Tech Brief FEDERAL MOTOR CARRIER SAFETY ADMINISTRATION

Office of Analysis, Research and 

Technology 

1200 New Jersey Ave. SE 

Washington, DC 20590 

FMCSA Contact: Dr. Martin Walker (MC RRR), 202 385 2364

Analysis of Risk as a Function of 

Driving-Hour: Assessment of Driving 

Hours 1 through 11 Final Report 

Background 

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-of-Service (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 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 two 

hours of off-duty time would provide drivers with “... substantially more 

opportunity to obtain restorative sleep.” 

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. 

Programs of the Federal Motor Carrier 

Safety Administration (FMCSA) 

encompass a range of issues and 

disciplines, all related to motor carrier 

safety and security. FMCSA's Office of 

Analysis, Research and Technology 

defines a "research program" as any 

systematic study directed toward fuller 

scientific discovery, knowledge, or 

understand-ing that will improve safety, 

and reduce the number and severity of 

commercial motor vehicle crashes. 

Similarly, a "technology program" is a 

program that adopts, develops, tests, 

and/or deploys innovative driver and/or 

vehicle best safety practices and 

technologies that will improve safety 

and reduce the number and severity of 

commercial motor vehicle crashes. An 

"analysis program" is defined as 

economic and environmental analyses 

done for the agency's rulemakings, as 

well as program effectiveness studies, 

state-reported data quality initiatives, 

and special crash and other motor 

carrier safety performance-related 

analyses. A “large truck” is any truck 

with a Gross Vehicle Weight rating or 

Gross Combination Weight rating of 

10,001 pounds or greater. 

Currently, FMCSA’s Office of Analysis, 

Research and Technology is 

conducting programs in order to 

produce safer drivers, improve safety 

of commercial motor vehicles, produce 

safer carriers, advance safety through 

information-based initiatives, and 

improve security through safety 

initiatives. The study described in this 

Tech Brief was designed and 

developed to support the strategic 

objective to produce safer drivers. The 

primary goals of this initiative are to 

ensure that commercial drivers are 

physically qualified, trained to perform 

safely, and mentally alert. 
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 = 24–60 

years). 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 = 24–60 years. 

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). 

Results

This study 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 driving-hours (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 

driving-hour. 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 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 as shown in the figure below: 

Critical-Incident Relative Frequency as a Function of Time-of-Day, with 

Traffic-Density Plot Superimposed 

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. The yellow line (Traffic) in the figure above 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 and 5,000 automatic 

traffic recorders sites across the United States, to estimate annual travel trends in the United 

States. 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). 

Conclusion

For this analysis the relative frequency of critical incidents were used as a surrogate for 

decrements in driver performance; this analysis 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 number of CMV driver population. Nonetheless, 

these are perhaps the best data available to investigate this important safety 

question. 

These results are consistent with Wylie et al. (1996) with regard to time-on-task 

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 criticalincident relative frequency was highest in the 1st driving-hour (significantly 

more than in driving-hours 2 though 11 for almost all analyses). 

Researcher: This study, “Analysis 

of Risk as a Function of DrivingHour: Assessment of Driving-Hours 

1 Through 11” was conducted by 

Virginia Tech Transportation 

Institute, 3500 Transportation 

Research Plaza (0536), 

Blacksburg, VA 24061 

Authored by: Richard J. Hanowski, 

Rebecca L. Olson, Joseph 

Bocanegra, and Jeffrey S. Hickman 

Key Words: Hours-of-Service, 

HOS, Truck, Commercial Vehicle, 

Regulations, Fatigue, Drowsiness, 

Time-on-task 

Notice: This Tech Brief is 

disseminated under the 

sponsorship of the Department of 

Transportation in the interest of 

information exchange. The Tech 

Brief provides a synopsis of the 

final publication for this study. The 

Tech Brief does not establish 

policies or regulations, nor does it 

imply USDOT endorsement of the 

conclusions or recommendations. 

The U.S. Government assumes no 

liability for its contents or their use. 

Web Site: All FMCSA Tech and 

Analysis Briefs may be accessed at 

www.fmcsa.dot.gov. 

Technical Writer: 

Peter Vaka 

C2 Technologies, Inc. 

Technical Editor: 

Kirse Kelly 

C2 Technologies, Inc. 

September 2008 

Publication No. FMCSA-RRR-08-006