentional Lane Deviation and 

Baseline 4704 4.38 0.001 4495 4.39 0.0001

Table 95. Mean Duration of Longest Glance Away from Forward Roadway—Any Task— 

across All Events and Vehicle 1 At-Fault (V1) Events 

Event Type ALL: 

df 

ALL: 

t-value 

ALL: 

p-value 

V1: 

df 

V1: 

t-value 

V1: 

p-value 

Crash and Near-crash 22616 2.60 0.070 21913 5.36 < 0.0001

Crash and Crash-Relevant Conflict 22616 3.58 0.003 21913 6.40 < 0.0001

Crash and Unintentional Lane 

Deviation 22616 0.34 0.997 21913 4.49 < 0.0001

Crash and Baseline 22616 5.00 < 0.0001 21913 7.75 < 0.0001

Near-Crash and Crash-Relevant 

Conflict 22616 2.39 0.118 21913 2.51 0.089

Near-Crash and Unintentional Lane 

Deviation 22616 6.51 < 0.0001 21913 3.36 0.007

Near-Crash and Baseline 22616 6.56 0.001 21913 6.83 < 0.0001

Crash-Relevant Conflict and 

Unintentional Lane Deviation 22616 19.57 < 0.0001 21913 15.90 < 0.0001

Crash-Relevant Conflict and Baseline 22616 14.93 < 0.0001 21913 18.07 < 0.0001

Unintentional Lane Deviation and 

Baseline 22616 32.36 < 0.0001 21913 32.76 < 0.0001
216 

Table 96. Mean Duration of Longest Glance Away from Forward Roadway— 

All Tertiary Tasks— across All Events and Vehicle 1 At-Fault (V1) Events 

Event Type ALL: 

df 

ALL: 

t-value 

ALL: 

p-value 

V1: 

df 

V1: 

t-value 

V1: 

p-value 

Crash and Near-crash 8277 0.82 0.926 8098 1.94 0.295

Crash and Crash-Relevant Conflict 8277 1.14 0.784 8098 2.46 0.100

Crash and Unintentional Lane 

Deviation 8277 3.20 0.012 8098 1.82 0.363

Crash and Baseline 8277 0.69 0.958 8098 3.50 0.004

Near-Crash and Crash-Relevant 

Conflict 8277 0.38 0.995 8098 2.46 0.100

Near-Crash and Unintentional Lane 

Deviation 8277 4.03 0.001 8098 0.67 0.962

Near-Crash and Baseline 8277 2.93 0.028 8098 4.87 < 0.0001

Crash-Relevant Conflict and 

Unintentional Lane Deviation 8277 11.75 < 0.0001 8098 8.19 < 0.0001

Crash-Relevant Conflict and Baseline 8277 15.35 < 0.0001 8098 18.48 < 0.0001

Unintentional Lane Deviation and 

Baseline 8277 27.64 < 0.0001 8098 27.76 < 0.0001

Table 97. Mean Duration of Longest Glance Away from Forward Roadway— 

Complex Tertiary Tasks— across All Events and Vehicle 1 At-Fault (V1) Events 

Event Type ALL: 

df 

ALL: 

t-value 

ALL: 

p-value 

V1: 

df 

V1: 

t-value 

V1: 

p-value 

Near-Crash and Crash-Relevant 

Conflict 492 1.55 0.409 487 1.47 0.459

Near-Crash and Unintentional Lane 

Deviation 492 1.35 0.534 487 1.35 0.530

Near-Crash and Baseline 492 2.17 0.132 487 2.19 0.129

Crash-Relevant Conflict and 

Unintentional Lane Deviation 492 1.06 0.714 487 0.60 0.933

Crash-Relevant Conflict and Baseline 492 3.16 0.009 487 3.60 0.002

Unintentional Lane Deviation and 

Baseline 492 4.57 < 0.0001 487 4.59 < 0.0001
217 

Table 98. Mean Duration of Longest Glance Away from Forward Roadway—Moderate Tertiary 

Tasks—across All Events and Vehicle 1 At-Fault (V1) Events 

Event Type ALL: 

df 

ALL: 

t-value 

ALL: 

p-value 

V1: 

df 

V1: 

t-value 

V1: 

p-value 

Crash and Near-crash 4218 0.77 0.939 4123 2.43 0.108

Crash and Crash-Relevant Conflict 4218 0.55 0.982 4123 2.86 0.035

Crash and Unintentional Lane 

Deviation 4218 0.90 0.898 4123 2.33 0.136

Crash and Baseline 4218 1.79 0.379 4123 3.70 0.002

Near-Crash and Crash-Relevant 

Conflict 4218 0.55 0.982 4123 0.66 0.966

Near-Crash and Unintentional Lane 

Deviation 4218 2.75 0.048 4123 0.659 0.965

Near-Crash and Baseline 4218 1.39 0.635 4123 2.77 0.044

Crash-Relevant Conflict and 

Unintentional Lane Deviation 4218 5.88 < 0.0001 4123 4.07 0.001

Crash-Relevant Conflict and Baseline 4218 8.24 < 0.0001 4123 9.92 < 0.0001

Unintentional Lane Deviation and 

Baseline 4218 12.83 < 0.0001 4123 12.86 < 0.0001

Table 99. Mean Duration of Longest Glance Away from Forward Roadway—Simple Tertiary Tasks 

—across All Events and Vehicle 1 At-Fault (V1) Events 

Event Type ALL: 

df 

ALL: 

t-value 

ALL: 

p-value 

V1: 

df 

V1: 

t-value 

V1: 

p-value 

Crash and Near-crash 2063 1.34 0.665 – – –

Crash and Crash-Relevant Conflict 2063 1.74 0.407 – – –

Crash and Unintentional Lane 

Deviation 2063 3.29 0.009 – – –

Crash and Baseline 2063 0.87 0.907 – – –

Near-Crash and Crash-Relevant 

Conflict 2063 0.23 0.999 2009 1.76 0.294

Near-Crash and Unintentional Lane 

Deviation 2063 2.57 0.077 2009 0.72 0.890

Near-Crash and Baseline 2063 1.11 0.801 2009 2.59 0.048

Crash-Relevant Conflict and 

Unintentional Lane Deviation 2063 7.64 < 0.0001 2009 5.86 < 0.0001

Crash-Relevant Conflict and Baseline 2063 6.79 < 0.0001 2009 6.83 < 0.0001

Unintentional Lane Deviation and 

Baseline 2063 14.35 < 0.0001 2009 13.29 < 0.0001
218 

Table 100. Mean Duration of Longest Glance Away from Forward Roadway—Secondary Tasks— 

across All Events and Vehicle 1 At-Fault (V1) Events 

Event Type ALL: 

df 

ALL: 

t-value 

ALL: 

p-value 

V1: 

df 

V1: 

t-value 

V1: 

p-value 

Crash and Near-crash 4704 5.17 < 0.0001 4495 5.09 < 0.0001

Crash and Crash-Relevant Conflict 4704 6.94 < 0.0001 4495 7.33 < 0.0001

Crash and Unintentional Lane 

Deviation 4704 5.27 < 0.0001 4495 5.56 < 0.0001

Crash and Baseline 4704 7.52 < 0.0001 4495 7.93 < 0.0001

Near-Crash and Crash-Relevant 

Conflict 4704 4.34 0.0001 4495 4.10 0.0004

Near-Crash and Unintentional Lane 

Deviation 4704 0.07 1.000 4495 0.34 0.997

Near-Crash and Baseline 4704 6.12 < 0.0001 4495 5.484 < 0.0001

Crash-Relevant Conflict and 

Unintentional Lane Deviation 4704 5.98 < 0.0001 4495 6.25 < 0.0001

Crash-Relevant Conflict and Baseline 4704 5.24 < 0.0001 4495 4.45 < 0.0001

Unintentional Lane Deviation and 

Baseline 4704 9.64 < 0.0001 4495 9.10 < 0.0001

Table 101. Complex Tertiary Tasks—Text Messaging— 

across All Events and Vehicle 1 At-Fault (V1) Events 

Event Type ALL: 

df 

ALL: 

t-value 

ALL: 

p-value 

V1: 

df 

V1: 

t-value 

V1: 

p-value 

Event with Text Messaging and Baseline 

with Text Messaging 22617 1.09 0.694 21914 1.31 0.557

Event with Text Messaging and Event 

without Text Messaging 22617 11.29 < 0.0001 21914 10.81 < 0.0001

Event with Text Messaging and Baseline 

without Text Messaging 22617 14.65 < 0.0001 21914 15.03 < 0.0001

Baseline with Text Messaging and Event 

without Text Messaging 22617 3.95 0.001 21914 3.58 0.002

Baseline with Text Messaging and Baseline 

without Text Messaging 22617 5.47 < 0.0001 21914 5.52 < 0.0001

Event without Text Messaging and Baseline 

without Text Messaging 22617 33.75 < 0.0001 21914 39.17 < 0.0001
219 

Table 102. Complex Tertiary Task—Other— 

across All Events and Vehicle 1 At-Fault (V1) Events 

Event Type ALL: 

df 

ALL: 

t-value 

ALL: 

p-value 

V1: 

df 

V1: 

t-value 

V1: 

p-value 

Event with Other—Complex Tertiary Task 

and Baseline with Other—Complex Tertiary 

Task 

22617 0.34 0.987 21914 0.34 0.986

Event with Other—Complex Tertiary Task 

and Event without Other—Complex Tertiary 

Task 

22617 5.46 < 0.0001 21914 5.04 < 0.0001

Event with Other—Complex Tertiary Task 

and Baseline without Other—Complex 

Tertiary Task 

22617 7.24 < 0.0001 21914 7.31 < 0.0001

Baseline with Other—Complex Tertiary Task 

and Event without Other—Complex Tertiary 

Task 

22617 3.45 0.003 21914 3.14 0.009

Baseline with Other—Complex Tertiary Task 

and Baseline without Other—Complex 

Tertiary Task 

22617 4.70 < 0.0001 21914 4.75 < 0.0001

Event without Other—Complex Tertiary Task 

and Baseline without Other—Complex 

Tertiary Task 

22617 34.45 < 0.0001 21914 39.95 < 0.0001

Table 103. Complex Tertiary Task—Interact with Dispatching Device— 

across All Events and Vehicle 1 At-Fault (V1) Events 

Event Type ALL: 

df 

ALL: 

t-value 

ALL: 

p-value 

V1: 

df 

V1: 

t-value 

V1: 

p-value 

Event with Interact with Dispatching Device 

and Baseline with Interact with Dispatching 

Device 

22617 2.46 0.067 21914 2.75 0.030

Event with Interact with Dispatching Device 

and Event without Interact with Dispatching 

Device 

22617 20.59 < 0.0001 21914 19.08 < 0.0001

Event with Interact with Dispatching Device 

and Baseline without Interact with 

Dispatching Device 

22617 27.00 < 0.0001 21914 27.97 < 0.0001

Baseline with Interact with Dispatching 

Device and Event without Interact with 

Dispatching Device 

22617 27.64 < 0.0001 21914 10.27 < 0.0001

Baseline with Interact with Dispatching 

Device and Baseline without Interact with 

Dispatching Device 

22617 16.23 < 0.0001 21914 16.37 < 0.0001

Event without Interact with Dispatching 

Device and Baseline without Interact with 

Dispatching Device 

22617 31.28 < 0.0001 21914 36.44 < 0.0001
220 

Table 104. Complex Tertiary Task—Write on pad, notebook, etc. — 

across All Events and Vehicle 1 At-Fault (V1) Events 

Event Type ALL: 

df 

ALL: 

t-value 

ALL: 

p-value 

V1: 

df 

V1: 

t-value 

V1: 

p-value 

Event with Writing and Baseline with Writing 22617 1.63 0.360 21914 1.65 0.352

Event with Writing and Event without Writing 22617 9.07 < 0.0001 21914 8.28 < 0.0001

Event with Writing and Baseline without 

Writing 22617 12.24 < 0.0001 21914 12.35 < 0.0001

Baseline with Writing and Event without 

Writing 22617 4.62 < 0.0001 21914 4.05 0.0003

Baseline with Writing and Baseline without 

Writing 22617 6.96 < 0.0001 21914 7.03 < 0.0001

Event without Writing and Baseline without 

Writing 22617 34.01 < 0.0001 21914 39.45 < 0.0001

Table 105. Complex Tertiary Task—Use Calculator— 

across All Events and Vehicle 1 At-Fault (V1) Events 

Event Type ALL: 

df 

ALL: 

t-value 

ALL: 

p-value 

V1: 

df 

V1: 

t-value 

V1: 

p-value 

Event with Use Calculator and Baseline with 

Use Calculator 22617 1.93 0.217 21914 1.95 0.029

Event with Use Calculator and Event without 

Use Calculator 22617 6.11 < 0.0001 21914 5.64 < 0.0001

Event with Use Calculator and Baseline 

without Use Calculator 22617 8.10 < 0.0001 21914 8.18 < 0.0001

Baseline with Use Calculator and Event 

without Use Calculator 22617 1.96 0.201 21914 1.61 < 0.0001

Baseline with Use Calculator and Baseline 

without Use Calculator 22617 3.37 0.004 21914 3.40 < 0.0001

Event without Use Calculator and Baseline 

without Use Calculator 22617 34.37 < 0.0001 21914 39.86 < 0.0001
221 

Table 106. Complex Tertiary Task—Look at Map— 

across All Events and Vehicle 1 At-Fault (V1) Events 

Event Type ALL: 

df 

ALL: 

t-value 

ALL: 

p-value 

V1: 

df 

V1: 

t-value 

V1: 

p-value 

Event with Look at Map and Baseline with 

Look at Map 22617 0.93 0.787 21914 0.94 0.782

Event with Look at Map and Event without 

Look at Map 22617 10.02 < 0.0001 21914 9.02 < 0.0001

Event with Look at Map and Baseline without 

Look at Map 22617 14.13 < 0.0001 21914 14.26 < 0.0001

Baseline with Look at Map and Event without 

Look at Map 22617 7.55 < 0.0001 21914 6.67 < 0.0001

Baseline with Look at Map and Baseline 

without Look at Map 22617 11.14 < 0.0001 21914 11.25 < 0.0001

Event without Look at Map and Baseline 

without Look at Map 22617 33.81 < 0.0001 21914 39.23 < 0.0001

Table 107. Complex Tertiary Task—Dial Cell Phone— 

across All Events and Vehicle 1 At-Fault (V1) Events 

Event Type ALL: 

df 

ALL: 

t-value 

ALL: 

p-value 

V1: 

df 

V1: 

t-value 

V1: 

p-value 

Event with Dial Cell Phone and Baseline with 

Dial Cell Phone 22617 3.43 0.003 21914 3.74 0.001

Event with Dial Cell Phone and Event 

without Dial Cell Phone 22617 16.28 < 0.0001 21914 14.81 < 0.0001

Event with Dial Cell Phone and Baseline 

without Dial Cell Phone 22617 22.98 < 0.0001 21914 23.23 < 0.0001

Baseline with Dial Cell Phone and Event 

without Dial Cell Phone 22617 10.36 < 0.0001 21914 8.82 < 0.0001

Baseline with Dial Cell Phone and Baseline 

without Dial Cell Phone 22617 16.41 < 0.0001 21914 16.56 < 0.0001

Event without Dial Cell Phone and Baseline 

without Dial Cell Phone 22617 32.29 < 0.0001 21914 37.56 < 0.0001
222 

Table 108. Complex Tertiary Task—Read book, newspaper, etc. — 

across All Events and Vehicle 1 At-Fault (V1) Events 

Event Type ALL: 

df 

ALL: 

t-value 

ALL: 

p-value 

V1: 

df 

V1: 

t-value 

V1: 

p-value 

Event with Reading and Baseline with 

Reading 22617 2.46 0.066 21914 2.53 0.055

Event with Reading and Event without 

Reading 22617 17.37 < 0.0001 21914 15.98 < 0.0001

Event with Reading and Baseline without 

Reading 22617 23.11 < 0.0001 21914 23.26 < 0.0001

Baseline with Reading and Event without 

Reading 22617 15.23 < 0.0001 21914 13.72 < 0.0001

Baseline with Reading and Baseline without 

Reading 22617 21.45 < 0.0001 21914 21.65 < 0.0001

Event without Reading and Baseline without 

Reading 22617 33.02 < 0.0001 21914 38.32 < 0.0001

Table 109. Moderate Tertiary Tasks—Use/Reach for Other Electronic Device— 

across All Events and Vehicle 1 At-Fault (V1) Events 

Event Type ALL: 

df 

ALL: 

t-value 

ALL: 

p-value 

V1: 

df 

V1: 

t-value 

V1: 

p-value 

Event with Use/Reach for Other Electronic 

Device and Baseline with Use/Reach for 

Other Electronic Device 

22617 5.07 < 0.0001 21914 4.99 < 0.0001

Event with Use/Reach for Other Electronic 

Device and Event without Use/Reach for 

Other Electronic Device 

22617 5.76 < 0.0001 21914 4.98 < 0.0001

Event with Use/Reach for Other Electronic 

Device and Baseline without Use/Reach for 

Other Electronic Device 

22617 7.85 < 0.0001 21914 7.52 < 0.0001

Baseline with Use/Reach for Other 

Electronic Device and Event without 

Use/Reach for Other Electronic Device 

22617 1.74 0.302 21914 2.23 0.116

Baseline with Use/Reach for Other 

Electronic Device and Baseline without 

Use/Reach for Other Electronic Device 

22617 0.03 1.000 21914 0.03 1.000

Event without Use/Reach for Other 

Electronic Device and Baseline without 

Use/Reach for Other Electronic Device 

22617 34.34 < 0.0001 21914 39.86 < 0.0001
223 

Table 110. Moderate Tertiary Tasks—Other— 

across All Events and Vehicle 1 At-Fault (V1) Events 

Event Type ALL: 

df 

ALL: 

t-value 

ALL: 

p-value 

V1: 

df 

V1: 

t-value 

V1: 

p-value 

Event with Other—Moderate Tertiary Task 

and Baseline with Other—Moderate Tertiary 

Task 

22617 2.72 0.033 21914 2.75 0.030

Event with Other—Moderate Tertiary Task 

and Event without Other—Moderate Tertiary 

Task 

22617 4.19 0.0002 21914 3.56 0.002

Event with Other—Moderate Tertiary Task 

and Baseline without Other—Moderate 

Tertiary Task 

22617 6.70 < 0.0001 21914 6.76 < 0.0001

Baseline with Other—Moderate Tertiary 

Task and Event without Other—Moderate 

Tertiary Task 

22617 0.16 0.999 21914 0.69 0.903

Baseline with Other—Moderate Tertiary 

Task and Baseline without Other—Moderate 

Tertiary Task 

22617 1.83 0.260 21914 1.85 0.252

Event without Other—Moderate Tertiary 

Task and Baseline without Other—Moderate 

Tertiary Task 

22617 34.36 < 0.0001 21914 39.86 < 0.0001

Table 111. Moderate Tertiary Tasks—Personal Grooming— 

across All Events and Vehicle 1 At-Fault (V1) Events 

Event Type ALL: 

df 

ALL: 

t-value 

ALL: 

p-value 

V1: 

df 

V1: 

t-value 

V1: 

p-value 

Event with Personal Grooming and Baseline 

with Personal Grooming 22617 5.66 < 0.0001 21914 5.72 < 0.0001

Event with Personal Grooming and Event 

without Personal Grooming 22617 4.71 < 0.0001 21914 4.20 0.0002

Event with Personal Grooming and Baseline 

without Personal Grooming 22617 6.80 < 0.0001 21914 6.86 < 0.0001

Baseline with Personal Grooming and Event 

without Personal Grooming 22617 3.29 0.006 21914 3.87 0.001

Baseline with Personal Grooming and 

Baseline without Personal Grooming 22617 1.21 0.619 21914 1.22 0.611

Event without Personal Grooming and 

Baseline without Personal Grooming 22617 34.37 < 0.0001 21914 39.87 < 0.0001
224 

Table 112. Moderate Tertiary Tasks—Reach for Object— 

across All Events and Vehicle 1 At-Fault (V1) Events 

Event Type ALL: 

df 

ALL: 

t-value 

ALL: 

p-value 

V1: 

df 

V1: 

t-value 

V1: 

p-value 

Event with Reach for Object and Baseline 

with Reach for Object 22617 11.41 < 0.0001 21914 12.68 < 0.0001

Event with Reach for Object and Event 

without Reach for Object 22617 12.21 < 0.0001 21914 10.99 < 0.0001

Event with Reach for Object and Baseline 

without Reach for Object 22617 22.28 < 0.0001 21914 23.41 < 0.0001

Baseline with Reach for Object and Event 

without Reach for Object 22617 1.10 0.687 21914 4.89 < 0.0001

Baseline with Reach for Object and Baseline 

without Reach for Object 22617 14.16 < 0.0001 21914 14.29 < 0.0001

Event without Reach for Object and Baseline 

without Reach for Object 22617 31.68 < 0.0001 21914 36.75 < 0.0001

Table 113. Moderate Tertiary Tasks—Look Back in Sleeper Berth— 

across All Events and Vehicle 1 At-Fault (V1) Events 

Event Type ALL: 

df 

ALL: 

t-value 

ALL: 

p-value 

V1: 

df 

V1: 

t-value 

V1: 

p-value 

Event with Look Back in Sleeper Berth and 

Baseline with Look Back in Sleeper Berth 22617 11.41 < 0.0001 21914 3.12 0.010

Event with Look Back in Sleeper Berth and 

Event without Look Back in Sleeper Berth 22617 12.21 < 0.0001 21914 4.41 < 0.0001

Event with Look Back in Sleeper Berth and 

Baseline without Look Back in Sleeper Berth 22617 22.28 < 0.0001 21914 7.31 < 0.0001

Baseline with Look Back in Sleeper Berth 

and Event without Look Back in Sleeper 

Berth 

22617 1.10 0.687 21914 0.74 0.881

Baseline with Look Back in Sleeper Berth 

and Baseline without Look Back in Sleeper 

Berth 

22617 14.16 < 0.0001 21914 4.67 < 0.0001

Event without Look Back in Sleeper Berth 

and Baseline without Look Back in Sleeper 

Berth 

22617 31.68 < 0.0001 21914 39.91 < 0.0001
225 

Table 114. Moderate Tertiary Tasks—Smoking–Lighting— 

across All Events and Vehicle 1 At-Fault (V1) Events 

Event Type ALL: 

df 

ALL: 

t-value 

ALL: 

p-value 

V1: 

df 

V1: 

t-value 

V1: 

p-value 

Event with Smoking—Lighting and Baseline 

with Smoking—Lighting 22617 1.26 0.590 21914 2.16 0.136

Event with Smoking—Lighting and Event 

without Smoking—Lighting 22617 0.01 0.999 21914 0.54 0.950

Event with Smoking—Lighting and Baseline 

without Smoking—Lighting 22617 2.92 0.019 21914 3.67 0.001

Baseline with Smoking—Lighting and Event 

without Smoking—Lighting 22617 3.70 0.001 21914 5.99 < 0.0001

Baseline with Smoking—Lighting and 

Baseline without Smoking—Lighting 22617 5.17 < 0.0001 21914 5.22 < 0.0001

Event without Smoking—Lighting and 

Baseline without Smoking—Lighting 22617 34.77 < 0.0001 21914 40.24 < 0.0001

Table 115. Moderate Tertiary Tasks—Talk/Listen to CB— 

across All Events and Vehicle 1 At-Fault (V1) Events 

Event Type ALL: 

df 

ALL: 

t-value 

ALL: 

p-value 

V1: 

df 

V1: 

t-value 

V1: 

p-value 

Event with Talk/Listen to CB and Baseline 

with Talk/Listen to CB 22617 1.63 0.363 21914 1.60 0.381

Event with Talk/Listen to CB and Event 

without Talk/Listen to CB 22617 3.46 0.003 21914 3.46 0.003

Event with Talk/Listen to CB and Baseline 

without Talk/Listen to CB 22617 0.49 0.962 21914 0.64 0.918

Baseline with Talk/Listen to CB and Event 

without Talk/Listen to CB 22617 15.17 < 0.0001 21914 18.16 < 0.0001

Baseline with Talk/Listen to CB and Baseline 

without Talk/Listen to CB 22617 3.73 0.001 21914 3.76 0.001

Event without Talk/Listen to CB and 

Baseline without Talk/Listen to CB 22617 34.58 < 0.0001 21914 40.11 < 0.0001
226 

Table 116. Moderate Tertiary Tasks—External Distraction— 

across All Events and Vehicle 1 At-Fault (V1) Events 

Event Type ALL: 

df 

ALL: 

t-value 

ALL: 

p-value 

V1: 

df 

V1: 

t-value 

V1: 

p-value 

Event with External Distraction and Baseline 

with External Distraction 22617 6.19 < 0.0001 21914 7.74 < 0.0001

Event with External Distraction and Event 

without External Distraction 22617 1.46 0.460 21914 0.01 1.000

Event with External Distraction and Baseline 

without External Distraction 22617 19.04 < 0.0001 21914 19.16 < 0.0001

Baseline with External Distraction and Event 

without External Distraction 22617 9.05 < 0.0001 21914 15.77 < 0.0001

Baseline with External Distraction and 

Baseline without External Distraction 22617 32.18 < 0.0001 21914 32.53 < 0.0001

Event without External Distraction and 

Baseline without External Distraction 22617 38.28 < 0.0001 21914 43.70 < 0.0001

Table 117. Moderate Tertiary Tasks—Talk/Listen to Hands-Free Phone— 

across All Events and Vehicle 1 At-Fault (V1) Events 

Event Type ALL: 

df 

ALL: 

t-value 

ALL: 

p-value 

V1: 

df 

V1: 

t-value 

V1: 

p-value 

Event with Talk/Listen to Hands-Free Phone 

and Baseline with Talk/Listen to Hands-Free 

Phone 

22617 4.01 0.0004 21914 4.12 0.0002

Event with Talk/Listen to Hands-Free Phone 

and Event without Talk/Listen to Hands-Free 

Phone 

22617 2.27 0.106 21914 2.49 0.061

Event with Talk/Listen to Hands-Free Phone 

and Baseline without Talk/Listen to HandsFree Phone 

22617 3.33 0.005 21914 3.52 0.002

Baseline with Talk/Listen to Hands-Free 

Phone and Event without Talk/Listen to 

Hands-Free Phone 

22617 19.32 < 0.0001 21914 23.39 < 0.0001

Baseline with Talk/Listen to Hands-Free 

Phone and Baseline without Talk/Listen to 

Hands-Free Phone 

22617 2.76 0.030 21914 2.79 0.027

Event without Talk/Listen to Hands-Free 

Phone and Baseline without Talk/Listen to 

Hands-Free Phone 

22617 34.28 < 0.0001 21914 39.83 < 0.0001
227 

Table 118. Moderate Tertiary Tasks—Put on/remove/adjust sunglasses or glasses— 

across All Events and Vehicle 1 At-Fault (V1) Events 

Event Type ALL: 

df 

ALL: 

t-value 

ALL: 

p-value 

V1: 

df 

V1: 

t-value 

V1: 

p-value 

Event with Put on/remove/adjust sunglasses 

or glasses and Baseline with Put 

on/remove/adjust sunglasses or glasses 

22617 3.24 0.007 21914 3.95 0.001

Event with Put on/remove/adjust sunglasses 

or glasses and Event without Put 

on/remove/adjust sunglasses or glasses 

22617 1.37 0.519 21914 1.50 0.438

Event with Put on/remove/adjust sunglasses 

or glasses and Baseline without Put 

on/remove/adjust sunglasses or glasses 

22617 4.59 < 0.0001 21914 5.35 < 0.0001

Baseline with Put on/remove/adjust 

sunglasses or glasses and Event without Put 

on/remove/adjust sunglasses or glasses 

22617 3.46 0.003 21914 4.55 < 0.0001

Baseline with Put on/remove/adjust 

sunglasses or glasses and Baseline without 

Put on/remove/adjust sunglasses or glasses 

22617 0.56 0.945 21914 0.56 0.943

Event without Put on/remove/adjust 

sunglasses or glasses and Baseline without 

Put on/remove/adjust sunglasses or glasses 

22617 34.44 < 0.0001 21914 39.92 < 0.0001

Table 119. Moderate Tertiary Tasks—Adjust Sunglasses— 

across All Events and Vehicle 1 At-Fault (V1) Events 

Event Type ALL: 

df 

ALL: 

t-value 

ALL: 

p-value 

V1: 

df 

V1: 

t-value 

V1: 

p-value 

Event with Adjust Instrument Panel and 

Baseline with Adjust Instrument Panel 22617 5.31 < 0.0001 21914 6.80 < 0.0001

Event with Adjust Instrument Panel and 

Event without Adjust Instrument Panel 22617 6.78 < 0.0001 21914 6.39 0.0002

Event with Adjust Instrument Panel and 

Baseline without Adjust Instrument Panel 22617 14.61 < 0.0001 21914 15.74 < 0.0001

Baseline with Adjust Instrument Panel and 

Event without Adjust Instrument Panel 22617 2.00 0.187 21914 1.82 0.263

Baseline with Adjust Instrument Panel and 

Baseline without Adjust Instrument Panel 22617 18.14 < 0.0001 21914 18.32 < 0.0001

Event without Adjust Instrument Panel and 

Baseline without Adjust Instrument Panel 22617 34.29 < 0.0001 21914 39.56 < 0.0001
228 

Table 120. Simple Tertiary Tasks—Other Personal Hygiene— 

across All Events and Vehicle 1 At-Fault (V1) Events 

Event Type ALL: 

df 

ALL: 

t-value 

ALL: 

p-value 

V1: 

df 

V1: 

t-value 

V1: 

p-value 

Event with Other Personal Hygiene and 

Baseline with Other Personal Hygiene 22617 5.84 < 0.0001 21914 6.72 < 0.0001

Event with Other Personal Hygiene and 

Event without Other Personal Hygiene 22617 5.24 < 0.0001 21914 6.40 < 0.0001

Event with Other Personal Hygiene and 

Baseline without Other Personal Hygiene 22617 5.45 < 0.0001 21914 6.38 < 0.0001

Baseline with Other Personal Hygiene and 

Event without Other Personal Hygiene 22617 25.06 < 0.0001 21914 30.23 < 0.0001

Baseline with Other Personal Hygiene and 

Baseline without Other Personal Hygiene 22617 1.86 0.245 21914 1.88 0.237

Event without Other Personal Hygiene and 

Baseline without Other Personal Hygiene 22617 34.28 < 0.0001 21914 39.89 < 0.0001

Table 121. Simple Tertiary Tasks—Bite Nails— 

across All Events and Vehicle 1 At-Fault (V1) Events 

Event Type ALL: 

df 

ALL: 

t-value 

ALL: 

p-value 

V1: 

df 

V1: 

t-value 

V1: 

p-value 

Event with Bite Nails and Baseline with Bite 

Nails 22617 0.83 0.838 21914 1.02 0.738

Event with Bite Nails and Event without Bite 

Nails 22617 2.77 0.029 21914 2.65 0.041

Event with Bite Nails and Baseline without 

Bite Nails 22617 0.32 0.989 21914 0.02 1.000

Baseline with Bite Nails and Event without 

Bite Nails 22617 12.01 < 0.0001 21914 14.18 < 0.0001

Baseline with Bite Nails and Baseline without 

Bite Nails 22617 4.00 0.0004 21914 4.04 < 0.0001

Event without Bite Nails and Baseline 

without Bite Nails 22617 34.60 < 0.0001 21914 40.12 < 0.0001
229 

Table 122. Simple Tertiary Tasks—Interact with Other Occupant(s)— 

across All Events and Vehicle 1 At-Fault (V1) Events 

Event Type ALL: 

df 

ALL: 

t-value 

ALL: 

p-value 

V1: 

df 

V1: 

t-value 

V1: 

p-value 

Event with Interact with Other Occupant(s) 

and Baseline with Interact with Other 

Occupant(s) 

22617 1.33 0.543 21914 1.50 0.440

Event with Interact with Other Occupant(s) 

and Event without Interact with Other 

Occupant(s) 

22617 0.03 1.000 21914 0.35 0.985

Event with Interact with Other Occupant(s) 

and Baseline without Interact with Other 

Occupant(s) 

22617 2.73 0.032 21914 2.78 0.028

Baseline with Interact with Other 

Occupant(s) and Event without Interact with 

Other Occupant(s) 

22617 4.85 < 0.0001 21914 7.39 < 0.0001

Baseline with Interact with Other 

Occupant(s) and Baseline without Interact 

with Other Occupant(s) 

22617 4.98 < 0.0001 21914 5.03 < 0.0001

Event without Interact with Other 

Occupant(s) and Baseline without Interact 

with Other Occupant(s) 

22617 34.79 < 0.0001 21914 40.30 < 0.0001

Table 123. Secondary Tasks—Look at Left Mirror/Out Left Window— 

across All Events and Vehicle 1 At-Fault (V1) Events 

Event Type ALL: 

df 

ALL: 

t-value 

ALL: 

p-value 

V1: 

df 

V1: 

t-value 

V1: 

p-value 

Event with Left Mirror/Window and Baseline 

with Left Mirror/Window 22617 8.58 < 0.0001 21914 9.21 < 0.0001

Event with Left Mirror/Window and Event 

without Left Mirror/Window 22617 3.90 0.0006 21914 0.30 0.990

Event with Left Mirror/Window and Baseline 

without Left Mirror/Window 22617 27.67 < 0.0001 21914 26.30 < 0.0001

Baseline with Left Mirror/Window and Event 

without Left Mirror/Window 22617 6.33 < 0.0001 21914 14.25 < 0.0001

Baseline with Left Mirror/Window and 

Baseline without Left Mirror/Window 22617 36.90 < 0.0001 21914 37.30 0.001

Event without Left Mirror/Window and 

Baseline without Left Mirror/Window 22617 35.89 < 0.0001 21914 42.45 < 0.0001
230 

Table 124. Secondary Tasks—Look at Right Mirror/Out Right Window— 

across All Events and Vehicle 1 At-Fault (V1) Events 

Event Type ALL: 

df 

ALL: 

t-value 

ALL: 

p-value 

V1: 

df 

V1: 

t-value 

V1: 

p-value 

Event with Right Mirror/Window and 

Baseline with Right Mirror/Window 

22617 4.64 < 0.0001 21914 4.14 0.0002

Event with Right Mirror/Window and Event 

without Right Mirror/Window 

22617 4.81 < 0.0001 21914 1.25 0.592

Event with Right Mirror/Window and 

Baseline without Right Mirror/Window 

22617 18.93 < 0.0001 21914 15.68 < 0.0001

Baseline with Right Mirror/Window and 

Event without Right Mirror/Window 

22617 0.10 1.000 21914 6.53 < 0.0001

Baseline with Right Mirror/Window and 

Baseline without Right Mirror/Window 

22617 30.81 < 0.0001 21914 31.13 < 0.0001

Event without Right Mirror/Window and 

Baseline without Right Mirror/Window 

22617 35.97 < 0.0001 21914 42.57 < 0.0001

Table 125. Secondary Tasks—Check Speedometer— 

across All Events and Vehicle 1 At-Fault (V1) Events 

Event Type ALL: 

df 

ALL: 

t-value 

ALL: 

p-value 

V1: 

df 

V1: 

t-value 

V1: 

p-value 

Event with Check Speedometer and 

Baseline with Check Speedometer 22617 1.98 0.194 21914 2.78 0.028

Event with Check Speedometer and Event 

without Check Speedometer 22617 0.98 0.763 21914 1.83 0.259

Event with Check Speedometer and 

Baseline without Check Speedometer 22617 7.26 < 0.0001 21914 7.68 < 0.0001

Baseline with Check Speedometer and 

Event without Check Speedometer 22617 9.10 < 0.0001 21914 14.71 < 0.0001

Baseline with Check Speedometer and 

Baseline without Check Speedometer 22617 18.71 < 0.0001 21914 18.90 < 0.0001

Event without Check Speedometer and 

Baseline without Check Speedometer 22617 36.71 < 0.0001 21914 42.12 < 0.0001
ACKNOWLEDGEMENTS 

This study would not have been possible without the trucking fleets and drivers who participated 

in the Drowsy Driver Warning System Field Operational Test and the Naturalistic Truck Driving 

Study. 

231 

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ANNOTATED BIBLIOGRAPHY 

assmeier, M., Poitschke, T., Wallhoff, F., Bengler, K., & Rigoll, G. (2007). Eye gaze studies 

comparing head-up and h

International Conference on Multimedia and Expo, Beijing, China, July 2–5, 2007, pp. 

2250–2252. Retrieved August 23, 2007, from: 

http://www.mmk.ei.tum.de/publ/pdf/07/07abl3.pdf 

This paper discusses the results of two studies conducted to evaluate the usability of a

heads-up display (HUD). The f

vehicle equipped with two heads-down displays and a HUD. Participants were asked to 

read road signs and control speed lim

participants “accept and desire” the HUD while driving. The second study investigated

the layout of the HUD and found that participants needed 4.9 s to mentally conceive fo

new symbols. 

Abdel-Aty, M. (2003). Investigating the relationship between cellular phone use and traffic 

safety. ITE (Institute of Transportation Engineers) Journal, 73(10):38–42. 

This investigation discusses the effects of cellular phone use on driver behavior and 

safety on the road. Study results show no significant difference in using hands-free 

mobile technology as opposed to a handheld cellular phone. Driver errors were shown to 

increase once a driver is distracted with using the cellular phone, and the dangers 

a cellular phone are still evident even after finishing a phone conversation on the road. In

terms of truck driving safety, cellular phone use is identif

considered a driver distraction and may lead to vehicle cras

Barr, L.C., Yang, C.Y.D., Hanowski, R.J., & Olson, R. (2005). Assessment of driver fatigue, 

distraction, and performance in a naturalistic setting. Transportation Research Record: 

Journal of the Transportation Research Board, Vol. 1937:51–60. Retrieved August 23, 2

from: http://trb.metapress.com/content/h716785086k1g603/ 

This study focused on the effects of fatigue and drowsiness on driving perform

naturalistic setting. Results show that highe

less experienced drivers and also drowsiness was twice as likely to occur between

a.m. and 9:00 a.m. The drowsy events were observed in a naturalistic setting in an effort 

to find effect

drowsiness and driver fatigue may be identified as distracters in driving performance. 

 conversation: The im

simulated driving performance. Accident Analysis and Prevention, 38(2):415–421. 

The research examined distracted driving behavior and its eff

safety. More specifically, the impact of cellular phones on driving performance indica

increased driver error 

divided attention, and delayed response. These driver errors are also common among 

commercial vehicle drivers, in which distraction such as cell phone use can lead to 

dangerous vehicle crashes. 

Abl

ead-down displays in vehicles. Proceedings of the 2007 IEEE 

irst study, a field study, had participants driving a test 

its on the displays. Results found that 85 percent of 

ur 

of using 

ied as a secondary task. It is 

hes and near-crashes. 

007, 

ance in a 

r levels of fatigue were found in younger and 

 6:00 

ive countermeasures for drowsy driving and reduce fatality rates as 

Beede, K.E., & Kass, S.J. (2006). Engrossed in pact of cell phones on 

ects on transportation 

tes 

in areas such as traffic violations, lane position maintenance, 

239 
Blanco, M., Hickman, J.S., Olson, R.L., Bocanegra, J.L., Hanowski, R.J., Nakata, A., Greening, 

M., Madison, P., Holbrook, G.T., & Bowman, D. (in press). Investigating critical incidents, 

e 

Safety 

e work/rest parameters relating 

n and 

primary goal of this on-road study was to 

Bun

. Studies conducted determined that these factors 

 Various 

ty 

, 35(4):495–500. 

s, 

d phones and hands-free 

Coo

 on their cognitive decision-making while being distracted by 

driver restart period, sleep quantity, and crash countermeasures in commercial vehicl

operations using naturalistic data collection. Washington DC: Federal Motor Carrier 

Administration, USDOT. 

This report presents the results of an on-road naturalistic driving data collection effort to 

investigate light vehicle-heavy vehicle (LV-HV) interactions and other safety issues 

related to CMV crash risk. Three primary focus areas wer

to driver fatigue and incident involvement, event causation, and LV-HV interactio

applicable functional countermeasures. The 

investigate crashes, near-crashes, and crash-relevant conflicts from the HV driver’s 

perspective to help determine functional countermeasures. 

n, T.L., Slavova, S., Struttmann, T.W., & Browning, S.R. (2005). Sleepiness/fatigue and 

distraction/inattention as factors for fatal versus nonfatal commercial motor vehicle driver 

injuries. Accident Analysis and Prevention, 37(5):862–869. 

This population-based case-control study observed fatigue and sleepiness as an element 

of inattention and driver distraction

greatly increased likelihood of fatalities in commercial motor vehicle collisions.

factors contributed to identifying crashes as fatal, such as driver sleepiness/fatigue, 

distraction/inattention, ages of drivers, and the nonuse or misuse of safety belts. Safe

belt law enactment and enforcement for all states and driver education focusing on 

fatigue and distraction for commercial vehicle drivers may decrease chances of 

commercial vehicle fatalities. 

Consiglio, W., Driscoll, P., Witte, M., & Berg, W.P. (2003). Effect of cellular telephone 

conversations and other potential interference on reaction time in a braking response. 

Accident Analysis and Prevention

This study focused on the effect of cellular telephone conversations on driver behavior. 

Reaction time in braking response was affected by in-car telephone conversation

listening to the radio, and talking with a passenger. Hand-hel

phones were found to have similar effect on reaction time, causing it to slow, whereas 

tasks such as listening to the radio did not show signs of slowing reaction time. 

per, P.J., & Zheng, Y. (2002). Turning gap acceptance decision-making: the impact of driver 

distraction. Journal of Safety Research, 33(3):321–335. 

This experiment in driver distraction was specific to turning gap acceptance. Participants

were exposed to a number of gaps in a stream of traffic and were given the task of 

deciding on a safe gap to turn left. The subjects experienced both dry and wet road 

conditions and were studied

various tasks. Results showed that although the scenario was very artificial, the act of 

turning into a stream of traffic while being distracted significantly increased unsafe 

decision making. 

240 
241 

Crundall, D. Van Loon, E., & Underwood, G. (2006). Attraction and distraction of attention with 

roadside advertisements. Accident Analysis & Prevention, 38(4):671-677. 

Roadside distractions such as raised-level advertisements (RLAs) and street-level 

advertisements (SLAs) act as agents in driver distraction, as shown in this study. 

Roadside advertisements have a tendency to distract passing drivers, therefore affecting 

their ability to control the vehicle. The effect of this distraction was studied on a number 

of participants primed to attend to the advertisements in the study. SLAs were found to 

elicit longer fixations than RLAs and were more likely to distract. 

De Waard, D., Brookhuis, K.A., & Hernández-Gress, N. (2001). The feasibility of detecting 

phone-use related driver distraction. International Journal of Vehicle Design, 26(1 special 

issue):85–95. 

Similar to work by Abdel-Aty (above), this study observed 20 participants in a driving 

simulator under two conditions: 1) driving in normal conditions, and 2) driving while 

engaged in a cell phone conversation. Results showed deteriorated driving performance 

while drivers handled cell phone conversations. 

Dingus, T.A., Antin, J.F., Hulse, M.C. & Wierwille, W.W. (1989). Attentional demand 

requirements of an automobile moving map navigation system. Transportation Research, 

Part A: Policy and Practice, 23A(4):301–315. 

The objective of this research was to assess the driver visual attentional demand 

requirements of an operational in-car navigation system. Thirty-two driver subjects, aged 

18–73 years, drove a specially instrumented vehicle on various types of public roadways 

with varying traffic conditions. Results show that the demand of most of the navigation 

tasks was comparable to that of one or more conventional tasks. Modifying the remaining 

navigation tasks to make information more readily available would reduce their demand 

Dingus, T., Neale, V., Garness, S., Hanowski, R., Keisler, A., Lee, S., Perez, M., Robinson, G., 

Belz, S., Casali, J., Pace-Schott, E., Stickgold, R., & Hobson, J. (2002). Impact of sleeper 

berth usage on driver fatigue: Final report (Document No. FMCSA-RT-02-050). 

Washington, DC: Federal Motor Carrier Safety Administration, USDOT. 

This report summarizes issues on driving safety and fatigue, resulting from a literature 

review and 10 focus groups. Specifically, the study focused on understanding the 

perspective of long-haul drivers, as opposed to Hanowski, Wierwille, Garness, and 

Dingus (2000, below). Focus groups results reveal concerns by drivers that include, but 

are not limited to, the quality of equipment, team driving, the lack of rest areas, driver 

education, cargo, and the pressure to drive. The results from the literature review and 

focus groups reveal a need for an on-road study to further evaluate and confirm the 

possible effects of fatigue. 

Fuse, T., Matsunaga, K., Shidoji, K., Matsuki, Y., & Umezaki, K. (2001). The cause of traffic 

accidents when drivers use car phones and the functional requirements of car phones for safe 

driving. International Journal of Vehicle Design, 26(1 special issue):48–56. 

The effects of cell phone use on driving behavior are examined in this study. Three 

situations were studied to measure reaction time: 1) cell phone conversation with a 
handset, 2) cell phone conversation during a manual task with handset, and 3) eyes on the 

road during a manual task with a handset. Results showed that the most significant effect 

of the car phone came from diverted visual attention rather than phone operation and 

conversation. This effect greatly influences truck safety, as complete visual attention is 

imperative in operating a CMV. 

Goodman, M.J., Tijerina, L., Bents, F.D., & Wierwille, W.W. (1999). Using cellular telephones 

in vehicles: Safe or unsafe? Transportation Human Factors, I(1):3–42. 

In this study, limited crash data revealed that the complexity of the conversation was the 

principal factor associated with most crashes. As opposed to dialing, hanging up, or 

reaching for the telephone, the complexity of the conversation proved to create a much 

greater risk for accidents; however, the general use of a cellular telephone in a vehicle 

increases risk of accidents and deteriorates driving performance. 

Greenberg, J., Tijerina, L., Curry, R., Artz, B., Cathey, L., Kochhar, D., Kozak, K., Blommer, 

M., & Grant, P. (2003). Driver distraction: Evaluation with event detection paradigm. 

Transportation Research Record, 1843:1–9. Retrieved August 23, 2007, from: 

http://pubsindex.trb.org/document/view/default.asp?lbid=682271 

Eight in-vehicle tasks and their effects on driver distraction were observed in this study. 

The study monitored participants of ages 16–66 as they coordinated cell phone tasks, 

took in sudden movements outside of the vehicle in surrounding traffic, tuned the radio, 

and adjusted the climate control. Performance of these in-vehicle tasks were compared 

between the adult group and the teenage group. Results showed the teens having poor 

vehicle control skills and are more prone to distraction than the adult group. These invehicle tasks are also seen as hazards in CMV safety and can lead to unsafe truck driving 

habits. 

Hancock, P.A., Simmons, L., Hashemi, L., Howarth, H., & Ranney, T. (1999). The effects of invehicle distraction on driver response during a crucial driving maneuver. Transportation 

Human Factors, 1(4):295–309. 

This study observed 10 volunteer drivers as they performed 60 repeated circuits on a 

light-controlled closed-loop test track. The study primarily observed the effects of an invehicle distracter (a telephone, number-matching task) on driver reaction. Vehicle speeds 

varied between approximately 20 to 30 mi/h. Results showed slower brake response 

times due to the presence of distracters. In addition, the margin of safety was significantly 

reduced by about 25 percent. These in-vehicle distracters can greatly affect reaction times 

for CMVs as they decrease the margin of safety. 

Hancock, P.A., Lesch, M., & Simmons, L. (2003). The distraction effects of phone use during a 

crucial driving maneuver. Accident Analysis and Prevention, 35(4):501–514. 

The study observed 42 drivers on a test track facilities. These participants were required 

to respond to an in-vehicle phone while negotiating a crucial stopping decision. To 

compensate for slowed response times, participants braked more intensely. An increased 

number of stop-light violations also resulted when participants were taken on the open 

road. The authors concluded that in-vehicle technologies such as telephones deteriorate 

242 
driving performance and is a crucial concern for drivers of all vehicles and all in-vehicle 

device designers. 

 VA: 

rtance of visual attention while driving. 

s 

Hanow

R.L

The drowsy driver warning system field operational test, data collection final report (Report 

on, 

/20

Han

ort 

n, 

h 

ts 

ith 

Hanowski, R.J. (2009). When Driving, Vision is King (commentary). In Knipling, R.R. (Ed.) 

Safety for the Long Haul: Large Truck Crash Risk, Causation, & Prevention. Arlington,

American Trucking Associations. 

In this commentary, the author stresses the impo

He cites data from the 100-Car study (Klauer et al., 2006), which found that eye glance

away from the forward roadway for greater than 2 s significantly increase the risk of a 

driver being involved in a crash or near-crash. The need for visual attention also applies 

to CMV drivers as traffic conflicts were more likely to occur in heavy traffic. 

ski, R.J., Blanco, M., Nakata, A., Hickman, J.S., Schaudt, W.A., Fumero, M.C., Olson, 

., Jermeland, J., Greening, M., Holbrook, G.T., Knipling, R.R., & Madison, P. (2008). 

No. DOT HS 810 035). Washington, DC: National Highway Traffic Safety Administrati

USDOT. Retrieved August 26, 2009, from: 

http://www.nhtsa.gov/staticfiles/DOT/NHTSA/NRD/Multimedia/PDFs/Crash%20Avoidance

08/810035.pdf 

The focus of this report is the description of the data collection procedures of a 

naturalistic field operational test (FOT) to determine the safety benefits and operational 

capabilities, limitations, and characteristics of a driver fatigue monitor. Forty-six trucks, 

operated by 103 drivers, were instrumented with a data acquisition system that recorded 

more than 100 data variables. Other collected measures were video, actigraphy, and 

questionnaires. Drivers were randomly assigned to either control (24 drivers) or 

experimental groups (79 drivers). 

owski, R.J., Perez, M.A., & Dingus, T.A. (2005). Driver distraction in long-haul truck 

drivers. Transportation Research Part F, 8(6):441-458. 

The study investigated driver fatigue during trucking operations via naturalistic data on 

41 drivers collected as they worked their normal routes. The data collected from the study

were used to examine a variety of fatigue-related factors in trucking operations. “Incident

type” classifications were developed surrounding crashes or near-crashes for the data. 

One classification was “driver distraction,” which was identified as a causal factor for 

incidents involving long-haul trucks. 

Hanowski, R.J., Wierwille, W.W., Garness, S.A., & Dingus, T.A. (2000). Impact of local/sh

haul operations of driver fatigue: Final report (Report no. DOT-MC-00-201). Washingto

DC: Federal Motor Carrier Safety Administration, USDOT. 

Before research for this particular report was conducted, focus groups assessed 

local/short-haul (L/SH) drivers and their concerns on safety and fatigue on the road. An 

on-road field study with 42 drivers was then conducted, the primary objective of whic

was to determine the significance of the presence of fatigue in L/SH operations. Resul

indicate fatigue was not caused by the job presented to the driver, but rather brought w

243 
the driver to the job. The results of the research have been used to help address fatigue 

and other safety issues involved in L/SH operations. 

Harbluk, J.L., Noy, Y.I., & Eizenman, M. (2002). The impact of cognitive distraction on driver 

visual behaviour and vehicle control. TP# 13889 E. Ottawa, Canada: Transport Canada. 

Twenty-one drivers participated in this on-road study in which they drove a designated 

route while performing difficult addition tasks, easy addition tasks, or no tasks; all tasks 

were communicated through a hands-free cell phone so drivers never had to remove their 

eyes from the forward roadway. Results show that while participants were under 

conditions of increased cognitive load, they spent less time checking instruments and the 

rear view mirror and also changed their inspection patterns of the forward view. In 

addition, drivers had more incidences of hard braking while experiencing an increased 

cognitive workload. 

Hashemi, L., Simmons, L.A., Howarth, H.D., & Hancock, P.A. (1998). Effects of an in-vehicle 

distractor upon driver performance. Proceedings of the Human Factors and Ergonomics 

Society 42nd Annual Meeting, 2:1626. 

The effects of in-vehicle distracters were observed in this study, in which 10 participants 

drove on a closed test-track. Results revealed a slow reaction time among the drivers in 

response to changing of stoplights at a light-controlled stop. Once realizing a light 

change, more intense braking and a decreased stopping time occurred. In handling 

CMVs, the intense braking would cause a hazardous situation in that the larger and 

heavier commercial vehicles require a longer stopping time. 

Hickman, J.S., Kipling, R.R., Olson, R.L., Fumero, M.C., Blanco, M., & Hanowski, R.J. (in 

press). Heavy vehicle-light vehicle interaction data collection and countermeasure research 

project: Preliminary analysis of data collected in the drowsy driver warning system field 

operational test. Washington, DC: FMCSA, USDOT. 

This project instrumented vehicles to collect data used to improve aspects of CMV safety. 

These aspects include but are not limited to safety events, traffic conflict assessments, 

and light vehicle-heavy vehicle interactions. The report includes data collected in the 

Drowsy Driver Warning System Field Operational Test (DDWS FOT) between May 

2004 and May 2005, and provides extensive data for the analysis of heavy-vehicle safety 

and driver risk. 

Horrey, W.J., Wickens, C.D., & Consalus, K.P. (2006). Modeling drivers’ visual attention 

allocation while interacting with in-vehicle technologies. Journal of Experimental 

Psychology: Applied, 12(2):67–78. 

The authors completed two experiments to examine a simulated traffic environment and 

driver performance on in-vehicle tasks. The first experiment dealt with task priority; the 

second experiment examined task complexity, introducing infrequent traffic hazards. 

Overall, this study emphasized in-vehicle tasks and their impact on driver distraction. 

These in-vehicle tasks and task priorities increased scanning in both experiments and also 

challenged lane keeping. In-vehicle tasks are also a major influence in hazards for 

244 
CMVS, which are much larger and more difficult to control under the influence of such 

in-vehicle tasks. 

Just, M.A., Keller, T.A., & Cynkar, J. (2008). A decrease in brain activation associated with 

driving when listening to someone speak. Brain Research Apr 18(1205):70–80. 

In this study, the authors used brain imaging to determine the effects of language 

comprehension on driving. Data were collected using a driving simulator; participants 

were asked to drive a curving road either undisturbed or while listening to spoken 

sentences that they judged as true or false. Results show that listening to the spoken 

sentences significantly decreased the driving accuracy and provides support for the idea 

that cognitive distraction plays a significant role is driver distraction as drivers were not 

using a hand-held device. 

Kantowitz, B. (1995). Simulator evaluation of heavy-vehicle driver workload. Proceedings of the 

Human Factors and Ergonomics Society 39th Annual Meeting. 

In this study, the author reviews heavy-vehicle driver workload among 12 CMV drivers 

using a simulator module. Reaction time and the immediate recall of a 7-digit auditory 

number were found to provide effective measures of the driver workload. Overall, 

primary-task performance measures were relatively uninfluenced by the addition of a 

secondary task within a driving simulator. 

Kass, S.J., Cole, K.S., & Stanny, C.J. (2007). Effects of distraction and experience on situation 

awareness and simulated driving. Transportation Research Part F: Traffic Psychology and 

Behaviour, 10(4):321-329. 

This study observed cell phone conversations as a driver distraction and the impact cell 

phones have on situation awareness. A sample of 25 novice drivers, aged 14–16, and 26 

experienced drivers, aged 21–52, were examined using a driving simulator where 

situation awareness was assessed. Subjects were observed using a driving simulator in 

which hands-free cell phone conversations were simulated. Novice drivers were found to 

make more driver errors than experienced drivers when faced with cell phone 

conversations. Regardless of the driver’s experience behind the wheel, these results are 

relevant in many driving situations, including CMV safety and in-vehicle distractions. 

Cell phones, hands-free or hand-held, still prove to be a major distraction for many 

vehicles. 

Klauer, S.G., Dingus, T.A., Neale, V.L., Sudweeks, J.D., & Ramsey, D.J. (2006). The impact of 

driver inattention on near-crash/crash risk: An analysis using the 100-car naturalistic driving 

study data. Washington, DC: National Highway Traffic Safety Administration, USDOT. 

Retrieved August 26, 2009, from: 

http://www.nhtsa.dot.gov/staticfiles/DOT/NHTSA/NRD/Multimedia/PDFs/Crash%20Avoida

nce/Driver%20Distraction/810594.pdf 

In the 100-Car Driving Study, naturalistic driving data were collected to evaluate nearcrash/crash risks involving drowsy drivers. These authors later evaluated the data to find 

results indicating drowsy drivers have four- to six-times higher near-crash/crash risks 

245 
246 

than alert drivers. Drivers distracted by visually and/or manually complex tasks had a 

three-times higher near-crash/crash risk than attentive drivers. 

Laberge, J., Scialfa, C., White, C., & Caird, J. (2004). Effects of passenger and cellular phone 

conversations on driver distraction. Transportation Research Record, (1899):109-116. 

This study examined the distracting effects of cellular phone conversations in vehicles. A 

sample of 80 participants was assigned to one of three driving conditions: 1) driving 

alone in a vehicle, 2) driving with a passenger, and 3) driving with a simulated cellular 

phone conversation. Contrary to other experiments, little evidence was found in which 

passengers had to adjust phone conversations due to traffic demands in the surrounding 

environment. The results of this experiment indicate a possible need for further research; 

however, this study still indicates that increased driving demands influence lane and 

speed maintenance, which can impact driver behavior in CMVs. 

Laberge-Nadeau, C., Maag, U., Bellavance, F., Lapierre, S.D., Desjardins, D., Messier, S., & 

Saidi, A. (2003). Wireless telephones and the risk of road crashes. Accident Analysis and 

Prevention, 35(5):649–660. 

A questionnaire and letter of consent was sent from the Societe de l’Assurance 

Automobile du Quebec (SAAQ) to 175,000 license holders for passenger vehicles. 

Questionnaire recipients were asked about exposure to risk, driving habits, opinions of 

hazardous driving activities, and accidents occurring in the last 24 months. The SAAQ 

received over 36,000 completed questionnaires and were provided with various company 

files for four wireless phone companies to verify the association between cell phone use 

and accidents. Overall, results from the study have shown the association between higher 

crash risks and cell phone use is justified. 

Llaneras, R.E., & Singer, J.P. (2002). Inventory of In-Vehicle Technology Human Factors 

Design Characteristics (Document No. DOT HS 809 457). Washington, DC: National 

Highway Traffic Safety Administration, USDOT. Retrieved August 26, 2009, from: 

http://www.nhtsa.gov/staticfiles/DOT/NHTSA/NRD/Multimedia/PDFs/Human%20Factors/R

educing%20Unsafe%20behaviors/DOT%20HS%20809%20457.pdf 

This is a review of an inventory of in-vehicle devices to better understand their design 

and implementation. The authors reviewed 80 in-vehicle devices, focusing on human 

factors characteristics and interface features. Results indicated that devices tend to 

incorporate a large number of features and options, creating a potential challenge for 

drivers to learn all of the capabilities of a system and resulting in lengthy manuals. 

Although devices also tended to provide large amounts of information, some designs may 

allow for increased information presentation without necessarily sacrificing performance. 

Warnings or cautions against interacting with systems while driving were common; 

however, relatively few systems disable equipment when vehicles are in operation. A 

number of other observations and “industry trends” are presented and discussed. 
Llaneras, R.E., Singer, J.P., & Bowers-Carnahan, R. (2005). Assessment of truck driver 

distraction problem and research needs. Washington, DC: National Highway Traffic Safety 

Administration, USDOT. Retrieved August 26, 2009, from: 

http://www.itsdocs.fhwa.dot.gov/jpodocs/repts_te/14260.htm 

The issue of driver distraction associated with the use of in-vehicle devices in heavy 

vehicles was explored through interviews with truck drivers and safety regulators. In 

order to characterize some of the interface designs and better understand their interaction 

demands, a sample of commercially available in-vehicle devices was examined. The 

extent to which these devices conformed to available human factors guidelines and 

accepted practices was assessed analytically. Industry device design and evaluation 

practices were also explored via contacts with equipment suppliers and industry original 

equipment manufacturers (OEMs). Truck driver distraction is perceived by many drivers 

and safety regulators to be a problem, although it is not generally viewed as a high 

priority issue. Fleet-based communication devices, which include text-based messaging 

functions, are widely available and used by the industry. These devices can potentially 

impose high levels of attentional demand if used while driving since they require 

numerous inputs and multi-line text displays which have been shown to impair driving 

performance. Manufacturers of these types of systems tend to provide the capability to 

restrict driver interactions with these systems while driving (e.g., lock-out the ability to 

read or send text messages); our interactions with drivers in our sample suggests that 

many organizations do not necessarily elect to fully implement these restrictions, and 

there is no uniformly adopted practice for dealing with these types of devices. Product 

developers and OEMs appear to involve drivers in product development and testing 

(primarily in order to ensure their products conform to the customers needs); however, 

objective testing to evaluate the attentional demands of devices may not be widely used. 

Mannering, F. L., Kilareski, W. P., & Washburn, S. S. (2004). Principles of Highway 

Engineering and Traffic Analysis (3rd ed.). Hoboken, NJ: John Wiley & Sons, pp. 170-219. 

[From the back cover] Given the continual and dramatic increase in vehicle ownership 

and travel, and the sheer number of people affected by our nation’s highway system, 

there are few more important and far-reaching fields than highway engineering. Rather 

than addressing the broad expanse of the transportation engineering field, this book 

focuses primarily on highway and traffic engineering, providing the depth needed to 

solve real highway-related problems. Taking a concise, accessible, example-oriented 

approach, the authors bring clarity to the subject matter. 

Patten, C.J.D., Kircher, A., Östlund, J., & Nilsson, L. (2004). Using mobile telephones: 

Cognitive workload and attention resource allocation. Accident Analysis and Prevention, 

36(3):341–350. 

This study examined the effects of mobile telephone conversations while driving. Forty 

participants were observed while operating a motor vehicle and engaged a phone 

conversation. The study focused on determining the effects of the conversation type 

versus the phone type itself. Results showed that the type of conversation the participant 

was having affected his/her control over the vehicle much more than the effect of the type 

of phone being used. The complexity of a phone conversation was more likely to have a 

247 
significant effect on the driver’s performance than whether or not the device being used 

was hand-held or hands-free. Overall, both of these effects can lead to driver distraction. 

Pedhazur, E. J. (1997). Multiple Regression in Behavioral Research: Explanation and Prediction

(3rd ed.). Fort Worth, TX: Harcourt Brace College Publishers 

An introduction book to linear regressions, this book contains chapters on variance 

partitioning, analysis of effect, categorical independent variables, curvilinear regression 

analysis, Attribute-Treatment Interaction, analysis of covariance, multilevel analysis, and 

logistic regression. 

Pettitt, M., Burnett, G., & Stevens, A. (2005). Defining driver distraction. Paper presented at 

World Congress on the Intelligent Transport Systems. San Francisco, CA. 

Unlike other articles cited here, this paper discusses issues surrounding the creation of a 

precise definition of driver distraction as opposed to its specific causes and effects. The 

growing interest and concerns from the research community in driver distraction have 

created the need to reliably monitor the problem; however, there is a need to correctly 

and comprehensively define the term. To properly define the term, accident statistics on 

work-related road traffic accidents, specifically from the United Kingdom, have been 

gathered and assessed. The authors found that driving-related distraction should be 

discussed in terms of: 1) the difference between distraction and inattention, 2) distraction 

within and outside of the vehicle, 3) categorization of distraction, and 4) the effects on 

driving performance. A comprehensive definition of driver distraction is presented. 

McEvoy, S.P., Stevenson, M.R., & Woodward, M. (2007). The prevalence of and factors 

associated with, serious crashes involving a distracting activity. Accident Analysis and 

Prevention, 39(3):475-482. 

This study began with a survey of 1,367 drivers in western Australia who were 

hospitalized after a vehicle crash between April 2002 and July 2004. The questionnaire 

results reveal that more than 30 percent of the hospitalized drivers took part in at least 

one distracting activity at the time of their accident. These distracting activities include 

carrying on a conversation with passengers, lack of concentration, and external factors 

(e.g., road conditions). Results show that such injuries will decrease with stricter 

enforcement of existing laws and new devices such as collision warning systems. 

Additionally, an increase in driver awareness can decrease the likelihood of crashes 

relating to distracted driving. 

McEvoy, S.P., Stevenson, M.R., McCart, A.T., Woodward, M., Haworth, C., Palamara, P., & 

Cercarelli, R. (2005). Role of mobile phones in motor vehicle crashes resulting in hospital 

attendance: A case-crossover study. BMJ, 2005 Aug 20;331(7514):428. 

This study investigated drivers who had been hospitalized as a result of a crash. 

Participants were interviewed and phone records were identified for the approximate time 

of the crash and for trips during the same time of day in the week prior to the crash. 

Results show that drivers who were using a cell phone up to 10 minutes prior to the crash 

were four times as likely to be involved in a crash than times when they were not using a 

248 
cell phone. Results also show that risk increased regardless of whether drivers were using 

a hands-free phone or hand-held phone. 

McKnight, A.J., & McKnight, A.S. (1993). The effect of cellular phone use upon driver 

attention. Accident Analysis and Prevention, 25(3):259-265. 

Complexity of cellular phone conversations is examined in a random sample of 150 

participants observed in a 25-minute long driving simulation of 45 traffic situations under 

the following conditions: 1) placing a phone call, 2) having a casual conversation, 3) 

having an intense phone conversation, 4) tuning a radio, and 5) no distraction. All of 

these conditions led to significant increases in the number of situations where the subjects 

failed to respond. Responses in driver error varied by age, as participants older than 50 

revealed many non-responses to cell phone distractions. Many younger participants were 

affected when driving while involved in an intense and complex conversation. The 

complexity of a cellular phone conversation may lead to higher likelihood in driver error. 

Rakauskas, M.E., Gugerty, L.J., & Ward, N.J. (2004). Effects of naturalistic cell phone 

conversations on driving performance. Journal of Safety Research, 35(4):453-464. 

In this study, a driving simulator was used to observe easy and difficult phone 

conversations and their effects on participants. Results show that intense cell phone 

conversation caused the driver to vary speeds more frequently. This research has 

concluded that the increasing use of cell phones in motor vehicles, especially CMVs, can 

cause disruptions in business and add to injury, disability, and personnel loss. 

Ranney, T.A., Mazzai, E., Garrott, R., & Goodman, M.J. (2000). NHTSA Driver distraction 

research: Past, present, and future. Washington, DC: National Highway Traffic Safety 

Administration, USDOT. Retrieved August 26, 2009, from: http://wwwnrd.nhtsa.dot.gov/departments/Human%20Factors/driver-distraction/PDF/233.PDF 

This report focuses on rising concerns about distracted driving and its potential to 

increase with the introduction of new in-vehicle technologies. The National Highway 

Traffic Safety Administration (NHTSA) conducted research on driver distraction to 

understand the contributing factors and to develop methods to curb it. These authors 

define driver distraction as an activity that takes part or all of the driver’s attention away 

from driving. This paper reviews past research performed by NHTSA on CMV driver 

workload, as there are many distractions (e.g., satellite tracking, wireless phones or CBs, 

and route guidance systems). The authors also outline further investigations to be 

performed on distracted driving to increase awareness of potential safety problems 

associated with the use of in-vehicle technologies. 

Redelmeier, D.A., & Tibshirani, R.J. (1997). Association between cellular-telephone calls and 

motor vehicle crashes. The New England Journal of Medicine, 336(7):453–458. 

This study of 699 drivers took place over a 14-month period to observe the risks of 

collision when using a cellular phone. During the study period, 26,798 cell phone calls 

were placed, resulting in a collision risk four times higher than when a phone was not 

being used by the driver. Overall, the association of use of a cellular telephone while 

driving was confirmed to be associated to a risk of a crash. Also, no safety advantages 

249 
were seen when observing hands-free versus hand-held cell phones. Both versions were 

found to have an impact on driving performance and reaction times to traffic signals and 

external driving conditions. This study is not specific in terms of types of motor vehicles 

being driven. 

Regan, M.A., Lee, J.D., & Young, K.L. (2009). Driver distraction: Theory, effects, and 

mitigation. Boca Raton, FL: CRC Press. 

[From back cover] This book provides a comprehensive overview of this important road 

safety problem. It defines distraction, explains the mechanisms underlying it, reviews its 

effects on driving performance and safety, suggests practical strategies for mitigating its 

effects, and provides directions for future research. It brings together into one, allinclusive, volume the wide array of literature on the topic. 

Sahai, H., & Khurshid, A. (1996). Statistics in Epidemiology: Methods, Techniques, and 

Applications. Boca Raton, FL: CRC Press. 

[From back cover] This book covers the broad range of data analytic topics in 

epidemiology. Written for epidemiologists and other researchers without extensive 

backgrounds in statistics, this new book provides a clear and concise description of 

statistical tools used in epidemiology. The book can be used as a text for students 

enrolled graduate programs in epidemiology, biostatistics and public health, as well as 

being a valuable reference for health science professionals and researchers. 

Sayer, J.R, Devonshire, J.M., & Flanagan, C.A. (2007). Naturalistic driving performance during 

secondary tasks. Proceedings of the Fourth International Driving Symposium on Human 

Factors in Driver Assessment, Training and Vehicle Design. 

This FOT investigates safety impacts of an integrated lane-departure system and curvespeed warning system. Video clips from 36 drivers were analyzed and secondary 

behaviors were identified. Researchers measured how often and how long drivers were 

not looking at the forward roadway and variability of steering wheel angle, lane position, 

speed and throttle position. Results show that drivers engage in secondary behaviors in 

about one-third of the video clips, with talking to a passenger being the most frequent. 

Glance durations away from the forward roadway were the shortest when drivers were 

talking on a cell phone. Overall, secondary behaviors showed little effect on basic driving 

performance. 

Schattler, K.L., Pellerito Jr., J., McAvoy, D., & Datta, T.K. (2006). Assessing driver distraction 

from cell phone use: A simulator-based study. Transportation Research Record, 1980:87–94. 

This study utilized a simulator to observe 37 drivers and their performance while 

handling a cell phone. The simulator engaged participants in typical real-life driving 

conditions and included a hand-held cell phone and scripted conversation with 

researchers. Drivers were instructed to handle conditions such as traffic signs and signals, 

pedestrians, and turns. Driver performance scores were significantly lower while subjects 

were using cell phones. 

250 
Shinar, D., Tractinsky, N., & Compton, R. (2005). Effects of practice, age, and task dema

interference from a phone task while driving. Accident Analysis and Prevention, 37(2):315–

326. 

This experimental research revised an earlier study to bette

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situations via a simulated driving environment. The simulator involved repeated 

experiences of a participant driving with multiple cell phone tasks of varying intensiti

Although it is known that driving while holding a cell phone conversation decreases 

driving performance, results of this experiment revealed that with continued practice a

multitasking while driving, the negative effects of multitasking would not be as seve

especially for experienced drivers. 

, J., Mayer, J., Laansoo, E., & Tijerina, L. (2009). Driver workload effects of cell phone, 

sic player, and text messaging tasks with the Ford SYNC voice interface versus hand

al-manual interfaces (paper presented at SAE World Congress & Exhibition, April 2009

roit, MI). Warrendale, PA: Society of Automotive Engineers International. As of Augus

 2009, available online via: http://www.sae.org/technical/papers/2009-01-0786 

This research utilized a driving simulator to compare driver performance and eye glance

behavior while participants performed various tasks using a voice-activated system 

available in Ford vehicles. Twenty-five participants were asked to perform seven 

different tasks, once while using the system and once while using their own cell phone

and hand-held music player. Results showed that driving distraction (i.e., eyes off road

time) was less during the system tasks than during the cell phone and hand-held music 

player tasks. 

, A. (2005). What is driver distraction? Paper presented at the International Conference on

Distracted Driving. Toronto, Ontario. 

This naturalistic study of more 100 drivers, vehicles, and 43,000 hours showed that 

distraction is a major factor in crashes. Numerous crashes and near-crashes resulted from

this study; video footage revealed distraction in 78 percent of the crashes and 65 percen

of the near-crashes. The report identifies distraction as “misallocated attention,” in whic

the driver’s focus is not solely on the driving task or environment. Forms of distraction, 

such as visual and auditory, and the impacts of distraction are also defined. The resul

indicate the need for further research to determine where the driver’s attention is 

allocated, if other than on the road. The driving environment must also be assessed, as

is essential to differentiate driving tasks between CMV d

drivers. 

yer, D.L. & Drews, F.A. (2004). Profiles in driver distraction: Effects of cell phone 

conversations on

A driving simulator was utilized to study the effects of cell phone use on younger and 

older drivers. Participants were asked to perform single-tasks conditions (driving-onl

and multi-task conditions (driving and talking on a cell phone). There were no significant

differences between younger and older participants, however when asked to talk on the 

cell phone while driving, their reactions were 18 percent slower, the following distance 

was 12 percent greater, and they took 17 percent longer to recover their speed after 

251 
braking. The participants were also twice as likely to be involved in a rear-end collision 

while talking on the cell phone. 

Strayer, D.L., Drews, F.A., & Johnston, W.A. (2003). Cell phone-induced failures of visual 

attention during simulated driving. Journal of Experimental Psychology, 9(1):23–32. 

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A driving simulator was used to examine the effects of hands-free cell phone 

conversations on driving performance. Driver inattention was analyzed using eyetracking data to determine if the decreased attention to the visual scene was a form of 

inattention blindnes

Strayer, D.L. & Johnston, W.A. (2001). Driver to distraction: Dual task studies of simulated 

driving and conversing on a cellular phone. Psychological Science, 12(6)46

This study examined 48 participants in a driving simulator to evaluate the effect of using 

a cell phone while driving. Participants were asked to engage in various conditions while

watching for either a green light, continue, or red light, stop, which was displayed on a 

computer screen. Participants were asked to engage in a dual-task where they were aske

to repeat words that an experimenter read to them over a hand-held cell phone, or they 

were asked to participate in a word-generation exercise where the experimenter would

read a word and the participant was asked to generate a new word that started with t

last letter of the previous word, also while using a hand-held cell phone. Results show 

that participants missed twice as many traffic signals when engaging in cell phone 

conversations and took longer to react to the signals that they did detect. 

Stutts, J., Feaganes, J., Reinfurt, D., Rodgman, E., Hamlett, C., Gish, K., & Staplin, L. (20

Driver’s exposure to distractions in their natural driving environment. Accident Analysis

Prevention, 37(6):1093-1101

This study sampled 70 participating drivers by outfitting vehicles with video cam

observe drivers’ exposure to distractions. As opposed to most studies revealing cell 

phones to be the greatest distracters, results show the most common distraction to be 

eating and drinking inside the vehicle. Other common distractions identified were 

reaching or looking for objects and manipulating vehicle controls. 

J.C., Reinfurt, D.W., Staplin, L., & Rodgman, E.A. (2001). The role of driver distra

raffic crashes. Washington, DC: AAA Foundation for Traffic Safety. 

This study includes data from the Crashwor

potential causes for traffic crashes. This report defines driver distraction as a form of 

inattention, in which the driver may become “lost in thought.” In addition to this 

psychological factor, environmental and situational factors (e.g., varying roadway 

conditions) play a role in driver distraction. Unlike other studies note

that demographic factors, such as age, were found to be a cause for distraction. D

gender was not found to be as significant when examining demographic factors. Results 

suggest that additional research may be needed to observe frequency and intensity of

various distractions, as new in-vehicle technologies may increase. 

252 
Stutts, J.C. & Hunter, W.W. (2003). Driver inattention, driver distraction and traffic crashes. ITE 

Journal, 73(7):34–45. 

These authors also used the CDS data to examine the role of driver distraction and its 

relationship with visual inattention, and the role of distraction to statistics on traffic 

crashes. 

Törnros, J.E.B. and Bolling, A.K. (2005). Mobile phone use: Effects of handheld and handsfree 

phones on driving performance. Accident Analysis and Prevention, 37(5):902–909. 

In this study, 48 participants were split into two groups, one group used a hand-held cell 

phone and the other used a hands-free cell phone; the majority of the participants 

participated in both a talking experiment and a dialing experiment. Participants were 

asked to drive two different routes using a driving similar; a longer route in an urban 

setting for the talking experiment and a shorter, straight route for the dialing experiment. 

During the talking experiment, participants were asked to use either a hands-free (only 

had to interact with the phone to pick up and hang up the call) or a hand-held (had to pick 

up and talk into the phone) cell phone to engage in an addition task. During the dialing 

experiment, the driver was asked to make a phone call (either on a hands-free or handheld phone) when they heard the word “ring.” Results show that participants engaging in 

the dialing task had more difficulty maintaining lane position and both the talking and 

dialing tasks led to reduced vehicle speed. 

Treat, JR., Tumbas, N.S., McDonald, S.T., Shinar, D., Hume, RD., Mayer, RE., Stansifer, RL., 

and Catellan, N.J. (1979). Tri-Level Study of the Causes of Traffic Accidents: Volume I: 

Causal Factor Tabulations and Assessment (Document No. DOT HS-805 085). Washington, 

DC: National Highway Traffic Safety Administration, USDOT. 

The final report presents analyses of data in terms of human and environmental factors. 

Major human errors found included improper lookout, excessive speed, and inattention to 

the situation. Environmental causes included obstructions in view and slick roads. 

Vehicular factors, like brake failure and under-inflation, were the least probable. The 

knowledge of the actual driving task at hand was seen as unrelated to other factors 

involved in the accident. 

Tseng, W.-S., Nguyen, H., Liebowitz, J., & Agresti, W. (2005). Distractions and motor vehicle 

accidents: Data mining application on fatality analysis reporting system (FARS) data files. 

Industrial Management and Data Systems, 105(9):1188–1205). 

This research applies data mining techniques to evaluate the relationship of driver 

distraction and car accidents. NHTSA’s Fatality Analysis Reporting System (FARS) was 

used to obtain data used in the research, which focused on the Maryland and Washington, 

DC area between years 2000 and 2003. Findings suggest that the combination of 

inattention and certain physical and mental conditions lead to a driver having a higher 

tendency to crash into non-moving objects. Also, collision into a moving vehicle is much 

more harmful than into a stationary object. These authors support research in CMV safety 

in that the same areas for concern (e.g., driver inattention). 

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Uno, H., & Hiramatsu, K. (2000). Effects of auditory distractions on driving behavior during 

lane change course negotiation: estimation of spare mental capacity as an index of attention 

distraction. JSAE Review, 21(2):219–224. 

Unlike other driver distraction studies, this study focused primarily on auditory 

distractions not accompanied by visual distractions in driving. Reaction times were 

observed on participants as their cognitive capabilities in dealing with auditory 

distractions were tested. As with previous studies, driving performance deteriorated with 

increased distractions. 

Wallace, B. (2003). Driver distraction by advertising: Genuine risk or urban myth? Proceedings 

of the Institution of Civil Engineers: Municipal Engineer, 156(3):185-190. 

As opposed to many studies focused on cell phones and in-vehicle tasks as driver 

distractions, this study examined driver distractions outside of the vehicle and addressed 

risks to safe driving caused by external distractions. Mainly theoretical discussions were 

produced and this will likely lead to future research on the topic. 

Wang, J-S., Knipling, R.R., & Goodman, M.J. (1996). The role of driver inattention in crashes: 

New statistics from the 1995 Crashworthiness Data System. Conference proceedings of the 

40th annual meeting of the Association for the Advancement of Automotive Medicine: 

Vancouver, British Columbia. 

NHTSA has used the CDS to obtain in-depth information and statistics on crashes 

relating to driver inattention. The paper reports the results from the 1995 CDS data and 

relays the three forms of driver inattention found from the study: 1) distraction, 2) looked 

but did not see, and 3) sleepy/fell asleep. The authors focused on the CDS to support 

hypothesized causes for driver inattention crashes. 

White, M.P., Eiser, J.R., & Harris, P.R. (2004). Risk perceptions of mobile phone use while 

driving. Risk Analysis, 24(2):323–334. 

Two studies were conducted to observe risk perceptions among drivers. The first 

observed 199 participants using hand-held mobile phones and found it to be one of the 

riskiest tasks to perform while driving, as opposed to the relatively small risks found with 

the use of hands-free mobile phone kits. The second study observed 1,320 participants 

and found half used a mobile phone while driving. Ultimately, the two studies determined 

that policy makers act strategically rather than develop “hazard-specific” policies and 

regulations. The authors focused on impact and controllability in hazardous driving 

situations and the regulations needed to be made in respect to those situations. 

Wogalter, M.S., & Mayhorn, C.B. (2005). Perceptions of driver distraction by cellular phone 

users and nonusers. Human Factors, 47(2):455–467. 

This study examined the behavior and perceptions of cell phone users and nonusers on 

vehicle control and driver distraction. Among the issues studied were how often the 

drivers used their cell phones and their beliefs about in-vehicle cell phone use. Among a 

sample of 330 participants, the 28 percent of the participants that were nonusers more 

strongly believed in the negative effects of driving while talking on a cell phone. Also, 

254 
cell phone users preferred no cell phone regulations. This study focused on driving risks 

and regulations that may be applied to limit driver distractibility. 

Wierwille, W.W., & Ellsworth, L.A. (1994).Evaluation of driver drowsiness by trained raters. 

Accident Analysis and Prevention, 26(5)571–581. 

This study develops a reliable drowsiness measure in CMV drivers during data analysis: 

the observer rating of drowsiness, a subjective measurement of how drowsy a driver 

appears in the video data. Six raters were viewed and rated 48 segments of video of the 

driver’s face from a previous driving-simulator study. Participants rated the level of 

drowsiness from “Not Drowsy” to “Extremely Drowsy,” and could choose any point on 

the continuous scale. Results found that the test-retest reliability was 0.80, showing that 

raters were consistent within themselves and that intrarater reliability correlations were 

0.88. Overall, raters were reliable within themselves and among other raters. 

Wierwille, W.W., Hanowski, R.J., Hankey, J.M., Kieliszewski, C.A., Lee, S.E., Medina, A, 

Keisler, A.S., & Dingus, T.A. (2002) Identification and evaluation of driver errors: Overview 

and recommendations. (Report no. FHWA-RD-02-003). Washington, DC: Federal Highway 

Administration, USDOT. Online executive summary retrieved August 26, 2009, from: 

http://www.tfhrc.gov/humanfac/02003execsum.htm 

This report offers a definition for “driver error” as a general term used to develop 

taxonomies of driver error and determine the associated causes. Instead of focusing on invehicle distractions, the report emphasizes causes and concerns for driver error and 

outlines many cognitive mistakes drivers make while behind the wheel. The data from 

this report supports hypothesized concerns on driver error for all vehicles. 

Young, K., Regan, M., & Hammer, M. (2003). Driver distraction: A review of the literature. 

(Report no. 206). Victoria, Australia: Monash University Accident Research Centre. 

Retrieved August 26, 2009, from: http://www.monash.edu.au/muarc/reports/muarc206.html 

The authors provide a comprehensive review of the available driver distraction literature. 

The focus of the review is internal driver distraction, such as cell phone use and GPS 

systems and non-technology based behaviors such as eating and talking to passengers. 

The authors look at various methods used to measure driver distraction and indicate the 

most promising methods. Recommendations for the management of driver distraction are 

also provided at the end of the report. 

Young, M.S., Mahfoud, J.M., Walker, G.H., Jenkins, D.P., & Stanton, N.A. (2008). Crash 

dieting: The effects of eating and drinking on driving performance. Accident Analysis and 

Prevention,40(1):142–148. 

Many driver distraction studies focus on the effect of cell phones on driving performance. 

However, eating and drinking while driving is common. Although it is seen as a lower 

risk by many drivers, this distracter can affect vehicle control as much as cell phones. 

Eating and drinking while driving increases the mental workload, leading to more crashes 

when a driver is faced with a safety-critical event. This effect may be greater in long haul 

drivers as it is more common for these drivers to drink coffee or a soft drink to ward off 

sleepiness. Further research may help to evaluate the detrimental effects on driver safety. 

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