Distraction in Commercial Trucks and Buses: 

Assessing Prevalence and Risk in Conjunction 

with Crashes and Near-Crashes

September 2010
FOREWORD

The purpose of the research presented herein was to analyze naturalistic data collected over a 1-

year period. Commercial trucks (3-axle and tractor trailer/tanker) and buses (transit and motor 

coaches) were the target vehicles in the analyses. These data are intended to provide the Federal 

Motor Carrier Safety Administration with descriptive data on the adverse consequences of 

cellular telephone use and other electronic device distractions while driving a commercial motor 

vehicle. The results of these analyses also provide information on the scope of cellular telephone 

use and other distractions during safety-critical events (i.e., crashes, near-crashes, and crashrelevant conflicts) in two data sets over a 1-year period. 

NOTICE

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

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

its contents or the use thereof.

The contents of this Report reflect the views of the contractor, who is responsible for the 

accuracy of the data presented herein. The contents do not necessarily reflect the official policy 

of the Department of Transportation.

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

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

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

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

1. Report No.

FMCSA-RRR-10-049

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

4. Title and Subtitle

DISTRACTION IN COMMERCIAL TRUCKS AND BUSES: 

ASSESSING PREVALENCE AND RISK IN CONJUNCTION WITH 

CRASHES AND NEAR-CRASHES

5. Report Date

September 2010

6. Performing Organization Code

7. Author(s)

Jeffrey S. Hickman, Richard J. Hanowski, and Joseph Bocanegra

8. Performing Organization Report No.

9. Performing Organization Name and Address

Virginia Tech Transportation Institute 

Center for Truck and Bus Safety 

3500 Transportation Research Plaza

Blacksburg, VA 24061

10. Work Unit No. (TRAIS)

11. Contract or Grant No.

DTMC75-09-J-00045

12. Sponsoring Agency Name and Address

U.S. Department of Transportation

Federal Motor Carrier Safety Administration

Office of Analysis, Research and Technology

1200 New Jersey Ave., SE

Washington, DC 20590

13. Type of Report and Period Covered

Final Report

September 2009–April 2010

14. Sponsoring Agency Code

FMCSA

15. Supplementary Notes

This project was funded by the Federal Motor Carrier Safety Administration. The Contracting Officer’s Technical 

Representative was Martin Walker, PhD. The Task Order Manager was Albert Alvarez.

16. Abstract

This research analyzed naturalistic data on commercial trucks (3-axle and tractor trailer/tanker) and buses (transit 

and motor coaches) over a 1-year period using two data sets: data set A from 207 truck and bus fleets comprising 

13,431 vehicles included 1,336 crashes, 15,864 near-crashes, and 173,591 crash-relevant conflicts; and data set B 

from 183 commercial truck and bus fleets comprising 13,306 vehicles included 1,085 crashes, 8,375 near-crashes, 

30,661 crash-relevant conflicts, and 211,171 baseline events (i.e., triggered non-safety critical events). Study results 

document the prevalence of cellular telephone distractions and the risk associated with performing related tasks 

while driving. Findings include the odds of involvement in a safety-critical event differed as a function of 

performing different cell phone-related sub-tasks while driving. More specifically, talking/listening on a cell phone 

while driving was generally found not to impact significantly the odds of involvement in a safety-critical event (and 

was even found to decrease the odds significantly in some cases), while other cell phone sub-tasks (e.g., texting, 

dialing, reaching) were found to increase significantly the odds of involvement in a safety-critical event. Analyses 

examine the likelihood of commercial drivers to use their cell phone under a fleet cell phone policy and State cell 

phone law.

17. Key Words

buses, cell phone, cell phone policy, commercial motor vehicle, CMV, crash 

avoidance, distraction, driver behavior, naturalistic, odds ratio, population 

attributable risk, trucks

18. Distribution Statement

No restrictions

19. Security Classif. (of this report)

Unclassified

20. Security Classif. (of this page)

Unclassified

21. No. of Pages

79

22. Price

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

SI* (MODERN METRIC) FACTORS CONVERSION

Table of APPROXIMATE CONVERSIONS TO SI UNITS

Symbol When You Know Multiply By To Find Symbol

LENGTH

in Inches 25.4 millimeters mm

ft Feet 0.305 meters m

yd Yards 0.914 meters m

mi Miles 1.61 kilometers km

AREA

in2 square inches 645.2 square millimeters mm2

ft2 square feet 0.093 square meters m2

yd2 square yards 0.836 square meters m2

ac Acres 0.405 hectares ha

mi2 square miles 2.59 square kilometers km2

VOLUME Note: Volumes greater than 

1000 L shall be shown in m3

fl oz fluid ounces 29.57 milliliters mL

gal Gallons 3.785 liters L

ft3 cubic feet 0.028 cubic meters m3

yd3 cubic yards 0.765 cubic meters m3

MASS

oz Ounces 28.35 grams g

lb Pounds 0.454 kilograms kg

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

TEMPERATURE Temperature is in exact degrees

°F Fahrenheit 5 × (F-32) ÷ 9

or (F-32) ÷ 1.8

Celsius °C

ILLUMINATION

fc foot-candles 10.76 lux lx

fl foot-Lamberts 3.426 candela/m2 cd/m2

Force and Pressure or Stress

lbf Poundforce 4.45 newtons N

lbf/in2 poundforce per square inch 6.89 kilopascals kPa

Table of APPROXIMATE CONVERSIONS FROM SI UNITS

Symbol When You Know Multiply By To Find Symbol

LENGTH

mm Millimeters 0.039 inches in

m Meters 3.28 feet ft

m Meters 1.09 yards yd

km Kilometers 0.621 miles mi

AREA

mm2 square millimeters 0.0016 square inches in2

m2 square meters 10.764 square feet ft2

m2 square meters 1.195 square yards yd2

ha Hectares 2.47 acres ac

km2 square kilometers 0.386 square miles mi2

VOLUME

mL Milliliters 0.034 fluid ounces fl oz

L Liters 0.264 gallons gal

m3 cubic meters 35.314 cubic feet ft3

m3 cubic meters 1.307 cubic yards yd3

MASS

g Grams 0.035 ounces oz

kg Kilograms 2.202 pounds lb

Mg (or “t”) megagrams (or “metric ton”) 1.103 short tons (2000 lb) T

TEMPERATURE Temperature is in exact degrees

°C Celsius 1.8C + 32 Fahrenheit °F

ILLUMINATION

lx Lux 0.0929 foot-candles fc

cd/m2 candela/m2 0.2919 foot-Lamberts fl

Force & Pressure or Stress

N Newtons 0.225 poundforce lbf

kPa Kilopascals 0.145 poundforce per square inch lbf/in2

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

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

TABLE OF CONTENTS

LIST OF ACRONYMS ..............................................................................................................VI

EXECUTIVE SUMMARY .......................................................................................................VII

1. BACKGROUND AND SIGNIFICANCE ..................................................................... XVI

1.1 INTRODUCTION .........................................................................................................1

1.1.1 Defining Driver Distraction .............................................................................. 2

1.1.2 Prior Distraction Studies Involving Trucks and Buses..................................... 3

1.1.3 Research Project Summary............................................................................... 6

2. METHODS............................................................................................................................9

2.1 CHARACTERIZATION OF THE DATA SET............................................................9

2.1.1 Data Collection Process.................................................................................... 9

2.1.2 Summary of Data Sets .................................................................................... 12

2.2 RESEARCH QUESTIONS AND ANALYSES..........................................................17

2.2.1 Analysis 1: Descriptive Analyses ................................................................... 17

2.2.2 Analysis 2: Odds Ratios of Tertiary Tasks..................................................... 17

2.2.3 Analysis 4: Odds Ratios of Fleet Policy and State Law ................................. 21

3. RESULTS ............................................................................................................................23

3.1 ANALYSIS 1: DESCRIPTIVE ANALYSES .............................................................23

3.1.1 Tertiary Tasks in Data Set A........................................................................... 23

3.1.2 Tertiary Tasks in Data Set B........................................................................... 27

3.1.3 Summary......................................................................................................... 39

3.2 ANALYSIS 2: ODDS RATIOS FOR TERTIARY TASKS .......................................40

3.2.1 Summary......................................................................................................... 45

3.3 ANALYSIS 3: POPULATION ATTRIBUTABLE RISK ..........................................45

3.4 ANALYSIS 4: ODDS RATIOS FOR FLEET POLICY AND STATE LAW............46

4. DISCUSSION......................................................................................................................47

4.1 SUMMARY.................................................................................................................47

4.2 CONCLUSIONS..........................................................................................................47

4.3 POSSIBLE LIMITATIONS ........................................................................................55

4.4 FUTURE RESEARCH................................................................................................57

REFERENCES.............................................................................................................................59
iv

LIST OF TABLES

Table 1. Frequency and percentage of tertiary tasks across each severity level in data set A....... ix

Table 2. Frequency and percentage of tertiary tasks across each severity level in data set B....... xi

Table 3. Odds ratios for each tertiary task in data set B. .............................................................. xii

Table 4. Odds ratios for cell phone use while driving in each cell phone policy variable in the 

365 day data set (safety-critical events). ...................................................................... xiii

Table 5. Odds ratios comparison for selected tertiary tasks and Olson et al. (2009)................... xiv

Table 6. Frequency and percentage of any tertiary task in “all” and “vehicle 1 at-fault” 

events................................................................................................................................4

Table 7. Odds ratios and 95 percent confidence intervals to assess likelihood of a safetycritical event while engaging in tertiary tasks..................................................................5

Table 8. Odds Ratios and 95 percent confidence intervals for all events to assess likelihood of 

a safety-critical event while eyes off forward roadway. ..................................................6

Table 9. Operational definition of each tertiary task coded by DRAs...........................................12

Table 10. Summary of data set B...................................................................................................14

Table 11. Summary of data set A...................................................................................................16

Table 12. Example of a 2 × 2 contingency table used to calculate odds ratio...............................18

Table 13. Example of the dialing a cell phone odds ratio calculation. ..........................................18

Table 14. Population attributable risk—confidence limits example..............................................21

Table 15. Example of 2 × 2 contingency table used to calculate the cell phone policy 

odds ratio. .......................................................................................................................22

Table 16. Frequency and percentage of tertiary tasks across each severity level in data set A.....23

Table 17. Frequency and percentage of tertiary tasks across each severity level in data set A 

(no fleet cell phone policy only). ...................................................................................24

Table 18. Frequency and percentage of tertiary tasks across each severity level in data set A 

(fleet cell phone policy only). ........................................................................................25

Table 19. Frequency and percentage of tertiary tasks in 3 axle or more trucks across each 

severity level in data set A. ............................................................................................25

Table 20. Frequency and percentage of tertiary tasks in buses across each severity level in 

data set A........................................................................................................................26

Table 21. Frequency and percentage of tertiary tasks in tractor trailers/tankers across each 

severity level in data set A. ............................................................................................26

Table 22. Frequency and percentage of tertiary tasks across each severity level in data set B.....28

Table 23. Frequency and percentage of tertiary tasks across each severity level in data set B 

(no fleet cell phone policy).............................................................................................30

Table 24. Frequency and percentage of tertiary tasks across each severity level in data set B 

(fleet cell phone policy)..................................................................................................32

Table 25. Frequency and percentage of tertiary tasks in 3 axle or more trucks across each 

severity level in data set B..............................................................................................34

Table 26. Frequency and percentage of tertiary tasks in buses across each severity level in 

data set B. .......................................................................................................................36
v

Table 27. Frequency and percentage of tertiary tasks in tractor trailers/tankers across each 

severity level in data set B..............................................................................................38

Table 28. Odds ratios for each tertiary task in data set B. .............................................................41

Table 29. Odds ratios for cell phone sub-tasks across each fleet cell phone policy in 

data set B. .......................................................................................................................42

Table 30. Odds ratios for each tertiary task in 3 axle or more trucks in data set B. ......................43

Table 31. Odds ratios for each tertiary task in buses in data set B. ...............................................44

Table 32. Odds ratios for each tertiary task in tractor trailers/tankers in data set B......................44

Table 33. PAR and 95 percent confidence intervals for each tertiary task in data set B for all 

safety-critical events and each fleet cell phone policy...................................................46

Table 34. Odds ratios for cell phone use while driving in each cell phone policy in the 365 

day data set (safety-critical events). ...............................................................................46

Table 35. Odds ratios comparison for selected tertiary tasks in this study and 

Olson et al. (2009)..........................................................................................................49

Table 36. Odds ratios and values for mean duration of eyes off the forward roadway for each 

tertiary task (numeric values from Figure 5)..................................................................53

LIST OF FIGURES

Figure 1. Photo. Event recorder (left) and typical installation of event recorder behind the 

vehicle’s rearview mirror (right). .....................................................................................9

Figure 2. Photo. Front camera view from the event recorder (left) and driver’s face view 

(right)..............................................................................................................................10

Figure 3. Flowchart. The data capture and review process............................................................10

Figure 4. Photos. Five screenviews captured by event recorders installed on test vehicle............40

Figure 5. Chart. Plot of the odds ratios and values for mean duration of eyes off the forward 

roadway for each tertiary task. .......................................................................................52
vi

LIST OF ACRONYMS

Acronym Definition

CMV commercial motor vehicle

DRA driver risk analyst

FMCSA Federal Motor Carrier Safety Administration

g gravity force

GPS Global Positioning System

IRB Institutional Review Board

LTCCS Large Truck Crash Causation Study

LCL lower confidence limit

NHTSA National Highway Traffic Safety Administration

NSC National Safety Council

OBSM onboard safety monitoring

SMB small to medium business

UCL upper confidence limit
vii

EXECUTIVE SUMMARY

Research on distracted driving has increased over the past several years, due, in part, to the 

increasing number of reported crashes involving cellular telephone (cell phone) use while 

driving, recent literature reviews that highlighted some key findings (e.g., Kircher, 2007; 

National Safety Council [NSC], 2010; Regan, Lee, & Young, 2009), and a study that described 

the magnitude of the problem in light (passenger) vehicles (Klauer et al., 2006). Although this 

research has shown the adverse safety implications of distracted driving, including cell phone use 

while driving, it has not focused on commercial motor vehicle (CMV) drivers, per se. As such, 

there is a knowledge gap regarding the operation of trucks (three-axle or more trucks and tractor 

trailers/tankers) and buses (including transit and motor coaches, but referred to as “buses” 

hereafter) in regards to distracted driving and, more specifically, cell phone use and texting while 

driving. Please note these vehicle types were supplied by the technology vendor, DriveCam, and 

may or may not conform to Federal definitions for these vehicles types.

SUMMARY OF RESEARCH PROJECT

The aim of this project was to use an existing data set to document the prevalence of distractions 

while driving a commercial motor vehicle (i.e., truck or bus). New in-vehicle technologies 

provide objective measures of driver behavior. These technologies collect data continuously on a 

wide variety of driving behaviors previously unavailable to fleet safety managers. Some of these 

systems use in-vehicle video technology to record driver behavior. Fleet safety managers may 

use these video recordings to provide feedback to their drivers on safe and at-risk driving 

behaviors. As such, existing naturalistic data sets can provide a wealth of data, including driver 

behavior during crashes, near-crashes, and crash-relevant conflicts, involving thousands of 

CMVs. DriveCam is a vendor of onboard safety monitoring (OBSM) systems for CMV fleets 

aimed at reducing risky driving behaviors using in-vehicle video technology. Video and 

kinematic data snippets, recorded with a kinematic trigger (e.g., hard brake at a pre-set 

threshold), archived by this vendor were made available for analysis in this study. Note that the 

authors of this study did not receive any video recordings from the vendor, but rather kinematic 

data and driver behavior data that did not identify the driver.

The data set did not include continuous data; it only included recorded events that met or 

exceeded a pre-set kinematic threshold (e.g., greater than or equal to │0.5 g│). These recorded 

events included safety-critical events (e.g., hard braking in response to another vehicle) and nonsafety triggered events (e.g., vehicle traveled over train tracks, which exceeded the kinematic 

threshold). One of the advantages of continuous data collection is that randomly selected 

baseline epochs (or control periods of uneventful driving) can be parsed from the continuous data 

set and be used to assess the risk involved in performing certain behaviors while driving (this 

approach was used by Olson, Hanowski, Hickman, & Bocanegra, 2009, and Klauer et al., 2006). 

While the non-safety triggered events (hereafter referred to as baseline events) collected by the 

technology vendor were not randomly selected, they may be used to evaluate the risk in 

performing various tertiary tasks while driving. There is bias associated with using these nonsafety triggered events (i.e., baseline events), as described in more detail below; however, 

Dingus et al. (2005) found that randomly selected baselines in the 100-Car Study had a similar 

kinematic profile as crash-relevant conflicts (the lowest severity safety-critical event). Note that 
viii

these baseline events were only available in one set of data, as the vendor did not begin to record 

distractions in these baseline events until later in the collection (data set B).

SUMMARY OF THE DATA SETS

The technology vendor sent to the authors two data sets collected over a consecutive 1-year 

period, identified as data set A collected during the first 273 days (September 6, 2008 to June 5, 

2009), and data set B collected during the last 92 days (June 6, 2009–September 5, 2009). Data 

set A was derived from 207 different truck and bus fleets comprising a total of 13,431 vehicles 

deployed by small to medium business (SMB) (32.2 percent), transit (30.4 percent), distribution 

(25.0 percent), energy (8.5 percent), municipalities (1.5 percent), waste (1.2 percent), 

utilities/telecommunication (0.9 percent), and construction (0.3 percent) fleets. A total of 1,336 

crashes, 15,864 near-crashes, and 173,591 crash-relevant conflicts were captured in data set A. 

Section 1 includes a detailed description of the two data sets.

Data set B included safety-critical events and baseline events from 183 different truck and bus 

fleets comprising a total of 13,306 trucks and buses deployed by distribution (33.7 percent), 

small to medium businesses (SMB; 31.3 percent), transit (27.5 percent), energy (3.7 percent), 

municipalities (2.0 percent), waste (1 percent), utilities/telecommunication (0.5 percent), and 

construction (0.3 percent) fleets. Note that the vendor released very limited demographic 

information to protect the fleets’ anonymity. A total of 1,085 crashes, 8,375 near-crashes, 30,661 

crash-relevant conflicts, and 211,171 baseline events were captured in data set B. Note that the 

vendor considers its operational definitions of crashes, near-crashes, and crash-relevant conflicts 

to be proprietary information. However, Olson et al. (2009) define crashes as any contact with an 

object, either moving or fixed, at any speed in which kinetic energy was measurably transferred 

or dissipated; near crashes are defined as any circumstance requiring a rapid, evasive maneuver 

by the subject vehicle, any other vehicle, pedestrian, cyclist, or animal, to avoid a crash, and 

crash-relevant conflicts are 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” to avoid a crash.

RESULTS

More detailed results, including additional analyses, are presented in section 3.

Descriptive Analyses

The analyses report the frequency and percentage of tertiary tasks in commercial trucks and 

buses in each data set. Ablassmeier, Poitschke, Wallhoff, Bengler, and Rigoll (2007) categorized 

driving tasks as primary, secondary, and tertiary. Primary tasks are those tasks necessary to 

operate the vehicle (e.g., looking at the forward roadway, steering, etc.), while secondary tasks 

are related to the driving task, but are not necessary to operate the vehicle (e.g., use turn signals, 

check speedometer, check mirrors, etc.). Tertiary tasks are not related to driving (e.g., eating, cell 

phone use, etc.). These descriptive analyses show the proportion of specific tertiary tasks 

performed by drivers, as well as the prevalence of tertiary tasks across safety-critical events and 
ix

baseline events. Table 1 shows the frequency and percentage of tertiary tasks across each 

severity level in data set A. Note that safety-critical events were defined as crashes, near-crashes, 

and crash-relevant events. However, the vendor did not perform video reduction on the 1,336 

crashes in data set A; thus, crashes are not shown in data set A tables. Also note that baseline 

events are not included in data set A as the vendor did not begin coding tertiary tasks in the 

baseline events until June 2009. More than one tertiary task could be coded during a safetycritical event in data set A; thus, the column totals in data set A data tables exceed 100 percent. 

Tertiary Tasks in Data Set A

As shown in Table 1 most of the safety-critical events did not involve a tertiary task (92.4 

percent). Of the tertiary tasks present during the safety-critical events, the most prevalent were 

other distraction (2.8 percent) and cell phone usage (3.9 percent).

Table 1. Frequency and percentage of tertiary tasks across each severity level in data set A.

Tertiary Tasks

Number 

(and percent)

of Near-Crashes

Number 

(and percent)

of Crash-Relevant 

Conflicts 

Total Number 

(and percent)

of Safety-Critical 

Events (near 

crashes + crashrelevant conflicts)

Cell Phone Usage 246

(1.55%)

7,278

(4.19%)

7,524

(3.97%)

Consuming Food/Drink 43

(0.27%)

1,367

(0.79%)

1,410

(0.74%)

Other Distraction 723

(4.56%)

4,635

(2.67%)

5,358

(2.83%)

Other Communication Device 0

(0.00%)

0

(0.00%)

0

(0.00%)

Passenger Distraction 41

(0.26%)

104

(0.06%)

145

(0.08%)

None 14,817

(93.40%)

160,236

(92.31%)

175,053

(92.40%)
x

Tertiary Tasks in Data Set B

Table 2 shows the frequency and percentage of tertiary tasks across each severity level in data set

B. More than one tertiary task could be coded during a baseline or safety-critical event in data set

B; thus, the column totals in data set B tables may exceed 100 percent. As shown in Table 2,

most of the baseline events and safety-critical events did not involve a tertiary task (97.4 percent 

and 94.0 percent, respectively). However, of the tertiary tasks recorded during crashes in data set

B, all were coded with a cell phone sub-task. More specifically, the crashes involved the driver 

using a cell phone to text/e-mail/access the Internet while driving (1.02 percent), followed by 

talking/listening on a hand-held cell phone (0.46 percent), reaching for a cell phone (0.28 

percent), and dialing a cell phone (0.18 percent). The most prevalent tertiary tasks during 

baseline events were talking/listening on a hand-held cell phone (1.1 percent), talking/listening 

on a hands-free cell phone (0.8 percent), and consuming food/drink (0.6 percent). Of the tertiary 

tasks present during the safety-critical events, the most prevalent were other distraction (3.0 

percent), talking/listening on a hand-held cell phone (1.0 percent), consuming food/drink (0.7 

percent), and talking/listening on a hands-free cell phone (0.5 percent).
xi

Table 2. Frequency and percentage of tertiary tasks across each severity level in data set B.

Tertiary Tasks

Number 

(and percent)

of Baseline 

Events 

Number 

(and percent)

of Crashes 

Number 

(and percent)

of Near-Crashes

Number 

(and percent)

of Crash-Relevant 

Conflicts 

Number (and 

percent) of SafetyCritical Events 

(crashes + nearcrashes +crashrelevant conflicts)

Dialing Cell Phone 256

(0.12%)

2

(0.18%)

3

(0.04%)

160

(0.52%)

165

(0.41%)

Consuming Food/Drink 1,320

(0.63%)

0

(0.00%)

8

(0.10%)

260

(0.85%)

268

(0.67%)

Other Distraction 13

(0.01%)

0

(0.00%)

32

(0.38%)

1,188

(3.87%)

1,220

(3.04%)

Passenger 0

(0.00%)

0

(0.00%)

0

(0.00%)

30

(0.10%)

30

(0.07%)

Reaching for Headset/Earpiece 168

(0.08%)

0

(0.00%)

0

(0.00%)

104

(0.34%)

104

(0.26%)

Reaching for Cell Phone 178

(0.08%)

3

(0.28%)

3

(0.04%)

116

(0.38%)

122

(0.30%)

Talking/Listening on Hand-Held 

Cell Phone

2,266

(1.07%)

5

(0.46%)

8

(0.10%)

359

(1.17%)

372

(0.93%)

Talking/Listening on Hands-Free 

Cell Phone

1,626

(0.77%)

0

(0.00%)

8

(0.10%)

186

(0.61%)

194

(0.48%)

Texting/E-mailing/Accessing the 

Internet on Phone

3

(0.00%)

11

(1.02%)

2

(0.02%)

77

(0.25%)

90

(0.22%)

Other Communication Device 0

(0.00%)

0

(0.00%)

0

(0.00%)

0

(0.00%)

0

(0.00%)

None 205,582

(97.35%)

1,064

(98.06%)

8,314

(99.27%)

28,330

(92.40%)

37,708

(93.99%)
xii

Odds Ratios for Tertiary Tasks

Table 3 shows the odds ratios for each tertiary task in data set B for all safety-critical events. 

Odds ratios greater than 1.0 where the range from the lower confidence limits (LCL) to the upper 

confidence limits (UCL) does not include 1.0 indicate the task significantly increases the odds of 

involvement in a safety-critical event. Conversely, odds ratios less than 1.0 where the range from 

the LCL to the UCL does not include 1.0 indicate that the task significantly decreases the odds of 

involvement in a safety-critical event.

Note that odds ratios were only calculated on tertiary tasks in data set B as baseline events were 

not available in data set A. As shown in Table 3, many of the tertiary tasks in data set B had a 

significant odds ratio. The odds ratios for other distraction and texting/e-mailing/accessing the 

Internet were very high. An odds ratio is a measure of association (not unlike a correlation),

which can be used under the correct circumstances as an estimate of the rate ratio (Guo & 

Hankey, 2009). In this situation, the odds ratios for other distraction and texting/

e-mailing/accessing the Internet indicate a strong relationship between these tertiary tasks and 

safety-critical events. As shown by the 95 percent confidence interval for these tertiary tasks, the 

error associated with these odds ratio estimates was extremely high; thus, it is difficult to report 

the odds ratio in any meaningful sense other than to report there was a very strong relationship 

between other distraction and texting/e-mailing/accessing the Internet and involvement in a 

safety-critical event.

Table 3. Odds ratios for each tertiary task in data set B.

Tertiary Task Odds Ratio

Lower 

Confidence 

Limit

Upper 

Confidence 

Limit

Frequency 

of Safety 

Critical 

Events

Frequency 

of Baseline 

Events

Any Cell Phone Usage 1.14* 1.06 1.23 895 4,262

Dialing Cell Phone 3.51* 2.89 4.27 165 256

Talking/Listening

Hands Free Cell 

Phone

0.65* 0.56 0.76 194 1,626

Talking/Listening Hand 

Held Cell Phone 0.89 0.80 1.00 372 2,266

Reaching for 

Headset/Earpiece 3.38* 2.64 4.31 104 168

Reaching for Cell 

Phone 3.74* 2.97 4.71 122 178

Texting/E-mailing/

Accessing the Internet 163.59* 51.77 516.73 90 3

Consuming Food/Drink 1.11 0.97 1.26 268 1,320

Other Distraction 511.64* 296.20 883.79 1,220 13

Passenger Distraction – – – 30 0

Note: Asterisk indicates a significant odds ratio. 
xiii

Odds Ratios for Fleet Cell Phone Policy and State Cell Phone Law

Odds ratios were calculated to approximate the effectiveness of a fleet cell phone policy or a 

State cell phone law prohibiting cell phone use while driving. These odds ratio did not look at 

safety-critical events, but rather evaluated the odds of cell phone usage given a fleet cell phone 

policy or State cell phone law versus the odds of cell phone usage given no fleet cell phone 

policy or State cell phone law. Table 4 shows the odds ratios for cell phone use while driving in 

each cell phone policy variable in the entire 365 days (i.e., data set A + data set B) for all safetycritical events and baseline events. As noted, crashes in data set A were not included in this 

analysis as the vendor did not re-review these crashes. 

As shown in Table 4, for truck and bus drivers the odds of using a cell phone while driving under 

a fleet cell phone policy were .83 times less, compared to no fleet cell phone policy. However, 

the State cell phone law did not significantly impact drivers’ likelihood in using their cell phone 

while driving compared to a State that did not have a law prohibiting cell phone use 

(odds ratio = 0.97). 

Table 4. Odds ratios for cell phone use while driving in each cell phone policy variable in the 365 

day data set (safety-critical events).

Cell Phone Policy Odds Ratio

Lower 

Confidence 

Limit

Upper 

Confidence 

Limit

Frequency of 

Cell Phone 

Use with 

Policy/Law

Frequency of 

No Cell 

Phone Use 

with 

Policy/Law

Fleet Cell Phone 

Policy 0.83* 0.78 0.87 8,787 1,897

State Cell Phone Law 0.97 0.94 1.01 4,526 2,987

Note: Asterisk indicates a significant odds ratio.

DISCUSSION

Naturalistic driving studies record drivers (through video and kinematic sensors) in actual 

driving situations in order to study driver behavior in driving conditions in the presence of realworld daily pressures. Naturalistic driving studies are difficult to conduct and only a few have 

analyzed driver distraction (Klauer et al., 2006; Olson et al., 2009; Sayer, Devonshire, & 

Flanagan, 2007). The results of this study are consistent with these other naturalistic studies. 

Several interesting findings invite further discussion. The results in this study were similar to the 

results found by Olson et al. (2009) regarding safety-critical event risk and performing a tertiary 

task while driving. Table 5 compares the odds ratios for selected tertiary tasks in this study and 

those found in Olson et al. Both studies found that talking/listening on a cell phone (hands-free 

or hand-held) did not increase the odds of involvement in a safety-critical event. More 

specifically, both found that talking/listening on a hands-free phone significantly decreased the 

odds of involvement in a safety-critical event (0.65 and 0.44, respectively) and talking/listening 

on a hand-held phone had no impact on the odds of involvement in a safety-critical event (0.90 

and 1.04, respectively). As shown in Table 5, this study also found that reaching for a 
xiv

headset/earpiece or a cell phone significantly increased the odds of involvement in a safetycritical event by 3.4 and 3.7 times, respectively. While Olson et al. did not classify these specific 

reaching tasks, the authors found that reaching for other electronic devices (which included 

headsets/earpieces) significantly increased the odds of involvement in a safety-critical event by 

6.7 times. The tertiary task, consuming food/drink, was also found to have no impact on the odds 

of involvement in a safety-critical event in this study and Olson et al.

Table 5. Odds ratios comparison for selected tertiary tasks and Olson et al. (2009).

Tertiary Task Odds Ratios Across all 

Vehicles in this Study

Odds Ratios in 

Olson et al. (2009)

Dialing Cell Phone 3.5* 5.93*

Talking/Listening Hands-Free Cell Phone 0.65* 0.44*

Talking/Listening Hand-Held Cell Phone 0.90 1.04

Reaching for Headset/Earpiece 3.4* 6.72*

Reaching for Cell Phone 3.7* Included in dialing 

a cell phone

Texting/E-mailing/Accessing the Internet 163.6* 23.24*

Consuming Food/Drink 1.1 1.01

Note: Asterisk indicates significant odds ratios.

The results clearly indicate that using a cell phone to text, e-mail, or access the Internet while 

driving is in a category of risk all by itself. Although the odds ratio for this tertiary task was very 

large in both this study and in Olson et al. (2009), it was also associated with a large amount or 

error (as shown by the 95 percent confidence interval). Very few instances of this behavior were 

observed during safety-critical events in this study and Olson et al. (90 and 23, respectively) and 

even fewer during control events (3 and 3, respectively). An odds ratio is a measure of 

association (not unlike a correlation) which can be used under the correct circumstances as an 

estimate of relative risk. In this situation, the odds ratio for texting/e-mailing/accessing the 

Internet indicates a strong relationship between this tertiary task and involvement in a safetycritical event. It is difficult to report the odds ratio in any meaningful sense other than to report 

there is a very strong relationship between texting/e-mailing/accessing the Internet while driving 

and involvement in a safety-critical event. The data suggests that truck and bus drivers who use 

their cell phone to text, e-mail, or access the Internet are very likely be involved in a safetycritical event. 

The significant decrease in the odds of involvement in a safety-critical event and/or nonsignificant results regarding the odds of talking/listening on a cell phone while driving have 

sparked controversy in the academic community and other traffic safety organizations. There is 

an extensive amount of published simulator and closed test track studies that have shown 

performance decrements while talking/listening on a cell phone (both hand-held and hands-free) 

(see National Safety Council, NCS, 2010 for a review) and many of safety organizations have 

advocated bans on cell phone use of any kind while driving. Simulator and closed test track 

studies have shown decrements in driver’s reaction time, lane keeping, and scanning ability; 

however, the question is not whether these performance decrements take place while 

talking/listening on a cell phone while driving, but do these performance decrements increase 

crash risk in the real world? When considering cell phone use as a dichotomous variable 
xv

(yes/no), this study found that cell phone use increased risk by 1.14 (a statistically significant 

finding). At first glance, this finding seemingly supports the simulator results. However, once the 

layers are peeled back and specific cell phone sub-tasks are individually considered, the study 

shows that the risk is not with the conversation (talking/listening sub-task), but the sub-tasks that 

require the driver to take his/her eyes off the road. 

It should not come as a surprise that commercial truck and bus drivers were far less likely to use 

their cell phone while driving under a fleet cell phone policy as the OBSM system allowed these 

fleets to monitor their drivers. This makes intuitive sense as the OBSM system afforded safety 

managers and direct supervisors an opportunity to monitor drivers and implement consequences 

that would directly impact the driver (e.g., punishment for policy violation). 

Some have argued that naturalistic studies are inconclusive as their data sets have relatively few 

crashes (as crashes are a rare event in the real-world) and risk is calculated using non-crash 

events (such as near-crashes and safety-critical events) (NSC, 2010). A key feature of this study 

was that the data set included more than 1,000 crashes; it is noteworthy that these results are 

consistent with previous naturalistic driving studies that contained far fewer crashes. 

Possible Limitations

There are six possible caveats or limitations in interpreting the results of this study. First, the data 

set involved an active safety intervention; thus, the data sets may be skewed from normative 

driving data as the safety managers have attempted to directly alter these behaviors. This 

translates into the results being, potentially, a “best case” scenario. Second, the fact that the truck 

and bus fleets in this data set purchased an OBSM system reflects a group of safety conscious 

truck and bus fleets; thus, the prevalence of tertiary tasks may, in fact, be more pronounced in the 

larger population. Third, the lack of continuous data collection or randomly collected video 

segments means there were no “true” baseline or control data. Baseline events were used in this 

study as a proxy measure of control data; however, the similarity in the odds ratios with Olson et 

al. (2009), which did used true baseline events, suggests this likely had little or no influence on 

the findings. Fourth, data set B was collected during a time frame when intense media attention

focused on the dangers of distracted driving. This intense media attention regarding distracted 

driving may have influenced safety managers and/or drivers behavior. However, again, this 

would translate into potentially less risky behavior being recorded in this study which would 

imply higher tertiary task involvement in the larger population. Fifth, driver exposure was not 

controlled in this study; thus, extremely unsafe drivers may have contributed far more safetycritical events than baseline events. However, as this study included at least 13,000 drivers (from 

over 13,000 trucks and buses), the affect of any outliers was minimized. Lastly, the length of the 

video window during safety-critical events and baseline epochs was longer than in other 

naturalistic studies (8 seconds before the trigger and 4 seconds after the trigger). It is possible 

that some tertiary tasks coded as an associative factor during the safety-critical events had no 

influence on safety-critical event occurrence (if the truck or bus driver engaged in the tertiary 

task at the very beginning of the video window). However, and as noted previously, the 

similarity of the odds ratios with the Olson et al. study that used a 6-second window suggests this 

likely had minimal influence on the results.
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1

1. BACKGROUND AND SIGNIFICANCE

1.1 INTRODUCTION

The most recent statistics show that the number of crashes and fatalities on our nation’s 

roadways involving large trucks is decreasing. In 2006, there were 42,642 traffic fatalities and 

2,575,000 injuries in the United States. Of these, 4,995 were fatal crashes and 106,000 were 

injury-only crashes resultant from large-truck collisions. These data show a reduction in large 

truck fatal crashes (4,229) and injury-only crashes (90,000) involving large-truck crashes in 2008 

(National Highway Traffic Safety Administration [NHTSA], 2009). These reductions are, in 

part, due to the success of the Federal Motor Carrier Safety Administration’s (FMCSA) mission 

to reduce the frequency and severity of crashes on our nation’s highways. Although these 

reductions in fatal and injury crashes are encouraging, more work is needed to further reduce 

large-truck traffic crashes and their associated injuries and fatalities. 

Research on distracted driving has increased over the past several years, due, in part, to the 

increasing number of reported crashes involving cell phone use while driving, and recent 

literature reviews that have highlighted some of the key findings (e.g., Kircher, 2007; National 

Safety Council [NSC], 2010; Regan, Lee, & Young, 2009) and a key naturalistic study that 

described the magnitude of the problem in light (passenger) vehicles (Klauer et al., 2006). 

Although this research has shown the adverse safety implications of distracted driving, including 

cell phone use while driving, few of these studies have focused on commercial motor vehicle 

(CMV) drivers, per se. As such, there is a gap in our knowledge that focuses on truck and bus 

(including transit and motor coaches, but referred to as “bus” hereafter) drivers in regards to 

distracted driving and, more specifically, cell phone use and texting while driving. 

In 2008 there were numerous high profile media reports of truck and bus crashes involving CMV 

drivers being distracted by cell phones and texting. For example, on December 11, 2008 it was 

reported in Commerce City, Colorado that a truck hauling radioactive material rolled over on the 

interstate; “The state patrol said that the truck driver was distracted by his cell phone when the 

crash happened” (Web site: http://cbs4denver.com/local/semi.crash.radioactive.2.884299.html). 

Similarly, it was also reported that a high profile crash in Ocala, Florida in September 2008 

occurred when a tractor-trailer driver, using his cell phone, crashed into the rear of a school bus 

and killed a middle school student 

(http://www.ocala.com/article/20080928/NEWS/809280295/1402/NEWS?Title=Bus_crash_rene

ws_debate_on_drivers_using_cell_phones). There have also been recently reported crashes 

involving cell phones and bus drivers, including school bus drivers. 

Distracted driving in trucks and buses has become a significant safety issue, in part, because of 

these high profile crashes and a recently published report, based on an FMCSA-funded study, on 

the dangers of driving a truck while distracted (Olson, Hanowski, Hickman, & Bocanegra, 2009). 

These recent media reports and published research findings on the prevalence and dangers of 

distracted driving resulted in U.S. Transportation Secretary Ray LaHood announcing a summit to 

address the dangers of text-messaging and other driver distractions behind the wheel. On 

September 30, 2009 and October 1, 2009, senior transportation officials, elected officials, safety 
2

advocates, law enforcement representatives, and academics convened in Washington, D.C. to 

discuss ideas about how to combat distracted driving. 

These high profile truck and bus crashes (and other similar crashes) and numerous research 

studies have illustrated the dangers of cell phone use while driving (e.g., Beede & Kass, 2006; 

Klauer et al., 2006; Olson et al., 2009; Patten et al., 2004; Strayer, Drews, & Johnson, 2003) 

have led many States to prohibit cell phone use while driving. As of April 2010, six States had 

prohibited hand-held cell phone use while driving: California, Connecticut, New Jersey, New 

York, Oregon, and Washington (note that Washington, D.C. and the U.S. Virgin Islands also 

prohibit drivers from using a hand-held cell phone while driving). All these hand-held cell phone 

laws are primary enforcement (i.e., a police officer may cite a driver for using a hand-held cell 

phone without other traffic offenses taking place). While no State completely bans all types of 

cell phone use (i.e., hand-held, hands-free, text messaging), some States prohibit cell phone use 

by certain drivers, such as novice drivers (24 States and Washington, D.C.) and school bus 

drivers when passengers are present (17 States and Washington, D.C.). As this report was 

drafted, 23 States, Washington, D.C., and Guam ban text messaging while driving for all drivers, 

9 States prohibit text messaging for novice drivers, and 1 State restricts text messaging for 

school bus drivers (Governors Highway Safety Association, 2010). 

Given the high-profile nature of this topic, many States and Federal agencies are deliberating 

modifications to existing cell phone laws and/or creating new laws. Readers are encouraged to 

visit www.distraction.gov for up-to-date information on legislative action regarding this issue 

and information on distracted driving.

While the original intent of this project was to assess the prevalence of cell phone use in 

commercial trucks and buses, as indicated in the original title Cell Phone Distraction in 

Commercial Trucks and Buses: Assessing Prevalence in Conjunction with Crashes and NearCrashes, feedback from FMCSA personnel during the project’s kickoff meeting revised the 

scope of this project to include as many driver distractions as possible. Thus, the study was not 

limited to cell phone use while driving and includes other distractions drivers engage in while 

driving (e.g., food/drink, passenger, other electronic device). 

1.1.1 Defining Driver Distraction

There are several “types” of driver distraction. Driver distraction can be grouped into one of four 

separate categories: visual, auditory, biomechanical, and cognitive (Ranney et al., 2000). 

However, the literature is inconsistent in the operational definition of “driver distraction.” Smiley 

(2005, p. 1) defined it as “misallocated attention,” while Ranney et al. (2000, p. 1) indicated that 

“driver distraction may be characterized as any activity that takes a driver’s attention away from 

the task of driving.” Pettitt, Burnett, and Stevens (2005) indicated that a more comprehensive 

definition of distraction included four components: 

• Impact—when a driver is delayed in the recognition of information necessary to safely 

maintain the lateral and longitudinal control of the vehicle.

• Agent—due to some event, activity, object or person, within or outside the vehicle.
3

• Mechanism—that compels or tends to induce the driver’s shifting attention away from 

fundamental driving tasks.

• Type—by compromising the driver’s auditory, biomechanical, cognitive or visual 

faculties, or combinations thereof (Pettitt, Burnett, & Stevens, 2005; p. 11).

Hanowski, Perez, and Dingus (2005) used the concepts in Pettitt, Burnett, and Stevens (2005) to 

develop a definition of distracted driving that could be implemented in the analysis of naturalistic 

driving data. More specifically, the inattentive behaviors observed in naturalistic driving reflect 

the Agents and underlying Mechanisms that can distract and lead to a safety-critical event (or 

Impact). This was the definition followed by Olson et al. (2009) and will be used to define driver 

distraction in this study 

1.1.2 Prior Distraction Studies Involving Trucks and Buses

There is a paucity of published research on the actual risk of distracted driving in commercial 

trucks and buses. There are only two published studies in this area and both of these studies were 

conducted with commercial trucks. For example, the Large Truck Crash Causation Study 

(LTCCS), while not directly a study on distracted driving, assessed the causal factors as well as 

associated factors in fatal crashes involving large trucks (FMCSA, 2005). Considered the most 

comprehensive safety database for crashes involving large trucks, the LTCCS collected data on 

truck crashes at 24 sites across 17 States from 2001 through 2003. Investigators traveled to crash 

sites to collect crash scene data, conducted thorough interviews with drivers about their 

conditions before the crash, and inspected the trucks. 

The results from the LTCCS indicated that 9 percent of the fatal truck crashes studied were 

attributed to driver inattention, 8 percent were attributed to an external distraction (i.e., the driver 

was looking at something outside of his/her truck), and 2 percent were attributed to an internal 

distraction. It is important to note that these driver errors were determined to be the causal factor 

in the crash (i.e., had they been absent, the crash could have been avoided), but if these driver 

errors had also been considered as an associated factor, they would likely result in higher 

percentages.

Olson et al. (2009) authored a report of driver distraction in CMV operations using data from 

two large-scale CMV naturalistic truck driving studies (Hanowski et al., 2008; Blanco et al., in 

press). These data sets represented 203 CMV drivers in seven trucking fleets across 16 fleet 

locations. In terms of data, the data set used included approximately 3 million miles of 

continuously collected kinematic and video data, and represents the most comprehensive 

naturalistic CMV driving set in the world.

The Olson et al. (2009) data set included 4,452 safety-critical events: 21 crashes, 197 nearcrashes, 3,019 crash-relevant conflicts, and 1,215 unintentional lane deviations, in addition to 

19,888 baseline epochs (non-events). Table 6 displays the percentage of any tertiary tasks that 

were present in all safety-critical events and all safety-critical events where the Vehicle 1 driver 

(i.e., the participant driver) was judged to be at fault in the safety-critical event. As can be seen in

Table 6, tertiary tasks were present in 46.2 percent to 77.5 percent of the safety-critical events 

(much higher than found in the LTCCS). Note that Ablassmeier et al. (2007) categorized driving 

tasks as primary, secondary, and tertiary. Primary tasks are those tasks necessary to operate the 
4

vehicle (e.g., looking at the forward roadway, steering, etc.), while secondary tasks are related to 

the driving task, but are not necessary to operate the vehicle (e.g., turn-signal use, check 

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

cell phone use, etc.).

Table 6. Frequency and percentage of any tertiary task in “all” and “vehicle 1 at-fault” events.

Event Type All SafetyCritical Events

Frequency 

(and Percent)

of All Safety 

Critical Events

All Vehicle 1 

At-Fault Events

Frequency 

(and Percent) of 

All Vehicle 1 

At-Fault Events

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

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

Crashes 71.4% n = 21 

(0.5%) 40.0% n = 10 

(0.3%)

Near-crashes 46.2% n = 197 

(4.4%) 50.0% n = 112 

(3.1%)

Crash-relevant conflicts 53.6% n = 3,019 

(67.8%) 57.4% n = 2,281

(63.0%)

Unintentional lane 

deviations 77.5% n = 1,215

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

Baseline epochs 56.5% n = 19,888 

(100.0%) 56.5% n = 19,888 

(100.0%)

Note: Adapted from Olson et al. (2009)

In a more detailed examination of the data, odds ratio analyses were calculated by Olson et al. 

(2009) to identify high-risk tertiary tasks. That is, tertiary tasks that were associated with an 

increased likelihood of involvement in a safety-critical event compared to non-event, baseline 

driving. Table 7 shows the results from the analyses that included all safety-critical events. Odds 

ratios, along with the lower confidence limits (LCL) and upper confidence limits (UCL) are 

shown in Table 7. Odds ratios greater than 1.0 that have LCL and UCL ranges that do not 

include “1.0” indicate the task significantly increases the odds of involvement in a safety-critical 

event. Conversely, odds ratios less than 1.0 that have LCL and UCL ranges that do not include 

“1.0” indicate that the task significantly decreases the odds of involvement in a safety-critical 

event. As shown in Table 7, many of the observed tertiary tasks had high odds ratios (e.g., text 

message on cell phone, interact with/look at dispatching device, write on pad, notebook, etc., use 

calculator, look at map, dial cell phone, and use/reach for other electronic device). The odds 

ratios in Table 7 that are marked with an asterisk were significant and did not have a 95 percent 

confidence interval that contained 1.0. 
5

Table 7. Odds ratios and 95 percent confidence intervals to assess likelihood of a safety-critical 

event while engaging in tertiary tasks.

Task Odds Ratio LCL UCL

Text message on cell phone 23.24* 9.69 55.73

Other—Complex Tertiary Task

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

Interact with/look at dispatching device 9.93* 7.49 13.16

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

Use calculator 8.21* 3.03 22.21

Look at map 7.02* 4.62 10.69

Use/reach for other electronic device

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

Dial cell phone 5.93* 4.57 7.69

Other—Moderate Tertiary Task

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

Personal grooming 4.48* 2.01 9.97

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

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

Reach for object in vehicle 3.09* 2.75 3.48

Look back in sleeper berth 2.30* 1.30 4.07

Adjust instrument panel 1.25* 1.06 1.47

Talk or listen to hand-held phone 1.04 0.89 1.22

Eat 1.01 0.83 1.21

Remove/adjust jewelry 1.68 0.44 6.32

Other—Simple Tertiary Task

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

Put on/remove/adjust hat 1.31 0.69 2.49

Use chewing tobacco 1.02 0.51 2.02

Put on/remove/adjust seat belt 1.26 0.60 2.64

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

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

Drink from a container 0.97 0.72 1.30

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

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

Bite nails/cuticles 0.45* 0.28 0.73

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

Talk or listen to CB radio 0.55* 0.41 0.75

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

Other personal hygiene 0.67* 0.59 0.75

Note: Adapted from Olson et al., (2009). Asterisk indicates a significant odds ratio. 

Olson et al. (2009) also conducted an eye glance analysis to determine, during a 6-second (s) 

interval (5 s before the event onset and 1 s after), the drivers’ mean duration of eyes off forward 

roadway (i.e., any time the driver’s eyes were not on the forward roadway, either from a single 

glance or multiple glances). CMV drivers’ mean duration of eyes off forward roadway was 2.1 s 

prior to the onset of a crash, 1.7 s prior to the onset of a near-crash, 1.6 s prior to the onset of a 

crash-relevant conflict, and 1.2 s during the baseline epoch. Table 8 illustrates the odds ratios 

across all safety-critical events in five different time bins. Not surprisingly, longer glances of 

more than 1.5 s were associated with a significant increase in the odds of involvement in a 
6

safety-critical event (odds ratio = 1.3) and very long glances of more than 2 s had the highest 

odds of involvement in a safety-critical event (odds ratio = 2.9). 

Table 8. Odds Ratios and 95 percent confidence intervals for all events to assess likelihood of a 

safety-critical event while eyes off forward roadway.

Total Eyes Off Forward Roadway Odds Ratio LCL UCL

Less than or equal to 0.5 s 1.36* 1.16 1.58

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

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

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

Greater than 2.0 s 2.93* 2.65 3.23

Note: Adapted from Olson et al. (2009). Asterisk indicates a significant odds ratio.

The Olson et al. (2009) study was the first naturalistic study to assess the odds associated with 

distracted driving in CMV operations. As such, the results have high ecological validity given 

the data collected in the study came from heavy trucks operating on our nation’s roadways as 

they made their revenue-producing deliveries. However, the results run counter to results 

published from other researchers using driving simulators that suggest talking and listening on a 

cell phone is dangerous (Beede & Kass, 2006; Patten et al., 2004; Strayer, Drews, & Johnston, 

2003). As shown above in Table 7, the results in the Olson et al. study suggest that talking and 

listening on a cell phone while driving does not increase risk, and may even provide a safety 

benefit. 

It is important to keep in mind that a driving simulator is not actual driving. Therefore, 

degradations in driving performance shown in simulators may not necessarily translate into 

actual crashes or near-crashes in the real world. Therefore, the risk posed by driver distractions 

must be assessed using real-world data. As naturalistic driving studies are still in their infancy, 

more data sets are needed verify the results found in Olson et al. (2009)

1.1.3 Research Project Summary

The aim of this project was to document the prevalence of distractions while driving a 

commercial truck or bus using an existing naturalistic data set. New in-vehicle technologies are 

available that provide objective measures of driver behavior under naturalistic driving 

conditions. These devices are able to provide continuous measures on a wide variety of driving 

behaviors previously unavailable to fleet safety managers. Some of these systems use in-vehicle 

video technology to record driver behavior. The video recordings obtained from these devices 

may be used by fleet safety managers to provide drivers with feedback on their safe and at-risk 

driving behaviors. As such, these existing naturalistic data sets can provide a wealth of 

naturalistic data, including driver behavior during crashes, near-crashes, and crash-relevant 

conflicts, involving thousands of commercial vehicles (including trucks and buses). DriveCam is 

a vendor of onboard safety monitoring (OBSM) systems for professional fleets aimed at reducing 

risky driving behaviors using in-vehicle video technology. Video and kinematic data snippets, 

recorded with a kinematic trigger (e.g., hard brake at a pre-set threshold), archived by the vendor 

were made available for analysis in this study. Note that the study authors did not receive any 

video data from the technology vendor, but rather anonymous, de-identified categorical data 

formulated from the reduction of videos by the vendor’s staff.
7

The data sets in the study did not include continuous data; they only included recorded events 

that met or exceeded a kinematic threshold. These recorded events included safety-critical events 

(e.g., hard braking in response to another vehicle) and non-safety triggered events (e.g., vehicle 

traveled over train tracks or a pothole and this exceeded the kinematic threshold, driver braked in 

response to no apparent traffic safety situation, etc.). One of the advantages of continuous data 

collection is that randomly selected baseline events (or control events) can be parsed from the 

continuous data set and be used to assess the risk involved in performing certain behaviors while 

driving (this was the approach used by Olson et al., 2009 and Klauer et al., 2006). While the nonsafety triggered events (hereafter referred to as baseline events) collected by the vendor were 

triggered and not randomly selected events, these baseline events can be used to evaluate the risk 

in performing various tertiary tasks while driving (section 4 includes a description of the 

limitations in using such an approach). There is bias associated with using these non-safety 

triggered events (i.e., baseline events), as described in more detail in section 4; however, Dingus 

et al. (2005) found that randomly selected baseline events in the 100-Car Study had a similar 

kinematic profile as crash-relevant conflicts (the lowest severity safety-critical event). Note that 

these baseline events were only available in data set B as the vendor did not begin to record 

distractions in these baseline events until June 2009.

1.1.3.1 Analysis Overview

Commercial trucks (3-axle and tractor trailer/tanker) and buses (transit and motor coach) were 

the target vehicles in the analyses. Please note these vehicle types were supplied by the 

technology vendor, and may or may not conform to Federal definitions for these vehicles types.

Note that safety-critical events included crashes, near-crashes, and crash-relevant conflicts that 

had been recorded by the system. The primary distraction analyses involved two data sets 

collected over a consecutive 1-year period: all safety-critical events in the first 273 days 

(September 6, 2008 to June 5, 2009; hereafter referred to as data set A), and all safety-critical 

events and baseline events in the last 92 days (i.e., June 6, 2009 to September 5, 2009; hereafter 

referred to as data set B). In data set B, the technology vendor re-reviewed all safety-critical 

events and baseline events where cell phone was noted as a distraction to determine the 

frequency of the following cell-phone sub-tasks: dialing cell phone, reaching for cell phone, 

reaching for headset/earpiece, talking/listening on hands-free cell phone, talking/listening on 

hand-held cell phone, and texting/e-mailing/accessing the Internet on cell phone. Note that the 

vendor’s normal data reduction only records cell phone use and does not classify the individual 

cell phone sub-tasks noted above. The vendor also re-reviewed all crashes in data set B and 

documented all the distractions that occurred during these crashes. Note that the vendor does not 

normally perform data reduction on crashes. Data set A was identical to data set B; however, the 

separate cell phone sub-tasks were not classified and data reduction was not conducted on 

crashes in data set A. Therefore, data set B was more detailed and broken out further than data 

set A.

The frequency and percentage of distractions during safety-critical events and baseline events

were documented in both data sets. The results of these analyses provide information on the 

scope of cell phone use, and other distractions, during safety-critical events and baseline epochs 

within the time periods noted above. This provides the FMCSA with descriptive data on the 

adverse consequences of cell phone use and other distractions while driving. However, important 

issues, such as assessing safety risk associated with engaging in distractions while driving, may 
8

not be fully understood given the lack of a randomly selected non-event (control) data set. 

However, the inclusion of baseline events provides information on the direction of these risks 

(although the actual risk may be greater and/or lesser than indicated). As such, the study can be 

viewed as using a very large naturalistic data set that will provide important information on the 

scope of the distracted driving problem in truck and bus operations. Below is a brief overview of 

the four analyses that were conducted. 

• Analysis 1: Descriptive Analyses — the first analysis was a descriptive analysis of the 

data set. The descriptive analyses reported the frequency and percentage of tertiary tasks 

across all commercial trucks and buses in each of the two data sets, as well as 

disaggregated results for each of the three vehicle types (i.e., 3 axle or more trucks, buses, 

and tractor trailers/tankers) and fleet cell phone policy (i.e., presence and absence). These 

descriptive analyses showed the proportion of specific tertiary tasks, as well as the 

prevalence of tertiary tasks across safety-critical events and baseline events. 

• Analysis 2: Odds Ratios of Tertiary Tasks — odds ratios were calculated to approximate 

the rate ratio of safety-critical event risk compared to baseline driving for each tertiary 

task (using baseline events) in data set B. Analysis 2 reports the odds ratios for each 

tertiary tasks across all commercial trucks and buses in data set B, as well as 

disaggregated results for each of the three vehicle types (i.e., 3 axle or more trucks, buses, 

and tractor trailers/tankers) and fleet cell phone policy (i.e., presence and absence).

• Analysis 3: Population Attributable Risk (PAR) — PAR was calculated on all significant 

odds ratios greater than 1.0. While odds ratios inform which tasks increase the likelihood 

of involvement in a safety-critical event, the PAR considers the frequency of occurrence 

of each task and shows the proportion of safety-critical events that could be avoided by 

limiting exposure to the risk factor.

• Analysis 4: Odds Ratios of Fleet Cell Phone Policy and State Cell Phone Law — odds 

ratios were also used to calculate the approximate effectiveness of a fleet cell phone 

policy and State cell phone law regarding cell phone use while driving. This odds ratio 

did not look at safety-critical events, but rather evaluated the odds of cell phone usage 

given a fleet cell phone policy or State cell phone law versus the odds of cell phone usage 

given no fleet cell phone policy or State cell phone law. 
9

2. METHODS

2.1 CHARACTERIZATION OF THE DATA SET

The data collected in the study was existing data collected by the vendor from September 6, 2008 

to September 5, 2009. No new data was collected; however, as indicated above, cell phone 

safety-critical events and baseline events in data set B (June 6, 2009 to September 5, 2009) were 

re-reviewed by the technology vendor’s personnel to classify discrete cell phone sub-tasks (e.g., 

dialing a cell phone, talking on a cell phone, reaching for a cell phone, etc.). Crashes in data set 

B were also re-reviewed by the vendor to classify any and all tertiary tasks during the crash. 

Below is a description of the procedures for collecting, validating, and reviewing the data. As the 

study involves the collection of existing data, a brief overview of the data collection process is 

provided below. 

2.1.1 Data Collection Process

The event recorder had two camera views, including: 

• Driver face view.

• Forward road-facing view. 

Figure 1 and Figure 2 show the event recorder and the two camera views captured by the event 

recorder, respectively. The event recorder had three accelerometers (y-, x-, and z-axis) that 

triggered an event to be recorded. If a certain criterion was met or surpassed (e.g., greater than or 

equal to │0.5 g│) the event recorder saved 12 s of video (i.e., 8 s prior to the criterion being met 

or surpassed and 4 s after). Note that this criterion was used for all vehicles in the study. Once 

events had been validated and reduced, the safety-critical events were uploaded to a secure 

server where fleet safety managers and other fleet personnel with the required access can view 

them. Fleet personnel used these videos to coach drivers to reduce risky driving behavior and 

praise appropriate responses to safety situations. Note that a light on the event recorder blinked 

once an event has been stored; thereby, providing the driver with immediate feedback. 

Figure 1. Photo. Event recorder (left) and typical installation of event recorder behind the vehicle’s 

rearview mirror (right).
10 

Figure 2. Photo. Front camera view from the event recorder (left) and driver’s face view (right). 

Figure 3 shows the basic flow of the review process to gather data in the study. The workflow is 

numbered with descriptions for each numbered item listed in the workflow. As shown in Figure 

3, potential events were captured by the event recorder via a 3-axis accelerometer-based 

triggering system. More specifically, potential events were captured when the accelerometer 

threshold was met or exceeded (e.g., hard braking, hard cornering, collision, rough road, etc.). 

The accelerometers in the event recorders were on 24 hours a day, 7 days a week. 

Potential events were typically transmitted at night over a cellular network from the event 

recorder to the technology vendor. The potential event included the 12-second video/audio for 

the clip as well as key kinematic data, including: serial number, settings, accelerometer data, 

Global Positioning System (GPS), speed (calculated via GPS), date/time stamp of recorded 

events, and event recorder logs. After potential events were transmitted to the technology vendor, 

a certified Driver Risk Analyst (DRA) reviewed the potential events to determine if the potential 

event was truly ‘risky’ and what behaviors contributed to the safety-critical event. DRAs 

performed event review on safety-critical event video clips and scored these video clips 

according to operational standards developed by the vendor. 

Figure 3. Flowchart. The data capture and review process. 
11

2.1.1.1 DRA Training

DRAs went through a month-long training program prior to reviewing videos unsupervised. The 

first two weeks of training were instructional and involved the following topics: common safe 

driving practices/standards according to the Smith System®

2.1.1.2 DRA Quality Control

, safety ride-a-long in an equipped 

vehicle, utilizing the vendor’s behavior definitions, proprietary software/ components, two 

milestone tests, correctly analyzing videos and writing effective reviewer notes, and the 

milestone 3 test (DRAs had to score >80 percent to move to the next training module). The 

following two weeks of on-the-job training include the following: 16 hours working with a 

mentor, assisting with analysis of various client videos (these events will be randomly selected 

for quality control), and a final certification test (DRAs must score >90 percent to complete 

training program).

As part of the vendor’s standard review process, each DRA went through statistical quality 

assurance sampling. The vendor had a tool that randomly selected video clips from each DRA to 

ensure a 97 percent quality rating (based on a 95 percent confidence interval). Quality assurance 

was completed daily on the safety-critical events reviewed by each DRA. Each day, DRAs 

received a list of the reviewed safety-critical events with their associated errors and successes. 

DRAs attended weekly meetings with their manager and quality assurance team to review their 

overall quality as well as to determine areas where refresher training might be needed. The 

vendor also tracked the most common mistakes made by all DRAs; this information was used to 

improve their training guidelines and behavioral definitions. The vendor had a quality rating of 

97 percent (<3 percent error rate) against their standards

2.1.1.3 DRA Review

The study included a re-review of all existing safety-critical events and baseline events in data 

set B that were coded with cell phone in order to classify specific cell phone sub-tasks, 

including: dialing cell phone, reaching for cell phone, reaching for headset/earpiece, 

talking/listening on hands-free cell phone, talking/listening on hand-held cell phone, texting/

e-mailing/accessing the Internet on cell phone. Note that all data provided by the vendor was 

existing data and none of these data were revised except the reclassification of specific cell 

phone sub-tasks in safety-critical events and baseline events that were already coded with cell 

phone. The description of the DRA quality control (above) and review (below) illustrates the 

process in which DRA’s reviewed and reduced existing video and kinematic data from 

commercial trucks and buses. 

DRAs were instructed to record or score a tertiary task if the tertiary task contributed to a safetycritical event trigger or significantly increased risk during the safety-critical event. Activities, 

such as checking a mirror, scanning to the side, or glances in the vehicle (such as looking at the 

instrument panel) for <1 second were considered normal driving activities and were not 

considered distractions. DRAs recorded a tertiary task in one of three situations: (a) if the 

behavior was noticed during the safety-critical event or baseline, (b) if the tertiary task was the 

cause of the event being triggered (note that if the driver reaction was within 1 second of the 

risky situation, this was not considered to be the cause of the trigger), and (c) when the duration 

of the tertiary task exposed the driver to substantially increased risk (i.e., eyes were off the 

roadway while moving for 2.5 consecutive seconds, >4 seconds in the first or last half of the clip, 
12

or a total of 6 seconds or more throughout the 12-second video clip). Table 9 shows the 

operational definition of each tertiary task scored or recorded by DRAs.

Table 9. Operational definition of each tertiary task coded by DRAs.

Behavior Description

Cell Phone

Anytime a driver is using a hand-held or hands-free cell phone select this 

behavior. Under all circumstances, cell phone should be marked as risky when 

the use of the cell phone and the associated inattention led to an abrupt action.

General guidance if the use of the cell phone is not risky.

Hand-held: If driver is holding the cell phone up to the ear or he/she is actively 

using the phone/speaker phone (texting, dialing, obviously in a phone 

conversation). 

Hands-free: If you can see the driver wearing the device and he/she appears 

to be in a phone conversation. 

Select Cell Phone Hands-free if you cannot see the device yet the driver is 

obviously in a phone conversation (clearly responding to a caller). Indications of 

this can be: the driver has conversational pauses and/or verbal 

acknowledgements such as uh-huh, hmm, yes, no, etc. 

Other Communication 

Device

If a driver is holding the communication device to listen or is actively using it, 

select this behavior. Examples include Nextel/ chirp devices, CB Radio, 2-way 

radios/walkie-talkies, etc. 

Food and Drink

Anytime a driver is eating/drinking, select this behavior. If you notice that the 

driver was eating or drinking when the event was triggered, you need to 

determine whether or not this was a contributing cause and/or unsafe driving 

behavior. 

Electronic Device

An event where the driver was distracted by an electronic device and the 

associated inattention led to an abrupt action or clearly increased risk. This 

includes any electronic device other than cell phone, CB radio or walkie-talkie. 

Examples include: GPS navigation system, laptops.

Passenger Select this if the driver’s visual attention was directed for too long on a 

passenger rather than on the roadway.

Other Distractions

Select this if some other form of distraction led to a risky event or caused the 

driver to have his/her eyes off the roadway for a significant amount of time. 

Some examples may be another vehicle distracts the driver, a pedestrian, 

changing channels on the radio station, fumbling with a cigarette, etc.

2.1.2 Summary of Data Sets

Two data sets collected over a consecutive 1-year period were provided by the vendor to the 

authors for this study: 

• Data set A (collected during the first 273 days, September 6, 2008–June 5, 2009).

• Data set B (collected during the last 92 days, June 6, 2009– September 5, 2009). 
13

As indicated above, data set A included only safety-critical events, while data set B included 

safety-critical events and baseline events. Data set B included reclassified cell phone sub-tasks 

and full reduction on crashes. No new data reduction occurred in this data set (i.e., crashes were 

not re-reviewed and safety-critical events coded with cell phone were not re-reviewed and 

classified into specific tasks). 

Table 10 shows the distribution of fleets, vehicles, sectors, company cell phone policy, safetycritical events, and baseline events across the three vehicle types (i.e., 3 axle or more truck, bus, 

tractor trailer/tanker) in data set B. As shown in Table 10, data set B came from 183 different 

truck and bus fleets comprising 13,306 vehicles. These 13,306 trucks and buses were distributed 

among distribution (33.7 percent), small to medium businesses (SMB; 31.3 percent), transit (27.5 

percent), energy (3.7 percent), municipalities (2.0 percent), waste (1 percent), 

utilities/telecommunication (0.5 percent), and construction (0.3 percent) fleets. Note that the 

vendor released very limited demographic information regarding these fleets to protect the fleets’ 

anonymity. A total of 1,085 crashes, 8,375 near-crashes, 30,661 crash-relevant conflicts, and 

211,171 baseline events were captured in data set B. Note that the vendor considers the 

operational definitions of crashes, near-crashes, and crash-relevant conflicts to be proprietary. 
14

Table 10. Summary of data set B.

Vehicle Type # of 

Fleets

# of 

Vehicles

Cell Phone 

Policy Sectors Crashes NearCrashes

CrashRelevant 

Conflicts

Baseline 

Events

3 Axle or More 

Truck 29 2,617

Yes = 15

No = 7

Unknown =7

76% Distribution

14% Energy

6% Municipalities

3% SMB

1% Waste

107 913 5,907 47,769

Bus 90 8,509

Yes = 55

No = 16

Unknown = 19

43% Transit

33% SMB

23% Distribution

1% Municipalities

740 6,145 18,576 99,662

Tractor 

Trailer/Tanker 64 2,180

Yes = 39

No = 13

Unknown = 12

58% SMB

25% Distribution

6% Energy

5% Waste

3% Utilities/Telecommunication

2% Construction

1% Municipalities

238 1,317 6,178 63,746

Total 183 13,306

Yes = 109

No = 36

Unknown = 38

33.7% Distribution

31.3% SMB

27.5% Transit

3.7% Energy

2.0% Municipalities

1% Waste

0.5% Utilities/Telecommunication

0.3% Construction

1,085 8,375 30,661 211,171
15

Table 11 shows the distribution of fleets, vehicles, sectors, company cell phone policy, and 

safety-critical events across the three vehicle types (i.e., 3 axle or more truck, bus, tractor 

trailer/tanker) in data set A. As shown in Table 11, the data in data set A came from 207 different 

truck and bus fleets comprising a total of 13,431 vehicles. These 13,431 trucks and buses were 

distributed among SMB (32.2 percent), transit (30.4 percent), distribution (25.0 percent), energy 

(8.5 percent), municipalities (1.5 percent), waste (1.2 percent), utilities/telecommunication (0.9 

percent), and construction (0.3 percent) fleets. A total of 1,336 crashes, 15,864 near-crashes, and 

173,591 crash-relevant conflicts were captured in data set A.
16

Table 11. Summary of data set A.

Vehicle Type # of 

Fleets

# of 

Vehicles

# w/Cell Phone 

Policy Sectors Crashes NearCrashes

CrashRelevant 

Conflicts

3 Axle or More 

Truck 31 2,511

Yes= 16

No = 8

Unknown = 7

59.9% Distribution

29.7% Energy

1.8% Municipalities

7.2% SMB

1.4% Waste

113 1,365 47,112

Bus 105 8,183

Yes = 52

No = 16

Unknown = 37

56.4% Transit

27.2% SMB

15.4% Distribution

1% Municipalities

1,008 12,260 76,445

Tractor 

Trailer/Tanker 71 2,737

Yes = 34

No = 11

Unknown = 26

56.1% SMB

22.4% Distribution

11.8% Energy

4% Waste

2.7% Utilities/Telecommunication

2.4% Municipalities

0.8% Construction

215 2,239 50,034

Total 207 13,431

Yes = 102

No = 35

Unknown = 70

32.2% SMB

30.4% Transit

25.0% Distribution

8.5% Energy

1.5% Municipalities

1.2% Waste

0.9% Utilities/Telecommunication

0.3% Construction

1,336 15,864 173,591
17

The distribution of the two data sets shown in Table 11 and Table 10 was similar. Also, there 

were more crashes and less near-crashes and crash-relevant conflicts than expected in data set B 

compared to data set A (given the time period in each data set). If the data in data set B was 

normalized to include the same period of time as in data set A, we would expect to find 

approximately 120,000 safety-critical events (compared to 190,791 safety-critical events in data 

set A). While the additional 125 trucks and buses in data set A explains some of this variability, 

it appears that drivers drove more safely and/or safety managers were more vigilant in their 

monitoring (and coaching) in data set B. 

2.2 RESEARCH QUESTIONS AND ANALYSES

There were four primary research questions in the study. Each of these research questions is 

described in more detail below. 

2.2.1 Analysis 1: Descriptive Analyses

The first analysis was a descriptive analysis of the two data sets. These descriptive analyses 

report the frequency and percentage of tertiary tasks in commercial trucks and buses in each data 

set across each vehicle type and fleet cell phone policy. These descriptive analyses show the 

proportion of specific tertiary tasks, as well as the prevalence of tertiary tasks across safetycritical events and baseline events (in data set B only). Below is a list of the descriptive analyses.

• Frequency and percentage of detailed cell phone tasks and other tertiary tasks in 

commercial trucks and buses across safety-critical events and baseline events in data set 

B. These results were also disaggregated by vehicle type and fleet cell phone policy. 

• Frequency and percentage of tertiary tasks in commercial trucks and buses across safetycritical events in data set A. These results were also disaggregated by vehicle type and 

fleet cell phone policy.

Note that the presence of each fleet’s cell phone policy (or lack thereof) was only coded in data 

set B. Thus, this information was cross-referenced with the fleets in data set A to determine 

which fleets in data set A had a cell phone policy (or conversely did not have a cell phone 

policy). As this information was not recorded for every fleet in the data set B, it was unknown if 

these fleets had a cell phone policy. Thus, the fleets where it was unknown if they had a cell 

phone policy were excluded from all descriptive analyses where the results were disaggregated 

by fleet cell phone policy (or lack thereof). There were various fleet cell phone policies present 

in data set B, including no cell phone use of any kind while driving and no hand-held cell phone 

use while driving (hands-free cell phone use was allowed). Limited sample size restricted the 

disaggregation by specific fleet cell phone policy; thus, the presence of a fleet cell phone policy 

in this analysis was defined as “any” fleet cell phone policy (e.g., no cell phone use of any kind 

while driving, no hand-held cell phone use while driving, etc.). 

2.2.2 Analysis 2: Odds Ratios of Tertiary Tasks

Odds ratios were calculated for each tertiary task to approximate the rate ratio of safety-critical 

event risk compared to normal, baseline driving (using baseline events). An odds ratio compares 

the odds of some outcome occurring (e.g., a safety-critical event), given the presence of some 
18

predictor factor, condition, or classification (e.g., cell phone use). An odds ratio is usually a 

comparison of the presence of a condition to its absence (e.g., driver cell phone use versus no 

driver cell phone use). As shown in Table 12, an odds ratio is a measure of association 

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

Table 12. Example of a 2 × 2 contingency table used to calculate odds ratio.

Outcome Driver Inattention No Driver Inattention

Incidence Occurrence n11 n12

No Incidence Occurrence n21 n22

Odds of occurrence were defined as the probability of safety-critical event occurrence divided by 

the probability of non-occurrence (baseline). The following formula was used to perform the 

calculation to determine the odds ratio in order to assess the increase (or decrease) in the 

probability of having a safety-critical event, compared to a baseline, in the presence of a tertiary 

task versus no tertiary task: Odds Ratio = (n11)(n22)/(n21)(n12

Odds ratios of “1.0” indicate the outcome is equally likely to occur given the condition. An odds 

ratio greater than “1.0” indicates the outcome is more likely to occur given the condition, while 

an odds ratio of less than “1.0” indicates the outcome is less likely to occur (Pedhazur, 1997). To 

determine whether the odds ratio is significant, the lower confidence limit (LCL) and the upper 

confidence limit (UCL) are calculated. If 1.0 is not included between the LCL and the UCL, the 

odds ratio is significant. The hypothetical data presented in 

).

Table 13 is used to illustrate how the 

odds ratio was calculated for dialing a cell phone while driving. In this hypothetical example, 

assume there were a total of 100 safety-critical events and 100 baseline events in the data set. 

Drivers were found to be dialing a cell phone while driving during 45 of the safety-critical 

events, while drivers were dialing a cell phone while driving in 23 of the baseline events.

Table 13. Example of the dialing a cell phone odds ratio calculation.

Outcome Dialing a Cell Phone No Dialing a 

Cell Phone

Safety-critical events 45 (A) 55 (B)

Baseline events 23 (C) 77 (D)

The formula for this calculation was as follows:

B C

A D Odds Ratio

×

× = (1a)

23 55

45 77

×

× Odds Ratio = (1b)

Odds Ratio = 2.74 (1c)
19

In order to determine if the odds ratio of 2.74 was significant, a 95-percent confidence interval 

was calculated, including the UCLs and LCLs. The formulas to calculate the UCL and LCL are 

shown below:

a b c d UCL Odds Ratio e

1 1 1 1 1.96 + + +

= × (2a)

77

1

23

1

55

1

45

1 1.96

2.74 + + +UCL = ×e (2b)

UCL = 5.04 (2c)

a b c d LCL OddsRatio e

1 1 1 1 −1.96 + + +

= × (3a)

77

1

23

1

55

1

45

1 1.96

2.74 − + + +LCL = × e (3b)

LCL =1.49 (3c)

Since 1.0 is not included between the LCL and the UCL, the odds ratio was significantly 

different than 1.0. If the study were conducted multiple times, 95 percent of the confidence 

intervals calculated would contain the true odds ratio. Therefore, in this example using data from 

Table 13, it can be interpreted that drivers who dial a cell phone while driving significantly 

increased their odds of involvement in a safety-critical event by 2.74, compared to a baseline, 

than if they were not dialing a cell phone while driving. Note that the odds ratio analysis follows 

an epidemiological approach. Odds ratios do not infer causation, they illustrate risk. This is 

analogous to retrospective research that shows a relationship between smoking cigarettes and 

cancer in the human population. The results from the odds ratio analyses can be interpreted the 

same way.

In order to approximate safety-critical event risk, compared to baseline driving, odds ratios were 

calculated on each tertiary task in data set B (when there was a sufficient sample size of safetycritical events). Odds ratios for each tertiary task were calculated with the absence and presence 

of that specific tertiary task. The results from these calculations show the odds ratio, LCL, UCL, 

and frequency of safety-related events and baseline events for each tertiary task. Note that odds 

ratios were only calculated on tertiary tasks in data set B.

As the study employs a case-control analysis approach (rather than a follow-on cohort approach), 

the use of odds ratios (rather than relative risk) is appropriate. The quantities obtained from a 

case-control and cohort approach are not equal. The odds ratio in a case-control approach is 

calculated with the probability of the risky behavior given a crash; however, the risk in a cohort 

study is calculated with the conditionality in the other direction. Since the total number of 

baseline events and safety-critical events is known in the study, exposure risk can be calculated 

(i.e., the risk of dialing a cell phone given the driving event is a baseline or a safety-critical 

event). In such situations, odds ratios are not used to approximate the risk ratio. Instead, the odds 

ratio is used to approximate the rate ratio. For time-variant factors, such as tertiary tasks, a more 
20

appropriate measure is rate ratio (i.e., the number of crashes per miles driven with cell phone 

use). The analysis framework used the odds ratio to approximate the rate ratio, which does not 

require the rare event assumption (Guo & Hankey, 2009). Analysis 3: Population Attributable 

Risk

The PAR was also calculated on all significant odds ratios and was defined as the risk of a 

safety-critical event in the total population (pt) minus the risk in the unexposed group (pu

Since this is a retrospective study, the odds ratio approaches the rate ratio (i.e., a ratio of the 

probability of the event occurring in the exposed group versus a non-exposed group); thus, odds 

ratios may be substituted for rate ratio and the PAR percentage was calculated as follows:

) (Sahai 

& Khurshid, 1996). For each odds ratio with an outcome greater than “1.0”, the percentage PAR 

was calculated. While the odds ratio is measured at the individual level, the PAR is measured at 

the population level. This analysis provided an assessment of the percentage of safety-critical 

events that are occurring in the population and that are directly attributable to the specific 

behavior measured. The PAR percentage is defined as the proportion of the risk to the safetycritical event in the study population that is attributable to the exposure to the distracting task, 

and thus could be avoided by limiting the exposure to the risk factor (Sahai & Khurshid).

100

(1 ( 1))

( ( 1)) ×

+ −

− = P OddsRatioP OddsRatio PAR percentage

e

e (4a)

Where Pe

Table 13

= population exposure estimate (e.g., number of baseline epochs with complex tertiary 

task/total number of baseline epochs). This calculation provides a percentage value which can 

then be generalized to the entire population. For example, drivers who dial a cell phone while 

driving significantly increase their odds of involvement in a safety-critical event by two times,

compared to not dialing a cell phone, but dialing a cell phone while driving is a rare occurrence 

in the entire population, which is explained by calculating the PAR percentage. Again, using the 

hypothetical data presented in , the PAR percentage is calculated below where:

0.23

100

23 = = total baseline epochsbaseline epochs with dialing a cell phone while driving present Pe (4b)

Odds Ratio = 2.74 (4c)

100

(1 0.23(2.74 1))

(0.23(2.74 1)) ×

+ −

− PAR percentage =

(4d)

PAR percentage =18.70 (4e)

In order to interpret the PAR percentage, the estimated sample variance and the UCL and LCL 

must first be calculated. Table 14 displays the hypothetical data used above in the odds ratio 

example; these data will be used to explain the 95 percent UCL and LCL calculations shown 

below.
21

Table 14. Population attributable risk—confidence limits example.

Outcome Dialing a Cell Phone No Dialing a Cell Phone Row Total

Safety-critical events 45 (A) 55 (B) 100 (m1)

Baseline events 23 (C) 77 (D) 100 (m2)

Column Total 68 (n1) 132 (n2) (n)

First, it is necessary to calculate the estimated sample variance using the following formula:

( ) 100

1 2

2

1

2

 ×



 





+ 













 = Dm

C

Bm

A

Dm

Bm Var PAR percentage (5a)

100

77 100

23

55 100

45

77 100

55 100 ( )

2

×



 





×

+

×





 





×

×

Var PAR percentage = (5b)

Var(PAR percentage) = 0.57 (5c)

Next, the 95 percent UCL and LCL is calculated using the estimated sample variance. The

formulas are as follows:

UCL = PAR percentage +1.96 Var(PAR percentage) (6a)

UCL =18.70 +1.96 0.57 (6b)

UCL = 20.18 (6c)

LCL = PAR percentage −1.96 Var(PAR percentage) (7a)

LCL =18.70 −1.96 0.57 (7b)

LCL =17.22 (7c)

Therefore, it can be reported using this hypothetical data that dialing a cell phone while driving 

significantly increases the probability of involvement in a safety-critical event. Note that PAR 

percentages were only calculated on tertiary tasks in data set B.

2.2.3 Analysis 4: Odds Ratios of Fleet Policy and State Law

Odds ratios were also used to calculate the approximate effectiveness of a fleet cell policy and 

State cell phone law regarding cell phone use while driving. Table 15 illustrates the 2 × 2 

contingency table employed in these analyses. In order to approximate the likelihood of 

commercial truck and bus drivers using a cell phone while driving under a fleet cell phone policy 

and State cell phone law, odds ratios for each cell phone policy were calculated. This odds ratio 

did not look at safety-critical events, but rather evaluated the odds of cell phone usage given a 
22

fleet cell phone policy or State cell phone law versus the odds of cell phone usage given no fleet 

cell phone policy or State cell phone law. Each of these calculations was performed using the 

safety-critical events in data sets A and B (i.e., the full 365 days) and baseline events in data set 

B (i.e., the two data sets were merged). Crashes in data set A were not included in this analysis 

as the vendor did not re-review these crashes. The results from these calculations show the odds 

ratios, LCL, UCL, and the frequency of cell phone use while driving and no cell phone use while 

driving. 

Table 15. Example of 2 × 2 contingency table used to calculate the cell phone policy odds ratio.

Outcome Cell Phone Policy No Cell Phone Policy

Cell phone use while driving n11 n12

No cell phone use while driving n21 n22

Note that the specific cell phone sub-tasks in data set B were re-classified as general “cell phone 

use” in this analysis in order to be merged with data set A. As indicated above, fleets with an 

unknown cell phone policy were excluded from this analysis. There were various fleet cell phone 

policies present in the merged data set, including no cell phone use while driving and no handheld cell phone use while driving (hands-free cell phone use was allowed). As the merged data 

set did not classify specific cell phone sub-tasks, the fleet cell phone policy in this analysis was 

defined as “any” fleet cell phone policy (e.g., no cell phone use while driving, no hand-held cell 

phone use while driving, etc.). Similarly, the State cell phone law in this analysis was defined as 

any State cell phone law (e.g., no hand-held cell phone use while driving, no texting while 

driving, etc.). Also note that baseline events and safety-critical events were not mutually 

exclusive in these analyses. More specifically, a baseline or safety-critical event that was coded 

with both a fleet cell phone policy and a State cell phone law was included in both analyses (i.e., 

fleet cell phone and State cell phone analyses).
23

3. RESULTS

As indicated, the authors completed four analyses using the two data sets provided by the 

vendor. These analyses included: descriptive statistics of tertiary tasks in both data sets, odds 

ratios on each tertiary task in data set B, PAR for each significant odds ratio greater than 1.0, and 

odds ratios of cell phone use while driving under company cell phone policy and State cell phone 

law in a merged data set. 

3.1 ANALYSIS 1: DESCRIPTIVE ANALYSES

The first analysis was a descriptive analysis of the data set. These descriptive analyses report the 

frequency and percentage of tertiary tasks in commercial trucks and buses in each data set across 

the three vehicle types. These descriptive analyses show the proportion of specific tertiary tasks 

performed by drivers, as well as the prevalence of tertiary tasks across safety-critical events and 

baseline events. 

3.1.1 Tertiary Tasks in Data Set A

Table 16 shows the frequency and percentage of tertiary tasks across each severity level in data 

set A. Note that safety-critical events were defined as crashes, near-crashes, and critical 

incidents. More than one tertiary task could be coded during a safety-critical event in data set A; 

thus, the column totals in the data set A tables may exceed 100 percent. 

As shown in Table 16, most of the safety-critical events did not involve a tertiary task (92.4 

percent). Of the tertiary tasks present during the safety-critical events, the most prevalent were 

other distraction (2.8 percent) and cell phone usage (4.0 percent). 

Table 16. Frequency and percentage of tertiary tasks across each severity level in data set A.

Tertiary Tasks

Number of 

Near-Crashes 

(and percent)

Number of 

Crash-Relevant 

Conflicts 

(and percent)

Number of SafetyCritical Events 

(near crashes + crashrelevant conflicts)

(and percent)

Cell Phone Usage 246

(1.55%)

7,278

(4.19%)

7,524

(3.97%)

Consuming Food/Drink 43

(0.27%)

1,367

(0.79%)

1,410

(0.74%)

Other Distraction 723

(4.56%)

4,635

(2.67%)

5,358

(2.83%)

Other Communication 

Device

0

(0.00%)

0

(0.00%)

0

(0.00%)

Passenger Distraction 41

(0.26%)

104

(0.06%)

145

(0.08%)

None 14,817

(93.40%)

160,236

(92.31%)

175,053

(92.40%)
24

Table 17 shows the frequency and percentage of tertiary tasks across each severity level in data 

set A for fleets with no cell phone policy. Note that fleets with an unknown cell phone policy 

were excluded from Table 17 and Table 18; thus, the frequencies in Table 17 and Table 18 will 

not sum to the frequencies found in Table 16. As shown in Table 17, most of the safety-critical 

events did not involve a tertiary task (93.3 percent). Of the tertiary tasks present during the 

safety-critical events, the most prevalent were cell phone usage (4.0 percent) and other 

distraction (2.1 percent).

Table 17. Frequency and percentage of tertiary tasks across each severity level in data set A (no 

fleet cell phone policy only).

Tertiary Tasks

Number of 

Near-Crashes

(and percent)

Number of 

Crash-Relevant 

Conflicts

(and percent)

Number of SafetyCritical Events

(near crashes + crashrelevant conflicts)

(and percent)

Cell Phone Usage 54

(3.71%)

715

(3.97%)

769

(3.95%)

Consuming Food/Drink 9

(0.62%)

129

(0.72%)

138

(0.71%)

Other Distraction 57

(3.91%)

345

(1.92%)

402

(2.07%)

Other Communication 

Device

0

(0.00%)

0

(0.00%)

0

(0.00%)

Passenger Distraction 3

(0.21%)

5

(0.03%)

8

(0.04%)

None 1,334

(91.56%)

16,816

(93.39%)

18,150

(93.25%)

Table 18 shows the frequency and percentage of tertiary tasks across each severity level in data 

set A for fleets with a cell phone policy. As shown in Table 18, most of the safety-critical events 

did not involve a tertiary task (91.4 percent). Of the tertiary tasks present during the safetycritical events, the most prevalent were cell phone usage (4.3 percent) and other distraction (3.4 

percent).
25

Table 18. Frequency and percentage of tertiary tasks across each severity level in data set A (fleet 

cell phone policy only).

Tertiary Tasks

Number of 

(and percent)

of Near-Crashes

Number 

(and percent)

of Crash-Relevant 

Conflicts

Number (and percent)

of Safety-Critical Events

(near crashes and 

crash-relevant conflicts)

Cell Phone Usage 159

(1.50%)

5,169

(4.58%)

5,328

(4.32%)

Consuming Food/Drink 28

(0.26%)

1,022

(0.91%)

1,050

(0.85%)

Other Distraction 499

(4.70%)

36,93

(3.28%)

4,192

(3.40%)

Other Communication 

Device

0

(0.00%)

0

(0.00%)

0

(0.00%)

Passenger Distraction 29

(0.27%)

75

(0.07%)

104

(0.08%)

None 9,915

(93.31%)

102,813

(91.18%)

112,728

(91.37%)

Table 19 shows the frequency and percentage of tertiary tasks in 3 axle or more trucks across 

each severity level in data set A. As shown in Table 19, most of the safety-critical events in 3 

axle or more trucks did not involve a tertiary task (91.9 percent). Of the tertiary tasks in 3 axle or 

more trucks during the safety-critical events, the most prevalent were other distraction (3.7 

percent) and cell phone usage (3.3 percent).

Table 19. Frequency and percentage of tertiary tasks in 3 axle or more trucks across each severity 

level in data set A.

Tertiary Tasks

Number of 

(and percent)

of Near-Crashes 

Number 

(and percent)

of Crash-Relevant 

Conflicts 

Number (and percent)

of Safety-Critical Events 

(near crashes and 

crash-relevant conflicts)

Cell Phone Usage 21

(1.54%)

1,576

(3.35%)

1,597

(3.29%)

Food/Drink 5

(0.37%)

653

(1.23%)

547

(1.13%)

Other Distraction 106

(7.77%)

1,676

(3.56%)

1,782

(3.68%)

Other Communication 

Device

0

(0.00%)

0

(0.00%)

0

(0.00%)

Passenger Distraction 1

(0.07%)

7

(0.01%)

8

(0.02%)

None 1,233

(90.33%)

43,316

(91.94%)

44,549

(91.90%)

Table 20 shows the frequency and percentage of tertiary tasks in buses across severity level in 

data set A. As shown in Table 20, most of the safety-critical events in buses did not involve a 
26

tertiary task (93 percent). Of the tertiary tasks in buses during the safety-critical events, the most 

prevalent were cell phone usage (3.6 percent) and other distraction (2.8 percent). 

Table 20. Frequency and percentage of tertiary tasks in buses across each severity level in data 

set A.

Tertiary Tasks

Number of 

(and percent)

of Near-Crashes 

Number 

(and percent)

of Crash-Relevant 

Conflicts 

Number (and percent)

of Safety-Critical Events 

(near crashes and 

crash-relevant conflicts)

Cell Phone Usage 157

(1.28%)

3,001

(3.93%)

3,158

(3.56%)

Food/Drink 26

(0.21%)

416

(0.54%)

442

(0.50%)

Other Distraction 485

(3.96%)

1,997

(2.61%)

2,482

(2.80%)

Other Communication 

Device

0

(0.00%)

0

(0.00%)

0

(0.00%)

Passenger Distraction 36

(0.29%)

78

(0.10%)

114

(0.13%)

None 11,558

(94.27%)

70,970

(92.84%)

82,528

(93.04%)

Table 21 shows the frequency and percentage of tertiary tasks in tractor trailers/tankers across 

each severity level in data set A. As shown in Table 21, most of the safety-critical events in 

tractor trailers/tankers did not involve a tertiary task (91.8 percent). Of the tertiary tasks in tractor 

trailers/tankers during the safety-critical events, the most prevalent were cell phone usage (5.3 

percent) and other distraction (2.1 percent).

Table 21. Frequency and percentage of tertiary tasks in tractor trailers/tankers across each 

severity level in data set A.

Tertiary Tasks

Number of 

(and percent)

of Near-Crashes 

Number 

(and percent)

of Crash-Relevant 

Conflicts 

Number (and percent)

of Safety-Critical Events 

(near crashes and 

crash-relevant conflicts)

Cell Phone Usage 68

(3.04%)

2,701

(5.40%)

2,769

(5.30%)

Food/Drink 12

(0.54%)

409

(0.82%)

421

(0.81%)

Other Distraction 132

(5.90%)

962

(1.92%)

1,094

(2.09%)

Other Communication 

Device

0

(0.00%)

0

(0.00%)

0

(0.00%)

Passenger Distraction 4

(0.18%)

19

(0.04%)

23

(0.04%)

None 2,026

(90.49%)

45,950

(91.84%)

47,976

(91.78%)
27

3.1.2 Tertiary Tasks in Data Set B

Table 22 shows the frequency and percentage of tertiary tasks across each severity level in data 

set B. More than one tertiary task could be coded during a baseline or safety-critical event in the 

data set B; thus, the column totals in the data set B tables may exceed 100 percent. 

As shown in Table 22, most of the baseline events and safety-critical events did not involve a 

tertiary task (97.4 percent and 94.0 percent, respectively). However, of the tertiary tasks recorded 

during crashes in data set B, all were coded with a cell phone sub-task. More specifically, the 

crashes involved the driver using a cell phone to text/e-mail/access the internet while driving 

(1.02 percent), followed by talking/listening on a hand-held cell phone (0.46 percent), reaching 

for a cell phone (0.28 percent), and dialing a cell phone (0.18 percent). The most prevalent 

tertiary tasks during baseline events were talking/listening on a hand-held cell phone (1.1 

percent), talking/listening on a hands-free cell phone (0.8 percent), and food/drink (0.6 percent). 

Of the tertiary tasks present during the safety-critical events, the most prevalent were other 

distraction (3.0 percent), talking/listening on a hand-held cell phone (1.0 percent), food/drink 

(0.7 percent), and talking/listening on a hands-free cell phone (0.5 percent).
28

Table 22. Frequency and percentage of tertiary tasks across each severity level in data set B.

Tertiary Tasks

Number 

(and percent)

of Baseline Events 

Number 

(and percent)

of Crashes

Number 

(and percent)

of Near-Crashes

Number 

(and percent)

of Crash-Relevant 

Conflicts

Number (and 

percent) of SafetyCritical Events

(crashes + nearcrashes +crashrelevant conflicts)

Dialing Cell Phone 256

(0.12%)

2

(0.18%)

3

(0.04%)

160

(0.52%)

165

(0.41%)

Consuming 

Food/Drink

1,320

(0.63%)

0

(0.00%)

8

(0.10%)

260

(0.85%)

268

(0.67%)

Other Distraction 13

(0.01%)

0

(0.00%)

32

(0.38%)

1,188

(3.87%)

1,220

(3.04%)

Passenger 0

(0.00%)

0

(0.00%)

0

(0.00%)

30

(0.10%)

30

(0.07%)

Reaching for 

Headset/Earpiece

168

(0.08%)

0

(0.00%)

0

(0.00%)

104

(0.34%)

104

(0.26%)

Reaching for Cell 

Phone

178

(0.08%)

3

(0.28%)

3

(0.04%)

116

(0.38%)

122

(0.30%)

Talking/Listening on 

Hand-Held Cell 

Phone

22,66

(1.07%)

5

(0.46%)

8

(0.10%)

359

(1.17%)

372

(0.93%)

Talking/Listening on 

Hands-Free Cell 

Phone

1,626

(0.77%)

0

(0.00%)

8

(0.10%)

186

(0.61%)

194

(0.48%)

Texting/E-mailing/

Accessing the Internet 

on Phone

3

(0.00%)

11

(1.02%)

2

(0.02%)

77

(0.25%)

90

(0.22%)

Other Communication 

Device

0

(0.00%)

0

(0.00%)

0

(0.00%)

0

(0.00%)

0

(0.00%)

None 205,582

(97.35%)

1,064

(98.06%)

8,314

(99.27%)

28,330

(92.40%)

37,708

(93.99%)
29

Table 23 shows the frequency and percentage of tertiary tasks across each severity level in data 

set B for fleets with no cell phone policy. Note that fleets with an unknown cell phone policy 

were excluded from Table 23 and Table 24; thus, the frequencies in Table 23 and Table 24 will 

not sum to the frequencies found in Table 22. As shown in Table 23, most of the baseline events

and safety-critical events did not involve a tertiary task (96.1 percent and 92.9 percent, 

respectively). However, of the tertiary tasks recorded during crashes in s data set B for fleets 

with no cell phone policy, all were coded with a cell phone sub-task. More specifically, the 

crashes involved the driver using a cell phone to text/e-mail/access the Internet while driving 

(2.1 percent), followed by talking/listening on a hand-held cell phone (1.4 percent), reaching for 

a cell phone (0.28 percent), and dialing a cell phone (0.7 percent). The most prevalent tertiary 

tasks during baseline events were talking/listening on a hand-held cell phone (2.3 percent), 

talking/listening on a hands-free cell phone (0.7 percent), and consuming food/drink (0.5 

percent). Of the tertiary tasks present during the safety-critical events, the most prevalent were 

other distraction (2.4 percent), talking/listening on a hand-held cell phone (1.8 percent), and 

talking/listening on a hands-free cell phone (0.8 percent). 
30

Table 23. Frequency and percentage of tertiary tasks across each severity level in data set B (no fleet cell phone policy).

Tertiary Tasks

Number 

(and percent)

of Baseline Events 

Number 

(and percent)

of Crashes 

Number 

(and percent)

of Near-Crashes

Number 

(and percent)

of Crash-Relevant 

Conflicts 

Number (and 

percent) of SafetyCritical Events 

(crashes + nearcrashes +crashrelevant conflicts)

Dialing Cell Phone 72

(0.26%)

0

(0.00%)

1

(0.10%)

37

(0.98%)

38

(0.78%)

Consuming 

Food/Drink

144

(0.52%)

0

(0.00%)

0

(0.00%)

28

(0.74%)

28

(0.57%)

Other Distraction 1

(0.00%)

0

(0.00%)

5

(0.52%)

113

(2.99%)

118

(2.42%)

Passenger 0

(0.00%)

0

(0.00%)

0

(0.00%)

7

(0.19%)

7

(0.14%)

Reaching for 

Headset/Earpiece

43

(0.15%)

0

(0.00%)

0

(0.00%)

28

(0.74%)

28

(0.57%)

Reaching for Cell 

Phone

54

(0.19%)

1

(0.71%)

1

(0.10%)

29

(0.77%)

31

(0.63%)

Talking/Listening on 

Hand-Held Cell 

Phone

649

(2.33%)

2

(1.43%)

2

(0.21%)

82

(2.17%)

86

(1.76%)

Talking/Listening on 

Hands-Free Cell 

Phone

184

(0.66%)

0

(0.00%)

2

(0.21%)

39

(1.03%)

41

(0.84%)

Texting/E-mailing/

Accessing the Internet 

on Phone

0

(0.00%)

3

(2.14%)

0

(0.00%)

2

(0.05%)

5

(0.10%)

Other Communication 

Device

0

(0.00%)

0

(0.00%)

0

(0.00%)

0

(0.00%)

0

(0.00%)

None 26,730

(96.12%)

134

(95.71%)

956

(98.96%)

3,448

(91.24%)

4,538

(92.90%)
31

Table 24 shows the frequency and percentage of tertiary tasks across each severity level in data 

set B for fleets with a cell phone policy. As shown in Table 24, most of the baseline events and 

safety-critical events did not involve a tertiary task (97.2 percent and 93.4 percent, respectively). 

However, of the tertiary tasks recorded during crashes in data set B for fleets with a cell phone 

policy, all were coded with a cell phone sub-task. More specifically, the crashes involved the 

driver using a cell phone to text/e-mail/access the Internet while driving (1.1 percent), followed 

by dialing a cell phone (0.3 percent), reaching for a cell phone (0.3 percent), and talking/listening 

on a hand-held cell phone (0.1 percent). The most prevalent tertiary tasks during baseline events

were talking/listening on a hand-held cell phone (1.0 percent), talking/listening on a hands-free 

cell phone (0.9 percent), and consuming food/drink (0.8 percent). Of the tertiary tasks present 

during the safety-critical events, the most prevalent were other distraction (3.5 percent), 

talking/listening on a hand-held cell phone (0.9 percent), and consuming food/drink (0.8 

percent).
32

Table 24. Frequency and percentage of tertiary tasks across each severity level in data set B (fleet cell phone policy).

Tertiary Tasks

Number 

(and percent)

of Baseline 

Events 

Number 

(and percent)

of Crashes 

Number 

(and percent)

of Near-Crashes

Number 

(and percent)

of Crash-Relevant 

Conflicts 

Number (and 

percent) of 

Safety-Critical 

Events (crashes + 

near-crashes 

+crash-relevant 

conflicts)

Dialing Cell Phone 161

(0.12%)

2

(0.27%)

2

(0.03%)

115

(0.53%)

119

(0.42%)

Consuming Food/Drink 1052

(0.76%)

0

(0.00%)

4

(0.07%)

209

(0.96%)

213

(0.75%)

Other Distraction 10

(0.01%)

0

(0.00%)

24

(0.41%)

969

(4.45%)

993

(3.49%)

Passenger 0

(0.00%)

0

(0.00%)

0

(0.00%)

16

(0.07%)

16

(0.06%)

Reaching for Headset/Earpiece 108

(0.08%)

0

(0.00%)

0

(0.00%)

70

(0.32%)

70

(0.25%)

Reaching for Cell Phone 96

(0.07%)

2

(0.27%)

2

(0.03%)

76

(0.35%)

80

(0.28%)

Talking/Listening on Hand-Held Cell 

Phone

1,388

(1.00%)

1

(0.14%)

5

(0.08%)

259

(1.19%)

265

(0.93%)

Talking/Listening on Hands-Free Cell 

Phone

1205

(0.87%)

0

(0.00%)

5

(0.08%)

132

(0.61%)

137

(0.48%)

Text/E-mail/Access the Internet on 

Phone

3

(0.00%)

8

(1.09%)

2

(0.03%)

74

(0.34%)

84

(0.30%)

Other Communication Device 0

(0.00%)

0

(0.00%)

0

(0.00%)

0

(0.00%)

0

(0.00%)

None 135,158

(97.21%)

720

(98.23%)

5,880

(99.29%)

19,948

(91.68%)

26,548

(93.43%)
33

Table 25 shows the frequency and percentage of tertiary tasks in 3 axle or more trucks across 

each severity level in data set B. As shown in Table 25, most of the baseline events and safetycritical events in 3 axle or more trucks did not involve a tertiary task (97 percent and 87.5 

percent, respectively). The 3 axle or more truck driver was using his/her cell phone to text/

e-mail/access the Internet during the one crash where a tertiary task was coded. The most 

prevalent tertiary tasks in 3 axle or more trucks during baseline events were consuming 

food/drink (1.4 percent) and talking/listening on a hand-held cell phone (1.1 percent). Of the 

tertiary tasks in 3 axle or more trucks during the safety-critical events, the most prevalent were 

other distraction/electronic device (8.1 percent), consuming food/drink (1.6 percent), 

talking/listening on a hand-held cell phone (1.0 percent), and texting/e-mailing/accessing the 

Internet (0.8 percent).
34

Table 25. Frequency and percentage of tertiary tasks in 3 axle or more trucks across each severity level in data set B.

Tertiary Tasks

Number 

(and percent)

of Baseline 

Events 

Number 

(and percent)

of Crashes 

Number 

(and percent)

of Near-Crashes

Number 

(and percent)

of CrashRelevant 

Conflicts 

Number (and 

percent) of 

Safety-Critical 

Events (crashes 

+ near-crashes 

+crash-relevant 

conflicts)

Dialing Cell Phone 40

(0.08%)

0

(0.00%)

0

(0.00%)

31

(0.52%)

31

(0.45%)

Consuming Food/Drink 683

(1.43%)

0

(0.00%)

2

(0.22%)

111

(1.88%)

113

(1.63%)

Other Distraction 1

(0.00%)

0

(0.00%)

11

(1.20%)

547

(9.26%)

558

(8.06%)

Passenger 0

(0.00%)

0

(0.00%)

0

(0.00%)

0

(0.00%)

0

(0.00%)

Reaching for Headset/Earpiece 28

(0.06%)

0

(0.00%)

0

(0.00%)

17

(0.29%)

17

(0.25%)

Reaching for Cell Phone 34

(0.07%)

0

(0.00%)

0

(0.00%)

18

(0.30%)

18

(0.26%)

Talking/Listening on Hand-Held Cell 

Phone

534

(1.12%)

0

(0.00%)

1

(0.11%)

68

(1.15%)

69

(1.00%)

Talking/Listening on Hands-Free Cell 

Phone

155

(0.32%)

0

(0.00%)

1

(0.11%)

27

(0.46%)

28

(0.40%)

Texting/E-mailing/Accessing the 

Internet on Phone

2

(0.00%)

1

(0.93%)

0

(0.00%)

57

(0.96%)

58

(0.84%)

Other Communication Device 0

(0.00%)

0

(0.00%)

0

(0.00%)

0

(0.00%)

0

(0.00%)

None 46,326

(96.98%)

106

(99.07%)

898

(98.36%)

5,060

(85.66%)

6,064

(87.54%)
35

Table 26 shows the frequency and percentage of tertiary tasks in buses across each severity level 

in data set B. As shown in Table 26, most of the baseline events and safety-critical events in 

buses did not involve a tertiary task (98.5 percent and 96.4 percent, respectively). However, of 

the tertiary tasks recorded in bus crashes in data set B, all were coded with a cell phone sub-task. 

More specifically, the bus crashes involved the driver using a cell phone to text/e-mail/access the 

Internet while driving (1.0 percent), followed by talking/listening on a hand-held cell phone 

(0.54 percent), reaching for a cell phone (0.27 percent), and dialing a cell phone (0.27 percent). 

The most prevalent tertiary tasks in buses during baseline events were talking/listening on a 

hand-held cell phone (0.7 percent), consuming food/drink (0.3 percent), and talking/listening on 

a hands-free cell phone (0.3 percent). Of the tertiary tasks in buses during the safety-critical 

events, the most prevalent were other distraction (1.5 percent) and talking/listening on a handheld cell phone (0.7 percent). 
36

Table 26. Frequency and percentage of tertiary tasks in buses across each severity level in data set B.

Tertiary Tasks

Number 

(and percent)

of Baseline 

Events 

Number 

(and percent)

of Crashes 

Number 

(and percent)

of Near-Crashes

Number 

(and percent)

of CrashRelevant 

Conflicts 

Number (and 

percent) of 

Safety-Critical 

Events (crashes 

+ near-crashes 

+crash-relevant 

conflicts)

Dialing Cell Phone 137

(0.14%)

2

(0.27%)

2

(0.03%)

80

(0.43%)

84

(0.33%)

Consuming Food/Drink 317

(0.32%)

0

(0.00%)

5

(0.08%)

93

(0.50%)

98

(0.38%)

Other Distraction 7

(0.01%)

0

(0.00%)

11

(0.18%)

379

(2.04%)

390

(1.53%)

Passenger 0

(0.00%)

0

(0.00%)

0

(0.00%)

24

(0.13%)

24

(0.09%)

Reaching for Headset/Earpiece 78

(0.08%)

0

(0.00%)

0

(0.00%)

55

(0.30%)

55

(0.22%)

Reaching for Cell Phone 92

(0.09%)

2

(0.27%)

1

(0.02%)

55

(0.30%)

58

(0.23%)

Talking/Listening on Hand-Held Cell Phone 724

(0.73%)

4

(0.54%)

4

(0.07%)

177

(0.95%)

185

(0.73%)

Talking/Listening on Hands-Free Cell Phone 247

(0.25%)

0

(0.00%)

2

(0.03%)

81

(0.44%)

83

(0.33%)

Texting/E-mailing/Accessing the Internet on 

Phone

1

(0.00%)

7

(0.95%)

2

(0.03%)

11

(0.06%)

20

(0.08%)

Other Communication Device 0

(0.00%)

0

(0.00%)

0

(0.00%)

0

(0.00%)

0

(0.00%)

None 98,186

(98.52%)

725

(97.97%)

6,120

(99.59%)

17,695

(95.26%)

24,540

(96.38%)
37

Table 27 shows the frequency and percentage of tertiary tasks in tractor trailers/tankers across 

each severity level in data set B. As shown in Table 27, most of the baseline events and safetycritical events in tractor trailers/tankers did not involve a tertiary task (95.8 percent and 91.9 

percent, respectively). However, of the tertiary tasks recorded in the tractor trailer/tanker crashes 

in data set B, all were coded with a cell phone sub-task. More specifically, the tractor 

trailer/tanker crashes involved the driver using a cell phone to text/e-mail/access the Internet

while driving (1.3 percent), followed by talking/listening on a hand-held cell phone (0.4 percent), 

and reaching for a cell phone (0.4 percent). The most prevalent tertiary tasks in tractor 

trailers/tankers during baseline events were talking/listening on a hands-free cell phone (1.9 

percent), talking/listening on a hand-held cell phone (1.6 percent), and consuming food/drink 

(0.5 percent). Of the tertiary tasks in tractor trailers/tankers during the safety-critical events, the 

most prevalent were other distraction (3.5 percent), talking/listening on a hand-held cell phone 

(1.5 percent), and talking/listening on a hands-free cell phone (1.1 percent).
38

Table 27. Frequency and percentage of tertiary tasks in tractor trailers/tankers across each severity level in data set B.

Tertiary Tasks

Number 

(and percent)

of Baseline 

Events 

Number 

(and percent)

of Crashes 

Number 

(and percent)

of Near-Crashes

Number 

(and percent)

of CrashRelevant 

Conflicts 

Number (and 

percent) of 

Safety-Critical 

Events (crashes 

+ near-crashes 

+crash-relevant 

conflicts)

Dialing Cell Phone 79

(0.12%)

0

(0.00%)

1

(0.08%)

49

(0.79%)

50

(0.65%)

Consuming Food/Drink 320

(0.50%)

0

(0.00%)

1

(0.08%)

56

(0.91%)

57

(0.74%)

Other Distraction 5

(0.01%)

0

(0.00%)

10

(0.76%)

262

(4.24%)

272

(3.52%)

Passenger 0

(0.00%)

0

(0.00%)

0

(0.00%)

6

(0.10%)

6

(0.08%)

Reaching for Headset/Earpiece 62

(0.10%)

0

(0.00%)

0

(0.00%)

32

(0.52%)

32

(0.41%)

Reaching for Cell Phone 52

(0.08%)

1

(0.42%)

2

(0.15%)

43

(0.70%)

46

(0.59%)

Talking/Listening on Hand-Held Cell Phone 1,008

(1.58%)

1

(0.42%)

3

(0.23%)

114

(1.85%)

118

(1.53%)

Talking/Listening on Hands-Free Cell Phone 1,224

(1.92%)

0

(0.00%)

5

(0.38%)

78

(1.26%)

83

(1.07%)

Texting/E-mailing/Accessing the Internet on

Phone

0

(0.00%)

3

(1.26%)

0

(0.00%)

9

(0.15%)

12

(0.16%)

Other Communication Device 0

(0.00%)

0

(0.00%)

0

(0.00%)

0

(0.00%)

0

(0.00%)

None 61,070

(95.80%)

233

(97.90%)

1,296

(98.41%)

5,575

(90.24%)

7,104

(91.87%)
39

3.1.3 Summary

The prevalence rates of tertiary tasks in this study were far below those found in LTCCS 

(FMCSA, 2005) and in a naturalistic truck study conducted by Olson et al. (2009). Olson et al. 

found that 59.9 percent and 56.5 percent of the safety-critical events and baseline epochs had at 

least one tertiary task present, respectively. The LTCCS found that 9 percent of the crashes were 

attributed to driver inattention, 8 percent were attributed to an external distraction (i.e., the driver 

was looking at something outside of his/her truck), and 2 percent were attributed to an internal 

distraction. Note that these driver errors in the LTCCS were determined to be the causal factor in 

the crash (i.e., had they not been present, the crash would not have happened). If these driver 

errors had also been considered as an associated factor, rather than a causal factor, they would 

likely result in much higher percentages.

It is likely that these data sets present a very conservative view of the scope of the distracted 

driver problem. The prevalence of driver distraction in these data sets was likely influenced by 

four factors. First, and most importantly, the fleets in the study were able to actively monitor 

their drivers. The system is an effective safety-management technique aimed at reducing risky 

driving behaviors. Hickman et al. (in press) found that the system reduced the mean-rate of 

safety-critical events/10,000 miles by up to 70 percent in a sample of long-haul and short-haul 

truck drivers. Similarly, McGehee et al. (2007) found that the same system was able to reduce 

the mean rate of safety-critical events/1,000 miles by up to 70 percent in a sample of novice teen 

drivers. Second, these reductions were likely compounded (at least in data set B) by the intense 

media attention regarding distracted driving. Third, the fact that the fleets in the data set 

purchased and used the system likely reflects a group of safety conscious fleets. Of course, it is 

possible the converse is true (i.e., fleet with significant safety issues decided they needed the 

driver monitoring system). Lastly, the available view of the driver from the system was less 

comprehensive as compared to the video views in Blanco et al. (in press). Figure 4 shows the 

five camera view available in Blanco et al. This limited view of the driver using the system (as 

shown in Figure 2), combined with the three other factors noted, may have resulted in fewer 

distracted driving events being recorded than may be occurring in the larger truck and bus 

population. 
40

Face View Forward View

Over-the-Shoulder

Right Mirror

Left Mirror

Adapted from Blanco et al. (in press)

Figure 4. Photos. Five screenviews captured by event recorders installed on test vehicle.

3.2 ANALYSIS 2: ODDS RATIOS FOR TERTIARY TASKS

Table 28 shows the odds ratios for each tertiary task in data set B for all safety-critical events. 

Note that crashes are included in the safety-critical events in data set B. Also, each cell phone 

safety-critical event and baseline in data set B was re-reviewed to classify specific cell phone 

sub-tasks.

As shown in Table 28, many of the tertiary tasks in data set B had a significant odds ratio. If 1.0 

is not included between the lower confidence limit and the upper confidence limit, the odds ratio 

is significant. The odds ratios for other distraction and texting/e-mailing/accessingthe Internet 

were very high. An odds ratio is a measure of association (not unlike a correlation) which can be 

used under the correct circumstances as an estimate of relative risk. In this situation, the odds 

ratios for other distraction and texting/e-mailing/accessingthe Internet indicated a strong 

relationship between these tertiary tasks and safety-critical events. As shown by the large 95 

confidence interval for these tertiary tasks, the error associated with these odds ratio estimates 

was extremely high (there were only 3 and 13 baseline events for texting/e-mailing/accessingthe 

Internet, and other distraction, respectively); thus, it was difficult to report the odds ratio in any 

meaningful sense other than to report there was a very strong relationship between other 

distraction and texting/e-mailing/accessingthe Internet and involvement in a safety-critical event.
41

Table 28. Odds ratios for each tertiary task in data set B.

Tertiary Task Odds Ratio

Lower 

Confidence 

Limit

Upper 

Confidence 

Limit

Frequency 

of Safety 

Critical 

Events

Frequency 

of Baseline 

Events

Any Cell Phone Usage 1.14* 1.06 1.23 895 4,262

Dialing Cell Phone 3.51* 2.89 4.27 165 256

Talking/Listening Hands Free 

Cell Phone

0.65* 0.56 0.76 194 1,626

Talking/Listening Hand Held 

Cell Phone

0.89 0.80 1.00 372 2,266

Reaching for 

Headset/Earpiece

3.38* 2.64 4.31 104 168

Reaching for Cell Phone 3.74* 2.97 4.71 122 178

Texting/E-mailing/Accessing 

the Internet

163.59* 51.77 516.73 90 3

Consuming Food/Drink 1.11 0.97 1.26 268 1,320

Other Distraction 511.64* 296.20 883.79 1,220 13

Passenger Distraction – – – 30 0

Note: Asterisk indicates a significant odds ratio. 

Truck and bus drivers reaching for a cell phone while driving were found to increase 

significantly their odds of involvement in a safety safety-critical event by 3.7 times. Dialing a 

cell phone while driving significantly increased the odds of involvement in a safety-critical event 

by 3.5 times, while reaching for a headset/earpiece increased the odds by 3.4 times. The tertiary 

task, any cell phone usage, includes all the specific cell phone sub-tasks. This is analogous to 

how cell phone usage was classified in data set A. As shown in Table 28, drivers significantly 

increased their odds of involvement in a safety-critical event by 1.14 times for any cell phone 

usage while driving.

One tertiary task was found to decrease significantly the odds of involvement in a safety-critical 

event. That is, drivers were less likely to be involved in a safety-critical event while 

talking/listening on a hands-free cell phone. Both consuming food/drink and talking/listening on 

a hand-held cell phone (odds ratios = 1.11 and 0.89, respectively) had non-significant odds ratios 

(i.e., no increase and/or decrease in risk). 

Table 29 shows the odds ratios for each cell phone sub-task across each fleet cell phone policy in 

data set B. Note that fleets with an unknown cell phone policy in data set B were excluded from 

this analysis. Also note that the tertiary tasks, consuming food/drink and other distraction, are not 

shown as these tertiary tasks would not be affected by the cell phone policy. The tertiary task, 

texting/e-mailing/accessing the Internet, is not shown as the sample size for this tertiary task

does not allow disaggregated analyses.

As shown in Table 29, many of the tertiary tasks in the disaggregated data set B had a significant 

odds ratio. Regardless of the fleet’s cell phone policy, drivers’ odds of being involved in a 

safety-critical event increased significantly while reaching for a cell phone (odds ratios = 4.24 if 

cell phone policy is in place and 3.38 if the company has no cell phone policy), dialing a cell 
42

phone (odds ratios = 3.76 and 3.11, respectively with or without a company policy), and reaching 

for a headset/earpiece while driving (odds ratios = 3.30 and 3.84, respectively with or without a 

company policy). The tertiary task, any cell phone usage, includes all the specific cell phone subtasks. As shown in Table 29, regardless of fleet cell phone policy, drivers’ odds of involvement 

in a safety-critical event increased significantly for any cell phone usage while driving (odds 

ratios = 1.16 and 1.22, respectively with or without a company policy).

Two tertiary tasks were found to decrease significantly the odds of involvement in a safetycritical event. Truck and bus drivers significantly decreased their odds of involvement in a 

safety-critical event while talking/listening on a hands-free cell phone in a fleet with a cell phone 

policy (odds ratio = 0.58) and while talking/listening on a hand-held cell phone in a fleet with no 

cell phone policy (odds ratio = 0.78). However, talking/listening on a hands-free cell phone in a 

fleet with no cell phone policy and talking/listening on a hand-held cell phone in a fleet with a 

cell phone policy had non-significant odds ratios (odds ratios = 1.3 and 0.97, respectively). 

Table 29. Odds ratios for cell phone sub-tasks across each fleet cell phone policy in data set B.

Tertiary Task Odds 

Ratio

Lower 

Confidence 

Limit

Upper 

Confidence 

Limit

Frequency 

of Safety 

Critical 

Events

Frequency 

of Baseline 

Events

Any Cell Phone Usage (No Cell 

Phone Policy) 1.22* 1.04 1.43 194 934

Any Cell Phone Usage (Cell 

Phone Policy) 1.16* 1.07 1.27 644 2,815

Dialing Cell Phone (No Cell 

Phone Policy) 3.11* 2.11 4.61 38 72

Dialing Cell Phone (Cell Phone 

Policy) 3.76* 3.0 4.77 119 161

Talking/Listening Hands Free 

Cell Phone (No Cell Phone 

Policy)

1.31 0.93 1.84 41 184

Talking/Listening Hands Free 

Cell Phone (Cell Phone Policy) 0.58* 0.48 0.70 137 1,205

Talking/Listening Hand Held 

Cell Phone (No Cell Phone 

Policy)

0.78* 0.62 0.98 86 649

Talking/Listening Hand Held 

Cell Phone (Cell Phone Policy) 0.97 0.85 1.11 265 1,388

Reaching for Headset/Earpiece 

(No Cell Phone Policy) 3.84* 2.38 6.18 28 43

Reaching for Headset/Earpiece 

(Cell Phone Policy) 3.30* 2.44 4.46 70 108

Reaching for Cell Phone (No 

Cell Phone Policy) 3.38* 2.17 5.27 31 54

Reaching for Cell Phone (Cell 

Phone Policy) 4.24* 3.15 5.71 80 96

Note: Asterisk indicates a significant odds ratio. 
43

Table 30 shows the odds ratios for each tertiary task in 3 axle or more trucks in the data set B. As 

shown in Table 30, many of the tertiary tasks in 3 axle or more trucks in data set B had a 

significant odds ratio. Three axle or more truck drivers significantly increased their odds of 

involvement in a safety-critical event by 5.9 times while dialing a cell phone while driving. 

Reaching for a headset/earpiece while driving a 3 axle or more truck increased risk by 4.6 times, 

while reaching for a cell phone and consuming food/drink increased the odds by 4.0 times and 

1.3 times, respectively. 

The tertiary task, any cell phone usage, includes all the specific cell phone sub-tasks. As shown 

in Table 30, 3 axle or more truck drivers significantly increased their odds of involvement in a 

safety-critical event by 1.9 times for any cell phone usage while driving. Both talking/listening 

on a hands-free cell phone and talking/listening on a hand-held cell phone (odds ratios = 1.4 and 

1.0, respectively) had non-significant odds ratios. 

Table 30. Odds ratios for each tertiary task in 3 axle or more trucks in data set B.

Tertiary Task Odds 

Ratio

Lower 

Confidence 

Limit

Upper 

Confidence 

Limit

Frequency of 

Safety 

Critical 

Events

Frequency of 

Baseline 

Events

Any Cell Phone Usage 1.93* 1.64 2.27 192 759

Dialing Cell Phone 5.92* 3.70 9.47 31 40

Talking/Listening HandsFree Cell Phone1.38 0.92 2.06 28 155

Talking/Listening HandHeld Cell Phone0.98 0.77 1.27 69 534

Reaching for 

Headset/Earpiece

4.64* 2.53 8.48 17 28

Reaching for Cell Phone 4.04* 2.28 7.17 18 34

Consuming Food/Drink 1.26* 1.03 1.55 113 683

Note: Asterisk indicates a significant odds ratio. 

Table 31 shows the odds ratios for each tertiary task in buses data set B. As shown in Table 31, 

many of the tertiary tasks in buses in data set B had a significant odds ratio. Bus drivers 

significantly increased their odds of involvement in a safety-critical event by 2.8 times while 

reaching for a headset/earpiece while driving. Reaching for a cell phone while driving a bus 

significantly increased the odds of involvement in a safety-critical event by 2.5 times. Dialing a 

cell phone and talking/listening on a hands-free cell phone increased the odds by 2.5 and 1.3 

times, respectively.

The tertiary task, any cell phone usage, includes all the specific cell phone sub-tasks. As shown 

in Table 31, bus drivers significantly increased their odds of involvement in a safety-critical 

event by 1.4 times for any cell phone usage while driving. Talking/Listening on a hand-held cell 

phone and consuming food/drink had a non-significant odds ratios (odds ratios = 1.1 and 1.24, 

respectively).
44

Table 31. Odds ratios for each tertiary task in buses in data set B.

Tertiary Task Odds 

Ratio

Lower 

Confidence 

Limit

Upper 

Confidence 

Limit

Frequency 

of Safety 

Critical 

Events

Frequency 

of Baseline 

Events

Any Cell Phone Usage 1.42* 1.27 1.59 409 1,152

Dialing Cell Phone 2.45* 1.87 3.22 84 137

Talking/Listening Hands-Free 

Cell Phone 1.34* 1.05 1.72 83 247

Talking/Listening Hand-Held 

Cell Phone 1.02 0.87 1.20 185 724

Reaching for Headset/Earpiece 2.82* 2.00 3.99 55 78

Reaching for Cell Phone 2.52* 1.81 3.50 58 92

Consuming Food/Drink 1.24 0.98 1.55 98 317

Note: Asterisk indicates a significant odds ratio. 

Table 32 shows the odds ratios for each tertiary task in tractor trailers/tankers in data set B. As 

shown in Table 32, many of the tertiary tasks in tractor trailers/tankers in data set B had a 

significant odds ratio. Tractor trailer/tanker drivers significantly increased their odds of 

involvement in a safety-critical event by 7.6 times while reaching for a cell phone while driving. 

Dialing a cell phone while driving a tractor trailer/tanker increased risk by 5.4 times, while 

reaching for a headset/earpiece device and consuming food/drink increased risk by 4.4 and 1.5 

times, respectively.

One tertiary task was found to decrease significantly the risk of involvement in a safety-critical 

event. That is, drivers were less likely to be involved in a safety-critical event while 

talking/listening on a hands-free cell phone (odds ratio = 0.58). The tertiary task, any cell phone 

usage, includes all the specific cell phone sub-tasks. As shown in Table 32, both any cell phone 

usage and talking/listening on a hand-held cell phone had a non-significant odds ratios (odds 

ratios = 1.1 and 1.1, respectively). 

Table 32. Odds ratios for each tertiary task in tractor trailers/tankers in data set B.

Tertiary Task Odds 

Ratio

Lower 

Confidence 

Limit

Upper 

Confidence 

Limit

Frequency 

of Safety 

Critical 

Events

Frequency 

of Baseline 

Events

Any Cell Phone Usage 1.08 0.95 1.22 294 2,351

Dialing Cell Phone 5.44* 3.81 7.76 50 79

Talking/Listening Hands-Free 

Cell Phone

0.58* 0.47 0.73 83 1224

Talking/Listening Hand-Held Cell 

Phone

1.01 0.83 1.22 118 1,008

Reaching for Headset/Earpiece 4.43* 2.89 6.80 32 62

Reaching for Cell Phone 7.60* 5.11 11.31 46 52

Consuming Food/Drink 1.53* 1.15 2.03 57 320

Note: Asterisk indicates a significant odds ratio. 
45

3.2.1 Summary

The odds ratio for other distraction was extremely high. While there was a very strong 

relationship between other distraction and involvement in a safety-critical event, the operational 

definition for this tertiary task was nebulous (included anything not listed in Table 9); thus, 

making any meaningful interpretation on this tertiary task was not possible.

The odds ratios for each vehicle type (e.g., 3 axle or more truck, bus, and tractor trailer/tanker) 

found some interesting trends. Although the magnitude of the odds ratios varied, the tertiary 

tasks, dialing a cell phone, reaching for a cell phone, and reaching for a headset/earpiece all 

increased the risk of involvement in a safety-critical event in 3 axle or more trucks, buses, and 

tractor trailers/tankers. The tertiary task, talking/listening on a hand-held cell phone, was 

consistently found to have no impact on the odds of involvement in a safety-critical event in each 

vehicle type. However, differences in risk between each vehicle type were found when 

evaluating the tertiary tasks, talking/listening on a hands-free cell phone and consuming 

food/drink. For example, talking/listening on a hands-free phone was found to increase the odds 

of involvement in a safety-critical event in buses (odds ratio = 1.3), decrease the odds in tractor 

trailers/tankers (odds ratio = 0.60), and have no impact on the odds in 3 axle or more trucks 

(odds ratio = 1.4). Consuming food/drink was found to increase significantly the odds of 

involvement in a safety-critical event in tractor trailers/tankers and 3 axle or more trucks (odds 

ratios = 1.5 and 1.3, respectively), but have no impact on the odds in buses (odds ratio = 1.2). 

It is also important to note this was an observational study that evaluated associations between 

various tertiary tasks and safety-critical event occurrence. The study did not evaluate cause and 

effect (i.e., cell phone use caused a safety-critical event), but rather showed which tertiary tasks 

increased commercial truck and bus drivers odds of being involved in safety-critical event if they 

engaged in those tertiary tasks while driving. As indicated above, the data sets may not reflect 

the actual prevalence of driver distractions in 3 or more axle trucks, buses, and tractor 

trailers/tankers. However, the presence of an OBSM system, such as DriveCam, does not change 

the riskiness of engaging in tertiary tasks while driving, it only affects the prevalence of drivers 

who engage in those tasks while driving, which in turn may alter the odds ratios.

3.3 ANALYSIS 3: POPULATION ATTRIBUTABLE RISK

In analysis 3, PAR percentages were calculated on all significant odds ratios greater than 1.0 in 

data set B. The PAR provides an assessment of the percentage of safety-critical events that 

occurred in the population and that were directly attributable to the specific tertiary tasks 

measured. Table 33 shows the PAR and 95 percent confidence interval for all odds ratios greater 

than 1.0 in data set B for all safety-critical events and each fleet cell phone policy. Note that a 

very large rate ratio estimate will result in a higher PAR estimate. Given how many safetycritical events and baseline events were in the data set and how rare some of the tertiary tasks 

were explains why some of the PAR estimates were rather low and contained a negative 

confidence interval (thereby uninterruptable); however, the rate ratio estimates for other 

distraction and texting/e-mailing/accessing the Internet were so large that it did not matter how 

frequently these tertiary tasks occurred. As indicated above, the error for other distraction and 

texting/e-mailing/accessing the Internet does not allow an interpretable PAR.”
46

Table 33. PAR and 95 percent confidence intervals for each tertiary task in data set B for all safetycritical events and each fleet cell phone policy.

Tertiary Tasks PAR Lower 

Confidence Limit

Upper 

Confidence Limit

Any Cell Phone Usage 0.30 -0.32 0.92

Any Cell Phone Usage (No Cell Phone Policy) 0.77 -0.48 2.02

Any Cell Phone Usage (Cell Phone Policy) 0.34 -0.39 1.07

Dialing Cell Phone 0.43 -0.13 0.99

Dialing Cell Phone (No Cell Phone Policy) 0.73 -0.53 2.00

Dialing Cell Phone (Cell Phone Policy) 0.48 -0.16 1.11

Reaching for Headset/Earpiece 0.26 -0.46 0.99

Reaching for Headset/Earpiece (No Cell Phone Policy) 0.64 -0.61 1.88

Reaching for Headset/Earpiece (Cell Phone Policy) 0.25 -0.66 1.16

Reaching for Cell Phone 0.34 -0.28 0.95

Reaching for Cell Phone (No Cell Phone Policy) 0.64 -0.67 1.95

Reaching for Cell Phone (Cell Phone Policy) 0.35 -0.35 1.05

Texting/E-mailing/Accessing the Internet 5.84 5.78 5.92

Other Distraction 72.02 72.01 77.04

3.4 ANALYSIS 4: ODDS RATIOS FOR FLEET POLICY AND STATE LAW

In analysis 4, odds ratios were calculated to approximate the effectiveness of a fleet cell phone 

policy and State cell phone law regarding cell phone use while driving. As indicated above, these 

odds ratio did not look at how cell phone use while driving affected involvement in a safetycritical event, but rather evaluated the odds of cell phone usage given a fleet cell phone policy or 

State cell phone law versus the odds of cell phone usage given no fleet cell phone policy or State

cell phone law. Table 34 shows the odds ratios for cell use while driving in each cell phone 

policy in the entire days data set (i.e., data set A + B) for all safety-critical events and baseline 

events. As shown in Table 34, for truck and bus drivers the odds of using a cell phone while 

driving under a fleet cell phone policy were .83 times less, compared to no fleet cell phone 

policy. However, the data shows that a State cell phone law did not significantly impact drivers’ 

likelihood in using their cell phone while driving compared to a State that did not have a law 

prohibiting cell phone use (odds ratio = 0.97). The results from the study suggest that these laws 

may not be effective because drivers may be ignoring or unaware of them.

Table 34. Odds ratios for cell phone use while driving in each cell phone policy in the 365 day data 

set (safety-critical events).

Cell Phone 

Policy

Odds 

Ratio LCL UCL

Frequency of Cell 

Phone Use with 

Policy/Law

Frequency of No 

Cell Phone Use with 

Policy/Law

Fleet Cell Phone 

Policy 0.83* 0.78 0.87 8,787 1,897

State Cell Phone 

Law 0.97 0.94 1.01 4,526 2,987

Note: Asterisk indicates a significant odds ratio. 
47

4. DISCUSSION

4.1 SUMMARY

The introduction of naturalistic driving studies that record drivers (through video and kinematic 

sensors) in actual driving situations has created a scientific method to study driver behavior in 

real-world driving conditions in the presence of real-world daily pressures. If the primary 

intention of transportation safety research is to understand driver behavior in the real-world, then 

naturalistic studies, conducted in the real-world, must be considered the gold standard. However, 

conducting large-scale naturalistic driving studies is complex and there have only been a few 

published naturalistic studies that have assessed driver distraction (Klauer et al., 2006; Olson et 

al., 2009; Sayer, Devonshire, & Flanagan, 2007). More naturalistic research is needed across 

different settings and research sites to validate the results found in these studies. 

The aim of this project was to document the prevalence of distractions while driving a 

commercial truck and bus using an existing naturalistic data set. Commercial trucks (3 axle and 

tractor trailer/tanker) and buses (transit and motor coaches) were the target vehicles in the 

analyses. Two large and diverse data sets were used in the study. The data in data set B came 

from 183 different truck and bus fleets comprising a total of 13,306 vehicles. A total of 1,085 

crashes, 8,375 near-crashes, 30,661 crash-relevant conflicts, and 211,171 baseline events were 

captured from these 13,306 vehicles in data set B. The data in data set A came from 207 different 

truck and bus fleets comprising a total of 13,431 vehicles. A total of 1,336 crashes, 15,864 nearcrashes, and 173.591 crash-relevant conflicts were captured from these 13,431 vehicles in data 

set A.

All safety-critical events and baseline events are in data set B (i.e., June 6, 2009–September 5, 

2009) and all safety-critical events are in data set A (September 6, 2008–June 5, 2009). In data 

set B, the vendor re-reviewed all safety-critical events and baseline events where cell phone was 

noted as a distraction to determine the frequency of the following cell-phone tasks: dialing cell 

phone, reaching for cell phone, reaching for headset/earpiece, talking/listening on hands-free cell 

phone, talking/listening on hand-held cell phone, and texting/e-mailing/accessing the Internet on 

cell phone. Note that the vendor’s normal data reduction only records cell phone use and does 

not classify the individual cell phone tasks noted above. The vendor also re-reviewed all crashes 

in data set B and documented all the distractions that occurred. Note that the vendor does not 

normally perform data reduction on crashes. Data set A was identical to data set B; except the 

separate cell phone tasks were not classified and data reduction was not conducted on crashes in 

data set A. Note that the results (above) and conclusions (below) only deal with professional 

drivers in a commercial motor vehicle mode and do not represent other drivers in other 

transportation modes.

4.2 CONCLUSIONS

There were several interesting findings in the data that require further discussion. First, while the 

OBSM system certainly influenced the prevalence of tertiary tasks, it should not influence the 

risk in performing these tasks while driving. However, some fleets with a cell phone policy were 
48

more stringent in their monitoring and modification of cell phone use than others. Under these 

circumstances risk would be affected as cell phone use in a fleet with a strict cell phone policy 

might be coded as a safety-critical event, while the identical cell phone use might be coded as a 

baseline in a fleet with a more liberal cell phone policy. More specifically, a fleet with the strict 

cell phone policy may have instructed the vendor to code any cell phone use as safety event, 

while a fleet with the liberal cell phone policy may have instructed the vendor to code cell phone 

use as a safety event only if it occurred in conjunction with a traffic safety event. 

This was not seen as a limitation as the study was able to control for this by disaggregating the 

results by fleet cell phone policy. The cell phone results for fleets with no cell phone policy 

should be viewed as the most accurate assessment of risk; thus, the conclusions below regarding 

cell phone use generally describe the results for these disaggregated results (i.e., fleets with no 

cell phone policy). Surprisingly, the odds of involvement in a safety-critical event associated 

with each non-talking/listening cell phone sub-task were very similar in fleets with and without a 

cell phone policy. Differences were shown with regard to talking/listening on hand-held and 

hands-free cell phone. For example, talking/listening on a hands-free phone was shown to 

decrease significantly the odds of involvement in a safety-critical event in fleets with a fleet cell 

phone policy (odds ratio = 0.58), while the same cell phone sub-task was shown not to have any 

impact on involvement in safety-critical events for fleets without a cell phone policy (odds ratio 

= 1.31; not significant). The converse was found with talking/listening on a hand-held phone 

(i.e., talking/listening on a hands-free phone was shown to not have any impact on risk for fleets 

with a fleet cell phone policy, while the same cell phone sub-task was shown to be protective for 

fleets without a fleet cell phone policy). However, it is important to stress that in either case, 

talking/listening was not associated with an increase in the odds of involvement in a safetycritical event (i.e., either a non-significant odds ratio or a significant decrease in the odds). Thus, 

in this study and in Olson et al. (2009), talking/listening on a hands-free cell phone was not 

associated with increased odds of involvement in a safety-critical event.

Second, the results in this study were very similar to the results found by Olson et al. (2009) 

regarding safety-critical event risk and performing a tertiary task while driving. Table 35 shows a 

comparison of the odds ratios for selected tertiary tasks in this study and those found in Olson et 

al. Both studies found a very strong relationship between texting/e-mailing/accessing the Internet

and involvement in a safety-critical event. This study found that reaching for a headset/earpiece 

or a cell phone significantly increased the odds of involvement in a safety-critical event by 3.4 

and 3.8 times, respectively, in fleets with no cell phone policy. While Olson et al. did not classify 

these specific reaching tasks, the authors found that reaching for other electronic devices (which 

included headsets/earpieces) significantly increased the odds of involvement in a safety-critical 

event by 6.7 times. The tertiary task, consuming food/drink, was also found to have a nonsignificant odds of involvement in a safety-critical event in this study and Olson et al. There 

were differences between the two studies regarding the odds of involvement in a safety-critical 

event while talking/listening on a cell phone while driving. As shown in Table 35, this study 

found that talking/listening on a hand-held cell phone significantly decreased the odds of 

involvement in a safety-critical event in fleets with no cell phone policy (odds ratio = 0.78), 

while Olson et al. found that talking/listening on a hand-held cell phone did not impact the odds 

of involvement in a safety-critical event (odds ratio = 1.0). The converse was found when 

comparing talking/listening on a hands-free cell phone (i.e., the study found no significant 

impact on the odds of involvement in a safety-critical event while talking/listening on a hands-
49

free phone in fleets with no cell phone policy, while Olson et al. found the same cell phone subtask to significantly decrease the odds of involvement in a safety-critical event. This study also 

found that truck and bus drivers significantly increased their odds of involvement in a safetycritical event if they engaged in any cell phone use while driving, while Olson et al. found any 

cell phone use to not significantly increase drivers’ odds of involvement in a safety-critical 

event. The reason for this discrepancy is the robust decrease in the odds of involvement in a 

safety-critical event for the cell phone sub-tasks, talking/listening on a hands-free device, in the 

Olson et al. study. Note that all trucks in the Olson et al. study were tractor-trailers. 

Table 35. Odds ratios comparison for selected tertiary tasks in this study and Olson et al. (2009).

Tertiary Task

Odds Ratios Across 

all Vehicles with no

Fleet Cell Phone 

Policy in this Study

Odds Ratios for 

Tractor 

Trailers/Tankers

Only in this Study

Odds Ratios in 

Olson et al. (2009)

Any Cell Phone Use 1.22* 1.08 1.04

Dialing Cell Phone 3.1* 5.44* 5.93*

Talking/Listening Hands-Free 

Cell Phone 1.31 0.58* 0.44*

Talking/Listening Hand-Held 

Cell Phone 0.78* 1.01 1.04

Reaching for 

Headset/Earpiece 3.4* 4.43* 6.72*

Reaching for Cell Phone 3.8* 7.60* Included in dialing

cell phone

Texting/E-mailing/Accessing 

the Internet 163.6* – 23.24*

Consuming Food/Drink 1.1 1.53* 1.01

Note: Asterisk indicates a significant odds ratio.

The results reported in the previous paragraph include all vehicle types. The Olson et al. (2009) 

study only included tractor-trailers; thus, the inclusion of 3 axle or more trucks and buses in this 

comparison might obscure any differences related to those specific vehicle types. As shown in

Table 35, the odds ratios for selected tertiary tasks in tractor trailers/tankers are also shown. The 

odds ratios for selected tertiary tasks in tractor trailers/tankers in this study were very similar to 

the odds ratios for tractor trailers found in Olson et al. Notably, the tertiary task, consuming 

food/drink, was found to significantly increase the odds of involvement in a safety-critical event 

in this study, while Olson et al. found that consuming food/drink did not have an impact on the 

odds of involvement in a safety-critical event. 

Third, it appears using a cell phone to text, e-mail, or access the Internet while driving is in a 

category of risk all by itself. Although the odds ratios for this tertiary task were very large in 

both this study and in Olson et al. (2009), it was also associated with a large amount or error (as 

shown by the large 95 percent confidence interval). There were very few instances of this 

behavior observed during safety-critical events in this study and Olson et al. (90 and 23, 

respectively) and even fewer during control events (3 and 3, respectively). As indicated above, 

an odds ratio is a measure of association (not unlike a correlation) which can be used under the 

correct circumstances as an estimate of rate ratio. In this situation, the odds ratio for texting/
50

e-mailing/accessing the Internet indicates a strong relationship between this tertiary task and 

involvement in a safety-critical event. It is difficult to report the odds ratio in any meaningful 

sense other than to report there is a very strong relationship between texting/e-mailing/accessing 

the Internet while driving and involvement in a safety-critical event. The data suggests that truck 

and bus drivers who use their cell phone to text, e-mail, or access the Internet are very likely be 

involved in a safety-critical event. 

Fourth, the finding that talking/listening on a cell phone while driving does not increase the odds

of involvement in a safety-critical event was not restricted to commercial truck and bus drivers 

(but only for hand-held cell phone in bus drivers). Klauer et al. (2006) found that talking or 

listening on a hand-held phone did not statistically elevate the odds of being involved in a safetycritical event in a naturalistic study with light vehicle drivers. Similarly, a naturalistic study by 

Sayer, Devonshire, and Flanagan (2007) found that light vehicle drivers improved their speed 

variance when using a cell phone while driving and maintained their eyes on the forward 

roadway. However, the results from these naturalistic studies are in opposition to results found in 

simulator studies that suggest talking/listening on a cell phone while driving does increase risk 

(e.g., Beede & Kass, 2006; Patten et al., 2004; Strayer & Johnston, 2001; Strayer, Drews, & 

Johnston, 2003), although this could be due how cell phone use is considered a dichotomous 

variable in some studies.

The Olson et al. (2009) study was the first published naturalistic study to quantify the role of 

visual distraction while commercial truck drivers engaged in various tertiary tasks. Olson et al.

and Hanowski et al. (in press) suggest a strong relationship between the odds ratios for tertiary 

tasks and mean duration of eyes off the forward roadway. Figure 5, adapted from Hanowski et al. 

(in press), shows a plot of odds ratios and mean durations of eyes of forward roadway times for 

each tertiary task in Olson et al. (For numeric values see Table 36.) The plot in Figure 5 shows a 

strong linear relationship between the odds ratios (dashed blue line using the left y-axis) and the 

mean duration of eyes off the forward roadway (solid red line using the right y-axis). For 

example, texting was associated with the highest odds ratio and longest mean eyes off the 

forward roadway time (4.6/6.0 seconds). As the odds ratios decrease, so does the mean eyes off 

the forward roadway times, such as reaching for other electronic device (4.10/6.0 seconds), 

dialing a cell phone (3.8/6.0 seconds), talking/listening on hands-free cell phone (1.60/6.0 

seconds), and talking/listening on a hand-held phone (1.27/6.0 seconds) (Olson et al.). 

In this study those tertiary tasks associated with the greatest visual attention had the greatest risk 

(e.g., texting/e-mailing/accessing the Internet, dialing a cell phone, reaching for cell phone, and 

reaching for a headset/earpiece), while those tertiary tasks associated with the least visual 

attention had no impact on the odds of being involved in a safety-critical event, in fact, one of 

these tasks was shown to decrease the odds of involvement in a safety-critical event (e.g., 

talking/listening on a hand-held and hands-free phone). It appears the primary difference 

between these high-risk and low-risk tertiary tasks involves the amount of visual distraction (as 

all tasks have a cognitive component). Although no eye glance analysis was performed in this 

study, the results lend support to the contention by Olson et al. (2009) and Hanowski et al. (in 

press) that tertiary tasks associated with significant visual attention regarding have the highest 

odds of involvement in a safety-critical event. 
51

A curious finding in the study was when the driver groups were disaggregated by vehicle type, 

bus drivers significantly increased their odds of involvement in a safety-critical event while 

talking/listening on a hands-free cell phone (but not an increased risk for hand-held). This 

finding was an anomaly when considering the results in the talking/listening sub-tasks in all 

other vehicle types. And, when all vehicle types (including buses) were collapsed (i.e., 

considered together), the talking/listening on a hands-free cell phone had a significant decrease 

in the odds of involvement in a safety-critical event. With the exception of bus drivers 

listening/talking on a hands-free cell phone, the results for all other disaggregated vehicle types 

showed no increase in the odds of involvement in a safety-critical event or had a significant 

decrease in the odds of involvement in a safety-critical event for talking/listening. As such, this 

result contradicts the preponderance of findings from the other analyses conducted in this study 

and the results from other naturalistic studies that show talking/listening on a cell phone does not 

increase the odds of involvement in a safety-critical event. Limited sample size prevented the 

disaggregation of bus data by fleet cell phone policy; thus, it is unknown if the fleet cell phone 

policy in buses would affect the direction of this result. Also, the 95 percent confidence interval 

for bus drivers talking/listening on a hands-free cell phone was 1.05 to 1.71, so a small amount 

of variability could impact the odds ratio. Nonetheless, because limited research has been 

directed at bus drivers specifically, it is a finding that should prompt further investigation. It may 

turn out to be a spurious finding, the result of outliers, or a statistical anomaly, but it nonetheless 

requires further research investigation, particularly in this unique group of commercial drivers. 
52

Adapted from Hanowski et al. (in press).

Figure 5. Chart. Plot of the odds ratios and values for mean duration of eyes off the forward roadway for each tertiary task.
53

Table 36. Odds ratios and values for mean duration of eyes off the forward roadway for each 

tertiary task (numeric values from Figure 5).

Tertiary Task Point Estimate

Odds Ratio

(range 0–25 points)

Mean Duration of Eyes 

off Forward Roadway 

(seconds)

Text message on cell phone 23.24* 4.60

Other—Complex Tertiary Task 10.07* 4.40

Interact with/look at dispatching device 9.93* 4.10

Write on pad, notebook, etc. 8.98* 4.20

Use calculator 8.21* 4.40

Look at map 7.02* 3.90

Use/reach for other electronic device 6.72* 4.10

Dial cell phone 5.93* 3.80

Other—Moderate Tertiary Task 5.86* 3.80

Personal grooming 4.48* 3.70

Read book, newspaper, paperwork, etc. 3.97* 4.30

Put on/remove/adjust sunglasses 3.63* 2.30

Reach for object in vehicle 3.09* 2.90

Look back in Sleeper Berth 2.30* 3.40

Other—Simple Tertiary Task 2.23 4.00

Remove/adjust jewelry 1.68 1.73

Put on/remove/adjust hat 1.31 1.56

Put on/remove/adjust seat belt 1.26 1.66

Adjust instrument panel 1.25* 2.60

Look at left-side mirror/out left window 1.09 2.10

Talk/sing/dance with no indication of passenger 1.05 1.60

Talk or listen to hand-held phone 1.04 1.27

Use chewing tobacco 1.02 1.89

Eating 1.01 2.43

Smoking-related behavior—cigarette in hand/mouth 0.97 1.97

Drink from a container 0.97 2.29

Look at right-side mirror/out left window 0.95 2.20

Other personal hygiene 0.67 1.60

Smoking-related behavior—lighting 0.60 1.60

Talk or listen to CB microphone 0.55 1.30

Look outside vehicle 0.54 2.00

Bite nails/cuticles 0.45 1.10

Talk or listen to hands-free phone 0.44 1.6

Interact with or look at other occupant(s) 0.35 2.00

Check speedometer 0.32 1.90

* Asterisk indicates a significant odds ratio

Distracted driving is an important safety issue, and many State and Federal organizations have 

passed and/or proposed bans on using cell phones while driving. So, the real question is whether 

it is safe to use a cell phone while driving? The answer depends on what is meant by the term 

“cell phone use.” If cell phone use implies use of the cell phone in any manner, then the data in 

this study suggest that cell phone use significantly increases the odds of involvement in a safetycritical event by 1.22 times (in fleets with no cell phone policy). However, if cell phone use is 
54

classified into specific cell phone tasks or sub-tasks (Hanowski et al., in press), then certain cell 

phone sub-tasks are shown to increase significantly the odds of involvement in a safety-critical 

event (e.g., texting/e-mailing/accessing the Internet, dialing a cell phone, reaching for cell phone, 

and reaching for a headset/earpiece), while others are not, and may, in fact, significantly decrease 

the odds of involvement in a safety-critical event (e.g., talking/listening on a hand-held and 

hands-free phone). Given the differences in odds ratios associated with the specific cell phone 

sub-tasks, researchers studying distracted driving should consider cell phone use as a series of 

specific sub-tasks, rather than one task, that are not associated with an equivalent degree of risk. 

Research that defines and records cell-phone use as one higher-level task and either ignores the 

specific cell phone sub-tasks will result in a skewed understanding of this issue.

Fifth, the odds ratio calculations revealed differences between each vehicle type. While most of 

the odds ratios were in the same direction, the magnitude of the odds ratios differed. This 

highlights the need to conduct naturalistic driving research in as many vehicle types (transport 

modes) and settings as possible as each are exposed to different work demands and driving 

situations. Unfortunately, the data set was not detailed enough to highlight these inter-modal (or 

vehicle type) difference. 

Sixth, it should not come as a surprise that commercial truck and bus drivers were far less likely 

to use their cell phone while driving under a fleet cell phone policy as the OBSM system allowed 

these fleets to accurately monitor their drivers. This make intuitive sense as the OBSM system 

afforded safety managers and direct supervisors an opportunity to monitor drivers and implement 

consequences that would directly impact the driver (e.g., punishment for policy violation). 

Seventh, as data set A only included 2 days in which media attention may have influenced the 

data, the data in data set B controls for this event and may document the influence of the media 

attention on distracted driving (i.e., the only difference between the two data sets was the widespread media attention). It appears the increased media attention may have influenced safety 

managers’ and/or drivers’ behavior. Overall, commercial truck and bus drivers in data set A used 

their cell phone in 4.0 percent of the safety-critical events, while the commercial truck and bus 

drivers in data set B used their cell phone in 2.6 percent of the total safety-critical events (a 35 

percent reduction). However, the driving force beyond this difference was clearly shown when 

the results are disaggregated by fleet cell phone policy. Commercial truck and bus drivers in 

fleets with no cell phone policy in data set A used their cell phone in 4.3 percent of the safetycritical events, while the commercial truck and bus drivers in fleets with no cell phone policy in 

the data set B used their cell phone in 4.7 percent of the total safety-critical events (a 8.5 percent 

increase). However, commercial truck and bus drivers in fleets with a cell phone policy in data 

set A used their cell phone in 4.0 percent of the safety-critical events, while the commercial truck 

and bus drivers in fleets with a cell phone policy in the data set B used their cell phone in 2.7 

percent of the total safety-critical events (a 32.5 percent decrease). Although interesting and in 

the correct direction, the possible media effect on the prevalence of cell phone use while driving 

in the two data sets cannot be precisely evaluated as the data set A did not contain baseline 

events. Nonetheless, and although further assessment is required, the data suggest that awareness 

of the distracted driving problem may have had an effect on curbing cell phone use while driving 

(at least temporarily).
55

Eighth, the significant decrease in the odds of involvement in a safety-critical event and/or nonsignificant results regarding the odds of involvement in a safety-critical event while 

talking/listening on a cell phone while driving have sparked controversy in the academic 

community and other traffic safety organizations. An extensive amount of published simulator 

and closed test track studies have shown performance decrements while talking/listening on a 

cell phone—both hand-held and hands-free (NSC, 2010)—and many safety organizations have 

advocated bans on cell phone use of any kind while driving. Simulator and closed test track 

studies have shown decrements in driver’s reaction time, lane keeping, and scanning ability; 

however, some of these same simulator studies have also found that drivers’ reduced their speed 

and/or following distance to a lead vehicle when talking on a cell phone while driving (Haigney, 

Taylor, & Westerman, 2000; Rakauskas, Gugerty, & Ward, 2004; Strayer, Drews, & Crouch, 

2006; Strayer, Drews, & Johnson, 2004). The question is not whether these performance 

decrements take place while talking/listening on a cell phone while driving, but do these 

performance decrements increase crash risk in the real world? As shown in this analysis, if cell 

phone use is treated as a dichotomous variable (yes/no), the results in the study support the 

results found in simulator studies that cell phone use significantly increases the odds of 

involvement in a safety-critical event. The study found that any cell phone use significantly 

increased the odds of involvement in a safety-critical event by 1.22 (in fleets with no cell phone 

policy); however, when the cell phone task is examined at a sub-task level, it is clear that not all 

cell phone sub-tasks are associated with an equal degree of risk. More specifically, those subtasks that require the driver to take his eyes off of the forward roadway (by reaching, dialing, 

texting) have significantly increased risk. Of equal importance is that talking/listening tasks, 

where the driver’s eyes can be maintained on the forward roadway, were generally associated 

with no significant increase in the odds of involvement in a safety-critical event. Therefore, 

future research on this topic must examine the cell phone task as a set of sub-tasks. If not, then 

the conclusions from such studies will be incorrect and misleading.

Finally, some have argued that naturalistic studies are limited as there are few crashes in these 

data sets (as crashes are a rare event in the real-world) and risk is calculated on non-crash safety 

events (such as near-crashes and safety-critical events) (NSC, 2010). A key feature of this study 

was that the data set that was used included over 1,000 crashes; and it is noteworthy that these 

results are consistent with previous naturalistic driving studies that used far fewer crashes.

4.3 POSSIBLE LIMITATIONS

The authors acknowledge six caveats when interpreting the results. First, the DriveCam data set 

involves an active safety intervention (via behavioral coaching through review of video clips by 

safety managers). More specifically, the frequency and distribution of tertiary tasks in the 

DriveCam data set was likely skewed from normative driving data as the safety managers 

attempted to directly alter these behaviors. Moreover, some commercial truck and bus fleets 

were likely to be more stringent in their modification of certain driver behaviors (e.g., cell phone 

use while driving) than other commercial truck and bus fleets in the two data sets. Hickman and 

Hanowski (in press) found that the DriveCam system could reduce the mean rate of safetycritical events/10,000 miles in large trucks by up to 70 percent, while McGehee et al. (2007) 

found similar results in novice teen drivers. Thus, the data found in the study likely reflects a 
56

“best case” scenario and the prevalence of driver distractions in the general population of 

commercial trucks and buses is likely to be higher. 

Second, the fact that the truck and bus fleets in the study purchased an OBSM monitoring system 

reflects a group of safety conscious truck and bus fleets. As with the first caveat noted above, the 

data was likely skewed from normal truck and bus fleets as the truck and bus fleets included in 

the study likely were more proactive regarding safety. As such, and consistent with the previous 

caveat, the prevalence of driver distractions in the study may be an underestimate of the normal 

commercial truck and bus driving population.

Third, the lack of continuous data collection or randomly collected video segments means there 

were no “true” baseline or control data (i.e., randomly selected baseline events). This limits most 

of the analyses to frequency distributions. However, DriveCam did reduce spurious trigger 

epochs that were not considered safety related. For example, a truck or bus could trigger an 

epoch by driving over a pot hole. While not safety related, DriveCam reviewed these epochs and 

recorded the ongoing tertiary tasks (if present). These non-safety-related epochs served as 

baseline events in the analyses. However, these baseline events were not truly random; thus, 

increasing possible biasing to certain situations that triggered the baseline (e.g., pot hole, train 

track, hard brake not in response to safety event, etc.). For that reason, these baseline events will 

not contain periods of driving where the driver was driving and nothing occurred in the roadway. 

This is considered a minor limitation as including these baseline events allows the assessment of

risk estimates for tertiary tasks that will greatly increase our understanding of risk as compared 

to limiting the analyses to frequency counts (which do little to assess risk). The similarity of the 

results in this study with those found in Olson et al (2009), which used true baseline events, 

suggests this had little or no influence on the findings. 

Fourth, data set B included safety-critical events and baseline events from June 6, 2009 to 

September 5, 2009. This was notable as the FMCSA hosted a webinar on June 3, 2009 to discuss 

the preliminary results from the Olson et al. (2009) study. This webinar was followed by intense 

media coverage and documentation regarding the dangers of driving while distracted. This 

intense media attention regarding distracted driving may have influenced safety managers to 

focus on reducing these behaviors more intently than before the media attention. Thus, as with 

the first limitation noted above, the prevalence of tertiary tasks in data set B may not reflect the 

actual prevalence of these tertiary tasks in commercial trucks and buses that do not have these 

systems installed in their vehicles. However, as data set A only included 2 days in which media 

attention may have influenced the data, the data in data set B controls for this event and actually 

documents the influence of the media attention on distracted driving (i.e., the only difference 

between the two data sets was the wide-spread media attention). Unfortunately, it is difficult to 

adequately evaluate this effect as data set A does not contain baseline events (i.e., the 

comparison was prevalence rates of safety-critical events between the two data sets). However, 

this would translate into potentially less risky behavior being recorded in the study, which would 

imply higher tertiary task involvement in the larger population of commercial trucks and buses. 

Fifth, driver exposure was not controlled in the study; thus, extremely unsafe drivers may have 

contributed far more safety-critical events than baseline events (or conversely, extremely safe 

drivers). However, as the study included at least 13,000 drivers (from over 13,000 trucks and 
57

buses), the effect of any outliers was minimized. More specifically, the affect of any one driver 

was minimized given the large number of drivers in the study.

Lastly, the length of the video window during safety-critical events and baseline epochs was 

longer than other naturalistic studies (8 seconds before the trigger and 4 seconds after the 

trigger). Olson et al. (2009) used a much shorter video window, 5 seconds prior to the trigger and 

1 second after the trigger. It is possible that some tertiary tasks coded as an associative factor 

during the safety-critical events had no influence on safety-critical event occurrence (if the truck 

or bus driver engaged in the tertiary task at the very beginning of the video window). As noted 

previously, the similarity of the odds ratios with the Olson et al study that used a 6-second 

window suggests this likely had minimal influence on the results

4.4 FUTURE RESEARCH

There were many important findings in the research project that informed our understanding of 

driver distraction in commercial trucks and buses. Most notably, this study used a diverse 

naturalistic data set to replicate and support findings from other naturalistic driving research 

(Klauer et al., 2006; Olson et al., 2009). While the goal of the study was to document the 

prevalence (and risk) of distracted driving in commercial trucks and buses using an existing 

naturalistic data set (to compare to Olson et al.), this study can also be viewed as a springboard 

for future studies using these existing naturalistic data sets. The following is a list of future 

research recommendations for studies that intend to use naturalistic data collected by OBSM 

technology vendors.

• Assess the risk of tertiary tasks on crash risk. Currently, naturalistic data sets are not large 

enough to perform any detailed analyses on crashes. While safety-critical events provide 

valuable information, almost all of the safety-critical events are non-crashes. Data set A 

had a total of 1,336 crashes. If these crashes could be re-reviewed and merged with data 

sets from other OBSM vendors (e.g., SmartDrive), then researchers could evaluate the 

crash risk of engaging in tertiary tasks while driving. 

• A prospective study using OBSM vendors is needed to avoid the use of baseline events. 

This was a retrospective observational study that relied on non-random control events 

(baseline events) to evaluate risk. To address the possible bias in using these non-random 

events as control events, a prospective study with OBSM vendors that collect noncontinuous data could be conducted where the OBSM system randomly records the driver 

while driving. It appears this would be possible by programming the OBSM device to 

randomly record the driver at intermittent time intervals while the truck or bus was 

moving. Note that the ongoing FMCSA-funded 250 truck study is collecting continuous 

data.

• The study did not perform an eye glance analysis. Future studies should perform an eye 

glance analysis, if possible, to investigate the eye glance findings in Olson et al. (2009). 

Currently, the Olson et al. study is the only published naturalistic study that evaluated 

visual distraction in commercial truck drivers. Other naturalistic studies involving 

commercial truck and bus drivers need to conduct eye glance analysis to confirm, or 

refute, the results found in Olson et al. 
58

• The tertiary task, other distraction, was found to be highly correlated with involvement in 

a safety-critical event. However, the operational definition for this tertiary task was so 

nebulous (essentially a catch-all) the interpretation of this result lacked any real meaning. 

Safety-critical events and baseline events coded with the tertiary task, other distraction, 

should be re-reviewed and classified using the data directory in Olson et al. (2009). 

• Future naturalistic driving studies should be conducted with various transport modes 

(e.g., buses, rail, air, etc.). It is clear that driver/operator distraction is an important issue 

that is not limited to passenger cars and heavy trucks. As with the recent FMCSA-funded 

naturalistic research conducted with heavy trucks (Olson et al., 2009), the goal of such 

investigations would include assessing the scope of the problem within the particular 

transport mode and identifying countermeasures that would ultimately lead to fewer 

crashes.
59

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