Analysis of Risk as a Function of Driving-Hour: 

Assessment of Driving-Hours 1 Through 11 

Final Report 

January 2008

FOREWORD 

Crashes involving large trucks constitute a significant risk to the driving public and an 

occupational risk to truck drivers. In 2005, some 442,000 large trucks (weighing over 10,000 lbs 

each) were involved in vehicle crashes; 4,951 of these large-truck crashes resulted in fatalities. 

Driver impairment due to drowsiness is a known contributing factor in many crashes involving 

commercial motor vehicle (CMV) drivers (Maycock, 1997). The Large Truck Crash Causation 

Studies found that 13 percent of truck drivers were coded as having been fatigued at the time of 

the crash (Federal Motor Carrier Safety Administration [FMCSA], 2006). 

On April 28, 2003, FMCSA published a revised set of regulations concerning the Hours-ofService (HOS) of CMV drivers. These published regulations were amended on September 30, 

2003 and implemented on January 4, 2004. One central component of the revisions was a twohour extension of off-duty time from eight to 10 hours. One rationale given in an FMCSA 

posting in the Federal Register (2005) was that the additional two hours of off-duty time would 

provide drivers with “...substantially more opportunity to obtain restorative sleep” (p. 3342). 

The results from Hanowski, Dingus, Sudweeks, Olson, and Fumero (2005) indicated that this 

indeed may be the case; their research found that drivers may be getting more sleep under the 

revised 2003 HOS regulation (6.28 h per day) as compared to the old regulations (5.18 h per day; 

Mitler et al., 1997). 

The current FMCSA-funded study examined some important issues pertaining to the HOS 

debate, particularly with regard to time-on-task or driving-hours. Hanowski et al. (2005) reported 

on an analysis that involved a comparison of critical incidents involving CMVs that occurred in 

the 10th and 11th driving-hours. This was a timely analysis effort as it provided insight into 

questions associated with the revised 2003 HOS regulations. Two limitations of the 2005 

analysis of Hanowski et al. were that: (i) it included only a partial dataset and (ii) it compared 

only the frequency of critical incidents that occurred in the 10th and 11th driving-hours. Since that 

report was written, data collection for the naturalistic study on which the 2005 Hanowski et al. 

analysis was based has been completed (Hanowski et al., in press). As a result, the two 

limitations cited above as being associated with the initial analysis can be addressed in the 

current study. In addition, to provide a comprehensive and thorough examination of the topic, 

additional analyses were conducted to address how critical incidents may vary as a function of 

driving shift and time-of-day. 

The current final report focused on the following research issues: 

1. Critical incidents as a function of driving-hour for hours 1 through 11; 

2. Critical incidents per driving-hour for hours 1 through 11, focusing on drivers that drove 

into the 11th hour; 

3. Modeling the data to look for significant differences in critical incidents across drivinghour; 

4. Critical incidents as a function of shift within the driver’s “work week” (i.e., “tour of 

duty”) and; 

5. Critical incidents as a function of time-of-day. 
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 the 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 

1. Report No. 

FMCSA-RRR-08-002 

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

5. Report Date 

January 2008 

4. Title and Subtitle 

Analysis of Risk as a Function of Driving-Hour: 

Assessment of Driving-Hours 1 Through 11 6. Performing Organization Code: 

7. Author(s) 

Richard J. Hanowski, Rebecca L. Olson, 

Joseph Bocanegra, and Jeffrey S. Hickman 

8. Performing Organization Report No. 

9. Performing Organization Name and Address 10. Work Unit No. 

Virginia Tech Transportation Institute 

3500 Transportation Research Plaza (0536) 

Blacksburg, VA 24061 

11. Contract or Grant No. 

DTMC75-07-D-00006 

13. Type of Report and Period Covered 

Final Report 

November 1, 2007–January 31, 2008 

12. Sponsoring Agency Name and Address 

Department of Transportation 

Federal Motor Carrier Safety Administration 

1200 New Jersey Ave., SE 

Washington, DC 20590 

14. Sponsoring Agency Code 

FMCSA 

15. Supplementary Notes 

This program was managed by the Federal Motor Carrier Safety Administration (FMCSA). 

16. Abstract 

The current FMCSA-funded study examined some important issues pertaining to the HOS debate, 

particularly with regard to time-on-task or driving-hours. Hanowski et al. (2005) reported on an analysis 

that involved a comparison of critical incidents involving CMVs that occurred in the 10th and 11th 

driving-hours. Two limitations of the Hanowski et al. (2005) analysis were that: (i) it included only a 

partial dataset and (ii) it compared only the frequency of critical incidents that occurred in the 10th and 

11th driving-hours. Since that report was written, data collection for the naturalistic study on which the 

Hanowski et al. (2005) analysis was based has been completed (Hanowski et al., in press). These two 

limitations associated with the initial analysis were addressed in the current study. This study has resulted 

in a major finding that is relevant to the assessment of the 2003 HOS regulations. Specifically, the results 

from the analysis on critical-incident relative frequency, used as a surrogate for driver performance 

decrement, generally showed no statistical difference in the hours between the 2nd through 11th drivinghours (for almost all analyses); and the statistical difference between the 1st driving-hour and the 11th 

driving-hour is in the opposite direction of what would be expected had there been a time-on-task effect. 

That is, the results of this study do not support the hypothesis that there is an increased risk resulting from 

CMV drivers driving in the 11th driving-hour as compared to the 10th driving-hour, or any hour. These 

results from this research suggest time-on-task as being a poor predictor of crashes and safety-related 

traffic events. In fact, a significant spike in the rate of critical incidents was found during the 1st drivinghour. This spike was found across all possible trips and on only those trips in which the truck driver drove 

into the 11th driving-hour. The latter statement is noteworthy because it suggests that even on those trips 

in which the driver drove into the 11th driving-hour, the critical-incident relative frequency was highest in 

the 1st driving-hour (significantly more compared to driving-hours 2 though 11 for almost all analyses). 

Additional analyses found a strong time-of-day effect which, upon closer examination, appeared to have 

resulted from hour-by-hour traffic density variations. The authors hypothesized that exposure to heavy 

traffic conditions, and possibly sleep inertia and an increase in complex driving situations that may be 

typical in the 1st driving-hour, may be the key predictors of critical- incident occurrence. 

17. Key Words 

Hours-of-Service, HOS, Truck, Commercial 

Vehicle, Regulations, Fatigue, Drowsiness, 

Time-on-task 

18. Distribution Statement 

No restrictions. This document is available through the 

National Technical Information Service; Springfield, 

VA 22161. 

19. Security Classif. (of this report) 

Unclassified 

20. Security Classif. (of this page) 

Unclassified 

21. No. of Pages 

98 

22. Price 

Form DOT F 1700.7 (8-72) Reproduction of completed pages authorized
SI* (Modern Metric) Conversion Factors 

APPROXIMATE CONVERSIONS TO SI UNITS APPROXIMATE CONVERSIONS FRO

Symbol When You Know Multiply By To Find Symbol Symbol When You Know Multiply By 

LENGTH LENGTH 

in inches 25.4 millimeters mm mm millimeters 0.039 

ft feet 0.305 meters m m meters 3.28 

yd yards 0.914 meters m m meters 1.09 

mi miles 1.61 kilometers km km kilometers 0.621 

AREA AREA 

in2 square inches 645.2 square millimeters mm2 mm2 square millimeters 0.0016 

ft2 square feet 0.093 square meters m2 m2 square meters 10.764 

yd2 square yards 0.836 square meters m2 m2 square meters 1.195 

ac acres 0.405 hectares ha ha hectares 2.47 

mi2 square miles 2.59 square kilometers km2 km2 square kilometers 0.386 

VOLUME VOLUME 

fl oz fluid ounces 29.57 milliliters ml ml milliliters 0.034 

gal gallons 3.785 liters l l liters 0.264 

ft3 cubic feet 0.028 cubic meters m3 m3 cubic meters 35.71 

yd3 cubic yards 0.765 cubic meters m3 m3 cubic meters 1.307 

MASS MASS 

oz ounces 28.35 grams g g grams 0.035 

lb pounds 0.454 kilograms kg kg kilograms 2.202 

T short tons (2,000 lbs) 0.907 megagrams Mg Mg megagrams 1.103 

TEMPERATURE (exact) TEMPERATURE (exac

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

 temperature or (F-32)/1.8 temperature temperature 

ILLUMINATION ILLUMINATION 

fc foot-candles 10.76 lux lx lx lux 0.0929 

fl foot-lamberts 3.426 candela/m2 cd/m2 cd/m2 candela/m2 0.2919 

FORCE and PRESSURE or STRESS FORCE and PRESSURE or S

lbf pound-force 4.45 newtons N N newtons 0.225 

psi pound-force per square inch 6.89 kilopascals kPa kPa kilopascals 0.145 

* SI is the symbol for the International System of Units. Appropriate rounding should be done to comply with Section 4 of A
i 

TABLE OF CONTENTS 

EXECUTIVE SUMMARY ........................................................................................................ XI

2003 HOURS-OF-SERVICE .............................................................................................. XI 

OVERVIEW OF THE CURRENT STUDY ....................................................................... XI 

METHODS .........................................................................................................................XII 

RESULT HIGHLIGHTS ....................................................................................................XII 

CONCLUSIONS ................................................................................................................XV 

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

1.1 2003 HOURS-OF-SERVICE.........................................................................................1 

1.2 FATIGUE AND CRASH RISK ....................................................................................1 

1.3 OVERVIEW OF THE CURRENT STUDY .................................................................3 

2. METHOD ..............................................................................................................................4

2.1 DRIVING HISTORY SOFTWARE AND DATA CAVEATS.....................................6 

3. RESULTS ..............................................................................................................................8

3.1 CRITICAL INCIDENTS AS A FUNCTION OF DRIVING-HOUR FOR 

HOURS 1 THROUGH 11 .............................................................................................8 

Analysis 1.1: All Data....................................................................................................9 

Analysis 1.2: All Data, Truck Driver at Fault..............................................................13 

Analysis 1.3: Baseline and Control Data .....................................................................14 

Analysis 1.4: Baseline and Control Data, Truck Driver at Fault.................................16 

Analysis 1.5: All Data, No Within-Hour Incidents......................................................17 

Analysis 1.6: All Data, Truck Driver at Fault, No Within-Hour Incidents .................19 

Analysis 1.7: Baseline and Control Data, No Within-Hour Incidents.........................20 

Analysis 1.8: Baseline and Control Data, Truck Driver at Fault, No Within-Hour 

Incidents.......................................................................................................................22 

3.2 CRITICAL INCIDENTS AS A FUNCTION OF DRIVING-HOUR FOR 

HOURS 1 THROUGH 11, FOR DRIVERS THAT DROVE 11 HOURS .................23 

Analysis 2.1: 11th Driving-Hour Trips, All Data .........................................................25 

Analysis 2.2: 11th Driving-Hour Trips, All Data, Truck Driver at Fault .....................26 

Analysis 2.3: 11th Driving-Hour Trips, Baseline and Control Data.............................28 

Analysis 2.4: 11th Driving-Hour Trips, Baseline and Control Data, Truck Driver 

at Fault .........................................................................................................................29 

Analysis 2.5: 11th Driving-Hour Trips, All Data, No Within-Hour Incidents .............31 

Analysis 2.6: 11th Driving-Hour Trips, All Data, Truck Driver at Fault, No WithinHour Incidents..............................................................................................................32 
ii 

Analysis 2.7: 11th Driving-Hour Trips, Baseline and Control Data, No Within-Hour 

Incidents.......................................................................................................................34 

Analysis 2.8: 11th Driving-Hour Trips, Baseline and Control Data, Truck Driver at 

Fault, No Within-Hour Incidents .................................................................................36 

3.3 MODELING THE DATA TO IDENTIFY SIGNIFICANT DIFFERENCES IN 

CRITICAL INCIDENTS ACROSS DRIVING-HOURS ...........................................38 

3.4 CRITICAL INCIDENTS AS A FUNCTION OF DRIVING-SHIFT .........................40 

Analysis 4.1: All Data..................................................................................................41 

Analysis 4.2: All Data, Truck Driver at Fault..............................................................45 

Analysis 4.3: Baseline and Control Data .....................................................................46 

Analysis 4.4: Baseline and Control Data, Truck Driver at Fault.................................47 

Analysis 4.5: All Data, No Within-Shift Incidents......................................................48 

Analysis 4.6: All Data, Truck Driver at Fault, No Within-Shift Incidents..................50 

Analysis 4.7: Baseline and Control Data, No Within-Shift Incidents .........................51 

Analysis 4.8: Baseline and Control Data, Truck Driver at Fault, No Within-Shift 

Incidents.......................................................................................................................52 

3.5 CRITICAL INCIDENTS AS A FUNCTION OF TIME-OF-DAY ............................54 

Analysis 5.1: All Data..................................................................................................55 

Analysis 5.2: All Data, Truck Driver at Fault..............................................................60 

Analysis 5.3: Baseline and Control Data .....................................................................61 

Analysis 5.4: Baseline and Control Data, Truck Driver at Fault.................................63 

Analysis 5.5: All Data, No Within-Hour Incidents......................................................64 

Analysis 5.6: All Data, Truck Driver at Fault, No Within-Hour Incidents .................66 

Analysis 5.7: Baseline and Control Data, No Within-Hour Incidents.........................67 

Analysis 5.8: Baseline and Control Data, Truck Driver at Fault, No Within-Hour 

Incidents.......................................................................................................................69 

4. CONCLUSIONS .................................................................................................................71
iii 

LIST OF TABLES 

Table 1. Trigger Types Used to Flag Critical Incidents in the FOT Dataset...................................5 

Table 2. Critical Incidents and Total Opportunities by Driving-Hour for Hours 1 Through 11 .....9 

Table 3. 2 × 2 Contingency Table Used to Calculate Odds Ratio .................................................11 

Table 4. Results from the Odds Ratio Analyses ............................................................................12 

Table 5. Critical Incidents and Total Opportunities by Driving-Hour for Hours 1 Through 11, 

in Which the Subject Driver was at Fault ......................................................................13 

Table 6. Results from the Odds Ratio Analyses, Including all Trips in Which the Subject 

Driver was at Fault .........................................................................................................14 

Table 7. Critical Incidents and Total Opportunities by Driving-Hour for Hours 1 Through 11 

for Baseline and Control Conditions..............................................................................14 

Table 8. Results from the Odds Ratio Analysis Across all Trips for Baseline and Control 

Conditions ......................................................................................................................15 

Table 9. Critical Incidents and Total Opportunities by Driving-Hour for Hours 1 Through 11, 

in Which the Subject Driver was at Fault, for Baseline and Control Conditions ..........16 

Table 10. Results from the Odds Ratio Analysis, Including All Trips in Which the Subject 

Driver was at Fault, for Baseline and Control Conditions .............................................17 

Table 11. Critical Incidents and Total Opportunities by Driving-Hour for Hours 1 Through 11, 

with Multiple Within-Hour Critical Incidents Removed ...............................................17 

Table 12. Results from the Odds Ratio Analysis with Multiple Within-Hour Critical Incidents 

Removed.........................................................................................................................18 

Table 13. Critical Incidents and Total Opportunities by Driving-Hour for Hours 1 Through 11, 

in Which the Subject Driver was at Fault, with Multiple Within-Hour Critical 

Incidents Removed.........................................................................................................19 

Table 14. Results from the Odds Ratio Analysis, in Which the Subject Driver was at Fault, 

with Multiple Within-Hour Critical Incidents Removed ...............................................20 

Table 15. Critical Incidents and Total Opportunities by Driving-Hour for Hours 1 Through 11, 

for Baseline and Control Conditions, with Multiple Within-Hour Critical Incidents 

Removed.........................................................................................................................20 

Table 16. Results from the Odds Ratio Analysis for Baseline and Control Conditions, with 

Multiple Within-Hour Critical Incidents Removed .......................................................21 

Table 17. Critical Incidents and Total Opportunities by Driving-Hour for Hours 1 Through 11, 

in Which the Subject Driver was at Fault, for Baseline and Control Conditions, with 

Multiple Within-Hour Critical Incidents Removed .......................................................22 

Table 18. Results from the Odds Ratio Analysis in Whichthe Subject Driver was at Fault, for 

Baseline and Control Conditions, with Multiple Within-Hour Critical Incidents 

Removed.........................................................................................................................23 

Table 19. Critical Incidents and Total Opportunities by Driving-Hour for Hours 1 Through 11, 

for Trips That Went into the 11th Driving-Hour.............................................................25 

Table 20. Results from Odds Ratio Analysis for Trips That Went into the 11th Driving-Hour ...26 
iv 

Table 21. Critical Incidents and Total Opportunities by Driving-Hour for Hours 1 Through 11, 

for Trips That Went into the 11th Driving-Hour, in Which the Subject Driver was at 

Fault................................................................................................................................26 

Table 22. Result from Odds Ratio Analysis for Trips That Went into the 11th Driving-Hour, In 

Which the Subject Driver was at Fault...........................................................................27 

Table 23. Critical Incidents and Total Opportunities by Driving-Hour for Driving-Hours 1 

Through 11, for Trips That Went into the 11th Driving-Hour, for Baseline and 

Control Conditions .........................................................................................................28 

Table 24. Results from the Odds Ratio for Trips That Went into the 11th Driving-Hour, for 

Baseline and Control Conditions....................................................................................29 

Table 25. Critical Incidents and Total Opportunities by Driving-Hour for Driving-Hours 1 

Through 11, for Trips That Went into the 11th Driving-Hour, in Which the Subject 

Driver was at Fault, for Baseline and Control Conditions .............................................29 

Table 26. Results from the Odds Ratio Analysis for Trips That Went into the 11th DrivingHour, In Which the Subject Driver was at Fault, for Baseline and Control 

Conditions ......................................................................................................................30 

Table 27. Critical Incidents and Total Opportunities by Driving-Hour for Driving-Hours 1 

Through 11, for Trips That Went into the 11th Driving-Hour, with Multiple WithinHour Critical Incidents Removed...................................................................................31 

Table 28. Results from the Odds Ratio Analysis for Trips That Went into the 11th DrivingHour, with Multiple Within-Hour Critical Incidents Removed .....................................32 

Table 29. Critical Incidents and Total Opportunities by Driving-Hour for Driving-Hours 1 

Through 11 for Trips That Went into the 11th Driving-Hour, in Which the Subject 

Driver was at Fault, with Multiple Within-Hour Critical Incidents Removed...............32 

Table 30. Results from the Odds Ratio Analysis in Which the Subject Driver was at Fault for 

Trips That Went into the 11th Driving-Hour, with Multiple Within-Hour Critical 

Incidents Removed.........................................................................................................33 

Table 31. Critical Incidents and Total Opportunities by Driving-Hour for Driving-Hours 

1 Through 11, for Trips That Went into the 11th Driving-Hour, for Baseline and 

Control Conditions, with Multiple Within-Hour Critical Incidents Removed...............34 

Table 32. Results from the Odds Ratio Analysis for Trips that went into the 11th DrivingHour, for Baseline and Control Conditions, with Multiple Within-Hour Critical 

Incidents Removed.........................................................................................................35 

Table 33. Critical Incidents and Total Opportunities by Driving-Hour for Driving-Hours 1 

Through 11, for Trips That Went into the 11th Driving-Hour, in Which the Subject 

Driver was at Fault, for Baseline and Control Conditions, with Multiple WithinHour Critical Incidents Removed...................................................................................36 

Table 34. Results From the Odds Ratio Analysis for Trips that went into the 11th DrivingHour, in Which the Subject Driver was at Fault, for Baseline and Control 

Conditions, with Multiple Within-Hour Critical Incidents Removed...........................37 

Table 35. Results from the Odds Ratio Analyses Using Logistic Regression Modeling ..............39 

Table 36. Results from the Odds Ratio Analyses Using Logistic Regression Modeling for 

Trips that Went into the 11th Driving-Hour....................................................................40 

Table 37. Critical Incidents and Total Opportunities by Driving-Shift for Shifts 1 Through 8 ....42 
v 

Table 38. Results from the Odds Ratio Analyses ..........................................................................43 

Table 39. Results From Odds Ratio Analysis Using Logistic Regression Modeling....................44 

Table 40. Critical Incidents and Total Opportunities by Driving-Shift for Shifts 1 Through 8 

In Which the Subject Driver was at Fault ......................................................................45 

Table 41. Results from the Odds Ratio Analyses, Including all Trips In Which the Subject 

Driver was at Fault .........................................................................................................46 

Table 42. Critical Incidents and Total Opportunities by Driving-Shift for Shifts 1 Through 8 

for Baseline and Control Conditions..............................................................................46 

Table 43. Results from the Odds Ratio Analysis Across all Trips for Baseline and Control 

Conditions ......................................................................................................................47 

Table 44. Critical Incidents and Total Opportunities by Driving-Shift for Shifts 1 Through 8, 

In Which the Subject Driver was at Fault, for Baseline and Control Conditions ..........47 

Table 45. Results from the Odds Ratio Analysis Including All Trips in Which the Subject 

Driver Was at Fault, for Baseline and Control Conditions ............................................48 

Table 46. Critical Incidents and Total Opportunities by Driving-Shift for Shifts 1 Through 8, 

with Multiple Within-Shift Critical Incidents Removed................................................49 

Table 47. Results from the Odds Ratio Analysis with Multiple Within-Shift Critical Incidents 

Removed.........................................................................................................................49 

Table 48. Critical Incidents and Total Opportunities by Driving-Shift for Shifts 1 Through 8, 

in Which the Subject Driver was at Fault, with Multiple Within-Shift Critical 

Incidents Removed.........................................................................................................50 

Table 49. Results from the Odds Ratio Analysis in Which the Subject Driver was at Fault, 

with Multiple Within-Shift Critical Incidents Removed................................................51 

Table 50. Critical Incidents and Total Opportunities by Driving-Shift for Shifts 1 Through 8, 

for Baseline and Control Conditions, with Multiple Within-Shift Critical Incidents 

Removed.........................................................................................................................51 

Table 51. Results from the Odds Ratio Analysis for Baseline and Control Conditions, with 

Multiple Within-Hour Critical Incidents Removed .......................................................52 

Table 52. Critical Incidents and Total Opportunities by Driving-Shift for Shifts 1 Through 8, 

in Which the Subject Driver was at Fault, for Baseline and Control Conditions, with 

Multiple Within-Shift Critical Incidents Removed........................................................52 

Table 53. Results from the Odds Ratio Analysis in Which the Subject Driver was at Fault, for 

Baseline and Control Conditions, with Multiple Within-Hour Critical Incidents 

Removed.........................................................................................................................53 

Table 54. Critical Incidents and Total Opportunities by Time-of-Day .........................................55 

Table 55. Results from the Odds Ratio Analyses ..........................................................................57 

Table 56. Critical Incidents and Total Opportunities by Time-of-Day In Which the Subject 

Driver was at Fault .........................................................................................................60 

Table 57. Critical Incidents and Total Opportunities by Time-of-Day for Baseline and 

Control Conditions .........................................................................................................61 

Table 58. Critical Incidents and Total Opportunities by Time-of-Day, In Which the Subject 

Driver was at Fault, for Baseline and Control Conditions .............................................63 
vi 

Table 59. Critical Incidents and Total Opportunities by Time-of-Day, with Multiple WithinHour Critical Incidents Removed...................................................................................64 

Table 60. Critical Incidents and Total Opportunities by Time-of-Day, in Which the Subject 

Driver Was at Fault, with Multiple Within-Hour Critical Incidents Removed..............66 

Table 61. Critical Incidents and Total Opportunities by Time-of-Day, for Baseline and 

Control Conditions, with Multiple Within-Hour Critical Incidents Removed...............67 

Table 62. Critical Incidents and Total Opportunities by Time-of-Day, in Which the Subject 

Driver Was at Fault, for Baseline and Control Conditions, with Multiple WithinHour Critical Incidents Removed...................................................................................69 
vii 

LIST OF FIGURES 

Figure 1. Critical-Incident Relative Frequency as a Function of Driving-Hour, in Which the 

Subject Driver was at Fault ..........................................................................................xiii 

Figure 2. Critical-Incident Relative Frequency as a Function of Time-of-Day, with TrafficDensity Plot Superimposed .......................................................................................... xiv 

Figure 3. Split-Screen Presentation of the Four Camera Views ..................................................... 4 

Figure 4. Total Opportunities (Trips) for Driving-Hours 1 Through 11....................................... 10 

Figure 5. Critical Incident Relative Frequency as a Function of Driving-Hour ........................... 11 

Figure 6. Critical-Incident Relative Frequency as a Function of Driving-Hour, in Which the 

Subject Driver was at Fault ........................................................................................... 13 

Figure 7. Critical-Incident Relative Frequency as a Function of Driving-Hour for Baseline and 

Control Conditions ........................................................................................................ 15 

Figure 8. Critical-Incident Relative Frequency as a Function of Driving-Hour in Which the 

Subject Driver was at Fault, for Baseline and Control Conditions ............................... 16 

Figure 9. Critical-Incident Relative Frequency as a Function of Driving-Hour, with Multiple 

Within-Hour Critical Incidents Removed ..................................................................... 18 

Figure 10. Critical-Incident Relative Frequency as a Function of Driving-Hour, in Which | the 

Subject Driver was at Fault, with Multiple Within-Hour Critical Incidents Removed. 19 

Figure 11. Critical-Incident Relative Frequency as a Function of Driving-Hour for Baseline 

and Control Conditions, with Multiple Within-Hour Critical Incidents Removed....... 21 

Figure 12. Critical-Incident Relative Frequency as a Function of Driving-Hour in Which the 

Subject Driver was at Fault, for Baseline and Control Conditions, with Multiple 

Within-Hour Critical Incidents Removed ..................................................................... 22 

Figure 13. Critical-Incident Relative Frequency as a Function of Driving-Hour for Trips That 

Went into the 11th Driving-Hour ................................................................................... 25 

Figure 14. Critical-Incident Relative Frequency as a Function of Driving-Hour, for Trips That 

Went into the 11th Driving-Hour, in Which the Subject Driver was at Fault................ 27 

Figure 15. Critical-Incident Relative Frequency as a Function of Driving-Hour, for Trips That 

Went into the 11th Driving-Hour, for Baseline and Control Conditions ....................... 28 

Figure 16. Critical-Incident Relative Frequency as a Function of Driving-Hour, for Trips That 

Went into the 11th Driving-Hour, in Which the Subject Driver was at Fault, for 

Baseline and Control Conditions................................................................................... 30 

Figure 17. Critical-Incident Relative Frequency as a Function of Driving-Hour, for Trips That 

Went into the 11th Driving-Hour, with Multiple Within-Hour Critical Incidents 

Removed........................................................................................................................ 31 

Figure 18. Critical-Incident Relative Frequency as a Function of Driving-Hour for Trips That 

Went into the 11th Driving-Hour, in Which the Subject Driver was at Fault, with 

Multiple Within-Hour Critical Incidents Removed ...................................................... 33 

Figure 19. Critical-Incident Relative Frequency as a Function of Driving-Hour, for Trips that 

went into the 11th Driving-Hour, for Baseline and Control Conditions, with Multiple 

Within-Hour Critical Incidents Removed ..................................................................... 34 
viii 

Figure 20. Critical-Incident Relative Frequency as a Function of Driving-Hour, for Trips That 

Went into the 11th Driving-Hour, in Which the Subject Driver was at Fault, for 

Baseline and Control Conditions, with Multiple Within-Hour Critical Incidents 

Removed........................................................................................................................ 36 

Figure 21. Odds Ratio as a Function of Driving-Hour Using Logistic Regression Modeling ..... 38 

Figure 22. Odds Ratio as a Function of Driving-Hour for Trips That Went into the 11th

Driving-Hour................................................................................................................. 39

Figure 23. Total Opportunities (Trips) for Shifts 1 Through 8..................................................... 42 

Figure 24. Critical-Incident Relative Frequency as a Function of Driving-Shift ......................... 43 

Figure 25. Odds Ratios as a Function of Driving-Shift Using Logistic Regression Modeling .... 44 

Figure 26. Critical-Incident Relative Frequency as a Function of Driving-Shift in Which the 

Subject Driver was at Fault ........................................................................................... 45 

Figure 27. Critical-Incident Relative Frequency as a Function of Driving-Shift for Baseline 

and Control Conditions ................................................................................................. 46 

Figure 28. Critical-Incident Relative Frequency as a Function of Driving-Shift, in Which the 

Subject Driver was at Fault, for Baseline and Control Conditions ............................... 48 

Figure 29. Critical-Incident Relative Frequency as a Function of Driving-Shift, with Multiple 

Within-Shift Critical Incidents Removed...................................................................... 49 

Figure 30. Critical-Incident Relative Frequency as a Function of Driving-Shift in Which the 

Subject Driver was at Fault, with Multiple Within-Shift Critical Incidents Removed . 50 

Figure 31. Critical Incident Relative Frequency as a Function of Driving-Shift, for Baseline 

and Control Conditions, with Multiple Within-Shift Critical Incidents Removed ....... 51 

Figure 32. Critical-Incident Relative Frequency as a Function of Driving-Shift, in Which the 

Subject Driver was at Fault, for Baseline and Control Conditions, with Multiple 

Within-Shift Critical Incidents Removed...................................................................... 53 

Figure 33. Number of Trips as a Function of Time-of-Day ......................................................... 56 

Figure 34. Critical-Incident Relative Frequency as a Function of Time-of-Day.......................... 56 

Figure 35. Circadian Rhythm Showing Alertness as a Function of Time-of-Day........................ 58 

Figure 36. Critical-Incident Relative Frequencies for the Circadian Lows (2 a.m. to 5 a.m., 3 

p.m. to 5 p.m.) and Highs (9 a.m. to 11 a.m. and 8 p.m. to 9 p.m.) .............................. 59 

Figure 37. Critical-Incident Relative Frequency as a Function of Time-of-Day, with TrafficDensity Plot Superimposed ........................................................................................... 59 

Figure 38. Critical-Incident Relative Frequency as a Function of Time-of-Day in Which the 

Subject Driver was at Fault ........................................................................................... 61 

Figure 39. Critical-Incident Relative Frequency as a Function of Time-of-Day for Baseline 

and Control Conditions ................................................................................................. 62 

Figure 40. Critical-Incident Relative Frequency as a Function of Time-of-Day, in Which the 

Subject Driver was at Fault, for Baseline and Control Conditions ............................... 64 

Figure 41. Critical-Incident Relative Frequency as a Function of Time-of-Day, with Multiple 

Within-Hour Critical Incidents Removed ..................................................................... 65 

Figure 42. Critical-Incident Relative Frequency as a Function of Time-of-Day, in Which the 

Subject Driver was at Fault, with Multiple Within-Hour Critical Incidents Removed. 67 
ix 

Figure 43. Critical-Incident Relative Frequency as a Function of Time-of-Day for Baseline 

and Control Conditions, with Multiple Within-Hour Critical Incidents Removed....... 68 

Figure 44. Critical-Incident Relative Frequency as a Function of Time-of-Day, in Which the 

Subject Driver was at Fault, for Baseline and Control Conditions, with Multiple 

Within-Hour Critical Incidents Removed ..................................................................... 70 
x 

ACRONYMS 

CMV Commercial Motor Vehicle 

DDWS Drowsy Driver Warning System 

FHWA Federal Highway Administration 

FMCSA Federal Motor Carrier Safety Administration 

FOT Field Operational Test 

GEE Generalized Estimating Equations 

GPS Global Positioning System 

HOS Hours of Service 

ITS Intelligent Transportation Systems 

JPO Joint Program Office 

LCL Lower Confidence Limit 

LTCCS Large Truck Crash Causation Study 

NHTSA National Highway Traffic Safety Administration 

UCL Upper Confidence Limit 

VMT Vehicle Miles Traveled 
xi 

EXECUTIVE SUMMARY 

2003 HOURS-OF-SERVICE 

On April 28, 2003, FMCSA published a revised set of regulations concerning the Hours-ofService (HOS) of commercial motor vehicle (CMV) drivers. These published regulations were 

amended on September 30, 2003 and implemented on January 4, 2004. One central component 

of the revisions was a two-hour extension of off-duty time from eight to 10 hours. One rationale 

given in an FMCSA posting in the Federal Register (2005) was that the additional 2 hours of 

off-duty time would provide drivers with “...substantially more opportunity to obtain restorative 

sleep” (p. 3342). The results from Hanowski, Dingus, Sudweeks, Olson, and Fumero (2005) 

indicated that this indeed may be the case; their research found that drivers may be getting more 

sleep under the revised 2003 HOS regulation (6.28 h per day) as compared to the old regulations 

(5.18 h per day; Mitler et al., 1997). 

FMCSA believed the extra off-duty time, with better opportunities to obtain restorative sleep, 

would also reduce the incidence of crashes wholly or partially attributable to drowsiness or 

fatigue (Federal Register, 2005). Several groups (Public Citizen, Citizens for Reliable and Safe 

Highways, and Parents Against Tired Truckers) challenged the 2003 HOS regulations, indicating 

that FMCSA failed to show how additional off-duty time compensated for more driving time 

(from 10 to 11 hours of driving). A naturalistic driving study performed under the 2003 HOS 

regulations by Hanowski et al. (2005) found no significant difference in critical incidents 

(crashes and near-crashes) involving CMVs that occurred in the 10th and 11th driving-hours. 

The current FMCSA-funded study examined some important issues pertaining to the HOS 

debate, particularly with regard to time-on-task or driving-hours. Hanowski et al. (2005) reported 

on an analysis that concerned a comparison of critical incidents involving CMVs that occurred in 

the 10th and 11th driving-hours. This was a timely analysis effort, as it provided insight into 

questions associated with the revised 2003 HOS regulations. Two limitations of the 2005 

analysis of Hanowski et al. were that: (i) it included only a partial dataset and (ii) it compared 

only the frequency of critical incidents that occurred in the 10th and 11th driving-hours. Since that 

report was written, data collection for the naturalistic study on which the analysis of Hanowski et 

al. (2005) was based has been completed (Hanowski et al., in press). These two limitations 

associated with the initial analysis were addressed in the current study. In addition, to provide a 

comprehensive and thorough examination of the topic, additional analyses were conducted to 

address how critical incidents may vary as a function of driving shift and time-of-day. 

OVERVIEW OF THE CURRENT STUDY 

The current final report focused on the following five research issues: (1) critical incidents as a 

function of driving-hour for hours 1 through 11, (2) critical incidents per driving-hour for hours 1 

through 11, focusing on drivers that drove into the 11th hour, (3) modeling the data to look for 

significant differences in critical incidents across driving-hour, (4) critical incidents as a function 

of shift within the driver’s “work week” (i.e., “tour of duty”), and (5) critical incidents as a 

function of time-of-day. 
xii 

METHODS 

Data for these analyses were collected during a Field Operational Test (FOT) of a Drowsy Driver 

Warning System (DDWS). A description of the methodology used in the FOT can be found in 

Hanowski, Nakata, and Olson (2004) and the final data collection report is in press (Hanowski et 

al., in press). Data collection for the FOT began in May 2004 and ended in September 2005. A 

total of 103 drivers participated in the study (102 males, 1 female; mean age = 40.03 years; age 

range = 24 to 60 years). Five drivers could not be used for the driving-hour analyses because of 

unreliable driving-time histories. The gender and age distribution of the 98 drivers included in 

the driving-hour analyses were: 97 males, 1 female; mean age = 39.96 years; age range = 24 to 

60 years. 

A naturalistic-data-collection approach was used in which data were collected as study 

participants drove company trucks during their normal revenue-producing runs. Unobtrusive data 

collection equipment was installed in 46 trucks. The data collection equipment included sensors 

to measure driver performance and video that recorded the driver’s face and three views outside 

of the truck. Driver participants were assigned to an instrumented truck to use on their normal 

delivery routes. The mean number of weeks that drivers participated in the study was 12.38 

weeks, with a standard deviation of 4.17 weeks. The final project dataset consisted of 2.3 million 

miles of driving data and 11.8 TB of video and dynamic sensor data. 

The data collection system, including the video cameras, became active when the ignition system 

of the vehicle was activated. The system was programmed to remain active and to gather data as 

long as the engine was on and the vehicle was in motion. The system was programmed to shut 

down when the ignition was turned off, and the system was programmed to pause if the vehicle 

ceased motion for a 10-minute period. Data collection resumed when the vehicle was again in 

motion. Driver performance was assessed through the occurrence of critical incidents (crashes, 

near-crashes, and crash-relevant conflicts). The critical incidents were identified using a software 

program to search through all data files for spikes in sensors that would indicate the possibility of 

a critical incident. For example, the program searched for all occurrences of longitudinal 

accelerations of >│0.35 g│to locate instances of hard braking. Once potential critical incidents 

had been identified by the software program, researchers viewed the video and other 

corresponding data to determine if the event was a valid critical incident. To help ensure that all 

critical incidents included in the current dataset were valid (that is, that an actual conflict had 

occurred), assessment of an event’s validity required the consensus of two researchers. 

RESULT HIGHLIGHTS 

The number of critical incidents varied on the analysis conducted. For example, Analysis 1 

included 819 critical incidents (12 crashes, 12 tire-strike crashes, 85 near-crashes, and 710 crashrelevant conflicts). To fully investigate this issue, the data were parsed and analyzed in eight 

different ways. This thorough investigative approach was undertaken to help ensure that 

significant findings were not overlooked. 

To adjust for the differences in opportunities across driving-hour, a rate was calculated which, in 

effect, normalized the data. That is, the frequency of critical incidents, in any given driving-hour, 

was divided by the total opportunities for that hour. This provided the relative frequency of 

having a critical incident in any given driving-hour. For example, there were 122 critical 
xiii 

incidents and 4,748 total opportunities in Driving-Hour 1. Thus, the rate of critical incidents in 

Driving-Hour 1 was 0.026 (122 critical incidents / 4,748 total opportunities = 0.026). 

Analysis 1 focused on critical incidents as a function of Driving-Hour for hours 1 through 11. 

Figure 1 shows the relative frequency of critical incidents as a function of driving-hour for Hours 

1 through 11, in which the subject-vehicle driver (i.e., the study truck driver) was judged, based 

on video review, to have been at fault. A breakdown in the frequency for each type of critical 

incident is shown in Figure 1, including crashes (red), crashes: tire strikes (green), near-crashes 

(purple), and crash-relevant conflicts (blue). The top of each bar in Figure 1 represents the 

combined total of all types of critical incidents (crashes, tire strikes, near-crashes, and crashrelevant conflicts). As can be seen from the results of this analysis, there was a notable “spike” in 

the relative frequency of critical incidents in the 1st driving-hour. Odds ratios were evaluated and 

found that all comparisons with the 1st driving-hour were statistically significant. This was a 

consistent finding throughout the eight sub-analyses conducted and suggests a robust and 

meaningful effect. Analysis 2, which focused on critical incidents as a function of driving-hour 

for hours 1 through 11, for drivers who drove 11 hours, found similar findings. 

Figure 1. Critical-Incident Relative Frequency as a Function of Driving-Hour, in Which 

the Subject Driver was at Fault 

As a follow-up to Analyses 1 and 2, Analysis 3 computed odds ratios using logistic regression 

modeling. One difference between this approach and the approach used in Analyses 1 and 2 is 

that an assumption of independence is not being made. That is, the approach used generalized 

estimating equations to account for correlations that might exist between drivers (i.e. the 

correlation between driver 1 and 2) and within drivers (i.e. the correlation between driver 1 

across days) (with respect to critical-incident occurrence). 

The logistic regression modeling approach was applied to two different datasets: (1) the entire 

dataset (Analysis 1), and (2) the subset of trips that went into the 11th driving-hour (Analysis 2). 

The output of the model is the odds ratios for each driving-hour, and not the critical-incident 

relative frequency, as in the previous analyses. Consistent with the general Analysis 1 results, 

there was a spike in the 1st driving-hour as compared to all other driving-hours. No other hourby-hour comparisons were statistically significant. The second logistic regression analysis 
xiv 

conducted was similar to Analysis 2, in that the focus was on trips that went into the 11th drivinghour. 

Analysis 4 investigated critical incidents as a function of driving-shift. Once again, the criticalincident data were parsed and analyzed in eight different ways. To summarize the findings from 

Analysis 4, there were no results that were consistent across all of the different analyses 

conducted. When there was a significant finding, it often involved the 5th or 8th driving-shift. 

However, when multiple within-shift critical incidents were accounted for, such significant 

findings were not found. Because of the inconsistency of the findings across the different 

analyses, these results do not seem to provide strong, convincing support for a driving-shift 

effect 

Analysis 5 assessed critical incidents as a function of time-of-day. As in Analyses 1, 2, and 4, 

Analysis 5 conducted eight separate analyses. At first, the time-of-day data plot and the results 

from the odds ratios did not seem to indicate any clear pattern; however there was obviously a 

strong time-of-day effect. Note that both a frequency odds ratio approach and a logistic 

regression modeling approach were used and similar results were found using both approaches. 

In an attempt to interpret what might be going on, follow-up analyses were conducted, one of 

which considered traffic density as a function of driving-hour. Traffic-density data were plotted 

against the hour-by-hour critical-incident relative frequency plots shown in Figure 2. Note that 

the traffic-density overlay in Figure 2 is based on visual inspection of plots found in Festin 

(1996). 

Figure 2. Critical-Incident Relative Frequency as a Function of Time-of-Day, 

with Traffic-Density Plot Superimposed 

The black line in Figure 2 displays the U.S. distribution of daily weekday traffic across each hour 

of the day in 1995 (Festin, 1996). Festin (1996) used two primary data sources, highway 

statistics for annual vehicle miles traveled (VMT), and 5,000 automatic traffic recorder sites 

across the United States, to estimate annual travel trends in the United States. Note that these 

traffic data represent national weekday averages and are not location-specific. The distribution is 

likely to change based on region, setting (i.e., urban vs. rural), and day of week (i.e., weekday vs. 
xv 

weekend). The traffic-density plot appears to follow the critical-incident relative frequency plot, 

particularly with regard to the sharp onset (around 6 a.m.) and slow decline after what 

presumably would be the evening rush period. As such, it is hypothesized that the hour-by-hour 

differences observed in the current study may be primarily a function of traffic density (i.e., 

increased exposure to other vehicles, leading to increased number of critical incidents). To 

investigate this hypothesis further, a Pearson correlation coefficient was calculated between the 

daily weekday traffic across each hour of the day and the relative frequency of safety-critical 

events across each hour of the day. As suggested by the plotted data, the results showed a strong 

positive linear relationship between the two variables with a correlation (r) of 0.83. With an R2

 of 

0.69, there is a strong linear relationship between critical- incident relative frequency and traffic 

density (i.e., the two increase and decrease with similar patterns). With regard to a strong timeof-day effect, similar results were found for the seven other analyses in Analysis 5. 

CONCLUSIONS 

This study has resulted in a major finding that is relevant to the assessment of the 2003 HOS 

regulations. Specifically, the results from the analysis on critical-incident relative frequency, 

used as a surrogate for driver performance decrement, generally showed no statistical difference 

in the 2nd through 11th driving-hours (for almost all analyses); and the statistical difference 

between the 1st driving-hour and the 11th driving-hour lies in the direction opposite to what 

would be expected, had there been a time-on-task effect. That is, the results from this study do 

not support the hypothesis that there is an increased risk resulting from CMV drivers driving in 

the 11th driving-hour as compared to the 10th driving-hour, or any hour, although caution must be 

used in interpreting these results, due to the small sample of drivers represented in the study as 

compared to the larger CMV driver population. 

These results from this research are consistent with Wylie et al. (1996) with regard to time-ontask being a poor predictor of crashes and safety-related traffic events. In fact, a significant spike 

in the rate of critical incidents was found during the 1st driving-hour. These results are not 

consistent with the contention that crash risk increases as hours of driving increase (see Kaneko 

and Jovanis, 1992; Lin, Jovanis, and Yang, 1993; Park et al., 2005). This spike was found across 

all possible trips and only those trips where the truck driver drove into the 11th driving-hour. The 

latter statement is noteworthy because it suggests that even on those trips in which the driver 

drove into the 11th driving-hour, the critical-incident relative frequency was highest in the 1st

driving-hour (significantly more, as compared to driving-hours 2 though 11 for almost all 

analyses). 

There are at least three possible explanations for the significant spike in the critical-incident 

relative frequency found in the 1st driving-hour. The first reason may be sleep inertia. Sleep 

inertia refers to the decrease or impairment of alertness and performance immediately upon 

waking from sleep. Sleep inertia refers to impairment in a variety of performance tasks, 

including short-term memory, vigilance, cognitive functioning, reaction time, and ability to resist 

sleep (Bonnet and Arand, 1995; Dinges, 1990; Mullington and Broughton, 1994). Obviously, 

personnel who are expected to perform work duties immediately on awaking, such as over-theroad drivers who sleep in sleeper berths in their trucks, may be most affected by sleep inertia. 

Another possible reason for the spike found in the 1st driving-hour could be related to road type 

and/or traffic density. Drivers may start their trips on local and other undivided roads with higher 
xvi 

traffic densities (as compared to what they might find on the open road). Similarly, for line-haul 

operations specifically, drivers may end their trips on local and other undivided roads, again 

experiencing higher traffic density than might occur on a highway. These can be conceived as 

“take-off” and “landing” effects. Hickman et al. (2005), using the same dataset as Hanowski et 

al. (2005), found that drivers were almost five times more likely to be involved in a critical 

incident on an undivided highway than on a divided highway. Additionally, the results from 

Analysis 5 in the current study on time-of-day indicated a potential explanation for the time-ofday effects as attributable to traffic density. These results suggest a possible “take-off” and 

“landing” effect. 

The third possible explanation for the spike found in the 1st driving-hour could reflect time-ofday effects. The current study found a strong time-of-day effect, but it seemed that these effects 

were related to traffic density. Wylie et al. (1996) indicated time-of-day effects to be the 

strongest and most consistent factor influencing driver fatigue. The current analysis did not focus 

on driver fatigue events specifically, so the impact of time-of-day on driver fatigue is unknown 

with respect to the dataset used for this analysis effort. An analysis was conducted looking at all 

critical incidents that occurred in the circadian low and high periods (presumably including both 

fatigue and alert incidents), but the results did not point to a circadian effect. 

Note that these three possible explanations for the spike in critical-incident relative frequency 

during the 1st driving-hour are not mutually exclusive. That is, it could very well be the case that 

a combination of these effects may be involved. For example, sleep inertia may be more 

attributable to the long-haul drivers who sleep in sleeper berths and who may go, in a relatively 

short period of time, from sleeping in the sleeper berth to driving. This may be exacerbated by 

complex driving environments (e.g., urban environments, loading areas, undivided highways, 

intersections) and higher traffic density levels that may occur in loading (i.e., “take-off”) and 

drop-off (i.e., “landing”) situations which occur at the beginning and, depending on the fleet 

operation, end of the shift. Time-of-day effects, which are influenced by internal factors (i.e., 

circadian rhythm) and external factors (e.g., rush hours, traffic density), also play a role. 

However, based on the current research and other findings in the literature, the impact of timeon-task, which is the basis for the 10th driving-hour vs. 11th driving-hour debate, does not seem 

to be an obvious or significant factor when considering increased critical-incident risk. 
1 

1. INTRODUCTION 

Crashes involving large trucks constitute a significant risk to the driving public and occupational 

risk to truck drivers. In 2005, some 442,000 large trucks (weighing over 10,000 lbs each) were 

involved in vehicle crashes; 4,932 of these large-truck crashes resulted in fatalities. A total of 

5,212 people died (12 percent of all traffic fatalities in 2005) and an additional 112,000 were 

injured (National Highway Traffic Safety Administration [NHTSA], 2007). Driver fatigue is a 

prominent factor in large-truck crashes. The Large Truck Crash Causation Study (LTCCS) found 

that 13 percent of truck drivers were coded as being fatigued at the time of the crash (Federal 

Motor Carrier Safety Administration [FMCSA], 2006). 

1.1 2003 HOURS-OF-SERVICE 

On April 28, 2003, FMCSA published a revised set of regulations concerning the Hours-ofService (HOS) of commercial motor vehicle (CMV) drivers. These published regulations were 

amended on September 30, 2003 and implemented on January 4, 2004. One central component 

of the revisions was a two-hour extension of off-duty time from eight to 10 hours. One rationale 

given in an FMCSA posting in the Federal Register (2005) was that the additional 2 hours of 

off-duty time would provide drivers with “...substantially more opportunity to obtain restorative 

sleep” (p. 3342). The results from Hanowski, Dingus, Sudweeks, Olson, and Fumero (2005) 

indicated that this indeed may be the case; their research found that drivers may be getting more 

sleep under the revised 2003 HOS regulation (6.28 h per day) as compared to the old regulations 

(5.18 h per day; Mitler et al., 1997). 

FMCSA believed the extra off-duty time, with better opportunities to obtain restorative sleep, 

would also reduce the incidence of crashes wholly or partially attributable to drowsiness or 

fatigue (Federal Register, 2005). Several groups (Public Citizen, Citizens for Reliable and Safe 

Highways, and Parents Against Tired Truckers) challenged the 2003 HOS regulations, indicating 

that FMCSA failed to show how additional off-duty time compensated for more driving time 

(from 10 to 11 hours of driving). A naturalistic driving study performed under the 2003 HOS 

regulations by Hanowski et al. (2005) found no significant difference in critical incidents 

(crashes and near-crashes) involving CMVs that occurred in the 10th and 11th driving-hours. 

1.2 FATIGUE AND CRASH RISK 

Driver impairment due to drowsiness is a known contributing factor in many crashes involving 

CMV drivers (Maycock, 1997). Fatigue affects mental alertness, thereby decreasing an 

individual’s ability to operate a vehicle safely. Drowsiness slows reaction time, decreases 

awareness, and impairs judgment (Balkin et al., 2000; Von Dongen et al., 2003). Many factors 

may affect driver alertness and fatigue, including time-of-day, previous hours of sleep, hours 

awake, health and wellness, caffeine intake, over-the-counter and prescription drug use, 

individual differences, and time-on-task (Orris et al., 2005). While all these factors are important 

in determining driver fatigue and alertness, one of the concerns in the challenge of the 2003 HOS 

regulations was driving an extra hour, from 10 to 11 hours (within a 14-hour work window). For 

CMV drivers, time-on-task can be conceptualized as hours driving. 
2 

Mackie and Miller (1978) studied the relationship between HOS and truck-crash risk. They 

found a higher proportion of crashes in the second half of trips, a strong increase in crash risk as 

continuous hours of driving increased, and consistent evidence of reduced driving performance 

as the number of consecutive shifts increased. Mackie and Miller (1978) also found a circadian 

effect (i.e., time-of-day) with the highest crash risk occurring between the hours of 2 and 6 a.m. 

However, the schedules assigned to drivers exceeded the U.S. legal HOS at the time, and most of 

the performance decrements they found occurred during “illegal” (i.e., over 11 hours) HOS 

driving-hours. 

Based on company-reported crash data, and within the parameters of U.S. legal HOS drivinghours, Jovanis and colleagues found that crash risk increased as driving-hours increased, most 

notably after the 4th driving-hour (see Kaneko and Jovanis, 1992; Lin, Jovanis, and Yang, 1993). 

Most recently, Park et al. (2005) used pre-existing crash data from the mid-1980s with 5,050 

drivers (954 accident-involved drivers and 1,506 non-accident-involved drivers in 1984; 887 

accident-involved drivers and 1,604 non-accident-involved drivers in 1985). As in prior research, 

Park et al. (2005) found an increase in crash risk associated with increasing driving-hours. Crash 

risk increased slightly between driving-hours 1 and 4, but increased significantly in the 5th hour 

and was sustained though the 10th driving-hour. 

However, the Driver Fatigue and Alertness Study casts doubt on whether the number of drivinghours has any effect on increased crash risk (Wylie et al., 1996). The Driver Fatigue and 

Alertness Study was an on-road study with 80 drivers in the United States and Canada. Trucks 

were instrumented with video cameras and several driving measures were collected (e.g., driving 

task performance, driving speed and distance, physiological measures, and self-report 

questionnaires) over a period of 16 weeks. The strongest and most consistent factor influencing 

driver fatigue was time-of-day; hours driving were not a consistent predictor of fatigue (Wylie et 

al., 1996). In fact, Dingus et al. (2002) found, during a naturalistic driving study with sleeperberth truck drivers, that the highest frequency of critical incidents occurred in the first drivinghour. While there was a “spike” in the frequency of critical incidents during the first drivinghour, when the data were normalized to account for exposure (i.e., dividing the frequency per 

hour by the number of times driving in each hour), statistical analyses found no significant 

difference between driving-hours. 

Analogous to time-on-task performance decrements is the contention that crash risk increases as 

the number of consecutive driving-shifts increases (i.e., the cumulative effect of fatigue increases 

over consecutive shifts). Jovanis and Kaneko (1991) used retrospective crash reports and drive 

histories and found a consistent trend in crash risk over four successive driving-shifts. On 

average, compared to the first shift, crash risk was 6 percent higher on the second shift, 17 

percent higher on the third shift, and 36 percent higher on the fourth shift. However, these effects 

were found only in drivers who drove during the night; there was no significant effect for drivers 

who drove during the day, suggesting time-of-day was a more likely explanation of the findings. 

The Driver Fatigue and Alertness Study found that the cumulative number of trips was not a 

strong or consistent predictor of fatigue (Wylie et al., 1996). Similarly, three different naturalistic 

driving studies with CMV drivers did not find an increase in critical-incident occurrence as the 

cumulative number of shifts increased (Dingus et al., 2002; Hanowski, Wierwille, Garness, and 

Dingus, 2000; Olson, 2006). These studies suggest that cumulative driving-shifts within the 

driver’s work week have little effect on crash risk. 
3 

1.3 OVERVIEW OF THE CURRENT STUDY 

The current study examines some important issues pertaining to the HOS debate, particularly 

with regard to time-on-task or driving-hours. Hanowski et al. (2005) reported on an analysis that 

concerned a comparison of critical incidents involving CMVs that occurred in the 10th and 11th

driving-hours. This was a timely analysis effort, as it provided insight into questions associated 

with the revised 2003 HOS regulations. Two limitations of the 2005 analysis of Hanowski et al. 

were that: (i) it included only a partial dataset and (ii) it compared only the frequency of critical 

incidents that occurred in the 10th and 11th driving-hours. Since that report was written, data 

collection for the naturalistic study on which the analysis of Hanowski et al. (2005) was based 

has been completed (Hanowski et al., in press). These two limitations associated with the initial 

analysis can be addressed in the current study. In addition, to provide a comprehensive and 

thorough examination of the topic, additional analyses were conducted to address how critical 

incidents may vary as a function of driving-shift and time-of-day. 

The current analysis focused on the following research issues: 

1. Critical incidents as a function of driving-hour for hours 1 through 11 

2. Critical incidents per driving-hour for hours 1 through 11, focusing on drivers that drove 

into the 11th hour 

3. Modeling the data to look for significant differences in critical incidents across drivinghour 

4. Critical incidents as a function of shift within the driver’s “work week” (i.e., “tour of 

duty”) 

5. Critical incidents as a function of time-of-day 
4 

2. METHOD 

Data for these analyses were collected during a Field Operational Test (FOT) of a Drowsy Driver 

Warning System (DDWS). This FOT was co-sponsored by NHTSA, FMCSA, and the Federal 

Highway Administration’s (FHWA) Intelligent Transportation System Joint Program Office 

(ITS JPO). A description of the methodology used in the FOT can be found in Hanowski, 

Nakata, and Olson (2004) and the final data collection report is in press (Hanowski et al., in 

press). Data collection for the FOT began in May 2004 and ended in September 2005. A total of 

103 drivers participated in the study (102 males, 1 female; mean age = 40.03 years; age range = 

24 to 60 years). As described below, data from five drivers could not be used for the drivinghour analyses (Analyses 1, 2, 3, and 4 described later) because of unreliable driving-time 

histories. The gender and age distribution of the 98 drivers included in the driving-hour analyses 

were: 97 males, 1 female; mean age = 39.96 years; age range = 24 to 60 years. 

A naturalistic-data-collection approach was used where data were collected as study participants 

drove company trucks during their normal revenue-producing runs. Unobtrusive data- collection 

equipment was installed in 46 trucks. The data-collection equipment included sensors to measure 

driver performance and video that recorded the driver’s face and three views outside of the truck 

(Figure 3). Driver participants were assigned to an instrumented truck to use on their normal 

delivery routes. Typically, once a driver had completed his or her participation in the study, a 

different driver would be assigned to the instrumented truck. The experimental design of the 

FOT called for drivers to operate an instrumented truck for up to 16 continuous weeks. However, 

for a variety of reasons, including attrition, truck downtime, and scheduling issues, some drivers 

drove more or less than this initially allotted time. The mean number of weeks that drivers 

participated in the study was 12.38 weeks, with a standard deviation of 4.17 weeks. The final 

project dataset consisted of 2.3 million miles of driving data and 11.8 TB of video and dynamic 

sensor data. 

Figure 3. Split-Screen Presentation of the Four Camera Views 

Clockwise from top left: driver’s face, forward road, rearward left, rearward right. 
5 

The data collection system, including the video cameras, became active when the ignition system 

of the vehicle was activated. The system was programmed to remain active and to gather data as 

long as the engine was on and the vehicle was in motion. The system was programmed to shut 

down when the ignition was turned off, and the system was programmed to pause if the vehicle 

ceased motion for a 10-minute period. Data collection resumed when the vehicle was again in 

motion. 

Driver performance was assessed through the occurrence of critical incidents (crashes, nearcrashes, and crash-relevant conflicts). Briefly, a crash was defined as any contact with an object, 

either moving or fixed, at any speed. A near-crash was defined as any circumstance that required 

a rapid, evasive maneuver (e.g., hard braking) by the subject vehicle or any other vehicle, 

pedestrian, cyclist, or animal, in order to avoid a crash. A crash-relevant conflict was defined as 

any circumstance that required 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. Examples of 

potential crash-relevant conflicts include hard braking by a driver because of a specific crash 

threat or proximity to other vehicles. Evasive maneuvers resulting in unsafe and/or illegal 

maneuvers or situations were included in this category (or as near-crashes, if more severe). All 

longitudinal accelerations of >│0.35 g│ were reviewed to assess whether they qualified as crashrelevant conflicts (or near-crashes); those with longitudinal acceleration of > │0.50 g│ were 

always coded as crash-relevant conflicts or near-crashes. Table 1 outlines all of the trigger types 

and thresholds used to identify critical incidents. 

Table 1. Trigger Types Used to Flag Critical Incidents in the FOT Dataset 

Trigger Type Description 

Longitudinal 

Acceleration 

(1) Acceleration or deceleration greater than or equal to │0.35 g│. Speed 

greater than or equal to 15 mph. 

(2) Acceleration or deceleration greater than or equal to │0.5 g│. Speed 

less than or equal to 15 mph. 

Time-to-Collision (3) A forward time-to-collision [TTC] value of less than or equal to 1.85 s, 

coupled with a range of less than or equal to 150 ft, a target speed of 

greater than or equal to 5 mph, a yaw rate of less than or equal to │4°/s│, 

and an azimuth of less than or equal to │0.8°│. 

(4) A forward TTC value of less than or equal to 1.85 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°/s│, and an azimuth of less than or equal to │0.8°│. 

Swerve (5) Swerve value of greater than or equal to 3. Speed greater than or equal 

to 15 mph. 

Critical-Incident 

Button 

(6) 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 (7) Event that was identified by a data reductionist viewing video footage; no 

other trigger listed above identified the event (i.e., Longitudinal Acceleration, 

TTC, etc.). 
6 

The critical incidents were identified using a software program to search through all data files for 

spikes in sensors that would indicate the possibility of a critical incident. For example, the 

program searched for all occurrences of longitudinal accelerations of >│0.35 g│to locate 

instances of hard braking. Once potential critical incidents had been identified by the software 

program, researchers viewed the video and other corresponding data to determine whether the 

event were a valid critical incident. To help ensure that all critical incidents included in the 

current dataset were valid (that is, that an actual conflict occurred), assessment of an event’s 

validity required the consensus of two researchers. 

2.1 DRIVING HISTORY SOFTWARE AND DATA CAVEATS 

Once critical incidents were identified, it was possible to obtain the driving-hour and time-of-day 

at the time of the critical incident. The full DDWS FOT dataset was processed through a 

software program which used the network speed on each truck to determine if the driver were 

driving. That is, the software program started with the first file for a given driver, and once the 

truck network speed moved above 0 mph, the software program assumed that the driver was 

driving and started counting driving time. The program then searched the data for > 6-hour gaps 

in the driving files (where the network speed = 0) and assumed that the driver had taken a break. 

The next driving file, as long as it was not less than 14 h from the start of the previous driving 

shift, was considered the start of the next driving shift or “trip.” The driving-hour and shift-hour 

data produced by this software were used in Analysis 1.4, described in the Results section. 

It is important to point out a number of caveats with regard to the data and the approach used: 

• There are only driving files; there was no record of non-driving work activity. 

• Embedded in breaks were non-driving work; there was no way to separate non-driving 

work from rest breaks, as there was only a record of driving. 

• A “break” (network speed = 0) of less than 10 min was ignored and counted as driving 

time. This was done to avoid considering time when the driver was stuck in traffic as a 

“break.” 

• In searching for the start of a “work week,” the software program searched for nondriving segments of >34 h and assumed this was the restart break (it is unknown whether 

this non-driving break included non-driving work). 

• For any shift, if the system did not collect data, that fact was unknown. 

• Data from five drivers were not included because the driving-time history was unreliable. 

For four of these drivers, the vehicle network speed sensor malfunctioned such that a 

non-zero speed was recorded when the truck was not moving. Because driving time was 

assessed, in part, by the speed variable, the driving times in these cases were unreliable 

and not used. In one other case, an error with the file-naming convention affected the 

method used to sort the data and calculate driving-time history. As a result, the drivingtime data for this driver were also unreliable and were not included in the analysis. 

• Each critical incident was checked by two data analysts to help ensure that the drivinghour produced by the software program was valid. For example, all files prior to a critical 

incident were checked to make sure that there were not any files with bad network speed, 

or any files where the subject driver could not be verified, that may have affected the 

calculation of the driving-hour. If any such files were found, then the associated critical 
7 

incident was not included in the dataset for analysis. Although all critical incidents were 

checked for accuracy, it was not possible to check all trips used to determine exposure 

values. However, any potential errors with respect to the exposure data would be 

assumed to be unbiased across trips. 
8 

3. RESULTS 

As noted previously, data analysis for this study focused on the following five areas: 

1. Critical incidents as a function of driving-hour for hours 1 through 11 

2. Critical incidents as a function of driving-hour for hours 1 through 11, for drivers that 

drove into the 11th hour 

3. Modeling the data to look for significant differences in critical incidents across drivinghour 

4. Critical incidents as a function of shift within the driver’s “work week” 

5. Critical incidents as a function of time-of-day 

6 Below we consider each of these areas in turn. 

3.1 CRITICAL INCIDENTS AS A FUNCTION OF DRIVING-HOUR FOR 

HOURS 1 THROUGH 11 

The dataset used in this analysis included all data that occurred in driving-hours 1 through 11. 

This included 819 critical incidents: 12 crashes, 12 tire-strike crashes, 85 near-crashes, and 710 

crash-relevant conflicts. For this analysis, critical incidents were assumed to be independent, 

although Analysis 3 used a logistic regression modeling approach that accounted for repeated 

measures to test this assumption. 

To fully investigate this issue, the data were parsed and analyzed in eight different ways. This 

thorough investigative approach was to help ensure that significant findings were not 

overlooked. The eight different analyses used the following datasets: 

• Analysis 1.1: All Data: This included the entire dataset, with the exception of information 

outlined in the Data Caveats section above. This provided the largest dataset available. 

• Analysis 1.2: All Data, Truck Driver at Fault: Using the dataset noted above, this analysis 

included only those critical incidents that were judged to have been the fault of the 

subject driver (i.e., the study truck driver). 

• Analysis 1.3: Baseline and Control Data: Only critical incidents that occurred in the 

control and baseline conditions (where no DDWS was used) were included. As the study 

for which these data were collected investigated a DDWS, it was possible that the 

presence of this in-vehicle technology may have affected the occurrence of critical 

incidents in an unknown manner. 

• Analysis 1.4: Baseline and Control Data, Truck Driver at Fault: Using the baseline and 

control data noted above, this analysis included only critical incidents in which the truck 

driver was judged to have been at fault. 

• Analysis 1.5: All Data, No Within-Hour Incidents: Although it was not a frequent 

occurrence, there were cases in which a given driving-hour had multiple critical incidents 

recorded. Using the dataset noted in Analysis 1.1, this analysis removed these multiples 

and treated critical incidents as a Bernoulli random variable (i.e., for each hour there are 

two possible outcomes—“yes, at least one incident occurred” or “no, an incident did not 
9 

occur”). The critical incident with the less severe classification was removed (e.g., a nearcrash would be removed instead of a crash). 

• Analysis 1.6: All Data, Truck Driver at Fault, No Within-Hour Incidents: Using the 

dataset noted in Analysis 1.1, this analysis considered only critical incidents in which the 

truck driver was at fault and removed multiple within-hour incidents. 

• Analysis 1.7: Baseline and Control Data, No Within-Hour Incidents: This analysis used 

the baseline and control data and removed multiple within-hour incidents. 

• Analysis 1.8: Baseline and Control Data, Truck Driver at Fault, No Within-Hour 

Incidents: Using the baseline and control data, this analysis considered only critical 

incidents in which the truck driver was at fault and removed muliple within-hour 

incidents. 

The findings from these eight analyses are outlined below. 

Analysis 1.1: All Data 

Table 2 shows the dataset for the first analysis, which included all critical incidents, and all 

driving opportunities (i.e., trips), that occurred in the 1st through 11th driving-hours. As shown in 

Table 2, each driving-hour has a different number of opportunities to be involved in a critical 

incident. For example, a driver may have driven into the 10th hour but not into the 11th hour; as a 

result, there were fewer opportunities to be involved in a critical incident in the 11th hour. To 

adjust for the differences in opportunities across driving-hour, a rate was calculated which, in 

effect, normalized the data. That is, the frequency of critical incidents, in any given driving-hour, 

is divided by the total opportunities for that hour. This provides the relative frequency of having 

a critical incident in any given driving-hour. As noted previously, it is important to point out the 

data in Table 2 include cases where multiple critical incidents were recorded in the same drivinghour; a follow-up analysis, presented later, removed the multiple cases to set up a dichotomous 

variable. 

Table 2. Critical Incidents and Total Opportunities by Driving-Hour for Hours 1 Through 11 

Hour Critical Incidents Per 

Driving-Hour 

Total Opportunities Per 

Driving-Hour 

Rate: Critical Incidents/ 

Total Opportunities 

1 155 4,748 0.033 

2 82 4,518 0.018 

3 94 4,393 0.021 

4 79 4,241 0.019 

5 84 4,022 0.021 

6 80 3,697 0.022 

7 62 3,320 0.019 

8 64 2,941 0.022 

9 52 2,510 0.021 

10 38 2,019 0.019 

11 29 1,535 0.019 
10 

Figure 4 plots the number of opportunities (also considered “trips”) for each driving-hour. Not 

surprisingly, the number of opportunities drops off as the number of driving-hours increases. 

Thus, by the time drivers reach the 11th hour, many of the trips had concluded. This suggests that 

drivers in the current study did not always drive 11 h, but rather stopped their trips short of the 

11th hour. However, there were 1,535 trips that did include the 11th hour. 

Figure 4. Total Opportunities (Trips) for Driving-Hours 1 Through 11 

Figure 5 shows the relative frequency of critical incidents as a function of driving-hour for hours 

1 through 11. A breakdown in the frequency for each type of critical incident is shown in Figure 

5, including crashes (red), crashes: tire strikes (green), near-crashes (purple), and crash-relevant 

conflicts (blue). The top of each bar in Figure 5 represents the combined total of all types of 

critical incidents (crashes, tire strikes, near-crashes, and crash-relevant conflicts). This number 

was used for all further analyses. The frequencies of crashes, tire strikes, and near-crashes were 

too small to conduct any meaningful analyses independent of the crash-relevant conflict data. As 

can be seen from the results of this analysis, there was a notable “spike” in the relative frequency 

of critical incidents in the 1st driving-hour. 
11 

Figure 5. Critical Incident Relative Frequency as a Function of Driving-Hour 

Odds ratios determined whether the critical-incident relative frequency was statistically different 

across driving-hours. Under the assumption that the occurrence of critical incidents was rare, the 

retrospective odds ratio provides an approximate estimate of the prospective relative risk; 

therefore, the relative risk of driving in the Ith hour vs. the Jth hour can be assessed with an odds 

ratio. 

To provide some background on this statistical technique: An odds ratio is a measure of 

association commonly employed in the analysis of 2 × 2 contingency tables (Agresti, 1996). The 

data for this analysis can be displayed as a 2 × 2 contingency table, as shown in Table 3. 

Table 3. 2 × 2 Contingency Table Used to Calculate Odds Ratio 

Incident Occurrence No Incident 

Occurrence 

Total Opportunity for 

Incident Occurrence 

I

th hour of shift n11 n12 n1. 

Jth hour of shift n21 n22 n2. 

n.1 n.2 n.. 

Odds of occurrence were defined as the probability of event occurrence divided by the 

probability of non-occurrence. The following formula was used to perform the calculation to 

determine the odds ratio in order to assess the increase in probability of having a critical incident 

in the Ith hour vs. the Jth hour: 

Odds Ratio = (n11)(n22)/(n21)(n12) 

Table 4 displays the results from the odds ratio analyses. The first two columns in Table 4 show 

the comparison hours (e.g., the first row compares the 1st and 2nd driving-hours). The third 

column shows the calculated odds ratio and the last two columns show the lower confidence 

limits (LCL) and upper confidence limits (UCL), respectively. Odds ratios with LCLs and UCLs 

that contain “1” are not statistically significant (note that 95 percent confidence level was used in 

these calculations). All significant odds ratios shown in Table 4 are highlighted and italicized. 
12 

Thus, the comparison of the 1st driving-hour to the 2nd indicates that drivers were 1.83 times 

more likely to be involved in a critical incident during the 1st hour than in the 2nd driving-hour. 

This was statistically significant as the LCL-UCL interval does not include “1.” 

Note that all significant effects are included in the odds ratio tables along with selected other 

analyses that are not significant. For example, in Table 4, it can be seen that all comparisons with 

the 1st driving-hour were statistically significant. Once that was determined, the next step was to 

look for the hour with the next highest relative frequency (8th driving-hour) and the lowest 

relative frequency (2nd driving-hour). Since this comparison was not significant, we would 

expect that all other comparisons would not be significant. This approach was followed for all 

analyses. It should also be noted that the 10th vs. 11th driving-hour comparison is always 

provided because this is such a controversial change in the HOS regulations. 

Table 4. Results from the Odds Ratio Analyses 

Driving-Hour Driving-Hour Odds Ratio LCL UCL 

1 2 1.83 1.39 2.39 

1 3 1.54 1.19 2.00 

1 4 1.78 1.35 2.34 

1 5 1.58 1.21 2.07 

1 6 1.53 1.16 2.01 

1 7 1.77 1.32 2.39 

1 8 1.52 1.13 2.04 

1 9 1.60 1.16 2.19 

1 10 1.76 1.23 2.52 

1 11 1.75 1.39 2.39 

8 2 1.20 0.86 1.67 

8 11 1.16 0.74 1.80 

10 11 1.00 0.61 1.62 
13 

Analysis 1.2: All Data, Truck Driver at Fault 

Analysis 1.2 included only critical incidents in which the subject-vehicle driver (i.e., the study 

truck driver) was judged, based on video review, to have been at fault. Note that these data do 

include multiple critical incidents that may have occurred within the same hour. The resulting 

dataset is shown in Table 5 below and plotted in Figure 6. 

Table 5. Critical Incidents and Total Opportunities by Driving-Hour for Hours 1 Through 11, 

in Which the Subject Driver was at Fault 

DrivingHour Critical Incidents Per 

Driving-Hour 

Total Opportunities Per 

Driving-Hour 

Rate: Critical Incidents/ 

Total Opportunities 

1 122 4,748 0.026 

2 61 4,518 0.014 

3 76 4,393 0.017 

4 56 4,241 0.013 

5 61 4,022 0.015 

6 60 3,697 0.016 

7 50 3,320 0.015 

8 44 2,941 0.015 

9 41 2,510 0.016 

10 24 2,019 0.012 

11 23 1,535 0.015 

Figure 6. Critical-Incident Relative Frequency as a Function of Driving-Hour, in Which 

the Subject Driver was at Fault 

Table 6 displays the results from the odds ratio analyses, including all trips in which the subject 

driver was at fault. Once again, the 1st driving-hour was found to be significantly greater in 

critical incidents than all other hours. No other significant differences were found between any 

other two hours. 
14 

Table 6. Results from the Odds Ratio Analyses, Including all Trips in Which 

the Subject Driver was at Fault 

Driving-Hour Driving-Hour Odds Ratio LCL UCL 

1 2 1.93 1.41 2.63 

1 3 1.50 1.12 2.00 

1 4 1.97 1.43 2.71 

1 5 1.71 1.26 2.34 

1 6 1.60 1.17 2.18 

1 7 1.72 1.24 2.40 

1 8 1.74 1.23 2.46 

1 9 1.59 1.11 2.27 

1 10 2.19 1.41 3.41 

1 11 1.73 1.11 2.72 

3 10 1.46 0.92 2.32 

9 10 1.38 0.83 2.29 

11 10 1.26 0.71 2.25 

Analysis 1.3: Baseline and Control Data 

Analysis 1.3 used only the data from the “control” and “baseline” conditions from the study. 

Recall that the study for which these data were collected investigated a DDWS; thus it was 

possible that the presence of this in-vehicle technology may have affected the occurrence of 

incidents in an unknown manner. To examine this possibility, only critical incidents that 

occurred in the control and baseline conditions (where no DDWS was used) were included, and 

the resulting dataset is shown in Table 7. Figure 7 plots the critical-incident relative frequency 

across driving-hours for the baseline and control conditions. 

Table 7. Critical Incidents and Total Opportunities by Driving-Hour for Hours 1 Through 11 

for Baseline and Control Conditions 

DrivingHour Critical Incidents Per 

Driving-Hour 

Total Opportunities Per 

Driving-Hour 

Rate: Critical Incidents/ 

Total Opportunities 

1 79 2,275 0.035 

2 43 2,154 0.020 

3 48 2,089 0.023 

4 37 2,021 0.018 

5 47 1,932 0.024 

6 43 1,788 0.024 

7 26 1,619 0.016 

8 39 1,458 0.027 

9 27 1,269 0.021 

10 20 1,031 0.019 

11 17 796 0.021 
15 

Figure 7. Critical-Incident Relative Frequency as a Function of Driving-Hour for Baseline 

and Control Conditions 

Table 8 displays the results from the odds ratio analyses, including all trips during the baseline 

and control conditions. Statistically significant differences were found between the 1st drivinghour and all other driving-hours, with the exception of the 8th and 11th driving-hours. No other 

significant results were found. 

Table 8. Results from the Odds Ratio Analysis Across all Trips 

for Baseline and Control Conditions 

Driving-Hour Driving-Hour Odds Ratio LCL UCL 

1 2 1.77 1.21 2.57 

1 3 1.58 1.10 2.27 

1 4 1.93 1.30 2.86 

1 5 1.44 1.00 2.08 

1 6 1.46 1.00 2.13 

1 7 2.20 1.41 3.45 

1 8 1.31 0.89 1.93 

1 9 1.65 1.06 2.58 

1 10 1.82 1.11 2.99 

1 11 1.65 0.97 2.80 

6 7 1.51 0.92 2.47 

3 7 1.44 0.89 2.33 

11 10 1.10 0.57 2.12 
16 

Analysis 1.4: Baseline and Control Data, Truck Driver at Fault 

Analysis 1.4 used only the data from the control and baseline conditions in which the subject 

driver was judged at fault. Table 9 displays the dataset used and Figure 8 plots the criticalincident relative frequency across the driving-hours. 

Table 9. Critical Incidents and Total Opportunities by Driving-Hour for Hours 1 Through 11, 

in Which the Subject Driver was at Fault, for Baseline and Control Conditions 

DrivingHour Critical Incidents Per 

Driving-Hour 

Total Opportunities Per 

Driving-Hour 

Rate: Critical Incidents/ 

Total Opportunities 

1 62 2,275 0.027 

2 34 2,154 0.016 

3 37 2,089 0.018 

4 26 2,021 0.013 

5 36 1,932 0.019 

6 33 1,788 0.018 

7 21 1,619 0.013 

8 25 1,458 0.017 

9 20 1,269 0.016 

10 11 1,031 0.011 

11 16 796 0.020 

Figure 8. Critical-Incident Relative Frequency as a Function of Driving-Hour in Which 

the Subject Driver was at Fault, for Baseline and Control Conditions 

Odds ratios were conducted on the critical-incident relative frequency data and the results are 

shown in Table 10. The 1st driving-hour was found to be statistically significant and greater in 

number of critical incidents than hours 2, 3, 4, 7, 8, 9, and 10. No other odds ratio comparisons 

were significant. 
17 

Table 10. Results from the Odds Ratio Analysis, Including All Trips in Which 

the Subject Driver was at Fault, for Baseline and Control Conditions 

Driving-Hour Driving-Hour Odds Ratio LCL UCL 

1 2 1.75 1.14 2.67 

1 3 1.55 1.03 2.34 

1 4 2.15 1.35 3.41 

1 5 1.48 0.97 2.24 

1 6 1.49 0.97 2.28 

1 7 2.13 1.29 3.51 

1 8 1.61 1.00 2.57 

1 9 1.75 1.05 2.91 

1 10 2.60 1.36 4.95 

1 11 1.37 0.78 2.38 

8 10 1.62 0.79 3.30 

9 10 1.48 0.71 3.11 

11 10 1.90 0.88 4.12 

Analysis 1.5: All Data, No Within-Hour Incidents 

As noted, the larger dataset included multiple critical incidents that may have been recorded in 

the same hour of the same trip. 

Table 11 shows the dataset where these multiple critical incidents (same hour, during the same 

trip), were removed. This resulted in a dichomotous variable, either “yes, at least one critical 

incident occurred” or “no critical incident occurred.” It is important to indicate that the 

occurrence of multiple critical incidents per hour was rare. In addition, as will be shown from the 

analysis results, re-analyzing the data with multiple within-hour critical incidents removed did 

not significantly affect the results. Figure 9 shows the plot of the critical-incident relative 

frequencies for each driving-hour. 

Table 11. Critical Incidents and Total Opportunities by Driving-Hour for Hours 1 Through 11, 

with Multiple Within-Hour Critical Incidents Removed 

DrivingHour Critical Incidents Per 

Driving-Hour 

Total Opportunities Per 

Driving-Hour 

Rate: Critical Incidents/ 

Total Opportunities 

1 145 4,748 0.031 

2 78 4,518 0.017 

3 89 4,393 0.020 

4 74 4,241 0.017 

5 79 4,022 0.020 

6 74 3,697 0.020 

7 58 3,320 0.017 

8 53 2,941 0.018 

9 49 2,510 0.020 

10 38 2,019 0.019 

11 27 1,535 0.018 
18 

Figure 9. Critical-Incident Relative Frequency as a Function of Driving-Hour, 

with Multiple Within-Hour Critical Incidents Removed 

As previously found, a spike of critical-incident relative frequency was seen in the 1st drivinghour. As shown in Table 12 below, odds ratios were analyzed and this spike was statistically 

significant across all other driving-hours. No other driving-hours differed with respect to criticalincident relative frequency. 

Table 12. Results from the Odds Ratio Analysis with Multiple Within-Hour 

Critical Incidents Removed 

Driving-Hour Driving-Hour Odds Ratio LCL UCL 

1 2 1.79 1.36 2.37 

1 3 1.52 1.17 1.99 

1 4 1.77 1.34 2.35 

1 5 1.57 1.19 2.07 

1 6 1.54 1.16 2.05 

1 7 1.77 1.30 2.41 

1 8 1.72 1.25 2.36 

1 9 1.58 1.14 2.20 

1 10 1.64 1.14 2.36 

1 11 1.76 1.16 2.66 

3 2 1.18 0.87 1.60 

9 11 1.11 0.69 1.79 

10 11 1.07 0.65 1.76 
19 

Analysis 1.6: All Data, Truck Driver at Fault, No Within-Hour Incidents 

Analysis 1.6 included critical incidents in which the subject driver was judged to have been at 

fault, and all multiple critical incidents that occurred within the same hour and the same trip were 

removed. The resulting dataset is shown in Table 13, and the plot of the relative frequency of 

critical incidents is shown in Figure 10. 

Table 13. Critical Incidents and Total Opportunities by Driving-Hour for Hours 1 Through 11, 

in Which the Subject Driver was at Fault, with Multiple Within-Hour Critical Incidents Removed 

DrivingHour Critical Incidents Per 

Driving-Hour 

Total Opportunities Per 

Driving-Hour 

Rate: Critical Incidents/ 

Total Opportunities 

1 115 4,748 0.024 

2 59 4,518 0.013 

3 72 4,393 0.016 

4 53 4,241 0.012 

5 57 4,022 0.014 

6 54 3,697 0.015 

7 48 3,320 0.014 

8 35 2,941 0.012 

9 39 2,510 0.016 

10 24 2,019 0.012 

11 21 1,535 0.014 

Figure 10. Critical-Incident Relative Frequency as a Function of Driving-Hour, in Which | 

the Subject Driver was at Fault, with Multiple Within-Hour Critical Incidents Removed 

Odds ratios were examined on the critical-incident relative frequencies across the driving-hours, 

and the results are shown in Table 14. Once again, the relative frequency of critical incidents in 

the 1st driving-hour was found to be significantly greater than those in all other hours and no 

other hour-to-hour comparisons were statistically significant. 
20 

Table 14. Results from the Odds Ratio Analysis, in Which the Subject Driver was at Fault, 

with Multiple Within-Hour Critical Incidents Removed 

Driving-Hour Driving-Hour Odds Ratio LCL UCL 

1 2 1.88 1.37 2.57 

1 3 1.49 1.11 2.01 

1 4 1.96 1.41 2.72 

1 5 1.73 1.25 2.38 

1 6 1.67 1.21 2.32 

1 7 1.69 1.20 2.38 

1 8 2.06 1.41 3.02 

1 9 1.57 1.09 2.27 

1 10 2.06 1.32 3.21 

1 11 1.79 1.12 2.86 

3 10 1.39 0.87 2.21 

9 10 1.31 0.79 2.19 

11 10 1.15 0.64 2.08 

Analysis 1.7: Baseline and Control Data, No Within-Hour Incidents 

Analysis 1.7 considered the control and baseline data and did not include any multiple withinhour critical incidents. The resulting dataset is shown in Table 15 and a plot of the critical 

incident relative frequencies is shown in Figure 11. 

Table 15. Critical Incidents and Total Opportunities by Driving-Hour for Hours 1 Through 11, 

for Baseline and Control Conditions, with Multiple Within-Hour Critical Incidents Removed 

DrivingHour Critical Incidents Per 

Driving-Hour 

Total Opportunities Per 

Driving-Hour 

Rate: Critical Incidents/ 

Total Opportunities 

1 73 2,275 0.032 

2 41 2,154 0.019 

3 44 2,089 0.021 

4 34 2,021 0.017 

5 44 1,932 0.023 

6 40 1,788 0.022 

7 25 1,619 0.015 

8 31 1,458 0.021 

9 24 1,269 0.019 

10 20 1,031 0.019 

11 15 796 0.019 
21 

Figure 11. Critical-Incident Relative Frequency as a Function of Driving-Hour for Baseline and 

Control Conditions, with Multiple Within-Hour Critical Incidents Removed 

Odds ratios were analyzed on this dataset and the results are shown in Table 16. The relative 

frequency of critical incidents in the 1st driving-hour was statistically greater than those in 

driving-hours 2, 3, 4, 7, 8, 9, and 10. No other hour-to-hour comparisons were significant. 

Table 16. Results from the Odds Ratio Analysis for Baseline and Control Conditions, 

with Multiple Within-Hour Critical Incidents Removed 

Driving-Hour Driving-Hour Odds Ratio LCL UCL 

1 2 1.71 1.16 2.52 

1 3 1.54 1.05 2.25 

1 4 1.94 1.28 2.92 

1 5 1.42 0.97 2.08 

1 6 1.45 0.98 2.14 

1 7 2.11 1.34 3.34 

1 8 1.53 1.00 2.34 

1 9 1.72 1.08 2.74 

1 10 1.68 1.02 2.76 

1 11 1.73 0.98 3.03 

5 7 1.49 0.91 2.44 

6 7 1.46 0.88 2.42 

11 10 1.03 0.52 2.02 
22 

Analysis 1.8: Baseline and Control Data, Truck Driver at Fault, No Within-Hour Incidents 

Analysis 1.8 assessed control and baseline data for subject drivers who were judged to have been 

at fault, with multiple within-hour critical incidents removed. The resulting dataset is shown in 

Table 17. Figure 12 is a plot of the critical-incident relative frequencies for the 11 driving-hours. 

Table 17. Critical Incidents and Total Opportunities by Driving-Hour for Hours 1 Through 11, 

in Which the Subject Driver was at Fault, for Baseline and Control Conditions, 

with Multiple Within-Hour Critical Incidents Removed 

DrivingHour Critical Incidents Per 

Driving-Hour 

Total Opportunities Per 

Driving-Hour 

Rate: Critical Incidents/ 

Total Opportunities 

1 58 2,275 0.025 

2 32 2,154 0.015 

3 34 2,089 0.016 

4 24 2,021 0.012 

5 33 1,932 0.017 

6 30 1,788 0.017 

7 20 1,619 0.012 

8 19 1,458 0.013 

9 18 1,269 0.014 

10 11 1,031 0.011 

11 14 796 0.018 

Figure 12. Critical-Incident Relative Frequency as a Function of Driving-Hour in Which the Subject 

Driver was at Fault, for Baseline and Control Conditions, with Multiple Within-Hour Critical 

Incidents Removed 

The results from the odds ratio analysis conducted on this dataset are shown in Table 18. Once 

again, the 1st driving-hour was found to be statistically significant (greater) in terms of 

occurrence of critical incidents than driving-hours 2, 3, 4, 7, 8, 9, and 10. There were no other 

statistically significant findings. 
23 

Table 18. Results from the Odds Ratio Analysis in Whichthe Subject Driver was at Fault, 

for Baseline and Control Conditions, with Multiple Within-Hour Critical Incidents Removed 

Hour Hour Odds Ratio LCL UCL 

1 2 1.73 1.12 2.68 

1 3 1.58 1.03 2.42 

1 4 2.18 1.35 3.52 

1 5 1.51 0.98 2.32 

1 6 1.53 0.98 2.39 

1 7 2.09 1.25 3.49 

1 8 1.98 1.18 3.34 

1 9 1.82 1.07 3.10 

1 10 2.43 1.27 4.64 

1 11 1.46 0.81 2.63 

11 4 1.49 0.77 2.89 

11 9 1.24 0.62 2.52 

11 10 1.66 0.75 3.68 

In summary, the key result from Analysis 1 was the finding of a 1st driving-hour spike in the 

critical-incident relative frequency, as compared to other driving-hours. This was a consistent 

finding throughout the eight different analyses conducted and suggests a robust and meaningful 

effect. 

3.2 CRITICAL INCIDENTS AS A FUNCTION OF DRIVING-HOUR FOR 

HOURS 1 THROUGH 11, FOR DRIVERS THAT DROVE 11 HOURS 

The second set of analyses considered only data in cases in which the driver drove into the 11th

driving-hour. This provided a powerful within-subject design approach. Critical incidents where 

the driver did not drive into the 11th driving-hour were not included in this analysis. However, 

partial 11th driving-hours were included. Twenty-nine critical incidents were recorded in the 11th

driving-hour. Five of these 29 incidents involved a partial 11th driving-hour, while 24 incidents 

involved a complete 11th driving-hour. The mean number of driving-hours for the 29 incidents 

that occurred in the 11th driving-hour was 10.93 h (SD = 0.2 h) calculated as follows: [(24 events 

× 11 h) + (5 events × 10.62 mean h)] ÷ 29. Since this value was, from an exposure perspective, 

inclusive of virtually the entire 11th driving-hour, we did not modify the statistical analyses 

conducted to compare these conditions. 

The same eight analyses conducted in the previous focus area were again conducted, although 

with the reduced dataset which only included trips into the 11th driving-hour. All hours of the 

11th driving-hour trips were analyzed, unlike in the previous work by Hanowski et al. (2005), 

which considered only the 10th and 11th driving-hours. 
24 

To fully investigate this issue, the data were parsed and analyzed in eight different ways. Once 

again, this thorough investigative approach was designed to help ensure that significant findings 

were not overlooked. The eight different analyses used the following datasets: 

• Analysis 2.1: 11th Driving-Hour All Data: This included the entire dataset in which 

drivers made trips into the 11th hour, with the exception of information outlined in the 

Data Caveats section. This provided the largest dataset available. 

• Analysis 2.2: 11th Driving-Hour All Data, Truck Driver at Fault: Using the dataset noted 

above, this analysis included only those critical incidents that were judged to have been 

the fault of the subject driver (i.e., the study truck driver). 

• Analysis 2.3: 11th Driving-Hour, Baseline and Control Data: Only critical incidents that 

occurred in the control and baseline conditions (where no DDWS was used) were 

included. 

• Analysis 2.4: 11th Driving-Hour, Baseline and Control Data, Truck Driver at Fault: Using 

the baseline and control data noted above, this analysis included only critical incidents in 

which the truck driver was judged to have been at fault. 

• Analysis 2.5: 11th Driving-Hour All Data, No Within-Hour Incidents: as in Analysis 1.5, 

mulitple within-hour critical incidents were removed treated critical incidents as a 

Bernoulli random variable (i.e., for each hour there are two possible outcomes— “yes, at 

least one incident occurred” or “no, an incident did not occur”). As previously noted, the 

critical incident with the less severe classification was removed (e.g., a near-crash would 

be removed instead of a crash). 

• Analysis 2.6: 11th Driving-Hour All Data, Truck Driver at Fault, No Within-Hour 

Incidents: Using the dataset noted in Analysis 2.1, this analysis considered only critical 

incidents in which the truck driver was at fault and removed muliple within-hour 

incidents. 

• Analysis 2.7: 11th Driving-Hour, Baseline and Control Data, No Within-Hour Incidents: 

Using the baseline and control data, this analysis removed multiple within-hour incidents. 

• Analysis 2.8: 11th Driving-Hour, Baseline and Control Data, Truck Driver at Fault, No 

Within-Hour Incidents: Using the baseline and control data, this analysis considered only 

critical incidents in which the truck driver was at fault and removed muliple within-hour 

incidents. 

The findings from these eight analyses are outlined below. 
25 

Analysis 2.1: 11th Driving-Hour Trips, All Data 

In Analysis 2.1, driving-hour was treated as a within-subject variable. That is, only the trips in 

which drivers drove into the 11th driving-hour were considered. Table 19 shows the dataset used 

for this analysis. As can be seen, the entire dataset is made up of 1,535 trips where the trip 

included an 11th driving-hour. Note that multiple critical incidents within an hour were included 

in this dataset but, as before, a follow-up analysis presented later removed the multiples. The rate 

data (critical-incident relative frequency) is shown plotted in Figure 13. 

Table 19. Critical Incidents and Total Opportunities by Driving-Hour for Hours 1 Through 11, 

for Trips That Went into the 11th Driving-Hour 

DrivingHour Critical Incidents Per 

Driving-Hour for 11th

Driving-Hour Trips 

Total Opportunities Per 

Driving-Hour for 11th

Driving-Hour Trips 

Rate: Critical Incidents/ 

Total Opportunities 

1 61 1,535 0.040 

2 31 1,535 0.020 

3 37 1,535 0.024 

4 26 1,535 0.017 

5 29 1,535 0.019 

6 34 1,535 0.022 

7 28 1,535 0.018 

8 22 1,535 0.014 

9 41 1,535 0.027 

10 33 1,535 0.021 

11 29 1,535 0.019 

Figure 13. Critical-Incident Relative Frequency as a Function of Driving-Hour for 

Trips That Went into the 11th Driving-Hour 

Similar to what was found with the dataset that considered all possible opportunities for 

involvement in a critical incident, and not just 11th driving-hour trips, there was an apparent 
26 

spike in the frequency of critical incidents in the 1st driving-hour. Odds ratios were analyzed to 

determine statistical differences between critical-incident relative frequencies in different hours. 

The results from the odds ratio analyses are shown in Table 20. Critical-incident relative 

frequency in the 1st driving-hour was significantly greater than all other driving-hours. In 

addition, a significant difference was found between driving-hours 8 and 9, and driving-hours 3 

and 8. There were no other significant differences. 

Table 20. Results from Odds Ratio Analysis for Trips That Went into the 11th Driving-Hour 

Driving-Hour Driving-Hour Odds Ratio LCL UCL 

1 2 2.01 1.30 3.11 

1 3 1.68 1.11 2.54 

1 4 2.40 1.51 3.82 

1 5 2.15 1.37 3.36 

1 6 1.83 1.19 2.80 

1 7 2.23 1.42 3.50 

1 8 2.85 1.74 4.66 

1 9 1.51 1.01 2.25 

1 10 1.88 1.23 2.89 

1 11 2.15 1.37 3.36 

9 8 1.89 1.12 3.18 

3 8 1.70 1.00 2.89 

10 11 1.14 0.69 1.89 

Analysis 2.2: 11th Driving-Hour Trips, All Data, Truck Driver at Fault 

Analysis 2.2 assessed only those critical incidents in which the subject driver was judged to have 

been at fault. The resulting dataset is shown in Table 21 and a plot of the critical-incident relative 

frequency is shown in Figure 14. 

Table 21. Critical Incidents and Total Opportunities by Driving-Hour for Hours 1 Through 11, 

for Trips That Went into the 11th Driving-Hour, in Which the Subject Driver was at Fault 

DrivingHour Critical Incidents Per 

Driving-Hour for 11th

Driving-Hour Trips 

Total Opportunities Per 

Driving-Hour for 11th

Driving-Hour Trips 

Rate: Critical Incidents/ 

Total Opportunities 

1 48 1,535 0.031 

2 26 1,535 0.017 

3 26 1,535 0.017 

4 18 1,535 0.012 

5 17 1,535 0.011 

6 25 1,535 0.016 

7 21 1,535 0.014 

8 17 1,535 0.011 

9 30 1,535 0.020 

10 20 1,535 0.013 

11 23 1,535 0.015 
27 

Figure 14. Critical-Incident Relative Frequency as a Function of Driving-Hour, 

for Trips That Went into the 11th Driving-Hour, in Which the Subject Driver was at Fault 

Odds ratio analyses were examined to determine statistical differences between driving-hours, 

and the results are shown in Table 22. As shown in the previous results, there was a significant 

difference in critical-incident relative frequency between the 1st driving-hour and all other 

driving-hours. There were no other significant findings. 

Table 22. Result from Odds Ratio Analysis for Trips That Went into the 11th Driving-Hour, 

In Which the Subject Driver was at Fault 

Driving-Hour Driving-Hour Odds Ratio LCL UCL 

1 2 1.87 1.16 3.04 

1 3 1.87 1.16 3.04 

1 4 2.72 1.58 4.70 

1 5 2.88 1.65 5.03 

1 6 1.95 1.20 3.18 

1 7 2.33 1.39 3.91 

1 8 2.88 1.65 5.03 

1 9 1.62 1.02 2.57 

1 10 2.45 1.44 4.14 

1 11 2.12 1.28 3.51 

9 5 1.78 0.98 3.24 

9 10 1.51 0.85 2.67 

11 10 1.15 0.63 2.11 
28 

Analysis 2.3: 11th Driving-Hour Trips, Baseline and Control Data 

Analysis 2.3 considered only the baseline and control data. This dataset is shown in Table 23. 

Figure 15 shows a plot of the critical-incident relative frequencies. 

Table 23. Critical Incidents and Total Opportunities by Driving-Hour 

for Driving-Hours 1 Through 11, for Trips That Went into the 11th Driving-Hour, 

for Baseline and Control Conditions 

DrivingHour Critical Incidents Per 

Driving-Hour for 11th

Driving-Hour Trips 

Total Opportunities Per 

Driving-Hour for 11th

Driving-Hour Trips 

Rate: Critical Incidents/ 

Total Opportunities 

1 31 796 0.039 

2 20 796 0.025 

3 20 796 0.025 

4 15 796 0.019 

5 15 796 0.019 

6 15 796 0.019 

7 11 796 0.014 

8 12 796 0.015 

9 24 796 0.030 

10 19 796 0.024 

11 17 796 0.021 

Figure 15. Critical-Incident Relative Frequency as a Function of Driving-Hour, 

for Trips That Went into the 11th Driving-Hour, for Baseline and Control Conditions 

The results from the odds ratio analysis are shown in Table 24. The critical-incident relative 

frequency in the 1st driving-hour was significantly greater than the frequencies for driving-hours 

4, 5, 6, 7, 8, and 11. In addition, significant differences were found for driving-hours 7 and 9, and 

driving-hours 8 and 9. No other significant differences were found. 
29 

Table 24. Results from the Odds Ratio for Trips That Went into the 11th Driving-Hour, 

for Baseline and Control Conditions 

Driving-Hour Driving-Hour Odds Ratio LCL UCL 

1 2 1.57 0.89 2.78 

1 3 1.57 0.89 2.78 

1 4 2.11 1.13 3.94 

1 5 2.11 1.13 3.94 

1 6 2.11 1.13 3.94 

1 7 2.89 1.44 5.79 

1 8 2.65 1.35 5.19 

1 9 1.30 0.76 2.24 

1 10 1.66 0.93 2.96 

1 11 1.86 1.02 3.38 

9 7 2.22 1.08 4.56 

9 8 2.03 1.01 4.09 

10 11 1.12 0.58 2.17 

Analysis 2.4: 11th Driving-Hour Trips, Baseline and Control Data, Truck Driver at Fault 

Analysis 2.4 considered only baseline and control data in which drivers were judged to have 

been at fault. The dataset for this analysis is shown in Table 25. Figure 16 shows a plot of the 

critical-incident relative frequency across the 11 driving-hours. 

Table 25. Critical Incidents and Total Opportunities by Driving-Hour 

for Driving-Hours 1 Through 11, for Trips That Went into the 11th Driving-Hour, 

in Which the Subject Driver was at Fault, for Baseline and Control Conditions 

DrivingHour Critical Incidents Per 

Driving-Hour for 11th

Driving-Hour Trips 

Total Opportunities Per 

Driving-Hour for 11th

Driving-Hour Trips 

Rate: Critical Incidents/ 

Total Opportunities 

1 23 796 0.029 

2 17 796 0.021 

3 12 796 0.015 

4 10 796 0.013 

5 10 796 0.013 

6 10 796 0.013 

7 8 796 0.010 

8 9 796 0.011 

9 17 796 0.021 

10 10 796 0.013 

11 16 796 0.020 
30 

Figure 16. Critical-Incident Relative Frequency as a Function of Driving-Hour, 

for Trips That Went into the 11th Driving-Hour, in Which the Subject Driver was at Fault, 

for Baseline and Control Conditions 

Odds ratios were analyzed to determine statistical significance between the critical-incident 

relative frequencies in different hours, and the results are shown in Table 26. The only 

significant differences between hours were between the 1st driving-hour and driving-hours 4, 5, 

6, 7, 8, and 10. No other odds ratios were statistically significant. 

Table 26. Results from the Odds Ratio Analysis for Trips That Went into the 11th Driving-Hour, 

In Which the Subject Driver was at Fault, for Baseline and Control Conditions 

Driving-Hour Driving-Hour Odds Ratio LCL UCL 

1 2 1.36 0.72 2.57 

1 3 1.94 0.96 3.93 

1 4 2.34 1.11 4.95 

1 5 2.34 1.11 4.95 

1 6 2.34 1.11 4.95 

1 7 2.93 1.30 6.59 

1 8 2.60 1.20 5.66 

1 9 1.36 0.72 2.57 

1 10 2.34 1.11 4.95 

1 11 1.45 0.76 2.77 

9 7 2.15 0.92 5.01 

9 8 1.91 0.85 4.31 

11 10 1.61 0.73 3.57 
31 

Analysis 2.5: 11th Driving-Hour Trips, All Data, No Within-Hour Incidents 

Analysis 2.5 shows the dataset in which multiple critical incidents that occurred in the same 

hour, during the same trip, were removed. The resulting dataset removed these multiples and is 

shown in Table 27. Figure 17 plots the critical-incident relative frequencies across the hours. 

Table 27. Critical Incidents and Total Opportunities by Driving-Hour 

for Driving-Hours 1 Through 11, for Trips That Went into the 11th Driving-Hour, 

with Multiple Within-Hour Critical Incidents Removed 

DrivingHour Critical Incidents Per 

Driving-Hour for 11th

Driving-Hour Trips 

Total Opportunities Per 

Driving-Hour for 11th

Driving-Hour Trips 

Rate: Critical Incidents/ 

Total Opportunities 

1 57 1,535 0.037 

2 29 1,535 0.019 

3 33 1,535 0.021 

4 24 1,535 0.016 

5 29 1,535 0.019 

6 31 1,535 0.020 

7 26 1,535 0.017 

8 20 1,535 0.013 

9 38 1,535 0.025 

10 33 1,535 0.021 

11 27 1,535 0.018 

Figure 17. Critical-Incident Relative Frequency as a Function of Driving-Hour, for Trips 

That Went into the 11th Driving-Hour, with Multiple Within-Hour Critical Incidents Removed 
32 

The results from the odds ratio analyses are shown in Table 28. As can be seen, the criticalincident relative frequency was significantly greater in the 1st driving-hour than in all other 

driving-hours. In addition, a significant difference was found between the 7th driving-hour and 

the 9th driving-hour. There were no other significant results. 

Table 28. Results from the Odds Ratio Analysis for Trips That Went into the 11th Driving-Hour, 

with Multiple Within-Hour Critical Incidents Removed 

Driving-Hour Driving-Hour Odds Ratio LCL UCL 

1 2 2.00 1.27 3.15 

1 3 1.76 1.14 2.71 

1 4 2.43 1.50 3.93 

1 5 2.00 1.27 3.15 

1 6 1.87 1.20 2.91 

1 7 2.24 1.40 3.58 

1 8 2.92 1.75 4.89 

1 9 1.52 1.00 2.30 

1 10 1.76 1.14 2.71 

1 11 2.15 1.36 3.42 

9 7 1.92 1.11 3.32 

9 4 1.60 0.95 2.68 

11 10 1.23 0.73 2.05 

Analysis 2.6: 11th Driving-Hour Trips, All Data, Truck Driver at Fault, No Within-Hour 

Incidents 

Analysis 2.6 assessed critical incidents in which the subject driver was at fault and removed any 

multiple incidents that occurred within the same hour for a given driver’s trip. The dataset for 

this analysis is shown in Table 29 and a plot of the critical-incident relative frequencies for the 

driving-hours is shown in Figure 18. 

Table 29. Critical Incidents and Total Opportunities by Driving-Hour 

for Driving-Hours 1 Through 11 for Trips That Went into the 11th Driving-Hour, 

in Which the Subject Driver was at Fault, with Multiple Within-Hour Critical Incidents Removed 

DrivingHour Critical Incidents Per 

Driving-Hour for 11th

Driving-Hour Trips 

Total Opportunities Per 

Driving-Hour for 11th

Driving-Hour Trips 

Rate: Critical Incidents/ 

Total Opportunities 

1 46 1,535 0.030 

2 25 1,535 0.016 

3 23 1,535 0.015 

4 17 1,535 0.011 

5 17 1,535 0.011 

6 22 1,535 0.014 

7 20 1,535 0.013 

8 15 1,535 0.010 

9 28 1,535 0.018 

10 20 1,535 0.013 

11 21 1,535 0.014 
33 

Figure 18. Critical-Incident Relative Frequency as a Function of Driving-Hour for Trips That Went 

into the 11th Driving-Hour, in Which the Subject Driver was at Fault, with Multiple Within-Hour 

Critical Incidents Removed 

The results from the odds ratio analysis are shown in Table 30. Once again, these results show 

that the 1st driving-hour had a significantly higher critical-incident relative frequency than any 

other driving-hour. In addition, the 8th driving-hour was significantly different than the 9th

driving-hour. There were no other statistical differences found. 

Table 30. Results from the Odds Ratio Analysis in Which the Subject Driver was at Fault 

for Trips That Went into the 11th Driving-Hour, with Multiple Within-Hour 

Critical Incidents Removed 

Driving-Hour Driving-Hour Odds Ratio LCL UCL 

1 2 1.87 1.14 3.05 

1 3 2.03 1.22 3.37 

1 4 2.76 1.57 4.83 

1 5 2.76 1.57 4.83 

1 6 2.12 1.27 3.55 

1 7 2.34 1.38 3.98 

1 8 3.13 1.74 5.63 

1 9 1.66 1.03 2.67 

1 10 2.34 1.38 3.98 

1 11 2.23 1.32 3.75 

9 8 1.88 1.00 3.54 

9 5 1.66 0.90 3.04 

11 10 1.05 0.57 1.95 
34 

Analysis 2.7: 11th Driving-Hour Trips, Baseline and Control Data, No Within-Hour 

Incidents 

Analysis 2.7 used baseline and control data only and removed any multiple incidents that 

occurred within the same hour for a given driver’s trip. The resulting dataset is shown in Table 

31. Figure 19 shows a plot of the critical-incident relative frequencies across the driving-hours. 

Table 31. Critical Incidents and Total Opportunities by Driving-Hour for Driving-Hours 

1 Through 11, for Trips That Went into the 11th Driving-Hour, for Baseline and Control Conditions, 

with Multiple Within-Hour Critical Incidents Removed 

DrivingHour Critical Incidents Per 

Driving-Hour for 11th

Driving-Hour Trips 

Total Opportunities Per 

Driving-Hour for 11th

Driving-Hour Trips 

Rate: Critical Incidents/ 

Total Opportunities 

1 29 796 0.036 

2 19 796 0.024 

3 16 796 0.020 

4 13 796 0.016 

5 15 796 0.019 

6 14 796 0.018 

7 11 796 0.014 

8 10 796 0.013 

9 21 796 0.026 

10 19 796 0.024 

11 15 796 0.019 

Figure 19. Critical-Incident Relative Frequency as a Function of Driving-Hour, for Trips 

that went into the 11th Driving-Hour, for Baseline and Control Conditions, 

with Multiple Within-Hour Critical Incidents Removed 
35 

Odds ratios were analyzed on the critical-incident relative frequencies, comparing one drivinghour to another. The results are shown in Table 32. Statistically significant differences were 

found between the 1st driving-hour and driving-hours 4, 5, 6, 7, 8, and 11. Also, the 8th and 9th

driving-hours were found to be significantly different. No other comparisons were statistically 

significant. 

Table 32. Results from the Odds Ratio Analysis for Trips that went into the 11th Driving-Hour, 

for Baseline and Control Conditions, with Multiple Within-Hour Critical Incidents Removed 

Driving-Hour Driving-Hour Odds Ratio LCL UCL 

1 2 1.55 0.86 2.78 

1 3 1.84 0.99 3.42 

1 4 2.28 1.17 4.41 

1 5 1.97 1.05 3.70 

1 6 2.11 1.11 4.03 

1 7 2.70 1.34 5.44 

1 8 2.97 1.44 6.14 

1 9 1.40 0.79 2.47 

1 10 1.55 0.86 2.78 

1 11 1.97 1.05 3.70 

9 8 2.13 1.00 4.55 

9 7 1.93 0.93 4.04 

10 11 1.27 0.64 2.52 
36 

Analysis 2.8: 11th Driving-Hour Trips, Baseline and Control Data, Truck Driver at Fault, 

No Within-Hour Incidents 

Analysis 2.8 used baseline and control data only and considered drivers who were at fault, with 

the removal of multiple within-hour incidents. The dataset for this analysis is shown in Table 33. 

Figure 20 shows a plot of the critical-incident relative frequencies across the different drivinghours. 

Table 33. Critical Incidents and Total Opportunities by Driving-Hour for 

Driving-Hours 1 Through 11, for Trips That Went into the 11th Driving-Hour, 

in Which the Subject Driver was at Fault, for Baseline and Control Conditions, 

with Multiple Within-Hour Critical Incidents Removed 

DrivingHour Critical Incidents Per 

Driving-Hour for 11th

Driving-Hour Trips 

Total Opportunities Per 

Driving-Hour for 11th

Driving-Hour Trips 

Rate: Critical Incidents/ 

Total Opportunities 

1 23 796 0.029 

2 16 796 0.020 

3 9 796 0.011 

4 9 796 0.011 

5 10 796 0.013 

6 9 796 0.011 

7 8 796 0.010 

8 7 796 0.009 

9 15 796 0.019 

10 10 796 0.013 

11 14 796 0.018 

Figure 20. Critical-Incident Relative Frequency as a Function of Driving-Hour, 

for Trips That Went into the 11th Driving-Hour, in Which the Subject Driver was at Fault, 

for Baseline and Control Conditions, with Multiple Within-Hour Critical Incidents Removed 
37 

Odds ratios were examined to compare the critical incident relative frequencies across drivinghours. The results are shown in Table 34. The critical incident relative frequency for the 1st

driving-hour was significantly greater than driving-hours 3, 4, 5, 6, 7, 8, and 10. No other 

statistically significant differences were found. 

Table 34. Results From the Odds Ratio Analysis for Trips that went into the 11th Driving-Hour, 

in Which the Subject Driver was at Fault, for Baseline and Control Conditions, 

with Multiple Within-Hour Critical Incidents Removed 

Driving-Hour Driving-Hour Odds Ratio LCL UCL 

1 2 1.45 0.76 2.77 

1 3 2.60 1.20 5.66 

1 4 2.60 1.20 5.66 

1 5 2.34 1.11 4.95 

1 6 2.60 1.20 5.66 

1 7 2.93 1.30 6.59 

1 8 3.35 1.43 7.86 

1 9 1.55 0.80 2.99 

1 10 2.34 1.11 4.95 

1 11 1.66 0.85 3.25 

9 8 2.16 0.88 5.34 

9 7 1.89 0.80 4.49 

11 10 1.41 0.62 3.19 

In summary, the results from Analysis 2 were consistent with the key findings from Analysis 1; 

that is, the 1st driving-hour was associated with an elevated critical-incident relative frequency as 

compared to the other driving-hours. In terms of significance, the specific driving-hours that 

were statistically different from the 1st driving-hour varied somewhat from one analysis to the 

next, and across the eight analyses. However, the finding that the 1st driving-hour was different 

from the other hours, but that the other driving-hours (i.e., 2 through 11) were not consistently 

different from each other, is an important finding. 
38 

3.3 MODELING THE DATA TO IDENTIFY SIGNIFICANT DIFFERENCES IN 

CRITICAL INCIDENTS ACROSS DRIVING-HOURS 

As a follow-up to Analyses 1 and 2, Analysis 3 computed odds ratios using logistic regression 

modeling. One difference between this approach and the approach used in Analyses 1 and 2 is 

that an assumption of independence is not being made. That is, the approach used generalized 

estimating equations (GEE) to account for correlations that might exist between drivers and 

within drivers (with respect to critical incident occurrence). The model that was used in these 

analyses was: 

Logit (P(Yt = SCE)) = αSCE + βt

Where t is driving-hours 1 through 11, αSCE is the intercept term, and βt is the effect of driving in 

the t

th driving-hour. 

Applying the logistic regression model to the data produces odds ratios for each driving-hour 

which can then be compared in a manner similar to the comparisons conducted in Analyses 1 and 

2 using lower and upper confidence limits to assess statistical significance. 

The logistic regression modeling approach was applied to two different datasets: (1) the entire 

dataset, or All Data, and (2) the subset of trips that went into the 11th driving-hour. Figure 21 

shows the resulting odds ratios as a function of driving-hour for the All Data dataset. As noted, 

the output of the model is the odds ratios for each driving-hour and not the critical-incident 

relative frequency, as in the previous analyses. 

Figure 21. Odds Ratio as a Function of Driving-Hour Using Logistic Regression Modeling 

The odds ratios were examined and the results are shown in Table 35. Consistent with the 

general Analysis 1 results, there was a spike in the critical-incident relative frequency in the st

driving-hour compared to those in all other driving-hours. No other hour-by-hour comparisons 

were statistically significant. 
39 

Table 35. Results from the Odds Ratio Analyses Using Logistic Regression Modeling 

Driving-Hour Driving-Hour Odds Ratio LCL UCL 

1 2 1.85 1.33 2.58 

1 3 1.56 1.09 2.22 

1 4 1.80 1.25 2.60 

1 5 1.60 1.18 2.17 

1 6 1.54 1.08 2.20 

1 7 1.81 1.26 2.60 

1 8 1.55 1.04 2.31 

1 9 1.64 1.10 2.45 

1 10 1.84 1.23 2.75 

1 11 1.91 1.12 3.28 

3 8 1.00 0.67 1.49 

9 8 0.95 0.60 1.49 

10 11 1.04 0.06 1.69 

The next analysis conducted was similar to Analysis 2, presented previously, in that the focus 

was on trips that went into the 11th driving-hour. Using this subset of data, logistic regression 

modeling was again used and the resulting odds ratios are shown in Figure 22. 

Figure 22. Odds Ratio as a Function of Driving-Hour for Trips That Went into the 11th Driving-Hour 

The odds ratios were examined and are shown in Table 36. Once again, the most prominent 

result is the high odds ratio in the 1st driving-hour as compared to all other driving-hours. 
40 

Table 36. Results from the Odds Ratio Analyses Using Logistic Regression Modeling for 

Trips that Went into the 11th Driving-Hour 

Driving-Hour Driving-Hour Odds Ratio LCL UCL 

1 2 2.00 1.31 3.07 

1 3 1.76 1.12 2.76 

1 4 2.43 1.59 3.72 

1 5 2.00 1.24 3.24 

1 6 1.87 1.24 2.83 

1 7 2.24 1.38 3.64 

1 8 2.92 1.68 5.08 

1 9 1.52 0.95 2.43 

1 10 1.76 1.15 2.69 

1 11 2.15 1.32 3.51 

3 8 1.66 0.97 2.85 

9 8 1.92 1.07 3.46 

10 8 1.66 1.06 2.61 

10 11 1.23 0.81 1.86 

In summary, the results of Analysis 3 using the logistic regression modeling approach were 

generally consistent with the results found in Analyses 1 and 2. Nonetheless, it was important to 

explore the possibility of assumed independence made in Analyses 1 and 2 that might impact the 

results. But, again, given the consistency in the findings using both methods, this does not appear 

to be the case. 

3.4 CRITICAL INCIDENTS AS A FUNCTION OF DRIVING-SHIFT 

As noted in the Data Caveats section, a new “driving-shift” was defined as consecutive drivinghours broken up by a non-driving (assumed break) of at least 6 h, as long as it was not less than 

14 h from the start of the previous driving-shift. The 1st driving-shift was always preceded by a 

break of at least 34 h of non-driving. 

For this analysis, critical incidents were investigated as a function of driving-shift. As outlined in 

the 2003 revised HOS regulations, a driver may drive up to 60 h in 7 consecutive days or 70 h in 

8 consecutive days before an off-duty period of time not less than 34 consecutive hours. The data 

used in this analysis were a subset of the data used in Analyses 1 and 2, resulting in a total of 747 

critical incidents that occurred in driving-shifts 1 through 8: 10 crashes, 11 tire-strike crashes, 77 

near-crashes, and 649 crash-relevant conflicts. 

Once again, the critical-incident data were parsed and analyzed in eight different ways, as 

follows: 

• Analysis 4.1: All Data: This included the entire dataset, with the exception of information 

outlined in the Data Caveats section. This provided the largest dataset available. Note that 

both a frequency-odds- ratio approach and a logistic regression modeling approach were 

used and the results of the two approaches compared. 
41 

• Analysis 4.2: All Data, Truck Driver at Fault: Using the dataset noted above, the analysis 

included only those critical incidents that were judged to have been the fault of the 

subject driver (i.e., the study truck driver). 

• Analysis 4.3: Baseline and Control Data: Only critical incidents that occurred in the 

control and baseline conditions (where no DDWS was used) were included. 

• Analysis 4.4: Baseline and Control Data, Truck Driver at Fault: Using the baseline and 

control data noted above, this analysis included only critical incidents in which the truck 

driver was judged to have been at fault. 

• Analysis 4.5: All Data, No Within-Shift Incidents: Using the dataset noted in Analysis 

4.1, this analysis removed multiple critical incidents that occurred in the same shift, and 

in the same trip. This analysis treated critical incidents as a dichotomous random variable 

(i.e., for each shift there are two possible outcomes—“yes, at least one incident occurred” 

or “no, an incident did not occur”). 

• Analysis 4.6: All Data, Truck Driver at Fault, No Within-Shift Incidents: Using the 

dataset noted in Analysis 4.1, this analysis considered only critical incidents where the 

truck driver was at fault and removed multiple within-shift incidents. 

• Analysis 4.7: Baseline and Control Data, No Within-Shift Incidents: This analysis used 

the baseline and control data and removed multiple within-shift incidents. 

• Analysis 4.8: Baseline and Control Data, Truck Driver at Fault, No Within-Shift 

Incidents: Using the baseline and control data, this analysis considered only critical 

incidents in which the truck driver was at fault and removed muliple within-shift 

incidents. 

The findings from these eight analyses are outlined below. 

Analysis 4.1: All Data 

Table 37 shows the dataset for the first analysis, which included all critical incidents, and all 

driving opportunities (i.e., trips), that occurred in the 1st through 8th driving-shifts. As shown in 

Table 37, each driving-shift had a different number of opportunities to be involved in critical 

incidents. For example, a driver could have seven shifts but not eight; as such, there were fewer 

opportunities to be involved in a critical incident as the shift number increased. The data were 

normalized to adjust for the differences in opportunities across driving-shift by calculating a rate 

measure. This provided the relative frequency of having a critical incident in any given drivingshift. Once again, using this first dataset, the data in Table 37 include cases where multiple 

critical incidents were recorded in the same driving-shift. 
42 

Table 37. Critical Incidents and Total Opportunities by Driving-Shift for Shifts 1 Through 8 

DrivingShift Critical Incidents Per 

Driving-Shift 

Total Opportunities Per 

Driving-Shift 

Rate: Critical Incidents/ 

Total Opportunities 

1 190 4748 0.040 

2 193 3552 0.054 

3 118 2522 0.047 

4 91 1693 0.054 

5 73 1087 0.067 

6 32 718 0.045 

7 23 571 0.040 

8 27 459 0.059 

Figure 23 plots the number of opportunities for each driving-shift. As with driving-hour, the 

number of opportunities drops off as the number of driving-shifts increased. This indicates that 

drivers in the current study often restarted their driving week (tour of duty) before they reached 

the 7th or 8th driving-shift. 

Figure 23. Total Opportunities (Trips) for Shifts 1 Through 8 

Figure 24 shows the relative frequency of critical incidents as a function of driving-shifts 1 

through 8. Once again, a breakdown in the frequency for each type of critical incident is 

indicated: crashes (red), crashes: tire strikes (green), near-crashes (purple), and crash-relevant 

conflicts (blue). The top of the bar is the total for all critical-incident types. 
43 

Figure 24. Critical-Incident Relative Frequency as a Function of Driving-Shift 

Table 38 shows the results from the odds ratio analyses. The first two columns in Table 38 show 

the comparison hours (e.g., the first row compares the 5th and 7th driving-shifts), while the third 

column shows the calculated odds ratio and the last two columns display the LCLs and UCLs, 

respectively. As a reminder, odds ratios with LCLs and UCLs that contain “1” are not 

statistically significant (again, a 95 percent confidence level was used in these calculations). All 

significant odds ratios shown in Table 38 are highlighted and in italics. As before, all significant 

effects are included in the odds ratio tables, along with selected other analyses that were not 

significant. The significant findings were that the odds ratio for the 5th driving-shift is 

significantly higher than the 1st, 3rd, 6th, and 7th driving-shifts. 

Table 38. Results from the Odds Ratio Analyses 

Driving-Shift Driving-Shift Odds Ratio LCL UCL 

5 7 1.72 1.06 2.77 

5 1 1.73 1.31 2.28 

5 6 1.54 1.01 2.36 

5 3 1.47 1.09 1.98 

5 2 1.25 0.95 1.66 

8 7 1.49 0.84 2.63 

8 1 1.50 0.99 2.27 

As discussed in Analysis 3, an alternative method of analyzing the data and calculating odds 

ratios is logistic regression modeling. This approach was applied to this dataset and the results 

are shown in Figure 25. As noted in Analysis 3, the logistic regression modeling approach 

determines odds ratios for each driving-shift which can then be compared for statistical 

differences between driving-shifts (Table 39). As can be seen, both the logistic regression 

modeling approach and the approach that does not account for repeated measures (Table 38) 

produce very similar results. The 5th driving-shift is significantly greater than the 1st and 3rd
44 

driving-shifts, and the 8th driving-shift is significantly different from the 1st driving shift (the 8th

driving-shift vs. the 1st driving-shift was marginally significant in the initial analysis). 

Figure 25. Odds Ratios as a Function of Driving-Shift Using Logistic Regression Modeling 

Table 39. Results From Odds Ratio Analysis Using Logistic Regression Modeling 

Driving-Shift Driving-Shift Odds Ratio LCL UCL 

5 1 1.59 1.11 2.26 

5 2 1.33 0.99 1.79 

5 3 1.37 1.00 1.88 

5 7 1.55 0.96 2.50 

5 6 1.37 0.90 2.09 

4 1 1.30 0.94 1.80 

4 7 1.27 0.81 1.99 

8 1 1.63 1.14 2.32 

8 2 1.36 0.93 2.00 

8 3 1.41 0.98 2.04 

8 4 1.25 0.83 1.90 

8 5 1.03 0.68 1.55 

8 6 1.41 0.89 2.23 

8 7 1.59 0.89 2.84 
45 

Analysis 4.2: All Data, Truck Driver at Fault 

Analysis 4.2 included only critical incidents in which the subject-vehicle driver (i.e., the study 

truck driver) was judged, based on video review, to have been at fault. Note that these data did 

not include multiple critical incidents that may have occurred within the same hour. The 

resulting dataset is shown in Table 40 below and plotted in Figure 26. 

Table 40. Critical Incidents and Total Opportunities by Driving-Shift for Shifts 1 Through 8 

In Which the Subject Driver was at Fault 

DrivingShift Critical Incidents Per 

Driving-Shift 

Total Opportunities Per 

Driving-Shift 

Rate: Critical Incidents/ 

Total Opportunities 

1 154 4,748 0.032 

2 141 3,552 0.040 

3 87 2,522 0.034 

4 68 1,693 0.040 

5 47 1,087 0.043 

6 26 718 0.036 

7 18 571 0.032 

8 24 459 0.052 

Figure 26. Critical-Incident Relative Frequency as a Function of Driving-Shift in Which 

the Subject Driver was at Fault 

Table 41 displays the results from the odds ratio analyses, including all trips in which the subject 

driver was at fault. The relative frequency of critical incidents in the 8th driving-shift was 

significantly greater than in the 1st driving-shift. 
46 

Table 41. Results from the Odds Ratio Analyses, Including all Trips 

In Which the Subject Driver was at Fault 

Driving-Shift Driving-Shift Odds Ratio LCL UCL 

8 1 1.65 1.06 2.56 

8 7 1.70 0.91 3.16 

5 1 1.35 0.97 1.88 

Analysis 4.3: Baseline and Control Data 

Analysis 4.3 used only the data from the “control” and “baseline” conditions from the study (i.e., 

data collected where no DDWS was used). The resulting dataset is shown in Table 42. Figure 27 

plots the critical-incident relative frequency across the driving-hours for the baseline and control 

conditions. 

Table 42. Critical Incidents and Total Opportunities by Driving-Shift for Shifts 

1 Through 8 for Baseline and Control Conditions 

DrivingShift Critical Incidents Per 

Driving-Shift 

Total Opportunities Per 

Driving-Shift 

Rate: Critical Incidents/ 

Total Opportunities 

1 103 2,275 0.045 

2 97 1,679 0.058 

3 65 1,183 0.055 

4 50 788 0.063 

5 37 501 0.074 

6 18 332 0.054 

7 16 254 0.063 

8 16 195 0.082 

Figure 27. Critical-Incident Relative Frequency as a Function of Driving-Shift 

for Baseline and Control Conditions 
47 

Table 43 displays the results from the odds ratio analyses. Statistically significant differences 

were found between the 8th and 1st driving-shifts. In looking at the plotted data in Figure 27, 

there appears to be an increasing trend in critical-incident relative frequency from the 1st through 

5th driving-shifts and the 6th through 8th driving-shifts. However, aside from the statistical 

difference betweent the 8th and 1st driving-shifts, these differences were not statistically 

significant. 

Table 43. Results from the Odds Ratio Analysis Across all Trips 

for Baseline and Control Conditions 

Driving-Shift Driving-Shift Odds Ratio LCL UCL 

8 1 1.88 1.09 3.26 

8 6 1.56 0.78 3.13 

8 3 1.54 0.87 2.72 

Analysis 4.4: Baseline and Control Data, Truck Driver at Fault 

Analysis 4.4 used only the data from the control and baseline conditions in which the subject 

driver was judged at fault. Table 44 displays the dataset used and Figure 28 plots the criticalincident relative frequency across the driving-shifts. 

Table 44. Critical Incidents and Total Opportunities by Driving-Shift for Shifts 1 Through 8, 

In Which the Subject Driver was at Fault, for Baseline and Control Conditions 

DrivingShift Critical Incidents Per 

Driving-Shift 

Total Opportunities Per 

Driving-Shift 

Rate: Critical Incidents/ 

Total Opportunities 

1 84 2,275 0.037 

2 69 1,679 0.041 

3 46 1,183 0.039 

4 40 788 0.051 

5 24 501 0.048 

6 14 332 0.042 

7 12 254 0.047 

8 13 195 0.067 
48 

Figure 28. Critical-Incident Relative Frequency as a Function of Driving-Shift, 

in Which the Subject Driver was at Fault, for Baseline and Control Conditions 

Odds ratios were conducted on the critical-incident relative frequency data and the results are 

shown in Table 45. As in the previous analysis, there was a statistically signifiant difference 

between the critical-incident relative frequencies in the 8th and 1st driving-shifts. 

Table 45. Results from the Odds Ratio Analysis Including All Trips in Which 

the Subject Driver Was at Fault, for Baseline and Control Conditions 

Driving-Shift Driving-Shift Odds Ratio LCL UCL 

8 1 1.86 1.02 3.41 

8 2 1.67 0.90 3.07 

8 3 1.77 0.94 3.33 

Analysis 4.5: All Data, No Within-Shift Incidents 

Analysis 4.5 removed multiple within-shift critical incidents such that a dichotomous varible was 

created (either “yes, at least one critical incident occurred” or “no critical incident occurred.”) 

Table 46 shows the resulting dataset. Figure 29 shows the plot of the critical-incident relative 

frequencies for each driving-shift. The resulting odds ratios are shown in Table 47. The spike in 

the critical-incident relative frequency in the 5th driving shift was significantly greater than the 

critical-incident relative frequencies in the 1st and 7th driving shifts. There was also a significant 

difference between the 4th and 7th driving shifts. 
49 

Table 46. Critical Incidents and Total Opportunities by Driving-Shift for Shifts 1 Through 8, 

with Multiple Within-Shift Critical Incidents Removed 

DrivingShift Critical Incidents Per 

Driving-Shift 

Total Opportunities Per 

Driving-Shift 

Rate: Critical Incidents/ 

Total Opportunities 

1 162 4,748 0.034 

2 144 3,552 0.041 

3 99 2,522 0.039 

4 73 1,693 0.043 

5 53 1,087 0.049 

6 23 718 0.032 

7 14 571 0.025 

8 19 459 0.041 

Figure 29. Critical-Incident Relative Frequency as a Function of Driving-Shift, 

with Multiple Within-Shift Critical Incidents Removed 

Table 47. Results from the Odds Ratio Analysis with Multiple 

Within-Shift Critical Incidents Removed 

Shift Shift Odds Ratio LCL UCL 

5 7 2.04 1.12 3.71 

5 1 1.45 1.06 1.99 

5 6 1.55 0.94 2.55 

5 3 1.25 0.89 1.77 

5 2 1.21 0.88 1.67 

8 7 1.72 0.85 3.47 

8 1 1.22 0.75 1.99 

4 7 1.79 1.00 3.20 

4 1 1.28 0.96 1.69 
50 

Analysis 4.6: All Data, Truck Driver at Fault, No Within-Shift Incidents 

Analysis 4.6 included critical incidents in which the subject driver was judged to have been at 

fault, and all multiple critical incidents that occurred within the same shift were removed. The 

resulting dataset is shown in Table 48 and the plot of the relative frequency of critical incidents 

is shown in Figure 30. 

Table 48. Critical Incidents and Total Opportunities by Driving-Shift for Shifts 1 Through 8, 

in Which the Subject Driver was at Fault, with Multiple Within-Shift Critical Incidents Removed 

DrivingShift Critical Incidents Per 

Driving-Shift 

Total Opportunities Per 

Driving-Shift 

Rate: Critical Incidents/ 

Total Opportunities 

1 129 4748 0.027 

2 105 3552 0.030 

3 74 2522 0.029 

4 55 1693 0.032 

5 32 1087 0.029 

6 18 718 0.025 

7 11 571 0.019 

8 17 459 0.037 

Figure 30. Critical-Incident Relative Frequency as a Function of Driving-Shift in Which 

the Subject Driver was at Fault, with Multiple Within-Shift Critical Incidents Removed 

Odds ratios were examined on the critical-incident relative frequencies across the driving-shifts 

and the results are shown in Table 49. None of the shift-by-shift comparisons was statistically 

significant. 
51 

Table 49. Results from the Odds Ratio Analysis in Which the Subject Driver was at Fault, 

with Multiple Within-Shift Critical Incidents Removed 

Driving-Shift Driving-Shift Odds Ratio LCL UCL 

8 1 1.38 0.82 2.30 

8 7 1.96 0.91 4.22 

4 1 1.20 0.87 1.66 

4 7 1.71 0.89 3.29 

Analysis 4.7: Baseline and Control Data, No Within-Shift Incidents 

Analysis 4.7 used the control and baseline data and removed all multiple within-shift critical 

incidents. The resulting dataset is shown in Table 50 and a plot of the critical-incident relative 

frequencies is shown in Figure 31. 

Table 50. Critical Incidents and Total Opportunities by Driving-Shift for Shifts 1 Through 8, 

for Baseline and Control Conditions, with Multiple Within-Shift Critical Incidents Removed 

DrivingShift Critical Incidents Per 

Driving-Shift 

Total Opportunities Per 

Driving-Shift 

Rate: Critical Incidents/ 

Total Opportunities 

1 88 2,275 0.039 

2 69 1,679 0.041 

3 53 1,183 0.045 

4 37 788 0.047 

5 28 501 0.056 

6 12 332 0.036 

7 10 254 0.039 

8 11 195 0.056 

Figure 31. Critical Incident Relative Frequency as a Function of Driving-Shift, for Baseline 

and Control Conditions, with Multiple Within-Shift Critical Incidents Removed 
52 

Odds ratios were analyzed on this dataset and the results are shown in Table 51. None of the 

shift-by-shift comparisons was significant. 

Table 51. Results from the Odds Ratio Analysis for Baseline and Control Conditions, 

with Multiple Within-Hour Critical Incidents Removed 

Shift Shift Odds Ratio LCL UCL 

8 1 1.49 0.78 2.83 

8 2 1.39 0.73 2.68 

8 6 1.59 0.69 3.69 

5 6 1.58 0.79 3.15 

5 1 1.47 0.95 2.28 

5 7 1.44 0.69 3.02 

Analysis 4.8: Baseline and Control Data, Truck Driver at Fault, No Within-Shift Incidents 

Analysis 4.8 analyzed control and baseline data for subject drivers who were judged to have been 

at fault, with multiple within-shift critical incidents removed. The resulting dataset is shown in 

Table 52. Figure 32 displays a plot of the critical-incident relative frequencies. 

Table 52. Critical Incidents and Total Opportunities by Driving-Shift for Shifts 1 Through 8, 

in Which the Subject Driver was at Fault, for Baseline and Control Conditions, 

with Multiple Within-Shift Critical Incidents Removed 

DrivingShift Critical Incidents Per 

Driving-Shift 

Total Opportunities Per 

Driving-Shift 

Rate: Critical Incidents/ 

Total Opportunities 

1 70 2,275 0.031 

2 50 1,679 0.030 

3 38 1,183 0.032 

4 31 788 0.039 

5 17 501 0.034 

6 9 332 0.027 

7 7 254 0.028 

8 9 195 0.046 
53 

Figure 32. Critical-Incident Relative Frequency as a Function of Driving-Shift, 

in Which the Subject Driver was at Fault, for Baseline and Control Conditions, 

with Multiple Within-Shift Critical Incidents Removed 

The results from the odds ratio analysis conducted on this dataset are shown in Table 53. There 

were no statistically significant differences between any of the shifts. 

Table 53. Results from the Odds Ratio Analysis in Which the Subject Driver was at Fault, 

for Baseline and Control Conditions, with Multiple Within-Hour Critical Incidents Removed 

Driving-Shift Driving-Shift Odds Ratio LCL UCL 

8 7 1.71 0.62 4.67 

8 1 1.52 0.75 3.10 

8 2 1.58 0.76 3.26 

4 7 1.44 0.63 3.32 

4 1 1.29 0.84 1.98 

4 2 1.33 0.85 2.11 

To summarize the findings from Analysis 4, there were no results that were consistent across all 

of the different analyses conducted. When there was a significant finding, it often involved the 

5th or 8th driving-shift. However, when multiple critical incidents were accounted for (as in 

Analyses 4.5 to 4.8), these significant findings were not found (with the exception of Analysis 

4.5, which did find significant differences between the 5th driving-shift and the 1st and 7th

driving-shifts). Because of the inconsistency of the findings across the different analyses, these 

results do not seem to provide strong, convincing support for a driving-shift effect. 
54 

3.5 CRITICAL INCIDENTS AS A FUNCTION OF TIME-OF-DAY 

Each truck in the study was equipped with a Global Positioning System (GPS). The data from 

the GPS provided not only location information of the truck, but also an accurate time-stamp. 

Using these data, it was possible to determine the time-of-day that each critical incident 

occurred. 

Since the time-of-day variable did not rely on the driver history software that was used to 

calculate driving-hour and driving-shift in Analyses 1 through 4, it was possible to use a larger 

critical-incident dataset for Analysis 5. The reader will note that for previous analyses, as 

outlined in the Data Caveats section, not all of the data were usable for Analyses 1 through 4, 

because of an unreliable time history data for five drivers. That being the case, the previous 

analyses included 819 critical incidents. For Analysis 5, 854 critical incidents were available for 

the analysis. This was not the entire critical-incident dataset collected in the study; some data 

could not be used due to unavailability or presumed unreliability of the GPS data. A breakdown 

of the 854 critical incidents that did have reliable GPS, (and consequently time-stamp) data, was 

as follows: 10 crashes, 12 tire-strike crashes, 86 near-crashes, and 746 crash-relevant conflicts. 

As in Analyses 1, 2, and 4, Analysis 5 conducted separate analyses with eight datasets, parsed in 

different ways to help ensure that no potentially significant findings were being overlooked. 

These eight datasets were: 

• Analysis 5.1: All Data: This included the entire dataset, with the exception of information 

outlined in the Data Caveats section. This provided the largest dataset available. 

• Analysis 5.2: All Data, Truck Driver at Fault: Using the dataset noted above, this analysis 

included only those critical incidents that were judged to have been the fault of the 

subject driver. 

• Analysis 5.3: Baseline and Control Data: Only critical incidents that occurred in the 

control and baseline conditions (where no DDWS was used) were included. 

• Analysis 5.4: Baseline and Control Data, Truck Driver at Fault: Using the baseline and 

control data noted above, this analysis included only critical incidents in which the truck 

driver was judged to have been at fault. 

• Analysis 5.5: All Data, No Within-Hour Incidents: Using the dataset noted in Analysis 

5.1, this analysis removed multiple critical incidents that occurred in the same hour, on 

the same day. This analysis treated critical incidents as a dichotomous random variable 

(i.e., for each shift there are two possible outcomes—“yes, at least one incident occurred” 

or “no, an incident did not occur”). As before, for the plots, the critical incident with the 

less severe classification was removed (e.g., a near-crash would be removed instead of a 

crash). 

• Analysis 5.6: All Data, Truck Driver at Fault, No Within-Hour Incidents: Using the 

dataset noted in Analysis 4.1, this analysis considered only critical incidents in which the 

truck driver was at fault and removed multiple within-hour incidents. 

• Analysis 5.7: Baseline and Control Data, No Within-Hour Incidents: This analysis used 

the baseline and control data and removed multiple within-hour incidents. 
55 

• Analysis 5.8: Baseline and Control Data, Truck Driver at Fault, No Within-Hour 

Incidents: Using the baseline and control data, this analysis considered only critical 

incidents in which the truck driver was at fault and removed muliple within-hour 

incidents. 

The findings from these eight analyses are outlined below. 

 Analysis 5.1: All Data 

Table 54 shows the dataset that included the largest number of critical incidents, and driving 

opportunities (i.e., trips), broken out as a function of the time-of-day (in one-hour segments). As 

in previous analyses, the data were normalized and a rate was calculated (critical incidents 

divided by opportunities). Figure 33 plots the opportunity data that show when drivers were 

driving, while Figure 34 plots the critical-incident relative frequency for each of the 24 hours. In 

Figure 34, a breakdown in the frequency for each type of critical incident is indicated: crashes 

(red), crashes: tire strikes (green), near-crashes (purple), and crash-relevant conflicts (blue). The 

top of the bar is the total for all critical incident types. 

Table 54. Critical Incidents and Total Opportunities by Time-of-Day 

Time-of-Day Critical Incidents Per 

Time-of-Day 

Total Opportunities Per 

Time-of-Day 

Rate: Critical Incidents/ 

Total Opportunities 

0:00–0:59 19 3,396 0.006 

1:00–1:59 9 2,866 0.003 

2:00–2:59 6 2,577 0.002 

3:00–3:59 8 2,479 0.003 

4:00–4:59 9 2,532 0.004 

5:00–5:59 6 2,625 0.002 

6:00–6:59 24 2,714 0.009 

7:00–7:59 16 3,045 0.005 

8:00–8:59 40 3,765 0.011 

9:00–9:59 50 4,457 0.011 

10:00–10:59 49 4,879 0.010 

11:00–11:59 62 5,157 0.012 

12:00–12:59 70 5,291 0.013 

13:00–13:59 58 5,469 0.011 

14:00–14:59 83 5,422 0.015 

15:00–15:59 64 5,376 0.012 

16:00–16:59 71 5,303 0.013 

17:00–17:59 54 5,120 0.011 

18:00–18:59 38 4,860 0.008 

19:00–19:59 36 4,702 0.008 

20:00–20:59 30 4,704 0.006 

21:00–21:59 25 4,621 0.005 

22:00–22:59 15 4,487 0.003 

23:00–23:50 12 3,968 0.003 
56 

Figure 33. Number of Trips as a Function of Time-of-Day 

Figure 34. Critical-Incident Relative Frequency as a Function of Time-of-Day 

Table 55 shows the results from the odds ratio analyses. The first two columns in Table 55 show 

the comparison hours (e.g., the first row compares 2:00 to 2:59 p.m. and midnight to 12:59 a.m.), 

while the third column shows the calculated odds ratio and the last two columns show the lower 

and upper confidence limits, respectively. As a reminder, odds ratios with LCLs and UCLs that 

contain “1” are not statistically significant (again, a 95 percent confidence level was used in 

these calculations). As before, all significant effects are included in the odds ratio tables along 

with selected other analyses that were not significant. 
57 

Table 55. Results from the Odds Ratio Analyses 

Hour Hour Odds Ratio LCL UCL 

14:00–14:59 0:00–0:59 2.76 1.68 4.56 

14:00–14:59 1:00–1:59 4.93 2.48 9.83 

14:00–14:59 2:00–2:59 6.66 2.90 15.28 

14:00–14:59 3:00–3:59 4.80 2.32 9.94 

14:00–14:59 4:00–4:59 4.36 2.19 8.68 

14:00–14:59 5:00–5:59 6.79 2.96 15.56 

14:00–14:59 6:00–6:59 1.74 1.10 2.75 

14:00–14:59 7:00–7:59 2.94 1.72 5.04 

14:00–14:59 8:00–8:59 1.45 0.99 2.12 

14:00–14:59 9:00–9:59 1.37 0.96 1.95 

14:00–14:59 10:00–10:59 1.53 1.07 2.19 

14:00–14:59 11:00–11:59 1.28 0.92 1.78 

14:00–14:59 12:00–12:59 1.16 0.84 1.60 

14:00–14:59 13:00–13:59 1.45 1.03 2.03 

14:00–14:59 15:00–15:59 1.29 0.93 1.79 

14:00–14:59 16:00–16:59 1.15 0.83 1.58 

14:00–14:59 17:00–17:59 1.46 1.03 2.06 

14:00–14:59 18:00–18:59 1.97 1.34 2.90 

14:00–14:59 19:00–19:59 2.01 1.36 2.99 

14:00–14:59 20:00–20:59 2.42 1.59 3.68 

14:00–14:59 21:00–21:59 2.86 1.82 4.48 

14:00–14:59 22:00–22:59 4.63 2.67 8.04 

14:00–14:59 23:00–23:50 5.12 2.79 9.40 

8:00–8:59 0:00–0:59 1.91 1.10 3.30 

8:00–8:59 1:00–1:59 3.41 1.65 7.04 

8:00–8:59 2:00–2:59 4.60 1.95 10.87 

8:00–8:59 3:00–3:59 3.32 1.55 7.10 

8:00–8:59 4:00–4:59 3.01 1.46 6.21 

8:00–8:59 5:00–5:59 4.69 1.98 11.07 

8:00–8:59 7:00–7:59 2.03 1.14 3.64 

8:00–8:59 20:00–20:59 1.67 1.04 2.69 

8:00–8:59 21:00–21:59 1.97 1.20 3.26 

8:00–8:59 22:00–22:59 3.20 1.77 5.80 

8:00–8:59 23:00–23:50 3.54 1.85 6.76 

9:00–9:59 0:00–0:59 2.02 1.19 3.43 

9:00–9:59 1:00–1:59 3.60 1.77 7.33 

9:00–9:59 2:00–2:59 4.86 2.08 11.35 

9:00–9:59 3:00–3:59 3.50 1.66 7.40 

9:00–9:59 4:00–4:59 3.18 1.56 6.48 

9:00–9:59 5:00–5:59 4.95 2.12 11.57 

9:00–9:59 7:00–7:59 2.15 1.22 3.78 

9:00–9:59 20:00–20:59 1.77 1.12 2.78 
58 

Hour Hour Odds Ratio LCL UCL 

9:00–9:59 21:00–21:59 2.09 1.29 3.38 

9:00–9:59 22:00–22:59 3.38 1.90 6.03 

9:00–9:59 23:00–23:50 3.74 1.99 7.03 

11:00–11:59 18:00–18:59 1.54 1.03 2.32 

11:00–11:59 19:00–19:59 1.58 1.04 2.38 

11:00–11:59 6:00–6:59 1.36 0.85 2.19 

12:00–12:59 6:00–6:59 1.50 0.94 2.39 

At first glance, the data plotted in Figure 34 and the results from the odds ratios do not seem to 

indicate any clear pattern; however, there is obviously a strong time-of-day effect. Note that both 

a frequency-odds-ratio approach and a logistic regression modeling approach were used and 

similar results were found using both approaches. 

In an attempt to interpret what might be going on, two follow-up analyses were conducted. The 

first analysis considered “circadian rhythm,” which refers to the human body’s natural tendency 

to be alert or drowsy at different points within the 24-hour cycle (Figure 35). This follow-up 

analysis compared the circadian low periods (2 a.m. to 5 a.m. and 3 p.m. to 5 p.m.; Missoula 

Technology and Development Center, n.d.) with the approximate circadian high periods (9 a.m. 

to 11 a.m. and 8 p.m. to 9 p.m.), as estimated from Figure 36. 

Figure 35. Circadian Rhythm Showing Alertness as a Function of Time-of-Day 

Using these circadian low and high periods, the relevant hours were combined. Figure 36 shows 

a plot of the critical-incident relative frequencies for each circadian low and high period. If 

circadian rhythm effects were significant, the plots would show significantly higher relative 

frequencies for the low periods and vice versa for the high periods. However, this was not the 

case and as a result, it does not appear that circadian rhythm effects had a meaningful impact on 

the critical-incident relative frequencies. 
59 

Figure 36. Critical-Incident Relative Frequencies for the Circadian Lows 

(2 a.m. to 5 a.m., 3 p.m. to 5 p.m.) and Highs (9 a.m. to 11 a.m. and 8 p.m. to 9 p.m.) 

A second follow-up analysis considered traffic density as a function of driving-hour. Trafficdensity data were plotted against the hour-by-hour critical-incident relative frequency plots 

shown in Figure 37. Please note that the traffic-density overlay in Figure 37 is based on visual 

inspection of plots found in Festin (1996). 

Figure 37. Critical-Incident Relative Frequency as a Function of Time-of-Day, 

with Traffic-Density Plot Superimposed 

The black line in Figure 37 displays the U.S. distribution of daily weekday traffic across each 

hour of the day in 1995 (Festin, 1996). Festin (1996) used two primary data sources, highway 
60 

statistics for annual vehicle miles traveled (VMT), and 5,000 automatic traffic recorder sites 

across the United States, to estimate annual travel trends in the United States. Please note that 

these traffic data represent national weekday averages and are not location-specific. The 

distribution is likely to change based on region, setting (i.e., urban vs. rural), and day of week 

(i.e., weekday vs. weekend). Nonetheless, the traffic-density plot follows the critical-incident 

relative frequency plot, particularly with regard to the sharp onset (around 6 a.m.) and slow 

decline after what presumably would be the evening rush period. As such, it is hypothesized that 

the hour-by-hour differences observed in the current study may be primarily a function of traffic 

density (i.e., increased exposure to other vehicles leading to increased critical incidents). To 

investigate this hypothesis further, a Pearson correlation coefficient was calculated between the 

daily weekday traffic across each hour of the day and the relative frequency of safety-critical 

events across each hour of the day. As suggested by the plotted data, the results showed a strong 

positive linear relationship between the two variables with a correlation (r) of 0.83. With an R2

 of 

0.69, there is a strong linear relationship between these two variables (i.e., both increase and 

decrease with a similar pattern). 

Analysis 5.2: All Data, Truck Driver at Fault 

Analysis 5.2 included only critical incidents in which the subject-vehicle driver (i.e., the study 

truck driver) was judged, based on video review, to have been at fault. The resulting dataset is 

shown in Table 56 below and plotted in Figure 38. 

Table 56. Critical Incidents and Total Opportunities by Time-of-Day 

In Which the Subject Driver was at Fault 

Time-of-Day Critical Incidents Per 

Time-of-Day 

Total Opportunities Per 

Time-of-Day 

Rate: Critical Incidents/ 

Total Opportunities 

0:00–0:59 12 3,396 0.004 

1:00–1:59 6 2,866 0.002 

2:00–2:59 2 2,577 0.001 

3:00–3:59 3 2,479 0.001 

4:00–4:59 7 2,532 0.003 

5:00–5:59 4 2,625 0.002 

6:00–6:59 18 2,714 0.007 

7:00–7:59 13 3,045 0.004 

8:00–8:59 33 3,765 0.009 

9:00–9:59 34 4,457 0.008 

10:00–10:59 41 4,879 0.008 

11:00–11:59 51 5,157 0.010 

12:00–12:59 55 5,291 0.010 

13:00–13:59 41 5,469 0.007 

14:00–14:59 57 5,422 0.011 

15:00–15:59 47 5,376 0.009 

16:00–16:59 53 5,303 0.010 

17:00–17:59 41 5,120 0.008 

18:00–18:59 31 4,860 0.006 

19:00–19:59 31 4,702 0.007 
61 

Time-of-Day Critical Incidents Per 

Time-of-Day 

Total Opportunities Per 

Time-of-Day 

Rate: Critical Incidents/ 

Total Opportunities 

20:00–20:59 25 4,704 0.005 

21:00–21:59 13 4,621 0.003 

22:00–22:59 9 4,487 0.002 

23:00–23:50 12 3,968 0.003 

Figure 38. Critical-Incident Relative Frequency as a Function of Time-of-Day in Which 

the Subject Driver was at Fault 

Analysis 5.3: Baseline and Control Data 

Analysis 5.3 used only the data from the “control” and “baseline” conditions from the study (i.e., 

data collected where no DDWS was used). The resulting dataset is shown in Table 57. Figure 39 

plots the critical-incident relative frequency across the driving-hours for the baseline and control 

conditions and the national traffic-density data described earlier are overlaid. 

Table 57. Critical Incidents and Total Opportunities by Time-of-Day 

for Baseline and Control Conditions 

Time-of-Day Critical Incidents Per 

Time-of-Day 

Total Opportunities Per 

Time-of-Day 

Rate: Critical Incidents/ 

Total Opportunities 

0:00–0:59 9 1,419 0.006 

1:00–1:59 4 1,146 0.003 

2:00–2:59 3 998 0.003 

3:00–3:59 0 971 0.000 

4:00–4:59 3 1,028 0.003 

5:00–5:59 4 1,125 0.004 

6:00–6:59 10 1,141 0.009 

7:00–7:59 7 1,275 0.005 

8:00–8:59 14 1,624 0.009 
62 

Time-of-Day Critical Incidents Per 

Time-of-Day 

Total Opportunities Per 

Time-of-Day 

Rate: Critical Incidents/ 

Total Opportunities 

9:00–9:59 20 1,933 0.010 

10:00–10:59 23 2,188 0.011 

11:00–11:59 33 2,311 0.014 

12:00–12:59 32 2,357 0.014 

13:00–13:59 26 2,470 0.011 

14:00–14:59 37 2,478 0.015 

15:00–15:59 32 2,421 0.013 

16:00–16:59 33 2,385 0.014 

17:00–17:59 24 2,293 0.010 

18:00–18:59 18 2,191 0.008 

19:00–19:59 15 2,096 0.007 

20:00–20:59 11 2,111 0.005 

21:00–21:59 12 2,068 0.006 

22:00–22:59 8 1,917 0.004 

23:00–23:50 1 1,643 0.001 

Figure 39. Critical-Incident Relative Frequency as a Function of Time-of-Day 

for Baseline and Control Conditions 
63 

Analysis 5.4: Baseline and Control Data, Truck Driver at Fault 

Analysis 5.4 used only the data from the control and baseline conditions in which the subject 

driver was judged at fault. Table 58 displays the dataset used and Figure 40 plots the criticalincident relative frequency across driving-hours. Again, a traffic-density-by-hour function is 

overlaid on the critical-incident relative frequency data. 

Table 58. Critical Incidents and Total Opportunities by Time-of-Day, 

In Which the Subject Driver was at Fault, for Baseline and Control Conditions 

Time-of-Day Critical Incidents Per 

Time-of-Day 

Total Opportunities Per 

Time-of-Day 

Rate: Critical Incidents/ 

Total Opportunities 

0:00–0:59 5 1,419 0.004 

1:00–1:59 1 1,146 0.001 

2:00–2:59 1 998 0.001 

3:00–3:59 0 971 0.000 

4:00–4:59 2 1,028 0.002 

5:00–5:59 2 1,125 0.002 

6:00–6:59 7 1,141 0.006 

7:00–7:59 5 1,275 0.004 

8:00–8:59 11 1,624 0.007 

9:00–9:59 13 1,933 0.007 

10:00–10:59 20 2,188 0.009 

11:00–11:59 28 2,311 0.012 

12:00–12:59 25 2,357 0.011 

13:00–13:59 19 2,470 0.008 

14:00–14:59 24 2,478 0.010 

15:00–15:59 26 2,421 0.011 

16:00–16:59 25 2,385 0.010 

17:00–17:59 21 2,293 0.009 

18:00–18:59 15 2,191 0.007 

19:00–19:59 12 2,096 0.006 

20:00–20:59 8 2,111 0.004 

21:00–21:59 6 2,068 0.003 

22:00–22:59 5 1,917 0.003 

23:00–23:50 1 1,643 0.001 
64 

Figure 40. Critical-Incident Relative Frequency as a Function of Time-of-Day, 

in Which the Subject Driver was at Fault, for Baseline and Control Conditions 

Analysis 5.5: All Data, No Within-Hour Incidents 

Analysis 5.5 removed multiple within-hour critical incidents such that a dichotomous variable 

was created (either “yes, at least one critical incident occurred” or “no critical incident 

occurred”). Table 59 displays the resulting dataset and Figure 41 plots the critical-incident 

relative frequencies for each hour of the day. 

Table 59. Critical Incidents and Total Opportunities by Time-of-Day, 

with Multiple Within-Hour Critical Incidents Removed 

Time-of-Day Critical Incidents Per 

Time-of-Day 

Total Opportunities Per 

Time-of-Day 

Rate: Critical Incidents/ 

Total Opportunities 

0:00–0:59 18 3,396 0.005 

1:00–1:59 7 2,866 0.002 

2:00–2:59 6 2,577 0.002 

3:00–3:59 8 2,479 0.003 

4:00–4:59 9 2,532 0.004 

5:00–5:59 6 2,625 0.002 

6:00–6:59 23 2,714 0.008 

7:00–7:59 16 3,045 0.005 

8:00–8:59 37 3,765 0.010 

9:00–9:59 48 4,457 0.011 

10:00–10:59 45 4,879 0.009 

11:00–11:59 57 5,157 0.011 

12:00–12:59 65 5,291 0.012 

13:00–13:59 55 5,469 0.010 

14:00–14:59 76 5,422 0.014 

15:00–15:59 57 5,376 0.011 

16:00–16:59 64 5,303 0.012 
65 

Time-of-Day Critical Incidents Per 

Time-of-Day 

Total Opportunities Per 

Time-of-Day 

Rate: Critical Incidents/ 

Total Opportunities 

17:00–17:59 49 5,120 0.010 

18:00–18:59 33 4,860 0.007 

19:00–19:59 33 4,702 0.007 

20:00–20:59 29 4,704 0.006 

21:00–21:59 25 4,621 0.005 

22:00–22:59 14 4,487 0.003 

23:00–23:50 11 3,968 0.003 

Figure 41. Critical-Incident Relative Frequency as a Function of Time-of-Day, 

with Multiple Within-Hour Critical Incidents Removed 
66 

Analysis 5.6: All Data, Truck Driver at Fault, No Within-Hour Incidents 

Analysis 5.6 included critical incidents in which the subject driver was judged to have been at 

fault and multiple critical incidents that occurred within the same hour of the day were removed. 

The resulting dataset is shown in Table 60 and the plot of the relative frequency of critical 

incidents is shown in Figure 42. 

Table 60. Critical Incidents and Total Opportunities by Time-of-Day, in Which 

the Subject Driver Was at Fault, with Multiple Within-Hour Critical Incidents Removed 

Time-of-Day Critical Incidents Per 

Time-of-Day 

Total Opportunities Per 

Time-of-Day 

Rate: Critical Incidents/ 

Total Opportunities 

0:00–0:59 11 3,396 0.003 

1:00–1:59 4 2,866 0.001 

2:00–2:59 2 2,577 0.001 

3:00–3:59 3 2,479 0.001 

4:00–4:59 7 2,532 0.003 

5:00–5:59 4 2,625 0.002 

6:00–6:59 17 2,714 0.006 

7:00–7:59 13 3,045 0.004 

8:00–8:59 30 3,765 0.008 

9:00–9:59 31 4,457 0.007 

10:00–10:59 38 4,879 0.008 

11:00–11:59 48 5,157 0.009 

12:00–12:59 51 5,291 0.010 

13:00–13:59 40 5,469 0.007 

14:00–14:59 54 5,422 0.010 

15:00–15:59 41 5,376 0.008 

16:00–16:59 50 5,303 0.009 

17:00–17:59 37 5,120 0.007 

18:00–18:59 26 4,860 0.005 

19:00–19:59 29 4,702 0.006 

20:00–20:59 24 4,704 0.005 

21:00–21:59 13 4,621 0.003 

22:00–22:59 8 4,487 0.002 

23:00–23:50 11 3,968 0.003 
67 

Figure 42. Critical-Incident Relative Frequency as a Function of Time-of-Day, 

in Which the Subject Driver was at Fault, with Multiple Within-Hour Critical Incidents Removed 

Analysis 5.7: Baseline and Control Data, No Within-Hour Incidents 

Analysis 5.7 used the control and baseline data and removed all multiple within-hour critical 

incidents. The resulting dataset is shown in Table 61 and a plot of the critical-incident relative 

frequencies is shown in Figure 43. 

Table 61. Critical Incidents and Total Opportunities by Time-of-Day, for Baseline and 

Control Conditions, with Multiple Within-Hour Critical Incidents Removed 

Time-of-Day Critical Incidents Per 

Time-of-Day 

Total Opportunities Per 

Time-of-Day 

Rate: Critical Incidents/ 

Total Opportunities 

0:00–0:59 8 1,419 0.006 

1:00–1:59 4 1,146 0.003 

2:00–2:59 3 998 0.003 

3:00–3:59 0 971 0.000 

4:00–4:59 3 1,028 0.003 

5:00–5:59 4 1,125 0.004 

6:00–6:59 9 1,141 0.008 

7:00–7:59 7 1,275 0.005 

8:00–8:59 12 1,624 0.007 

9:00–9:59 20 1,933 0.010 

10:00–10:59 20 2,188 0.009 

11:00–11:59 30 2,311 0.013 

12:00–12:59 27 2,357 0.011 

13:00–13:59 24 2,470 0.010 

14:00–14:59 33 2,478 0.013 

15:00–15:59 27 2,421 0.011 
68 

Time-of-Day Critical Incidents Per 

Time-of-Day 

Total Opportunities Per 

Time-of-Day 

Rate: Critical Incidents/ 

Total Opportunities 

16:00–16:59 30 2,385 0.013 

17:00–17:59 20 2,293 0.009 

18:00–18:59 16 2,191 0.007 

19:00–19:59 13 2,096 0.006 

20:00–20:59 10 2,111 0.005 

21:00–21:59 12 2,068 0.006 

22:00–22:59 7 1,917 0.004 

23:00–23:50 1 1,643 0.001 

Figure 43. Critical-Incident Relative Frequency as a Function of Time-of-Day for Baseline and 

Control Conditions, with Multiple Within-Hour Critical Incidents Removed 
69 

Analysis 5.8: Baseline and Control Data, Truck Driver at Fault, No Within-Hour Incidents 

Analysis 5.8 analyzed control and baseline data for subject drivers who were judged to have been 

at fault, with multiple within-hour critical incidents removed. The resulting dataset is shown in 

Table 62, while Figure 44 displays a plot of the critical-incident relative frequencies with traffic 

density overlaid. 

Table 62. Critical Incidents and Total Opportunities by Time-of-Day, 

in Which the Subject Driver Was at Fault, for Baseline and Control Conditions, 

with Multiple Within-Hour Critical Incidents Removed 

Time-of-Day Critical Incidents Per 

Time-of-Day 

Total Opportunities Per 

Time-of-Day 

Rate: Critical Incidents/ 

Total Opportunities 

0:00–0:59 4 1,419 0.003 

1:0–1:59 1 1,146 0.001 

2:00–2:59 1 998 0.001 

3:00–3:59 0 971 0.000 

4:00–4:59 2 1,028 0.002 

5:00–5:59 2 1,125 0.002 

6:00–6:59 6 1,141 0.005 

7:00–7:59 5 1,275 0.004 

8:00–8:59 9 1,624 0.006 

9:00–9:59 13 1,933 0.007 

10:00–10:59 17 2,188 0.008 

11:00–11:59 26 2,311 0.011 

12:00–12:59 21 2,357 0.009 

13:00–13:59 18 2,470 0.007 

14:00–14:59 23 2,478 0.009 

15:00–15:59 22 2,421 0.009 

16:00–16:59 24 2,385 0.010 

17:00–17:59 18 2,293 0.008 

18:00–18:59 13 2,191 0.006 

19:00–19:59 11 2,096 0.005 

20:00–20:59 7 2,111 0.003 

21:00–21:59 6 2,068 0.003 

22:00–22:59 4 1,917 0.002 

23:00–23:50 1 1,643 0.001 
70 

Figure 44. Critical-Incident Relative Frequency as a Function of Time-of-Day, 

in Which the Subject Driver was at Fault, for Baseline and Control Conditions, 

with Multiple Within-Hour Critical Incidents Removed 

To summarize the findings from Analysis 5: There was a strong effect of critical-incident 

occurrence as a function of time-of-day. In suggesting why this may be the case, an hour-by-hour 

plot of traffic density seems to follow a similar pattern. Keep in mind that the current study was 

not designed to look specifically for traffic density, and therefore, available national data were 

used. However, based on the similarity of the functions, it is hypothesized that hour-by-hour 

fluctuations in critical-incident relative frequency can be explained by comparable fluctuations in 

traffic density on an hour-by-hour basis. This hypothesis makes intuitive sense in that, as the 

number of vehicles increases, so does the chance of being involved in a multi-vehicle incident. 
71 

4. CONCLUSIONS 

This study has resulted in a major finding that is relevant to the assessment of the 2003 HOS 

regulations. Specifically, the results from the analysis on critical incident relative frequency, 

used as a surrogate for driver performance decrement, generally showed no statistical difference 

between the 2nd through 11th driving-hours (for almost all analyses); and the statistical difference 

between the 1st driving-hour and the 11th driving-hour lies in the direction opposite to what 

would be expected, had there been a time-on-task effect. That is, the results from this study do 

not support the hypothesis that there is an increased risk resulting from CMV drivers driving in 

the 11th driving-hour as compared to the 10th driving-hour, or any hour, although caution must be 

used in interpreting these results, due to the small sample of drivers represented in the study as 

compared to the larger CMV driver population. That is, although a dataset of over 2 million 

VMT collected from 98 drivers driving 46 instrumented trucks is the largest known continuously 

collected naturalistic dataset available, it captures only a small segment of the approximately 3 

million current Commercial Driver’s License holders (ATRI, 2007), the 8.5 million registered 

vehicles, and the 222 billion miles driven each year (FMCSA, 2007a). Nonetheless, these are 

perhaps the best data available to investigate this important safety question. 

These results from this research are consistent with Wylie et al. (1996) with regard to time-ontask being a poor predictor of crashes and safety-related traffic events. In fact, a significant spike 

in the rate of critical incidents was found during the 1st driving-hour. These results are not 

consistent with the contention that crash risk increases as hours of driving increase (see Kaneko 

and Jovanis, 1992; Lin, Jovanis, and Yang, 1993; Park et al., 2005). This spike was found across 

all possible trips and only those trips on which the truck driver drove into the 11th driving-hour. 

The latter statement is noteworthy because it suggests that even in those trips on which the driver 

drove into the 11th driving-hour, the critical-incident relative frequency was highest in the 1st

driving-hour (significantly more than in driving-hours 2 though 11 for almost all analyses). 

A cursory examination of FMCSA’s LTCCS database shows a finding similar to the current 

study. When looking at truck crashes by hours driving—that is, across all hours—we see that 

most crashes occurred in the 1st driving-hour (FMCSA, 2007b). More specifically, findings from 

the LTCCS database indicate the 1st driving-hour is associated with the highest raw percentage of 

crashes (14.7 percent). Note that exposure (opportunities) was not accounted for, per se, in the 

LTCCS dataset. However, the current study, which does account for exposure, found a similar 

spike in critical-incident relative frequency during the 1st driving-hour. 

There are at least three possible explanations for the significant spike in the critical-incident 

relative frequency found in the 1st driving-hour. The first reason may be sleep inertia. Sleep 

inertia refers to the decrease or impairment of alertness and performance immediately upon 

waking from sleep. Sleep inertia refers to impairment in a variety of performance tasks, 

including short-term memory, vigilance, cognitive functioning, reaction time, and ability to resist 

sleep (Bonnet and Arand, 1995; Dinges, 1990; Mullington and Broughton, 1994). Dinges (1990) 

suggested sleep inertia can endure for up to 15 min after waking. However, more recent studies 

by Bruck and Pisani (1999) and Jewett et al. (1999) indicate, respectively, that the effects of 

sleep inertia may last for 30 min or 60 min after waking. Obviously, personnel who are expected 

to perform work duties immediately on awaking, such as over-the-road drivers who sleep in 

sleeper berths in their trucks, may be most affected by sleep inertia. Moreover, sleep deprivation 

can compound the effects of sleep inertia. Wertz et al. (2006) found the effects of sleep inertia 
72 

were detectable for at least 2 h after waking when subjects were sleep-deprived. While Hanowski 

et al. (2005) found that drivers were getting more sleep under the 2003 HOS regulations (6.28 h 

vs. 5.18 h each night), the amount of sleep they were getting was still below the 7 to 9 h 

recommended by the National Sleep Foundation for healthy adults (NSF, 2007). 

Another possible reason for the spike found in the 1st driving-hour could be related to road type 

and/or traffic density. Drivers may start their trips on local and other undivided roads with higher 

traffic densities (as compared to what they might find on the open road). Similarly, for line-haul 

operations specifically, drivers may end their trips on local and other undivided roads, again 

experiencing higher traffic density than might occur on a highway. These can be conceived as 

“take-off” and “landing” effects. Hickman et al. (2005), using the same dataset as Hanowski et 

al. (2005), found that drivers were almost five times as likely to be involved in a critical incident 

on an undivided highway as on a divided highway. Additionally, the results from Analysis 5 in 

the current study on time-of-day indicated a potential explanation for the time-of-day effects as 

attributable to traffic density. These results suggest a possible “take-off” and “landing” effect. 

While the spike in the 1st driving-hour reflects a constant “take-off” effect across all trips 

(regardless of trip length), the “landing” effect would be indistinguishable, as it would be 

distributed across different trip lengths. This hypothesis is supported when we look at the 

critical-incident relative frequency across all trip lengths. However, the study was not set up to 

have drivers ending at a set time or being at a set destination after, say, 10 or 11 driving-hours. 

For the line-haul operators, who represented approximately 24 percent of the drivers in the study, 

this was indeed the case. However, for the other 76 percent of the drivers, who were typically out 

on the road for a week at a time, there was no known consistent set destination at the end of a 

shift. Under these circumstances, it was difficult to investigate the hypothesis of “landing” 

effects with the current dataset. Future research is needed, using a methodology that would 

facilitate further investigation of this hypothesis. 

The third possible explanation for the spike found in the 1st driving-hour could reflect time-ofday effects. The current study found a strong time-of-day effect, but it seemed that these effects 

were related to traffic density. Wylie et al. (1996) indicated time-of-day effects to be the 

strongest and most consistent factor influencing driver fatigue. The current analysis did not focus 

on driver fatigue events specifically, so the impact of time-of-day on driver fatigue is unknown 

with respect to the dataset used for this analysis effort. An analysis was conducted looking at all 

critical incidents that occurred in the circadian low and high periods (presumably including both 

fatigue and alert incidents), but the results did not point to a circadian effect. Future research 

could be directed at pulling out the critical incidents, or other drowsy episodes not associated 

with a critical incident, that occurred during circadian lows to investigate this further. 

Nonetheless, for the current analysis, time-of-day effects seemed to be associated with traffic 

density. 

Note that these three possible explanations for the spike in critical-incident relative frequency 

during the 1st driving-hour are not mutually exclusive. That is, it could very well be the case that 

a combination of these effects may be involved. For example, sleep inertia may be more 

attributable to the long-haul drivers who sleep in sleeper berths and who may go, in a relatively 

short period of time, from sleeping in the sleeper berth to driving. This may be exacerbated by 

complex driving environments (e.g., urban environments, loading areas, undivided highways, 

intersections) and higher traffic density levels that may occur in loading (i.e., “take-off”) and 

drop-off (i.e., “landing”) situations which occur at the beginning and, depending on the fleet 
73 

operation, end of the shift. Time-of-day effects, which are influenced by internal factors (i.e., 

circadian rhythm) and external factors (e.g., rush hours, traffic density), also play a role. 

However, based on the current research and other findings in the literature, the impact of timeon-task, which is the basis for the 10th vs. 11th driving-hour debate, does not seem to be an 

obvious or significant factor when considering increased critical-incident risk. 

75 

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