Evaluating the Safety Benefits of a Low-Cost 

Driving Behavior Management System in 

Commercial Vehicle Operations 

June 2010
FOREWORD 

Motor vehicle crashes are often predictable and preventable. Yet, many drivers choose to behave 

in ways that put themselves and others at risk for a vehicle crash and/or serious injuries. At-risk 

driving behaviors include violating speed limits, excessive speed/lateral acceleration on curves, 

unplanned lane departures, frequent hard braking, close following distances, lateral 

encroachment (e.g., during attempted lane changes, perhaps due to improper mirror use), failure 

to yield at intersections, general disobedience of the rules-of-the-road, etc. Performing at-risk 

driving behaviors is likely to increase crash risk. 

Behavioral approaches to safety have provided robust positive results when applied in 

organizations seeking to reduce employee injuries due to at-risk behaviors. However, almost all 

prior behavioral safety research has been applied in work settings where employees can 

systematically observe the safe versus at-risk behavior of their coworkers. In contrast, 

commercial truck and bus drivers typically work alone in relative isolation and thus require 

alternative strategies. Until recently, the primary problem with implementing behavior-based 

approaches has been getting quality behavioral data on driving behaviors. New technologies are 

available that provide objective measures of driver behavior. These in-vehicle technologies are 

able to provide continuous measures on a wide variety of driving behaviors previously 

unavailable to fleet safety managers. Some driving behavior management systems (DBMSs) use 

in-vehicle video technology to record driver behavior. These recordings can be used by fleet 

safety managers to provide feedback on safe and at-risk driving behaviors. 

The Federal Motor Carrier Safety Administration funded this project to provide an independent 

evaluation of a commercially available low-cost DBMS. Participating drivers drove an 

instrumented vehicle for 17 consecutive weeks while they made their normal, revenue-producing 

deliveries. During the 4-week baseline phase, the DBMS recorded safety-related events; 

however, the feedback light on the event recorder was disabled and safety managers did not have 

access to the recorded safety-related events to provide feedback to drivers. During the 13-week 

intervention phase, the feedback light on the event recorder was activated and safety managers 

had access to the recorded safety-related events (following a recommended coaching protocol 

with drivers when necessary). The primary analyses in the current report determined the safety 

benefits of a commercially available low cost DBMS. 

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 use thereof. 

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

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

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

of this report. 
Technical Report Documentation Page 

1. Report No. 

FMCSA-RRR-10-033 

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

5. Report Date 

 June 2010 

4. Title and Subtitle 

EVALUATING THE SAFETY BENEFITS OF A LOW-COST 

DRIVING BEHAVIOR MANAGEMENT SYSTEM IN 

COMMERCIAL VEHICLE OPERATIONS 6. Performing Organization Code 

7. Author(s) 

Jeffrey S. Hickman and Richard J. Hanowski 

8. Performing Organization Report No. 

9. Performing Organization Name and Address 10. Work Unit No. (TRAIS) 

Virginia Tech Transportation Institute 

3500 Transportation Research Plaza (0536) 

Blacksburg, VA 24061 11. Contract or Grant No. 

DTMC75-07-D-00006, 

Task Order # 1 

13. Type of Report and Period Covered 

Final Report 

July 2007–July 2009 

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 

14. Sponsoring Agency Code 

15. Supplementary Notes 

This program was administered through the Federal Motor Carrier Safety Administration (FMCSA). The 

FMCSA Program Manager was Olu Ajayi and the Contracting Officer Technical Representative was Martin 

Walker, PhD. 

16. Abstract 

This project provides an independent evaluation of a commercially available low-cost driving behavior 

management system. Participating drivers from two carriers (identified as Carrier A and Carrier B) drove an 

instrumented vehicle for 17 consecutive weeks while they made their normal, revenue-producing deliveries. 

During the 4-week baseline phase, the event recorder recorded safety-related events; however, the feedback 

light on the event recorder was disabled and safety managers did not have access to the recorded safety-related 

events to provide feedback to drivers. During the 13-week intervention phase, the feedback light on the event 

recorder was activated and safety managers had access to the recorded safety-related events and followed a 

recommended coaching protocol with drivers (when necessary). Carrier A significantly reduced the mean rate 

of recorded safety-related events/10,000 vehicle miles travelled (VMT) from baseline to intervention by 38.1 

percent (p = 0.046), while Carrier B significantly reduced the mean rate of recorded safety-related events/10,000 

VMT from baseline to intervention by 52.2 percent (p = 0.03). The results suggest the combination of onboard 

safety monitoring and behavioral coaching were responsible for the reduction in the mean rate of safety-related 

events/10,000 VMT at Carriers A and B. 

17. Key Words 

behavioral safety, carrier safety, CMV, commercial motor vehicle, 

driver behavior, low-cost driving behavior management system, 

onboard safety monitoring, truck 

18. Distribution Statement 

No restrictions 

19. Security Classif. (of this report) 

Unclassified 

20. Security Classif. (of this page) 

Unclassified 

21. No. of Pages 

68 

22. Price 

N/A 

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

Table of APPROXIMATE CONVERSIONS TO SI UNITS 

Symbol When You Know Multiply By To Find Symbol 

LENGTH 

In inches 25.4 millimeters mm 

Ft feet 0.305 meters m 

Yd yards 0.914 meters m 

Mi miles 1.61 kilometers km 

AREA 

in2 square inches 645.2 square millimeters mm2 

ft2 square feet 0.093 square meters m2 

yd2 square yards 0.836 square meters m2 

Ac acres 0.405 hectares ha 

mi2 square miles 2.59 square kilometers km2 

VOLUME Note: Volumes greater than 

1000 L shall be shown in m3 

fl oz fluid ounces 29.57 milliliters mL 

Gal gallons 3.785 liters L 

ft3 cubic feet 0.028 cubic meters m3 

yd3 cubic yards 0.765 cubic meters m3 

MASS 

Oz ounces 28.35 grams g 

Lb pounds 0.454 kilograms kg 

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

TEMPERATURE temperature is in exact degrees 

°F Fahrenheit 5 × (F-32) ÷ 9 

or (F-32) ÷ 1.8 

Celsius °C 

ILLUMINATION 

Fc foot-candles 10.76 lux lx 

Fl foot-Lamberts 3.426 candela/m2 cd/m2 

Force and Pressure or Stress 

Lbf poundforce 4.45 Newtons N 

lbf/in2 poundforce per square inch 6.89 kilopascals kPa 

Table of APPROXIMATE CONVERSIONS FROM SI UNITS 

Symbol When You Know Multiply By To Find Symbol 

LENGTH 

Mm millimeters 0.039 inches in 

M meters 3.28 feet ft 

M meters 1.09 yards yd 

Km kilometers 0.621 miles mi 

AREA 

mm2 square millimeters 0.0016 square inches in2 

m2 square meters 10.764 square feet ft2 

m2 square meters 1.195 square yards yd2 

Ha hectares 2.47 acres ac 

km2 square kilometers 0.386 square miles mi2 

VOLUME 

mL milliliters 0.034 fluid ounces fl oz 

L liters 0.264 gallons gal 

m3 cubic meters 35.314 cubic feet ft3 

m3 cubic meters 1.307 cubic yards yd3 

MASS 

G grams 0.035 ounces oz 

Kg kilograms 2.202 pounds lb 

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

TEMPERATURE temperature is in exact degrees 

°C Celsius 1.8C + 32 Fahrenheit °F 

ILLUMINATION 

Lx lux 0.0929 foot-candles fc 

cd/m2 candela/m2 0.2919 foot-Lamberts fl 

Force & Pressure or Stress 

N Newtons 0.225 poundforce lbf 

kPa Kilopascals 0.145 poundforce per square inch lbf/in2 

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

(Revised March 2003, Section 508 accessible version August 2009). 

ii 
TABLE OF CONTENTS 

ACRONYMS...............................................................................................................................VI

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

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

1.1 BACKGROUND AND SIGNIFICANCE.....................................................................1

1.1.1 Behavioral Approaches to Safety...................................................................... 2

1.2 ON-BOARD SAFETY MONITORING DEVICES......................................................3

1.2.1 Summary........................................................................................................... 4

2. METHODOLOGY ...............................................................................................................5

2.1 TECHNOLOGY SCAN.................................................................................................5

2.2 PARTICIPANTS AND SETTING................................................................................5

2.2.1 Power Analysis................................................................................................. 5

2.2.2 Participating Drivers......................................................................................... 6

2.3 RESEARCH DESIGN...................................................................................................6

2.4 PROCEDURES..............................................................................................................7

2.4.1 Driver Recruitment ........................................................................................... 7

2.4.2 Driver Briefing.................................................................................................. 7

2.4.3 Fleet Safety Manager Recruitment ................................................................... 8

2.4.4 Fleet Safety Manager Training Seminar........................................................... 8

2.4.5 Installation of Event Recorders......................................................................... 9

2.4.6 Data Collection Process.................................................................................... 9

2.4.7 Data Reduction................................................................................................ 12

2.5 DATA ANALYSIS......................................................................................................13

HYPOTHESIS 1: There will be a significant reduction in the mean rate of safetyrelated events from baseline to intervention. .................................................. 13

HYPOTHESIS 2: There will be a significant reduction in the mean rate of severe 

safety-related events from baseline to intervention. ....................................... 14

3. RESULTS ............................................................................................................................15

3.1 DRIVER DEMOGRAPHIC QUESTIONNAIRE.......................................................15

3.2 SAFETY MANAGER DEMOGRAPHIC QUESTIONNAIRE..................................16

3.2.1

........................................ 16

HYPOTHESIS 1: There will be a significant reduction in the mean rate of 

safety-related events from baseline to intervention

3.2.2

............................. 21

HYPOTHESIS 2: There will be a significant reduction in the mean rate of 

severe safety-related events from baseline to intervention

iii 
iv 

3.3 IN-STUDY DRIVER QUESTIONNAIRE..................................................................23

3.4 POST-STUDY QUESTIONNAIRES..........................................................................25

3.4.1 Drivers............................................................................................................. 25

3.4.2 Fleet Safety Managers..................................................................................... 27

3.5 POST-HOC ANALYSES............................................................................................29

4. SUMMARY AND CONCLUSIONS .................................................................................33

4.1 SUMMARY.................................................................................................................33

4.2 CONCLUSIONS..........................................................................................................34

4.3 RECOMMENDATIONS.............................................................................................37

ACKNOWLEDGEMENTS ........................................................................................................49

REFERENCES.............................................................................................................................51

LIST OF APPENDICES 

APPENDIX A. DRIVER DEMOGRAPHIC QUESTIONNAIRE.........................................39

APPENDIX B. IN-STUDY QUESTIONNAIRE......................................................................41

APPENDIX C. POST-STUDY DRIVER QUESTIONNAIRE...............................................43

APPENDIX D. SAFETY MANAGER DEMOGRAPHIC QUESTIONNAIRE...................45

APPENDIX E. POST-STUDY SAFETY MANAGER QUESTIONNAIRE.........................47
LIST OF FIGURES 

Figure 1. Image. Event Recorder (Left) and Typical Installation of Event Recorder behind the 

Vehicle’s Rearview Mirror (Right)................................................................................10

Figure 2. Image. Front Camera View (Left) and Driver's Face View (Right)...............................10

Figure 3. Image. Screen Shot of the Event List Page ....................................................................11

Figure 4. Image. Screen Shot of the Event Analysis Page.............................................................12

Figure 5. Image. Scoring Options Available to Data Analysts during Data Reduction ................13

Figure 6. Image. Flow Diagram of Data Reduction Process .........................................................13

Figure 7. Graph. Mean Rate of Safety-Related Events/10,000 VMT across 17 Weeks of Data 

Collection at Carrier A ...................................................................................................19

Figure 8. Graph. Mean Rate of Safety-Related Events/10,000 VMT across 17 Weeks of Data 

Collection at Carrier B ...................................................................................................21

Figure 9. Graph. Mean Rate of Severe Safety-Related Events/10,000 VMT across 17 Weeks 

of Data Collection at Carrier A ......................................................................................22

Figure 10. Graph. Mean Rate of Severe Safety-Related Events/10,000 VMT across 17 Weeks 

of Data Collection at Carrier B ......................................................................................23

Figure 11. Graph. Frequency of Driver Comments in the Post-Study Driver Questionnaire........27

Figure 12. Graph. Mean Rate of Safety-Related Events/10,000 VMT during the Baseline and 

Intervention Phases for Drivers in the Fleet Manager Coaching and No Fleet 

Manager Coaching Groups at Carrier A.........................................................................30

Figure 13. Graph. Mean Rate of Severe Safety-Related Events/10,000 VMT during the 

Baseline and Intervention Phases for Drivers in the Fleet Manager Coaching and 

No Fleet Manager Coaching Groups at Carrier A..........................................................31

LIST OF TABLES 

Table 1. Selected Results from the Driver Demographic Questionnaire at Carriers A and B.......15

Table 2. Selected Results from Safety Manager Demographic Questionnaire at Carriers 

A and B...........................................................................................................................16

Table 3. Frequency and Percentage of Safety-Related Events at Carrier A..................................18

Table 4. Frequency and Percentage of Safety-Related Events at Carrier B ..................................20

Table 5. Selected Results from the In-Study Driver Questionnaire at Carriers A and B ..............24

Table 6. Selected Results from the Post-Study Driver Questionnaire at Carriers A and B...........26

Table 7. Selected Results from the Post-Study Safety Manager Questionnaire at Carriers 

A and B...........................................................................................................................28

v 
ACRONYMS 

Acronym Definition 

ATRI American Transportation Research Institute 

CMV commercial motor vehicle 

DBMS Driving Behavior Management System 

FMCSA Federal Motor Carrier Safety Administration 

ICF Informed Consent Form 

LTCCS Large-Truck Crash Causation Study 

VMT Vehicle Miles Traveled 

VTTI Virginia Tech Transportation Institute 

vi 
EXECUTIVE SUMMARY 

INTRODUCTION 

Motor vehicle crashes are often predictable and preventable. Yet, many drivers choose to behave 

in ways that put themselves and others at risk for a vehicle crash and/or serious injuries. One of 

the most significant studies on the factors that contribute to motor vehicle crashes was the 

Indiana Tri-Level Study (Treat et al., 1979). To provide insight into the factors that contribute to 

traffic crashes, collision data were examined across three different levels to assess causal factors 

as being definite, probable, or possible. The study determined that 90.3 percent of the crashes 

involved some type of human error, such as at-risk driving behavior, inadvertent errors, and 

impaired states. While the vehicles in Treat et al. (1979) were predominantly passenger vehicles, 

the same relationship can be found in heavy vehicles. The Large Truck Crash Causation Study 

(LTCCS) performed by the Federal Motor Carrier Safety Administration (FMCSA), assessed the 

causes of, and contributing factors to, crashes involving commercial motor vehicles (CMVs). 

The LTCCS (FMCSA, 2006) found that 87.3 percent of the critical reasons assigned to the largetruck driver were driver errors, including decision errors (38 percent; driver drove too fast for 

conditions), recognition errors (28.4 percent; driver did not recognize the situation due to not 

paying proper attention), non-performance errors (11.6 percent; driver fell asleep), and 

performance errors (9.2 percent; driver exercised poor directional control). 

Behavioral Approaches to Safety 

A review of published behavioral safety studies by Sulzer-Azaroff and Austin (2000) found that 

96.9 percent of the studies reviewed showed significant reductions in work-related injuries after 

the implementation of behavioral safety techniques. Geller (2001) found that behavioral safety 

programs are advantageous because they are easy to implement, easy to teach, and may be 

implemented in the setting where the problem occurs. Behavioral safety programs have 

successfully increased safety-related work behaviors in a variety of organizational settings. In a 

review of 53 occupational safety and health studies covering various safety approaches, Gustello 

(1993) found that behavioral safety approaches had the highest average reduction in injury rate 

(59.6 percent). 

However, almost all prior behavioral safety research has been applied in work settings where 

employees can systematically observe the safe versus at-risk behaviors of their coworkers. In 

contrast, commercial truck and bus drivers typically work alone and in relative isolation and thus 

require alternative strategies. Implementing new behavior based strategies for truck drivers will 

require acquiring quality behavioral data on driving behaviors. If behavioral approaches can be 

integrated with technologies that monitor driver behavior, fleet safety managers would have an 

effective tool to improve safety-related behaviors that occur when there is little or no opportunity 

for interpersonal observation and feedback. 

On-Board Safety Monitoring Devices 

New technologies are available that provide objective measures of driver behavior. These invehicle technologies are able to provide continuous measures on a wide variety of driving 

behaviors previously unavailable to fleet safety managers. Some driving behavior monitoring 

vii 
systems (DBMS) use in-vehicle video technology to record driver behavior. These recordings 

can be used by fleet safety managers to provide feedback on safe and at-risk driving behaviors. 

Behavioral approaches to safety are directed at modifying at-risk driving behaviors to reduce 

crash and injury risk. Thus, DBMSs have the potential to be used in conjunction with behavioral 

safety techniques to reduce a variety of at-risk behaviors. The current study provides an 

independent evaluation of a commercially available low-cost DBMS with CMV drivers. 

METHOD 

This quasi-experiment (i.e., no participant randomization) used a simple A4B13 design; where 

“A” and “B” referred to the baseline and intervention phases, respectively. The superscript refers 

to the number of weeks in each phase (i.e., “4” referred to four weeks). During the 4-week 

baseline phase, drivers from two carriers (identified in text as Carrier A and Carrier B) drove an 

instrumented vehicle during their normal, revenue-producing deliveries. An event recorder was 

configured to record safety-related events as normal; however, the feedback light (a light on the 

event recorder, visible to the driver, flashed each time an event was recorded) was disabled and 

no driver coaching occurred. Immediately following the 4-week baseline, the intervention phase 

began. During the 13-week intervention phase, drivers drove an instrumented vehicle during 

their normal, revenue-producing deliveries. During this time, the event recorder recorded safetyrelated events as normal and the DBMS program was enabled (i.e., the feedback light was 

activated and safety managers followed a recommended coaching protocol when necessary). As 

the independent evaluators in this research, procedures described below were limited to those 

performed by the authors. 

Participants and Setting 

Carrier A was a long-haul carrier located in the Southeastern United States that primarily 

delivered dry goods. A total of 50 drivers had an event recorder installed in their trucks (36 

drivers completed data collection). A total of 46 drivers at Carrier A signed an Informed Consent 

Form (ICF) that allowed researchers to send questionnaires to participating drivers. The mean 

age of these 46 drivers was 44 years old (range = 23 to 61 years old). Carrier B was a local/shorthaul carrier located in the Northwestern United States that primarily delivered beverage and 

paper goods. A total of 50 drivers had an event recorder installed in their trucks (41 drivers 

completed data collection). A total of 30 drivers at Carrier B signed an ICF that allowed 

researchers to send questionnaires to participating drivers. The mean age of these 30 drivers was 

50 years old (range = 27 to 71 years old). 

Procedure 

Prior to the event recorders being installed in the vehicles, drivers attended an initial project 

briefing. The project briefing lasted approximately 2 hours and included details regarding the 

project, informed consent, how the DBMS worked, and the recommended coaching process. 

Drivers indicated their interest in participating in the study by signing the ICF. Drivers who 

signed the ICF were asked to complete a Driver Demographic Questionnaire. Fifty event 

recorders were installed in 50 trucks at both participating carriers. Prior to the start of the 4-week 

baseline phase, drivers were instructed to make their normal, revenue-producing deliveries. 

During this time, the event recorders captured safety-related events; however, the feedback lights 

viii 
on the recorders were deactivated, no coaching occurred, and fleet safety managers did not have 

access to the data collected by the event recorders (unless a crash occurred). 

Prior to the start of the 13-week intervention phase, safety managers attended a training seminar 

that lasted approximately 3 hours. The safety manager training seminar included details 

regarding the project, informed consent, how the DBMS worked, how to use the technology 

vendor’s software, and how to “coach” drivers using the video data. Safety managers indicated 

their interest in participating in the study by signing the ICF. Safety managers who signed the 

ICF were asked to complete the Safety Manager Demographic Questionnaire. After the safety 

manager training seminar, the 13-week intervention phase began. During this phase, drivers were 

instructed to make their normal, revenue-producing deliveries. However, the feedback light on 

the event recorder was activated, safety managers coached drivers (when necessary), and safety 

managers had access to all the data collected by the event recorders during the 13-week 

intervention phase. 

Data Collection Process 

DriveCam®, a product vendor, was responsible for all data collection and reduction. The event 

recorder had two camera views: (1) driver’s face view, and (2) forward-facing view. The event 

recorder had three accelerometers (y-, x-, and z-axis) that triggered an event to be recorded. If 

the 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). The video 

and quantitative data were automatically sent to the vendor’s headquarters in San Diego, CA, via 

cellular transmission. Once received, the data were reviewed, reduced (i.e., data analysts marked 

the presence of specific variables pertaining to the event), and uploaded to a server. While all 

safety-related events were uploaded to the server for review, only those safety-related events that 

exceeded a certain threshold (or “Event Score”) were reviewed with the driver. Event Scores in 

the current study ranged from 0 to 11 (e.g., 0 = collision; 3 = driver unbelted; and 11 = driver 

involved in a near-crash, while talking on a cell phone and unbelted). Typically, an Event Score 

greater than or equal to 5 was marked to be reviewed by the safety manager to determine with 

the driver present; however, it was ultimately up to the safety manager which safety-related 

events were reviewed with the driver. Note that collisions were not reduced or scored by 

DriveCam personnel due to client liability concerns. Once on the server, the authors and safety 

managers had on-line access to the data via proprietary software. Researchers checked the 

software each day and recorded the frequency of safety-related events, severity, driving 

behaviors, date, driver#, and quantitative data. 

RESULTS 

Hypothesis 1: There will be a significant reduction in the mean rate of safety-related events 

from baseline to intervention 

Carrier A 

During the 4-week baseline phase, a total of 58 safety-related events were captured by event 

recorders (2 collisions and 56 risky driving events) from the 36 drivers who completed the study 

(14 drivers quit, resigned, withdrew, had a malfunctioning event recorder, and/or did not meet 

ix 
the criteria for inclusion in the analyses). These 36 drivers drove a total of 291,869 miles during 

the baseline phase. A rate was calculated to account for exposure (i.e., frequency of safetyrelated events/10,000 vehicle miles traveled [VMT]). The mean rate of safety-related 

events/10,000 VMT during the baseline phase was 1.9 safety-related events. 

During the 13-week intervention phase, 141 safety-related events were captured by event 

recorders (2 collisions and 139 risky driving events) over the course of 1,170,721 miles. The 

mean rate of safety-related events/10,000 VMT during the intervention phase was 1.2. A paired 

sample t test found the 38.1 percent reduction in the mean rate of safety-related events/10,000 

VMT from the baseline phase (1.9) to the intervention phase (1.2) to be statistically significant. 

Carrier B 

During the 4-week intervention phase a total of 65 safety-related events were captured by the 

event recorder (1 collision and 64 risky driving events) from the 41 drivers who completed the 

study (9 drivers quit, resigned, withdrew, had a malfunctioning event recorder, and/or did not 

meet the criteria for inclusion in the analyses). These 41 drivers drove a total of 162,492 miles 

during the baseline phase. As indicated above, a rate was calculated to account for exposure. The 

mean rate of safety-related events/10,000 VMT during the baseline phase was 4.02. 

During the 13-week intervention phase, 117 safety-related events were captured by event 

recorder (2 collisions and 115 risky driving events) over the course of 615,403 miles. The mean 

rate of safety-related events/10,000 VMT during the intervention phase was 1.93. A paired 

sample t test found the 52.2 percent reduction in the mean rate of safety-related events/10,000 

VMT from the baseline phase (4.0) to the intervention phase (1.93) to be statistically significant. 

Note that additional analyses are presented in the main body of the current report. These include 

analyses of questionnaire data, severe safety-related events, and post-hoc analyses of drivers who 

did and did not receive feedback. 

Hypothesis 2: There will be a significant reduction in the mean rate of severe safety-related 

events from baseline to intervention 

The mean rate of severe safety-related events (i.e., with an Event Score > 3) during the baseline 

and intervention phases were compared in Hypothesis 2. A “severe” event was defined as any 

safety-related event with an Event Score > 3. For each participant the frequency of severe safetyrelated events during the baseline phase was divided by the number of VMT during the baseline 

phase. The same procedures were used for data collected during the intervention phase. A paired 

sample t test was used to assess if there was a significant reduction in the mean frequency of 

severe safety-related events/10,000 VMT from baseline to intervention (α = 0.05). 

Carrier A 

At Carrier A there were a total of 16 severe safety-related events out of a total of 199 safetyrelated events (8.0 percent). The mean rate of severe safety-related events/10,000 VMT during 

the baseline phase was 0.22, while the mean rate of severe safety-related events/10,000 VMT 

during the intervention phase was 0.09. The 59.1 percent reduction in the mean rate of severe 

safety-related events/10,000 VMT from the baseline to the intervention phase was not 

statistically significant. The lack of sufficient statistical power could be the reason why 

x 
Hypothesis 2 was not supported at Carrier A. Note the power analysis indicated that 30 drivers 

would be sufficient to detect a significant difference; however, this analysis did not consider 

severe safety-related events. Nonetheless, despite the lack of a significant reduction, a 59.1 

percent decrease in the mean rate of severe safety-related events/10,000 VMT is noteworthy. 

Carrier B 

At Carrier B there were a total of 28 severe safety-related events out of a total of 179 safetyrelated events (15.6 percent). The mean rate of severe safety-related events/10,000 VMT during 

the baseline phase was 0.36, while the mean rate of severe safety-related events/10,000 VMT 

during the intervention phase was 0.2. The 44.4 percent reduction in the mean rate of severe 

safety-related events/10,000 VMT from the baseline to the intervention phase was not 

statistically significant. As with the Carrier A results, Hypothesis 2 was not supported at Carrier 

B. As indicated above, this could be due to limited statistical power. However, as with the 

Carrier A findings, a substantial reduction of 44.4 percent in the mean rate of severe safetyrelated events/10,000 VMT was observed at Carrier B. While not significant, due to the small 

number of severe events, the percentage reduction in severe safety-related events at Carriers A 

and B have practical significance. 

CONCLUSIONS 

In interpreting these results, two issues are noteworthy. First, it appears Carrier B had superior 

decreases to Carrier A in the mean rate of safety-related events/10,000 VMT (based on 

percentage reduction); however, concluding differential intervention impact is risky because 

Carrier A drove more safely than Carrier B during the baseline phase (1.9 versus 4.0 safetyrelated events/10,000 VMT). For example, Carriers A and B likely experienced different safetyrelated environmental conditions due to the predominant roads driven. A naturalistic study by 

Hanowski, Olson, Hickman, and Dingus (2006) reported that long-haul drivers typically drive on 

rural divided roads (e.g., highways), while local/short-haul drivers typically drive on urban 

undivided roads. Nonetheless, both carriers had substantial safety improvements from the 

DBMS. 

Second, drivers were aware the instrumented vehicles were recording their driving behaviors; 

thus, it is possible that drivers altered their performance accordingly (i.e., subject reactivity). 

However, it is unlikely this awareness influenced intervention impact as any reactivity to being 

observed is likely to be most prominent at the beginning of such procedures (Campbell, 1957). In 

fact, the data obtained during the baseline phase may have been understated, resulting in a less 

robust effect during the intervention phase. If this was the case, note that event recorders were 

installed in vehicles at Carriers A and B several weeks prior to the start of data collection. Thus, 

drivers would have become familiar with the presence of the event recorders by the time data 

collection began. As such, it is unlikely the results were impacted by reactivity effects since the 

strong, positive benefits of the DBMS in reducing safety-related events in this study were robust. 

xi 
xii 

FUTURE RECOMMENDATIONS 

The goal of the current study was to assess the efficacy of a commercially available low-cost 

DBMS in an applied setting while normal, revenue-producing deliveries were made. Thus, no 

attempt was made to deviate significantly from the existing DBMS. As prior research has found 

the combination of goal setting and feedback to be the optimal approach, future studies assessing 

the efficacy of a DBMS should consider the addition of goal setting training and directly 

assessing participants’ goals. 

The current study did not assess implicit goal setting; thus, variations in goal setting among 

drivers could have been the reason for differential behavior change among drivers. The current 

DBMS was successful in significantly reducing the mean rate of safety-related events/10,000 

VMT (by 37 and 52.2 percent at Carriers A and B, respectively). Though the safety benefits 

identified in this study were significant, it is possible that carriers may be reluctant to adopt such 

programs without a compelling case for return-on-investment. That is, though improved safety is 

a key outcome of the DBMS used in this study, it may not be sufficient to evoke widespread 

adoption of the technology. As such, the authors recommend that a follow-on cost-benefit 

analysis research be directed at assessing the return-on-investment of a DBMS. The authors 

recommend that such an assessment include the costs associated with implementing and 

maintaining the DBMS program as well as the direct (e.g., damage, health care, etc.) and indirect 

(e.g., legal fees, insurance costs, etc.) costs associated with reduced crashes and violations. If it 

can be shown that there a significant safety benefit from a DBMS and associated cost savings to 

carriers due to the associated reduction in safety events, then a strong case may be made for the 

efficacy of a DBMS program. 

This current study was an exploratory study with limited scope and budget. The authors 

recommend that future follow-on studies consider utilizing a longer time frame for the baseline 

and intervention phases. This would provide ample opportunity to collect more data on safetyrelated events across both phases and possibly provide a better assessment of the intervention 

impacts of the DBMS program. Also, there is the possibility that driver behavior could be 

positively or negatively impacted during the study period by factors that are unrelated to the 

DBMS. Future studies should consider having a separate sample of truck drivers who are in the 

baseline condition throughout the entire study period. This would provide an alternate control 

group and help draw out the effect of other possible confounding factors. 
1. INTRODUCTION 

1.1 BACKGROUND AND SIGNIFICANCE 

Motor vehicle crashes are often predictable and preventable. Yet, many drivers choose to behave 

in ways that put themselves and others at risk for a vehicle crash and/or serious injuries. At-risk 

driving behaviors include violating speed limits, excessive speed/lateral acceleration on curves, 

unplanned lane departures, frequent hard braking, close following distances, lateral 

encroachment (e.g., during attempted lane changes, perhaps due to improper mirror use), failure 

to yield at intersections, and general disobedience of the rules-of-the-road. Performing at-risk 

driving behaviors is likely to increase crash risk. 

One of the most significant studies on the factors that contribute to motor vehicle crashes was the 

Indiana Tri-Level Study (Treat et al., 1979). To provide insight into the factors that contribute to 

traffic crashes, collision data were collected across three different levels to assess causal factors 

as being definite, probable, or possible. The study determined that 90.3 percent of the crashes 

involved some type of human error, such as at-risk driving behavior, inadvertent errors, and 

impaired states. Hendricks, Fell, and Freedman (1999) replicated the epidemiological method 

employed in the Indiana Tri-Level Study using the National Automotive Sampling System 

protocol. Similar to the Indiana Tri-Level Study, Hendricks, Fell, and Freedman found that 

human error was the most frequently cited contributing factor in these crashes (99.2 percent). 

While the above studies were predominantly light-vehicle crashes, the same relationship can be 

found in heavy-vehicle crashes. The Large Truck Crash Causation Study (LTCCS), performed 

by the Federal Motor Carrier Safety Administration (FMCSA), assessed the causes of, and 

contributing factors to, crashes involving commercial motor vehicles (CMVs). The LTCCS 

(FMCSA, 2006) found that 87.3 percent of the critical reasons assigned to the large-truck driver 

were driver errors, including 38 percent that were decision errors (e.g., the truck driver was 

traveling too fast for conditions), 28.4 percent recognition errors (e.g., the truck driver did not 

recognize the situation due to not paying proper attention), 11.6 percent non-performance errors 

(e.g., the truck driver fell asleep), and 9.2 percent performance errors (e.g., the truck driver 

exercised poor directional control). Similarly, a naturalistic study by Hickman et al. (in press) 

found that 91.5 percent of the critical reasons assigned to the large-truck driver were driver 

errors, including 47.5 percent that were decision errors, 30.5 percent recognition errors, 11.9 

percent performance errors, and 1.6 percent non-performance errors. Moreover, an American 

Trucking Research Institute (ATRI) study found similar support that driver behavior is the 

primary contributing factor in large-truck crashes (ATRI, 2005). The ATRI study analyzed data 

on 540,750 truck drivers—including driver traffic violations and convictions—gathered over a 3-

year time frame to determine future crash predictability. The four convictions with the highest 

associations in future crash involvement were: (1) improper or erratic lane change, (2) failure to 

yield right of way, (3) improper turn, and (4) failure to maintain a proper lane. When a truck 

driver received a conviction for one of these behaviors, the likelihood of a future crash increased 

to between 91 to 100 percent. These studies suggest that driver behavior is the primary 

contributing factor in CMV crashes and that safety management approaches should focus on 

behavior to improve safety. 

1 
1.1.1 Behavioral Approaches to Safety 

Behavioral safety programs are advantageous because they are easy to implement, easy to teach, 

and may be implemented in the setting where the problem occurs (Daniels, 1999; Geller, 2001). 

Behavioral safety programs have been successfully used to increase safety-related work 

behaviors in a variety of organizational settings, including: 

 Pizza stores (Ludwig & Geller, 1991, 1997). 

 Paper mill (Fellner & Sulzer-Azaroff, 1984). 

 Mining industry (Fox, Hopkins, & Anger, 1987; Hickman & Geller, 2003). 

 Railroad (Peterson, 1984). 

 Gas pipeline company (McSween, 1995). 

 Manufacturing plants (Reber & Wallin, 1984). 

 Chemical research laboratory (Sulzer-Azaroff, 1978). 

 Food manufacturing plant (Komaki, Barwick, & Scott, 1978). 

 Infirmary at a residential center for mentally disabled individuals (Alavosius & SulzerAzaroff, 1986). 

 Building construction (Mattila & Hyödynmaa, 1988). 

 Telecommunication parts manufacturing plant (Sulzer-Azaroff, Loafman, Merante, & 

Hlavacek, 1990). 

 Shipyard (Saarela, 1990). 

 Utility company (Loafmann, 1998). 

In a review of 53 occupational safety and health studies covering various safety approaches, 

Gustello (1993) found behavioral safety approaches had the highest average reduction in injury 

rate (59.6 percent) compared to other approaches. A review of published behavioral safety 

studies by Sulzer-Azaroff and Austin (2000) found that 96.9 percent of the studies reviewed 

showed significant reductions in work-related injuries after the implementation of behavioral 

safety techniques. Behavioral safety programs have also been shown to reduce workers’ 

compensation claims. Behavioral Science Technology, Inc. (1998) found a 70 percent reduction 

in workers’ compensation claims in the third year after the introduction of a behavioral safety 

program; and Hantula, Rajala, Kellerman, and Bragger (2001) showed reductions in workers’ 

compensation claims after the introduction of a behavioral safety intervention. Clearly, 

behavioral safety programs can be effective in reducing injuries and their associated costs. 

Behavioral approaches to safety have provided robust positive results when applied in 

organizations seeking to reduce employee injuries due to at-risk behaviors. Primary techniques 

include peer observation and feedback, goal setting, and training and education sessions (Geller, 

2001; Krause, Robin, & Knipling, 1999). Almost all prior behavioral safety research has been 

applied in work settings where employees can systematically observe the safe versus at-risk 

behavior of their coworkers. Drawbacks to this approach include: nonobjective, unreliable, or 

biased observation; the need for extensive training of observers; paid employee time needed to 

2 
make interpersonal behavioral observations; lack of motivation to make behavioral observations

and deliver feedback; and resistance to accept nonobjective and potentially biased feedback. 

These drawbacks are exacerbated in workers who operate heavy trucks and buses as they are 

typically solitary workers or workers with little supervision. Since most employees who opera

a CMV as part of their job duties work alone, and because of the large human and economic 

costs associated with large-truck and passenger bus crashes, there would be great potential 

benefit from research developing practical behavioral safety techniques with CMV drivers. 

te 

The challenge, until recently, has been getting quality behavioral data on driving behaviors. Most 

CMV organizations use reactive approaches to assess safety outcomes. These include the 

frequency and severity of crashes and violations. However, crashes and violations only show a 

snapshot of driver behavior and it is too late to intervene on driver behavior after a crash occurs. 

A proactive approach focuses on specific driver behaviors—a leading indicator of driver safety 

that can address at-risk driving behaviors as they occur, prior to a crash and/or violation. If 

behavioral approaches can be integrated with technologies that monitor driver behavior, carrier 

safety managers would have an effective tool to improve safety-related behaviors that occur 

when there is little or no opportunity for interpersonal observation and feedback. Moreover, 

these data provide safety managers with leading indicators of driver safety; thus, safety managers 

can address potential safety issues prior to the occurrence of a crash and/or violation. 

1.2 ON-BOARD SAFETY MONITORING DEVICES 

New technologies are available that provide objective measures of driver behavior. These invehicle technologies provide continuous measures on a wide variety of driving behaviors 

previously unavailable to carrier safety managers. Some driving behavior management systems 

(DBMSs) use in-vehicle video technology to record driver behavior. These recordings can be 

used by safety managers to provide feedback on specific safe and at-risk driving behaviors. 

Behavioral approaches to safety posit that modification of safe and/or at-risk driving behaviors 

will greatly reduce crash and injury risk. 

McGehee, Raby, Carney, Lee, and Reyes (2007) used in-vehicle video technology with newly 

licensed teen drivers. This technology provided novice teen drivers and their parents with a 

means of identifying their risky driving behaviors so that feedback could be provided to reduce 

future at-risk driving behaviors. McGehee et al. paired this new technology with parental 

feedback in the form of a weekly video review and a graphical report card. Each teen driver had 

his/her personal vehicle equipped with an event-triggered video device, designed to capture 20-

second clips of the forward and cabin views whenever the vehicle exceeded lateral or forward 

threshold accelerations. Results indicated that the combination of video feedback and a graphical 

report card significantly decreased the rate of safety-related events in teen drivers. In the first 9 

weeks of the intervention, the teen drivers reduced their rate of safety-relevant events from an 

average of 8.6 safety-related events per 1,000 VMT during baseline to 3.6 safety-related events 

per 1,000 VMT (58 percent reduction). The group further reduced the mean rate of safety-related 

events to 2.1 per 1,000 VMT in the following nine weeks (76 percent reduction). The decrease 

from 8.6 to 2.1 safety-related events per 1,000 miles was statistically significant (t = 4.15, 

p = 0.0007). 

3 
4 

The McGehee et al. (2007) study illustrates the power of behavioral approaches to safety, in 

conjunction with DBMSs, to greatly reduce a variety of at-risk behaviors. Knipling, Hickman, 

and Bergoffen (2003) suggested the combination of DBMSs with other safety management 

techniques (especially behavioral safety techniques) is likely to be one of the most powerful 

approaches in reducing CMV crashes. The current study will provide an independent evaluation 

of a commercially available low-cost DBMS with CMV drivers. 

1.2.1 Summary 

This research effort assessed the efficacy of a commercially available low-cost DBMS to 

determine the improvements in driving safety in CMV operations (measured through improved 

driver performance). 

More specifically, a DBMS is comprised of three components: (1) in-vehicle video technology, 

(2) driving performance management software, and (3) driver counseling. The in-vehicle video 

technology records safety-related events (i.e., crashes, near-crashes, and safety-relevant 

conflicts) that involved the instrumented vehicles. Information about the safety-related event 

(e.g., level of longitudinal acceleration, video clip of event, etc.) is saved by the event recorder 

and accessed by safety managers via the performance management software. As part of the 

program, the safety managers engage the involved driver in counseling (also called coaching or 

feedback) aimed at correcting the driving behavior(s) that led to, or may have contributed to, the 

safety-related event. The safety manager may use the recorded and saved video and detailed 

information to pinpoint what the driver did or did not do to avoid the safety-related event, and 

coach the driver to avoid making the same mistake in the future. 

This research is directed at determining if a commercially available low-cost DBMS was 

effective in reducing safety-related events caused by CMV drivers, including the methods 

necessary to accomplish this goal. 
2. METHODOLOGY 

2.1 TECHNOLOGY SCAN 

Prior to the start of data collection, the authors investigated the available technology to identify 

commercially available low-cost DBMSs that could be used in the current study. As more than 

one technology vendor existed for a low-cost DBMS (i.e., less than $1,000), the authors 

published an online “Sources Sought” announcement on July 17, 2007, that was removed after 

30 days. The announcement requested a brief proposal from interested technology vendors to 

participate in the current research project. 

Of critical importance was the technology vendor’s willingness to provide, free of charge, the 

following: 

 At least 100 data collection units. 

 Installation of all data collection units. 

 All associated video data reduction from recorded events. 

 Safety manager and driver training. 

 All necessary support and/or maintenance. 

Additionally, the technology vendor had to provide two CMV fleets (one long-haul; one 

local/short-haul) ready to participate in a pilot test of their system. DriveCam®, headquartered in 

San Diego, CA, was the only technology vendor to submit a proposal. After reviewing its 

proposal and conducting detailed discussions, this technology vendor was selected for this 

project. 

2.2 PARTICIPANTS AND SETTING 

2.2.1 Power Analysis 

Prior to the start of data collection, a power analysis was calculated on the number of required 

participants to detect a significant difference in the mean rate of safety-related events between 

the baseline and intervention phases. The Lipsey (1990) method was selected to calculate the 

sample size required to reach the estimated power, given estimates from the technology vendor’s 

prior research and given the hypothesized differences between baseline (e.g., data were collected, 

but no counseling occurred) and intervention phases (data were collected and counseling 

occurred when necessary). The Lipsey method uses the following equation: 

Using this equation, Effect Size was the hypothesized effect size, XBaseline was the mean 

frequency of safety-related events during the baseline phase, XIntervention was the mean frequency 

of safety-related events during the Intervention phase, SBaseline

2

 was the variance for the baseline 

phase, and S 2

Intervention was the variance for the intervention phase. A safety-related event was 

5 
operationally defined as an unexpected event resulting in a close call requiring fast action on th

part of the driver to avoid a crash, near-crash, or other traffic event. Safety-related events are 

likely to require emergency steering or braking, or both, by at least one of the drivers involved

Safety-related events are far more numerous than crashes, in which the driver could not avoid a 

collision through their emergency actions, or lack thereof (Hanowski, Keisler, & Wierwille, 

2004). 

e 

. 

The following estimated values were derived from the technology vendor’s prior research with 

over 21,000 drivers: XBaseline = 7.99; XIntervention = 1.87; S 2

Baseline = 48.44; and S 2Intervention = 5.46. 

Using an alpha of 0.05 for a two-tailed t test and given the estimated effect size of 1.18, a power 

of 0.95 would be reached with 30 participants (Lipsey, 1990; page 91). Thus, an adequate sample 

of 50 participating drivers in each fleet type was chosen as a sufficient sample size to account for 

possible dropouts. 

2.2.2 Participating Drivers 

e data (i.e., safety-related events), both participating carriers 

al 

s. A 

2.3 RESEARCH DESIGN 

This quasi-experiment used a simple A B design; where “A” and “B” refer to the baseline and 

intervention phases, respectively. The superscript refers to the number of weeks in each phase 

(i.e., “4” refers to 4 weeks). During the 4-week baseline phase, drivers drove an instrumented 

vehicle during their normal, revenue-producing deliveries. The event recorder was configured to 

record safety-related events as normal; however, the feedback light was disabled (under normal 

operation, a light on the event recorder flashed each time an event was recorded) and no driver 

counseling occurred. Immediately following the 4-week baseline, the 13-week intervention phase 

began, during which drivers drove an instrumented vehicle during their normal, revenueproducing deliveries. During this time, the event recorder recorded safety-related events as 

normal and the “coaching” program was enabled (i.e., the feedback light was activated and 

safety managers followed the recommended coaching protocol). 

4 13

Given the sensitive nature of th

signed a non-disclosure agreement, which allowed the authors to collect data; however, both 

participating carriers were to remain anonymous in all research reports. Carrier A was a longhaul carrier located in the Southeastern United States that primarily delivered dry goods. A tot

of 50 drivers had an event recorder installed in their trucks at Carrier A (36 drivers completed 

data collection). A total of 46 drivers at Carrier A signed an informed consent form (ICF) that 

allowed researchers to send questionnaires to participating drivers. The mean age of these 46 

drivers was 44 years old (range = 23–61 years old). Carrier B was a local/short-haul carrier 

located in the Northwestern United States. that primarily delivered beverage and paper good

total of 50 drivers had an event recorder installed in their trucks (41 drivers completed data 

collection). A total of 30 drivers at Carrier B signed an ICF that allowed researchers to send 

questionnaires to participating drivers. The mean age of these 30 drivers was 50 years old 

(range = 27–71 years old). 

6 
2.4 PROCEDURES 

As indicated above, the authors were independent evaluators during the current research; thus, 

the procedures described below are limited to those performed by the authors. 

2.4.1 Driver Recruitment 

As the participating carriers made a fleet decision to participate in the pilot test, each 

participating carrier made the final decision regarding which 50 vehicles (100 total) were 

instrumented with data collection equipment. Thus, the 100 drivers assigned to the instrumented 

trucks participated in the technology vendor’s safety program. As such, the authors sought each 

driver’s permission to send questionnaires and view identifiable video and quantitative data. 

Permission was received when drivers signed the ICF. Drivers who signed the ICF also 

completed a Driver Demographic Questionnaire (appendix A). Those drivers who declined to 

sign the ICF still participated in the pilot study; however, the authors did not send questionnaires 

to these drivers and received de-identified data from the technology vendor regarding safetyrelated events (i.e., no video and an anonymous driver #, such as ACH1). 

Prior to the start of data collection and the installation of event recorders in each driver’s truck, 

the authors met with drivers to discuss the nature of the project, technology, benefits of 

participation, and informed consent rights. A detailed description of the driver briefing is shown 

below. Given that Carrier A was a long-haul carrier, it was difficult to have all drivers attend a 

group meeting. The regional safety manager at Carrier A scheduled several conference calls with 

drivers. During these conference calls, the researcher discussed the current project and answered 

drivers’ questions. A technology vendor representative also participated in the conference call. 

During the installation process at Carrier A, a researcher met with each driver to review informed 

consent procedures and the nature of the study. It was at this time that drivers indicated their 

willingness to participate in the study. As indicated above, a total of 46 drivers at Carrier A 

signed the ICF. 

Carrier B scheduled a driver meeting at one of their fleet terminal locations. A researcher and 

technology vendor representative were present and reviewed the concepts described in the driver 

briefing below. This meeting was poorly attended and misinformation regarding the project 

spread quickly. Several weeks later the researcher traveled to Carrier B to meet each driver 

individually to describe the study and answer questions. A total of 30 drivers at Carrier B signed 

the ICF. 

2.4.2 Driver Briefing 

The driver briefings lasted approximately 1.5–2 hours. These briefings involved both education 

and training through the use of visual (e.g., videos on a DVD) and lecture (e.g., PowerPoint 

slides) materials where appropriate. The driver briefing followed the technology vendor’s typical 

presentation agenda, but differed in two distinct ways: discussion about the feedback light and 

driver coaching being disabled during the baseline phase and enabled during the intervention 

phase, and informed consent instructions. The informed consent instructions included: 

confidentiality and anonymity of the data, freedom to withdraw from the study at any time, and 

instructions on how to maintain confidentiality and anonymity (e.g., do not report to any 

7 
unauthorized personnel that data collection equipment has been installed on the vehicle). The 

driver briefing agenda included the following topics: 

 Introduction and background material. 

 Facts about driving. 

 How the technology works. 

 An overview of how the program works. 

 The benefits of using the technology. 

 Examples of what the event recorder captures. 

 Informed consent procedures. 

 Final questions. 

Participating drivers were also informed they would be solicited to complete two questionnaires 

during data collection. During the intervention phase, each driver who was involved in a 

coaching session with his/her safety manager was sent an In-Study Driver Questionnaire, which 

served as a manipulation check to assess if safety managers followed the coaching protocol 

(appendix B). Upon the completion of the intervention phase, participating drivers completed a 

Post-Study Driver Questionnaire (appendix C), which assessed participating drivers’ opinions 

and perceptions of the program. To increase participation, each completed and returned In-Study 

Driver Questionnaire was entered into a monthly raffle for $50.00 at each participating carrier, 

and each completed and returned Post-Study Driver Questionnaire was entered in a raffle for 

$50.00 at each participating carrier. 

2.4.3 Fleet Safety Manager Recruitment 

Again, as each participating carrier made a fleet decision to participate in the technology 

vendor’s pilot test, each safety manager who had one of their drivers assigned to an instrumented 

truck participated in the technology vendor’s safety program. The authors sought each safety 

manager’s permission to send questionnaires. Permission was received when fleet safety 

managers signed the ICF. Safety managers who signed the ICF also completed a Safety Manager 

Demographic Questionnaire (appendix D). No fleet safety managers declined to sign the ICF. 

Prior to the start of the Intervention phase and during the baseline phase, the authors met with the 

safety managers to discuss the nature of the project, technology, the recommended coaching 

protocol, use of performance software, benefits of participation, and informed consent rights. A 

detailed description of the safety manager briefing is below. At that time the safety managers 

indicated their willingness to participate in the study. Three safety managers at each participating 

carrier signed the ICF (6 total). 

2.4.4 Fleet Safety Manager Training Seminar 

The safety manager training seminar lasted approximately 2–3 hours. This training seminar 

involved both education and training through the use of visual (e.g., videos on a DVD) and 

lecture (e.g., PowerPoint slides) materials. The fleet safety manager training seminar followed 

the technology vendor’s typical presentation agenda, but differed in two distinct ways: 

8 
discussion about the feedback light and driver counseling being disabled during the baseline 

phase and enabled during the intervention phase, and informed consent instructions. The 

informed consent instructions included: confidentiality and anonymity of the data, freedom to 

withdraw from the study at any time, and instructions on how to maintain confidentiality and 

anonymity (e.g., do not report to any unauthorized personnel that data collection equipment has 

been installed on the vehicle). The fleet safety manager training seminar included the following 

topics: 

 Introduction and background material. 

 Facts about driving. 

 How the technology works. 

 An overview of how the program works. 

 The benefits of using the technology. 

 Examples of what the event recorder captures. 

 Using the DBMS software. 

 Face-to-face coaching. 

 Informed consent procedures. 

 Final questions. 

Participating fleet safety managers were also informed they would be solicited to complete one 

questionnaire during data collection. Upon the completion of the intervention phase, 

participating fleet safety managers completed a Post-Study Safety Manager Questionnaire 

(appendix E), which assessed participating fleet safety managers’ opinions and perceptions of the 

program. To increase participation, each safety manager who completed and returned the End of 

Study Safety Manager Questionnaire received a flat fee of $25.00. 

2.4.5 Installation of Event Recorders 

Installation of event recorders commenced after the driver briefing. A total of 100 trucks (50 at 

each participating carrier) had an event recorder installed in each participating driver’s truck. The 

technology vendor was responsible for the installation process. The event recorders were sent via 

the U.S. Postal Service to each participating carrier. Once these event recorders were received, a 

technology vendor representative arrived on-site and trained carrier mechanics in the installation 

process and the steps involved in system validation (i.e., the event recorder was functioning and 

capable of transmitting data). The installation process was fairly easy and took approximately 

60–120 min. Given the distributed nature of deliveries at each participating carrier and the time 

needed to install each event recorder, all 100 event recorders were installed over a period of 

several weeks. Note that data collection did not begin until all 50 event recorders were installed 

at each site. The event recorders were deactivated prior to the start of the baseline phase. 

2.4.6 Data Collection Process 

The technology vendor was responsible for all data collection. The event recorder had two 

camera views: a driver face view and a forward-facing view. Figure 1 and Figure 2 show the 

9 
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). The 

threshold was determined by the technology vendor, based on their prior experience with over 

60,000 installed event recorders. Note the threshold value remained constant throughout data 

collection. 

Figure 1. Image. Event Recorder (Left) and Typical Installation of Event Recorder behind the 

Vehicle’s Rearview Mirror (Right) 

Figure 2. Image. Front Camera View (Left) and Driver's Face View (Right) 

The video and quantitative data from all 100 instrumented trucks were automatically sent to the 

technology vendor via cellular transmission. The received data were reviewed, reduced (i.e., data 

analysts marked the presence of specific variables pertaining to the event), and uploaded to a 

server. While all safety-related events were uploaded into the software for review, only those 

safety-related events that exceeded a certain threshold “Event Score” were requested to be 

reviewed by the safety manager with the driver in question. Event scores in the current study 

ranged from 0 to 11 (e.g., 0 = collision; 3 = driver unbelted; and 11 = driver involved in a near 

crash, while talking on a cell phone and unbelted). Typically, an Event Score greater than or 

equal to 5 was marked for review; however, Event Scores lower than 5 were also marked for 

review depending on the severity of the safety-rated event. Note that collisions were not reduced 

or scored by DriveCam personnel due to client liability concerns; however, all collisions were 

uploaded into the software for review by fleet safety managers. Once on the server, the authors 

10 
and safety managers had access to these data via the software (accessible via the Internet). The 

authors checked the software each day and recorded the frequency of safety-related events, the 

Event Score, driving behaviors, date, driver #, and quantitative data from participating drivers 

who signed an ICF and input these data into an internal database. Figure 3 and Figure 4 display 

screen shots of the Event List and Event Analysis pages, respectively, from the DBMS software. 

Figure 3. Image. Screen Shot of the Event List Page 

11 
Figure 4. Image. Screen Shot of the Event Analysis Page 

2.4.7 Data Reduction 

The technology vendor was responsible for all data reduction (except, as noted above, collision 

data was not reduced). Once the data were received, a trained data analyst reviewed the event. 

Data analysts underwent an extensive 5-week training regimen prior to reducing “real” data. The 

data analyst reviewed the event to determine if represented a valid safety-related event or a 

spurious trigger value (e.g., hit a pothole in the street, driving on a bumpy road, etc.). Spurious 

events were not reduced, while valid safety-related events were reduced by the trained data 

analyst. Data reduction involved reviewing the video and recording the trigger type, outcome, 

root cause, demeanor, risky behaviors, adverse weather conditions (if necessary), and any custom 

classifications determined by the client. Figure 5 displays the scoring options available to data 

analysts during data reduction. The date, time, fleet#, and driver ID # were all automatically 

tagged to the safety-related event. An Event Score was automatically determined, based on the 

inputs from the data analyst. Figure 6 displays a flow diagram of the data reduction steps once an 

event was captured by the event recorder. Safety-related event were uploaded approximately 24–

48 hours from the time an event was captured by the event recorder in the instrumented truck. 

12 
Figure 5. Image. Scoring Options Available to Data Analysts during Data Reduction 

Figure 6. Image. Flow Diagram of Data Reduction Process 

Event

Captured 

Data Transmitted 

via 

Cellular Service

Video 

Reviewed

Event

Reduced 

Event

Uploaded

to Server 

NO

YES Authors & Safety

Manager Access via 

HindSight software

No

Further 

Action

Valid 

Event 

2.5 DATA ANALYSIS 

The current evaluation focused on determining if there was a reduction in safety-related events 

recorded by the event recorder that were attributed to the DBMS (i.e., the system and 

counseling). The key analyses to be conducted included a significant reduction in the frequency 

and severity of safety-related events during the baseline phase compared to the intervention 

phase; thus, there were two primary hypotheses. One potential challenge in the data analyses was 

how to deal with participating drivers who dropped out of the program (e.g., quit, terminated, 

etc.). For the proposed analyses, drivers who did not complete at least 3 weeks of data collection 

during the baseline phase and 4 weeks of data collection during the intervention phase were not 

included in any analyses. Below is a description of the study hypotheses. 

HYPOTHESIS 1: There will be a significant reduction in the mean rate of safety-related 

events from baseline to intervention. 

In Hypothesis 1, the mean rates of safety-related events during the baseline and intervention 

phases were compared. The dependent measure was the mean rate of safety-related 

events/10,000 VMT. Thus, for each participant the frequency of safety-related events during the 

baseline phase was divided by the number of VMT during data collection in the baseline phase. 

This normalized the data and accounted for missing days, dropouts, and/or exposure. The same 

procedures were used for data collected during the intervention phase. A paired sample t test was 

used to assess if a there was a significant reduction in the mean rate of safety-related 

events/10,000 VMT during the baseline compared to the intervention phase (α = 0.05). 

13 
14 

HYPOTHESIS 2: There will be a significant reduction in the mean rate of severe safetyrelated events from baseline to intervention. 

In Hypothesis 2, the mean rates of severe safety-related events (i.e., with an Event Score > 3) 

during the baseline and intervention phases were compared. The dependent measure was the 

mean rate of severe safety-related events/10,000 VMT. The Event Score is a proxy measure of 

safety-related event severity (i.e., the higher the Event Score, the greater the severity of the 

safety-related event). Thus, for each participant, the frequency of safety-related events with an 

Event Score > 3 during the baseline phase was divided by the number of VMT during the 

baseline phase. The same procedures were used for data collected during the intervention phase. 

A paired sample t test was used to assess if there was a significant reduction in the mean rate of 

severe safety-related events/10,000 VMT from the baseline phase to the intervention phase 

(α = 0.05). 
3. RESULTS 

3.1 DRIVER DEMOGRAPHIC QUESTIONNAIRE 

The Driver Demographic Questionnaire, a 16-item questionnaire given to participating drivers 

after signing the ICF, requested basic demographic information as well as work history and prior 

crash and violation involvement. All information collected was self-reported. A total of 46 and 

30 drivers completed the Driver Demographic Questionnaire at Carriers A and B, respectively. 

Table 1 displays selected results from the Driver Demographic Questionnaire at Carriers A and 

B. As shown in Table 1, participants in Carriers A and B were similar. However, participants in 

Carrier A were slightly younger (mean age of 44.3 versus 50.8 years old), had less CMV driving 

experience (mean experience of 8.4 versus 12.8 years), self-reported fewer moving violations 

(mean violations of 0.23 versus 0.5), but more crashes (mean crashes of 0.29 versus 0.18) and atfault crashes (mean at-fault crashes of 0.32 versus 0.1) in the previous 36 months. 

Table 1. Selected Results from the Driver Demographic Questionnaire at Carriers A and B 

Variable Time 

Period 

Carrier A Carrier B 

Age N/A Mean = 44.3 years old 

(Range = 23 to 61) 

Mean = 50.8 years old 

(Range = 37-71) 

Gender N/A 2 females 

44 males 

1 female 

29 males 

Ethnicity N/A 76.0% - Caucasian 

17.4% - African-American 

2.2% - Hispanic 

2.2% - Native-American 

2.2% - No response 

80.0% - Caucasian 

6.7% - Hispanic 

3.3% - Native-American 

10.0% - No response 

Experience Driving 

a Commercial 

Motor Vehicle 

N/A Mean = 8.4 years 

(Range = 1 to 40 years) 

Mean = 12.8 years 

(Range = 1 to 54 years) 

Employment at 

Current Carrier 

N/A Mean = 5.93 years 

Range = (0.83 to 30 years) 

Mean = 5.65 years 

Range = (0.33 to 21 years) 

Moving Violations Last 12 

Months 

Mean = 0.24 violations 

(Range = 0 to 2 violations) 

Mean = 0.12 violations 

(Range = 0 to 1 violations) 

Moving Violations

g Violations 

Last 36 

Months 

Mean = 0.23 violations 

(Range = 0 to 1 violations) 

Mean = 0.5 violations 

(Range = 0 to 2 violations) 

Crashes Last 12 

Months 

Mean = 0.25 crashes 

(Range = 0 to 3 crashes) 

Mean = 0.04 crashes 

(Range = 0 to 1 crashes) 

Crashes Last 36 

Months 

Mean = 0.29 crashes 

(Range = 0 to 2 crashes) 

Mean = 0.18 crashes 

(Range = 0 to 1 crashes) 

At-Fault Crashes Last 12 

Months 

Mean = 0.23 at-fault crashes 

(Range = 0 to 3 at-fault crashes) 

Mean = 0.0 at-fault crashes 

At-Fault Crashes Last 36 

Months 

Mean = 0.32 at-fault crashes 

(Range = 0 to 1 at-fault crashes) 

Mean = 0.1 at-fault crashes 

(Range = 0 to 1 at-fault crashes)

15 
3.2 SAFETY MANAGER DEMOGRAPHIC QUESTIONNAIRE 

The Safety Manager Demographic Questionnaire, a 10-item questionnaire given to participating 

safety managers after signing the ICF, requested basic demographic information as well as work 

history and prior crash and violation involvement. All information collected was self-reported. A 

total of two and three safety managers completed the Safety Manager Demographic 

Questionnaire at Carriers A and B, respectively. 

Table 2 displays selected results from the Safety Manager Demographic Questionnaire at 

Carriers A and B. As shown in Table 2, safety managers at Carrier A had more management 

experience (mean years of 16.6 versus 5.9) and tenure at the participating carrier (mean years of 

18.8 versus 2.1) than safety managers at Carrier B (in fact, the primary safety manager at Carrier 

B had been at Carrier B for less than one year). This discrepancy was evident during the initial 

roll-out of this study. It appeared that safety managers at Carrier A had developed a good rapport 

and level of trust with the drivers. The drivers at Carrier A were less suspicious and more willing 

to “give the DBMS a chance” than the drivers at Carrier B. It was evident that safety managers at 

Carrier B did not have the same level of trust or rapport with their drivers. Drivers at Carrier B 

were suspicious of the DBMS program and actively subverted the program. For example, at 

Carrier B there were 278 events where the driver blocked the driver face camera, while drivers at 

Carrier A had 10 of these events. Drivers at both carriers were instructed to not block the driver 

face camera while participants were operating the truck. 

Table 2. Selected Results from Safety Manager Demographic Questionnaire at Carriers A and B 

Variable Carrier A Carrier B 

Age Mean = 45 years old Mean = 33.7 years old 

Gender 2 females 3 males 

Ethnicity 100% - Caucasian 100% - Caucasian 

Experience as a Manager in 

Commercial Motor Vehicle 

Mean = 16.6 years 

(Range = 14 to 19.2 years) 

Mean = 5.9 years 

(Range = 3 to 10.6 years) 

Employment at Current Carrier Mean = 18.8 years 

Range = (14 to 23.6 years) 

Mean = 2.1 years 

Range = (0.4 to 4 years) 

3.2.1 HYPOTHESIS 1: There will be a significant reduction in the mean rate of safetyrelated events from baseline to intervention 

As indicated above, the mean frequency of safety-related events during the baseline and 

intervention phases were compared in Hypothesis 1. The dependent measure was the mean rate 

of safety-related events/10,000 VMT. Thus, for each participant the frequency of safety-related 

events during the baseline phase was divided by the number of VMT traveled during data 

collection in the baseline phase. This normalized the data and accounted for missing days, 

dropouts, and/or exposure. The same procedures were used for data collected during the 

Intervention phase. A paired sample t test was used to assess if a there was a significant 

16 
reduction in the mean frequency of safety-related events during the baseline compared to the 

intervention phase (α = 0.05). 

3.2.1.1 Carrier A 

Frequency of Safety-Related Events: A total of 36 drivers at Carrier A, from the original 50, 

were included in the data analyses. A total of 14 drivers quit, resigned, withdrew, had a 

malfunctioning event recorder, and/or did not meet the minimum requirements to be included in 

the data analyses. There were technical issues with several of the event recorders at Carrier A. 

These technical issues precluded five drivers from being included in the data analyses and an 

additional eight drivers included in the data analyses had missing safety-related events during the 

baseline phase. Thus, the frequency of safety-related events in the baseline phase was lower than 

if these event recorders had functioned properly.

Table 3 shows the frequency and percentage of event type, root cause, and risky driving behavior 

coded by data analysts. The percentages in Table 3 add up to more than 100 percent as more than 

one root cause and/or risky behavior could be selected for each safety-related event. During the 

4-week baseline phase, a total of 58 valid safety-related events were captured by the event 

recorder (2 collisions and 56 risky driving events). During the 13-week intervention phase, a 

total of 141 valid safety-related events were captured by the event recorder (2 collisions and 139 

risky driving events). Note that it is difficult to interpret on the raw frequencies of safety-related 

events given the unbalanced data collection periods in the baseline and intervention phases (4 

versus 13 weeks). As such, the data were normalized (as shown below) to allow more 

appropriate comparisons between the baseline and intervention phases. 

17 
Table 3. Frequency and Percentage of Safety-Related Events at Carrier A 

Variable Type Variable Baseline 

Phase 

N 

Baseline 

Phase 

% 

Intervention 

Phase 

N 

Intervention 

Phase 

% 

Event Type Collision 2 3.4 2 1.4 

Event Type Risky Driving 56 96.6 139 98.6 

Root Cause Blind Area Not Checked 0 0.0 2 1.4 

Root Cause Cell Phone 5 8.6 10 7.1 

Root Cause Food/Drink 3 5.2 1 0.7 

Root Cause Not Looking Far Ahead 1 1.7 9 6.4 

Root Cause Not Scanning Roadway 1 1.7 0 0.0 

Root Cause Not Scanning Intersection 3 5.2 3 2.1 

Root Cause Judgment Error 2 3.5 0 0.0 

Root Cause Mirrors Not Checked 0 0.0 1 0.7 

Root Cause No Root Cause 43 74.1 115 81.6 

Risky Behavior Driver Unbelted 21 36.2 34 24.1 

Risky Action Following Too Close 20 34.5 78 55.3 

Risky Action Traffic Violation 2 3.5 6 4.2 

Risky Action Poor Lane Selection 1 1.7 0 0.0 

Normalized Data: The 36 drivers at Carrier A drove a total of 291,869 VMT during the baseline 

phase. A rate was calculated to account for exposure (i.e., frequency of safety-related events / 

VMT). Figure 7 displays the mean rate of safety-related events/10,000 VMT per week across the 

17 weeks of data collection. As shown in Figure 7, the mean rate of safety-related events/10,000 

VMT during the baseline phase was 1.9 safety-related events. During the intervention phase, 

these same 36 drivers drove a total of 1,170,721 miles. The mean rate of safety-related 

events/10,000 VMT during the intervention phase was 1.2 safety-related events. A paired sample 

t test found the mean rate of safety-related events/10,000 VMT during the intervention phase (1.2 

safety-related events/10,000 VMT) was significantly lower than the mean rate of safety-related 

events/10,000 VMT during the baseline phase (1.9 safety-related events/10,000 VMT; t(35) = 1.7, 

p = 0.046). Hypothesis 1 was supported at Carrier A since the 38.1 percent reduction in the mean 

rate of safety-related events/10,000 VMT from the baseline to intervention phase was significant.

18 
Figure 7. Graph. Mean Rate of Safety-Related Events/10,000 VMT across 17 Weeks of Data 

Collection at Carrier A 

3.2.1.2 Carrier B 

Frequency of Safety-Related Events: A total of 41 drivers at Carrier B, from the original 50, 

were included in the data analyses. A total of nine drivers quit, resigned, withdrew, had a 

malfunctioning event recorder, and/or did not meet the minimum requirements to be included in 

the data analyses. Table 4 shows the frequency and percentage of event type, root cause, and 

risky actions coded by data reductionists. The percentages in Table 4 add up to more than 100 

percent as more than one root cause and/or risky action could be selected for each safety-related 

event. During the 4-week baseline phase, 65 valid safety-related events were captured by the 

event recorder (1 collision and 64 risky driving events). During the 13-week intervention phase, 

a total of 117 valid safety-related events were captured by the event recorder (2 collisions and 

115 risky driving events). As indicated above, it is difficult to make interpretations on the raw 

frequencies of safety-related events given the unbalanced data collection periods in the baseline 

and intervention phases.

19 
Table 4. Frequency and Percentage of Safety-Related Events at Carrier B 

Variable Type Variable 

Baseline 

Phase 

N 

Baseline 

Phase 

% 

Intervention 

Phase 

N 

Intervention 

Phase 

% 

Event Type Collision 1 1.5 2 1.7 

Event Type Risky Driving 64 98.5 115 98.3 

Root Causes Blind Area Not Checked 8 12.3 1 0.9 

Root Causes Cell Phone 2 3.1 6 5.1 

Root Causes Food/Drink 1 1.5 3 2.6 

Root Causes Not Looking Far Ahead 1 1.5 17 14.5 

Root Causes Not Scanning Roadway 2 3.1 9 7.7 

Root Causes Not Scanning Intersection 0 0.0 8 6.8 

Root Causes Judgment Error 0 0.0 1 0.9 

Root Causes No Root Cause 51 78.5 75 64.1 

Risky Behavior Driver Unbelted 49 75.4 31 26.5 

Risky Action Following Too Close 5 7.7 44 37.6 

Risky Action Traffic Violation 3 4.6 10 8.5 

Risky Action Unsafe Speed 1 1.5 0 0.0 

Risky Action Failed to Keep an Out 0 0.0 1 0.9 

Normalized Data: The 41 drivers at Carrier B drove a total of 162,492 miles during the baseline 

phase. As indicated above, a rate was calculated to account for exposure. Figure 8 displays the 

mean rate of safety-related events per week across the 17 weeks of data collection. As shown in 

Figure 8, the mean rate of safety-related events/10,000 VMT during the baseline phase was 4.02 

safety-related events. During the intervention phase, these same 41 drivers drove a total of 

615,403 miles. The mean rate of safety-related events/10,000 VMT during the Intervention phase 

was 1.93 safety-related events. A paired sample t test found the mean rate of safety-related 

events/10,000 VMT during the intervention phase (1.9 safety-related events/10,000 VMT) was 

significantly lower than the mean rate of safety-related events/10,000 VMT during the baseline 

phase (4.0 safety-related events/10,000 VMT; t(40) = 1.88, p = 0.03). Hypothesis 1 was also 

supported at Carrier B since the 52.2 percent reduction in the mean rate of safety-related 

events/10,000 VMT from the baseline to intervention phase was significant.

20 
Figure 8. Graph. Mean Rate of Safety-Related Events/10,000 VMT across 17 Weeks of 

Data Collection at Carrier B 

3.2.2 HYPOTHESIS 2: There will be a significant reduction in the mean rate of severe 

safety-related events from baseline to intervention 

As indicated above, the mean rate of severe safety-related events (i.e., with an Event Score > 3) 

during the baseline and intervention phases were compared in Hypothesis 2. A “severe” event 

was defined as any safety-related event with an Event Score > 3. For each participant the 

frequency of severe safety-related events during the baseline phase was divided by the number of 

miles during the baseline phase. The same procedures were used for data collected during the 

intervention phase. A paired sample t test was used to assess if there was a significant reduction 

in the mean frequency of severe safety-related events/10,000 VMT from baseline to intervention 

(α = 0.05). 

3.2.2.1 Carrier A 

Drivers at Carrier A had 16 severe safety-related events out of a total of 199 safety-related events 

(8.0 percent). Figure 9 displays the mean rate of severe safety-related events/10,000 VMT across 

the 17 weeks of data collection at Carrier A. The mean rate of severe safety-related 

events/10,000 VMT during the baseline phase was 0.22, while the mean rate of severe safetyrelated events/10,000 VMT during the intervention phase was 0.09. The 59.1 percent reduction 

in the mean rate of severe safety-related events/10,000 VMT from the baseline to the 

Intervention phase was not significant (paired sample t test(35) = 1.19, p = 0.121). The lack of 

sufficient statistical power could be the reason why Hypothesis 2 was not supported at Carrier A. 

Note the power analysis above indicated that 30 drivers would be sufficient to detect a 

significant difference; however, this analysis did not consider severe safety-related events. 

21 
Nonetheless, despite the lack of a significant reduction, a 59.1 percent decrease in the mean rate 

of severe safety-related events/10,000 VMT is noteworthy. 

Figure 9. Graph. Mean Rate of Severe Safety-Related Events/10,000 VMT across 17 Weeks of Data 

Collection at Carrier A 

3.2.2.2 Carrier B 

Drivers at Carrier B had 28 severe safety-related events out of 179 safety-related events (15.6 

percent). Figure 10 displays the mean rate of severe safety-related events/10,000 VMT across the 

17 weeks of data collection. The mean rate of severe safety-related events/10,000 VMT during 

the baseline phase was 0.36, while the mean rate of severe safety-related events/10,000 VMT 

during the intervention phase was 0.2. The 44.4 percent reduction in the mean rate of severe 

safety-related events/10,000 VMT from the baseline to the intervention phase was not significant 

(paired sample t test(40) = 1.02, p = 0.16). As with the Carrier A results, Hypothesis 2 was not 

supported at Carrier B. As indicated above, this could be due to limited statistical power. 

However, as with the Carrier A findings, a substantial reduction of 44.4 percent in the mean rate 

of severe safety-related events/10,000 VMT was observed at Carrier B. While not significant, 

due to the small number of severe events, the percentage reduction in severe safety-related 

events at Carriers A and B have practical significance.

22 
Figure 10. Graph. Mean Rate of Severe Safety-Related Events/10,000 VMT across 17 Weeks of Data 

Collection at Carrier B 

3.3 IN-STUDY DRIVER QUESTIONNAIRE 

The In-Study Driver Questionnaire was a 10-item questionnaire that served as a manipulation 

check of fleet safety managers’ adherence to the recommended coaching protocol provided by 

the researchers and assessed drivers’ perceptions of the coaching process (appendix B). A 

researcher contacted drivers, via phone and/or email, to complete the In-Study Driver 

Questionnaire each time they participated in a coaching session with the safety manager (note 

that only drivers who signed an ICF were contacted by researchers). As the independent 

evaluators, contact between the authors and the participating carriers was held to a minimum and 

only involved essential communication. Thus, the authors relied on the associated software for 

indication of a coaching session (i.e., the safety manager would check a box and/or make a 

notation to indicate coaching had taken place). 

Carrier A safety managers conducted 32 coaching sessions during the study’s intervention phase. 

The average time between the safety-related event and the coaching session was 5.8 days at 

Carrier A. Twenty-five of the coaching sessions involved a driver who signed an ICF; thus, 

eligible to complete the In-Study Driver Questionnaire. Although there was an incentive for 

completing the In-Study Questionnaire and researchers attempted to contact drivers several 

times, only 10 In-Study Driver Questionnaires were completed at Carrier A (40 percent). 

Carrier B safety managers conducted 37 coaching sessions during the intervention phase. The 

average time between the safety-related event and the coaching session was 10.1 days at 

Carrier B. Twenty-eight of the coaching sessions involved a driver who signed an ICF; thus, 

23 
eligible to complete the In-Study Driver Questionnaire. Fourteen In-Study Driver Questionnaires 

were completed at Carrier B (50 percent). 

Table 5 shows selected results from the In-Study Driver Questionnaire at Carriers A and B. The 

first four items in Table 5 illustrate the recommended coaching protocol: review the video with 

the driver, clearly identify the “root cause” in the safety-related event, identify ways to prevent 

the safety-related event in the future, and keep the coaching session positive. As can be seen in 

Table 5, safety managers at each carrier differed vastly in their ability to follow the coaching 

protocol. 

Table 5. Selected Results from the In-Study Driver Questionnaire at Carriers A and B 

Item Carrier A Carrier B 

1) Reviewed video during coaching session 8 out of 10 (80%) 1 out of 14 (7%) 

2) How clearly was the “root cause” identified Mean = 7.1 

(Moderately Clear) 

Mean = 1.0 

(Very Unclear) 

3) Identified ways to prevent future events 9 out of 10 (90%) 1 out of 14 (7%) 

4) The coaching session was positive Mean = 6.25 

(Positive) 

Mean = 3.0 

(Moderately 

Negative) 

5) How likely are you to use the information learned in 

coaching session 

Mean = 7.8 

(Moderately Likely) 

Mean = 2.0 

(Very Unlikely) 

6) Length of coaching session Mean = 10 minutes Mean = 10 minutes 

Of the completed and returned In-Study Driver Questionnaires, 80 percent of the drivers at 

Carrier A indicated the fleet safety manager at Carrier A reviewed the video of the safety-related 

event during the coaching session, whereas only 7 percent of the drivers at Carrier B indicated 

the same. The second item asked drivers to indicate, on a nine-point scale (1 = Very Unclear; 9 = 

Very Clear), how clearly the root cause in the safety-related event was identified by the fleet 

safety manager. As shown in Table 5, drivers at Carrier A indicated the root cause in the safetyrelated event was identified “moderately clearly” by fleet safety managers at Carrier A, whereas 

drivers at Carrier B indicated “very unclear.” The third item asked drivers to indicate (yes or no) 

if the fleet safety manager identified ways to prevent the safety-related event in the future. 

Ninety percent of drivers at Carrier A indicated that fleet safety managers at Carrier A identified 

ways to prevent the safety-related event in the future, whereas only 7 percent of the drivers at 

Carrier B indicated the same. The fourth item asked drivers to indicate, on a nine-point scale (1 = 

Very Negative; 9 = Very Positive), if the fleet safety manager kept the coaching session positive. 

As shown in Table 5, drivers at Carrier A indicated that fleet safety managers at Carrier A kept 

the coaching session “positive,” whereas drivers at Carrier B indicated “moderately negative.” 

Lastly, the fifth item asked drivers to indicate, on a nine-point scale (1 = Very Unlikely; 9 = 

Very Likely), how likely they were to use the information learned in the coaching session. 

Drivers at Carrier A indicated they were “moderately likely” to use the information learned 

during the coaching session, whereas drivers at Carrier B indicated “very unlikely.” 

24 
Note that data were only obtained from a small portion of the coaching sessions at Carriers A 

and B. Thus, the results should be interpreted with caution. The results from the In-Study Driver 

Questionnaire suggest that fleet safety managers at Carrier A adhered to the coaching protocol, 

while fleet safety managers at Carrier B did not. However, drivers at Carrier B experienced a 

52.2 percent reduction in the mean rate of safety-related events/10,000 VMT from the baseline to 

the Intervention phase. There was essentially no difference between those drivers who received a 

coaching session (as indicated by the fleet safety manager) and those who did not receive a 

coaching session at Carrier B. For example, very few drivers who received a coaching session at 

Carrier B viewed a video of the safety-related event. Feedback usually entailed a message from 

the fleet safety manager to all participating drivers via the dispatching device (e.g., please obey 

Carrier B’s policy on safety-belt use). Thus, all drivers at Carrier B received some sort of 

feedback and information that they were being monitored. It appears this was sufficient to reduce 

significantly the mean rate of safety-related events/10,000 VMT. Conversely, the coaching 

sessions at Carrier A had a powerful effect on safety-related events, as will be shown in the posthoc analyses below. 

3.4 POST-STUDY QUESTIONNAIRES 

Post-study questionnaires were sent to drivers and fleet safety managers at both carriers who 

signed an ICF. The post-study questionnaires assessed perceptions and opinions regarding the 

DBMS. The Post-Study Driver Questionnaire had 11 items, while the Post-Study Safety 

Manager Questionnaire had 14 items. 

3.4.1 Drivers 

A total of 13 and 9 drivers at Carriers A and B, respectively, completed the Post-Study Driver 

Questionnaire. Table 6 shows selected results from the Post-Study Driver Questionnaire at 

Carriers A and B. As can be seen in Table 6, drivers at Carriers A and B differed in their 

perceptions and opinions regarding the DBMS. These perceptions and opinions of the DBMS 

were likely influenced by drivers’ rapport and trust with their safety managers and the 

differences at each carrier in following the coaching protocol. Again, as data were only obtained 

from a small portion of drivers at Carriers A and B, these results should be interpreted with 

caution. 

25 
Table 6. Selected Results from the Post-Study Driver Questionnaire at Carriers A and B 

Item Carrier A Carrier B 

1) Reviewed video during coaching session 12 out of 13 (92.3%) 3 out of 9 (25%) 

2) Video was helpful in identifying risky driving 

behavior* 

Mean = 8.0 

(Very Helpful) 

Mean = 4.3 

(Neutral) 

3) Trusted the accuracy of the video* Mean = 7.0 

(Moderately Trustful) 

Mean = 5.3 

(Neutral) 

4) Clarity of the root cause Mean = 7.0 

(Moderately Clear) 

Mean = 6.0 

(Clear) 

5) Safer driver after participating in program Mean = 7.2 

(Moderately Safer) 

Mean = 7.1 

(Moderately Safer) 

6) DBMS was useful in reducing risky driving 

behavior 

Mean = 6.7 

(Useful) 

Mean = 5.7 

(Neutral) 

7) Overall opinion of DBMS Mean = 7.4 

(Moderately Positive) 

Mean = 5.9 

(Positive) 

* based on drivers who answered “yes” to the first item. 

As in the In-Study Driver Questionnaire, drivers at Carrier A were more likely than drivers at 

Carrier B to have reviewed video of a safety-related event during the coaching session (92.3 

percent versus 25 percent). The second item asked drivers to indicate, on a nine-point scale 

(1 = Very Useless; 9 = Very Helpful), if the video was helpful in identifying the risky driving 

behavior (note this was only answered by drivers who reported viewing a video during a 

coaching session). Drivers at Carrier A reported the video was “very helpful” in identifying the 

risky driving behavior, while drivers at Carrier B were “neutral”. The third item asked drivers to 

indicate, on a nine-point scale (1 = Extremely Distrustful; 9 = Extremely Trustful), if they trusted 

the accuracy of the video shown in the coaching session (note this was only answered by drivers 

who reported viewing a video during a coaching session). Drivers at Carrier A reported they 

found the accuracy of the video “moderately trustful”, while drivers at Carrier B were “neutral”. 

The fourth item asked drivers to indicate, on a nine-point scale (1 = Very Unclear; 9 = Very 

Clear), how clearly fleet safety managers identified the root cause in the safety-related event. 

Drivers at Carrier A reported that fleet safety managers at Carrier A were “moderately clear” in 

identifying the root cause in the safety-related event, while drivers at Carrier B indicated fleet 

safety managers were “clear”. The fifth item asked drivers to indicate, on a nine-point scale (1 = 

Much Riskier; 9 = Much Safer), if they were safer drivers after participating in the DBMS 

program. Drivers at Carriers A and B reported they believed they were “moderately safer” 

drivers after participating in the program. The sixth item asked drivers to indicate, on a ninepoint scale (1 = Completely Useless; 9 = Extremely Useful), if the DBMS was useful in reducing 

their risky driving behavior. Drivers at Carrier A reported the DBMS was “useful” in reducing 

their risky driving behaviors, while drivers at Carrier B thought it was “neutral”. The seventh 

item asked drivers to indicate, on a nine-point scale (1 = Very Negative; 9 = Very Positive), their 

overall opinion of the DBMS. Drivers at Carrier A reported that, overall, the DBMS was 

“useful”, while drivers at Carrier B found it to be “neutral”. 

26 
3.4.1.1 Driver Comments 

Drivers were also asked three open-ended questions regarding improvements and general 

comments regarding the DBMS. Figure 11 illustrates a content analysis of these comments. Note 

that comments from drivers at Carriers A and B were combined. As shown in Figure 11, most 

drivers cited privacy issues and the size of the event recorder as issues to be addressed. Also, 

drivers thought the addition of extra cameras to the side of the truck would be beneficial in 

identifying common interactions with passenger car drivers. 

Figure 11. Graph. Frequency of Driver Comments in the Post-Study Driver Questionnaire

3.4.2 Fleet Safety Managers 

Four fleet safety managers completed the Post-Study Safety Manager Questionnaire (two each 

from Carriers A and B). Due to the low sample size, the data were combined to protect the 

identity of the fleet safety managers. Table 7 shows selected results from the Post-Study Safety 

Manager Questionnaire at Carriers A and B. 

27 
Table 7. Selected Results from the Post-Study Safety Manager Questionnaire at Carriers A and B 

Item Carriers A and B 

Time consumption involved in coaching drivers Mean = 7.0 

(Moderately Time Consuming) 

Interactions with drivers while reviewing videos with drivers Mean = 7.0 

(Moderately Positive) 

Driver interest in reviewing videos Mean = 7.8 

(Moderately Interested) 

Difficulty in identifying the root cause in safety-related event Mean = 3.3 

(Easy) 

Improved in management of drivers as a result of the DBMS program Mean = 6.5 

(Improved) 

Recommend DBMS program to other safety managers 4 out of 4 

(100%) 

Drivers who participated in the DBMS program are safer drivers Mean = 6.3 

(Safer) 

Was the DBMS useful in identifying drivers’ risky driving behaviors Mean = 8.8 

(Extremely Useful) 

Overall opinion of the DBMS program Mean = 7.3 

(Moderately Positive) 

The first item in Table 7 asked fleet safety managers to indicate, on a nine-point scale 

(1 = Not Time Consuming; 9 = Very Time Consuming), the time consumption involved in 

coaching drivers. Fleet safety managers found the coaching process “moderately time 

consuming.” The second item asked fleet safety managers to indicate, on a nine-point scale 

(1 = Very Negative; 9 = Very Positive), their interactions with drivers while reviewing the video 

of the safety-related event. Fleet safety managers reported their interactions with drivers while 

reviewing the safety-related event as “moderately positive.” The third item asked fleet safety 

managers to indicate, on a nine-point scale (1 = Very Indifferent; 9 = Very Interested), their 

perceptions of drivers’ interest in reviewing the video of the safety-related event. Fleet safety 

managers reported that drivers were “moderately interested” in reviewing the video of the safetyrelated event. The fourth item asked fleet safety managers to indicate, on a nine-point scale 

(1 = Very Easy; 9 = Very Difficult), the difficulty in identifying the root cause in the safetyrelated event. Fleet safety managers reported that identifying the root cause in the safety-related 

event was “easy.” 

The fifth item asked fleet safety managers to indicate, on a nine-point scale (1 = Much Worse; 

9 = Much Improved), their improved management of drivers as a result of the DBMS. Fleet 

safety managers reported an “improved” management approach as a result of the DBMS. The 

sixth item asked fleet safety managers to indicate, yes or no, if they would recommend the 

DBMS to other fleet safety managers. All fleet safety managers indicated they would 

recommend the DBMS to other fleet safety managers. The seventh item asked fleet safety 

managers to indicate, on a nine-point scale (1 = Much Riskier; 9 = Much Safer), if drivers who 

participated in the DBMS program were safer drivers. Fleet safety managers reported that drivers 

28 
who participated in the DBMS program were “safer” drivers. The eighth item asked fleet safety 

managers to indicate, on a nine-point scale (1 = Completely Useless; 9 = Extremely Useful), if 

the DBMS was useful in identifying drivers’ risky driving behaviors. Fleet safety managers 

reported the DBMS was “extremely useful” in identifying drivers’ risky driving behaviors. The 

ninth item asked fleet safety managers to indicate, on a nine-point scale (1 = Very Negative; 

9 = Very Positive), their overall opinion of the DBMS. Fleet safety managers reported that, 

overall, the DBMS program was “moderately positive.” 

3.4.2.1 Fleet Safety Manager Comments 

Fleet safety managers were also asked five open-ended questions regarding improvements and 

general comments regarding the DBMS program. Several fleet safety managers indicated the 

quality of the videos needed to be improved. Fleet safety managers cited poor video quality at 

night and the need for better perception of the vehicle in space. Fleet safety managers also 

indicated the length of the video recording (12 s) should be increased. Most fleet safety managers 

found the software user-friendly; however, several fleet safety managers were frustrated by slow 

downloads during “peak” hours. Overall, fleet safety managers found the camera to be a very 

useful tool in identifying their drivers’ at-risk driving behaviors. Privacy issues could be 

alleviated, if the event recorder was active only during the operation of the vehicle and not while 

drivers were in the sleeper berth. Note that drivers were allowed to cover the camera while they 

were in the sleeper berth; however, the camera was still active and could record audio and video 

if activated. This was a prominent issue with drivers (i.e., privacy while not on the job). 

3.5 POST-HOC ANALYSES 

Additional analyses were performed on data from Carrier A (note: these were not indicated in the 

work plan, thus, there were no specific hypotheses). Drivers at Carrier A were grouped into one 

of two groups: (1) drivers who participated in a coaching session where a video was reviewed (n

= 13 drivers) and (2) drivers who did not participate in a coaching session where a video was 

reviewed or did not participate in a coaching session of any kind (n = 23 drivers). Figure 12

displays the mean rate of safety-related events/10,000 VMT during the baseline and intervention 

phases for drivers who received a coaching session with review of video (i.e., Fleet Manager 

Coaching) and those drivers who did not receive a coaching session with review of a video (i.e., 

No Fleet Manager Coaching). As shown in Figure 12, drivers in the Fleet Manager Coaching 

group had a mean rate of 1.65 safety-related events/10,000 VMT during the baseline phase and a 

mean rate of 0.95 safety-related events/10,000 VMT during the intervention phase. The 42.4 

percent reduction in the mean rate of safety-related events/10,000 VMT from the baseline to the 

intervention phase was significant (paired sample t test(12) = 2.13, p = 0.027). 

As shown in Figure 12, drivers in the No Fleet Manager Coaching group had a mean rate of 2.11 

safety-related events/10,000 VMT during the baseline phase and a mean rate of 1.39 safetyrelated events/10,000 VMT during the intervention phase. The 34.1 percent reduction in the 

mean rate of safety-related events/10,000 VMT from the baseline to the intervention phase was 

not significant (paired sample t test(22) = 1.13, p = 0.136). Thus, the coaching sessions that did 

not include videos and/or the feedback light alone were insufficient to reduce (to a statistically 

significant level) the mean rate of safety-related events/10,000 VMT at Carrier A. Again, note 

29 
the power analyses above indicated a minimum of 30 drivers and these post-hoc analyses were 

not considered in the power analysis. 

Figure 12. Graph. Mean Rate of Safety-Related Events/10,000 VMT during the Baseline and 

Intervention Phases for Drivers in the Fleet Manager Coaching and No Fleet Manager Coaching 

Groups at Carrier A 

What is interesting in Figure 12 is the mean rate of safety-related events/10,000 VMT during the 

baseline phase in the No Fleet Manager Coaching group was higher than found in the Fleet 

Manager Coaching group, which would seem counterintuitive as the drivers who have the most 

safety-related events would be more likely to receive a coaching session. However, Figure 13

illustrates the reason for this discrepancy.

Figure 13 displays the mean rate of severe safety-related events/10,000 VMT during the baseline 

and intervention phases for the Fleet Manager Coaching and No Fleet Manager Coaching groups. 

As shown in Figure 13, drivers in the Feedback group had a mean rate of 0.53 severe safetyrelated events/10,000 VMT during the baseline phase and a mean rate of 0.13 severe safetyrelated events/10,000 VMT during the intervention phase. The 75.5 percent reduction in the 

mean rate of severe safety-related events/10,000 VMT from the baseline to the intervention 

phase was not significant (paired sample t test(12) = 1.56, p = 0.073). Drivers in the No Fleet 

Manager Coaching group had a mean rate of 0.05 severe safety-related events/10,000 VMT 

during the baseline phase and a mean rate of 0.07 severe safety-related events/10,000 VMT 

during the intervention phase. The 28.6 percent increase in the mean rate of severe safety-related 

events/10,000 VMT from the baseline to the intervention phase was not significant (paired 

sample t test (22) = 0.27, p = 0.605). 

30 
Figure 13. Graph. Mean Rate of Severe Safety-Related Events/10,000 VMT during the Baseline and 

Intervention Phases for Drivers in the Fleet Manager Coaching and No Fleet Manager Coaching 

Groups at Carrier A 

Limited power restricted the ability to detect a statistically significant difference. However, 

Figure 13 illustrates that those drivers involved in higher severity safety-related events, thus in 

need of a coaching session, received a coaching session where the video of the safety-related 

event was reviewed. Though the results were not significant, the trend was clearly in the right 

direction and, in fact, accounted for a 75.5 percent reduction in the rate of severe safety-related 

events/10,000 VMT in the Fleet Manager Coaching group. These sub-analyses were only 

performed at Carrier A as there was objective and anecdotal evidence that Carrier A followed the 

coaching protocol provided by the researchers. These sub-analyses were not performed at Carrier 

B as there was essentially no difference between those drivers who received a coaching session 

(as indicated by the fleet safety manager) and those who did not. As shown above, very few 

drivers who received a coaching session at Carrier B included review of the video. Feedback 

usually entailed a message from the safety manager to all participating drivers via the 

dispatching device (e.g., please obey Carrier B’s policy on safety-belt use). 

31 
32 

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4. SUMMARY AND CONCLUSIONS 

4.1 SUMMARY 

In this quasi-experiment (i.e., lack of random assignment of drivers) the effectiveness of a 

DBMS to decrease the risky driving behaviors of local/short-haul and long-haul truck drivers 

was evaluated. Almost all prior behavioral safety research has targeted work behaviors in 

settings where employees can systematically observe the safe versus at-risk behavior of their 

coworkers (e.g., Geller, 1998; Krause, Hidley, & Hodson, 1996). However, employees who work 

in relative isolation or have little oversight from a supervisor or peer require a process where 

objective data can be obtained to provide feedback and coaching. 

The current study addressed this limitation with a commercially available low-cost DBMS. The 

technology vendor provided in-vehicle video technology to record behavior of drivers for two 

carriers (identified as Carrier A and Carrier B). These recordings were used by fleet safety 

managers to provide feedback on the safe and at-risk driving behaviors of participating drivers. 

During the 4-week baseline phase, the event recorder recorded safety-related events; however, 

the feedback light on the event recorder was disabled and safety managers did not have access to 

the recorded safety-related events to provide feedback to drivers. During the 13-week 

intervention phase, the feedback light on the event recorder was activated and safety managers 

had access to the recorded safety-related events and followed the coaching protocol with drivers 

(when necessary). 

Based on a total of 1,462,590 and 777,895 miles driven at Carriers A and B, respectively, the 

DBMS was effective in decreasing the mean rate of safety-related events/10,000 VMT. At 

Carrier A, the mean rate of safety-related events/10,000 VMT during the intervention phase was 

significantly reduced by 37 percent compared to the baseline phase. Similarly, drivers at Carrier 

B significantly reduced the mean rate of safety-related events/10,000 VMT during the 

Intervention phase by 52.2 percent compared to the baseline phase. Thus, Hypothesis 1 was 

supported. 

Unfortunately, Hypothesis 2, a significant reduction in the severity of safety-related events, was 

not supported statistically because of the low number of severe safety-related events. 

Nonetheless, at Carrier A, the mean rate of severe safety-related events/10,000 VMT during the 

intervention phase was reduced by 59.1 percent compared to the baseline phase. Similarly, 

drivers at Carrier B reduced the mean rate of severe safety-related events/10,000 VMT during 

the Intervention phase by 44.4 percent compared to the baseline phase. Although these 

reductions were not statistically significant, they are noteworthy and in the expected direction 

and, with additional data, would be expected to result in statistical, as well as practical, 

significance. 

The power of the videos in reducing safety-related events was shown in post-hoc analyses at 

Carrier A. Drivers in the Fleet Manager Coaching group at Carrier A significantly reduced the 

mean rate of safety-related events/10,000 VMT by 42.4 percent from the baseline to the 

intervention phase. The 34.1 percent reduction in the mean rate of safety-related events/10,000 

VMT from the baseline to the intervention phase with drivers in the No Fleet Manager Coaching 

33 
group at Carrier A was substantial, though not statistically significant. These findings show that a 

DBMS can lead to practical safety benefits, but the recommended coaching program can further 

bolster the results achieved. Put another way, the coaching sessions where drivers reviewed a 

video of a safety-related event resulted in significant safety benefits, whereas the feedback light 

alone and/or coaching sessions without videos were less robust. 

What is interesting is that the mean rate of safety-related events/10,000 VMT during the baseline 

phase in the No Fleet Manager Coaching group was higher than the Fleet Manager Coaching 

group. At first this seems counterintuitive as the drivers who have the most events would be 

more likely to receive a coaching session. However, the drivers in the Fleet Manager Coaching 

group had a rate of severe safety-related events/10,000 VMT that was 10 times higher than 

drivers in the No Feedback group. This illustrated that drivers in the Fleet Manager Coaching 

group were involved in more severe safety-related events, and thus were in need of a coaching 

session (which they received). Drivers in the Fleet Manager Coaching group at Carrier A 

reduced the mean rate of severe safety-related events/10,000 VMT from the baseline to the 

intervention phase by 75.5 percent (remarkable, though not significant, due to lack of statistical 

power). Drivers in the No Fleet Manager Coaching group increased the mean rate of severe 

safety-related events/10,000 VMT from the baseline to the intervention phase by 28.6 percent; 

however, this was also found to be non-significant. Though the results in the Fleet Manager 

Coaching group were not significant, the trend was clearly in the right direction and accounted 

for a 75.5 percent reduction in the rate of severe safety-related events/10,000 VMT. While not 

significant, due to the small number of severe events, the percentage reduction in severe safetyrelated events in the Fleet Manager Coaching groups at Carrier A has practical significance. 

4.2 CONCLUSIONS 

In interpreting these results, eight issues are noteworthy. First, while it appears Carrier B had 

substantial decreases, compared to Carrier A, in the mean rate of safety-related events/10,000 

VMT (based on percentage reduction), concluding differential intervention impact is risky 

because Carrier A drove more safely than Carrier B during the baseline phase (1.9 versus 4.0 

safety-related events/10,000 VMT). For example, drivers at Carrier A and B likely experienced 

different safety-related environmental conditions due to the predominant roads driven. A 

naturalistic study by Hanowski, Olson, Hickman, and Dingus (2006) reported that long-haul 

drivers typically drive on rural divided roads (e.g., highways), while local/short-haul drivers 

typically drive on urban undivided roads. 

Second, drivers were aware the instrumented vehicles were recording their driving behaviors; 

thus, the issue of “subject reactivity” should be considered. However, it is unlikely this 

awareness influenced intervention impact as any reactivity to the event recorders or the DBMS 

was constant across both phases, and any effect of reactivity to being observed is likely to be 

most prominent at the beginning of such procedures (Campbell, 1957). In fact, the data obtained 

during the baseline phase may have been understated (i.e., if behavior was affected because 

drivers were conscious that a camera was present, the impact would be felt much more during 

the early stages of the study—the baseline phase. As a camera is in place over time, drivers 

become less conscious of it. As such, subject reactivity would diminish as the study progresses, 

resulting in a less robust effect during the intervention phase.) Third, technical difficulties with 

34 
the event recorders at Carrier A likely had an adverse effect on the positive impact of the 

intervention. Missing events due to these technical difficulties during the baseline phase would 

have likely increased the mean rate of safety-related events/10,000 VMT during the baseline 

phase, thereby increasing the impact of the DBMS intervention at Carrier A. 

Fourth, although evidence suggests that safety managers at Carrier B did not strictly adhere to 

the coaching protocols, drivers significantly reduced the mean rate of safety-related 

events/10,000 VMT from the baseline phase to the Intervention phase. Why? The results from 

the post-hoc analyses at Carrier A suggest that drivers in the No Feedback group did not 

experience a significant reduction in the mean rate of safety-related events/10,000 VMT. This 

group included drivers that received a coaching session, but did not review a video in the 

coaching session (similar to the drivers in Carrier B). However, this is a flawed comparison as 

drivers in the No Feedback group at Carrier A also included drivers that received no feedback of 

any kind. In fact, all drivers at Carrier B received some feedback, though informally, through 

dispatching device messages and other communication devices. Apparently, this was sufficient 

to alter the driving behaviors at Carrier B. However, the high rate of sabotage casts some doubt 

on the results at Carrier B (278 events at Carrier B; 2 and 276 events in the baseline and 

intervention phases, respectively). As the driver face camera was blocked, data reductionists 

could not discern driver behaviors; thus, in-cab driver behaviors were unknown during these 

events. While out-of-cab driving behaviors, such as following too close, could be seen in these 

situations (and were reduced as such), drivers’ in-cab behaviors could not be seen (e.g., cell 

phone use, asleep at the wheel, driver unbelted, etc.). It is likely that some of these in-cab, at-risk 

driving behaviors were ongoing during these events; however, data reductionists were blind to 

their occurrence as the driver face camera was obstructed. 

Fifth, the results replicate those found by McGehee et al. (2007), who also used a DriveCam 

system with novice teen drivers. However, there were some critical differences between the 

current study and McGehee at al. The participants in McGehee et al. were novice teen drivers 

who were still acquiring basic knowledge and skills in regards to driving a motor vehicle, 

whereas the participants in the current study were experienced professional drivers (the mean 

rate of safety-related events/10,000 VMT was 10-fold greater with the novice teen drivers 

compared to the experienced professional drivers in the current study). McGehee et al. also 

included social comparison feedback during the intervention (i.e., feedback on the performance 

of the other teen drivers in the study); this was not part of the coaching protocol in the current 

study. Williams and Geller (2000) found that the addition of social comparison feedback 

regarding workers’ safety performance was sufficient to improve safety-related behaviors 

beyond levels attained with individual behavioral feedback alone. Williams and Geller explained 

their unexpected finding by assuming that social comparison feedback added a motivational 

element to select the safe alternatives. While the individual feedback provided the teen drivers 

with specific information on safe versus at-risk driving behaviors, the public accountability 

provided by general comparisons with similar teen drivers provided an effective motivational 

consequence. 

Sixth, the current study relied on the power of feedback to alter drivers’ at-risk driving 

behaviors. The delivery of feedback to increase safety-related work behaviors has received great 

attention in the organizational behavior management literature (Fellner & Sulzer-Azaroff, 1984; 

Ludwig & Geller, 1991, 1997; Reber & Wallin, 1984; Williams & Geller, 2000). However, the 

35 
motivational effects of feedback have been questioned. Bandura (1986) suggested that 

dissatisfaction with one’s prior attainments can motivate increased effort and vigilance. Without 

goals people do not have a standard against which to compare prior behavior; thus, selfevaluative reactions are not engaged. Without feedback people do not have information allowing 

them to gauge progress toward the goal. Therefore, goals or feedback alone do not activate selfregulatory processes (Bandura & Cervone, 1983; Cervone & Wood, 1995). 

A weakness in studies that use feedback as an intervention component is their failure to assess 

goal setting. A goal is an object, aim, or endpoint of action that describes what people are trying 

to accomplish. Several reviews and meta-analyses have supported the basic tenets of goal-setting 

theory (Locke, Shaw, Saari, & Latham, 1981; Tubbs, 1986; Wood, Mento, & Locke, 1987). The 

basic theory proposes that goals and performance have a linear relationship (i.e., higher goals 

lead to higher performance). Locke and Bryan (1969) had participants drive an instrumented car 

over a prescribed course for three trips. During trips two and three, goals were assigned to 

different driving behaviors. However, participants received feedback on all driving-related 

behaviors. Participants only improved on those driving behaviors for which the experimenter 

assigned goals. Several reviews of the goal-setting literature have supported the interdependent 

relationship of goals and feedback (Locke & Latham, 1990; Tubbs, 1986; Wood, Mento, & 

Locke, 1987). Locke and Latham hypothesized that participants in feedback-only interventions 

reporting beneficial behavior change were spontaneously setting goals. They further state, “The 

unmistakable message of such [feedback] interventions must be, here is something which you 

should improve!” (p. 196). 

The current study made no attempt to assess drivers’ goals and/or provide goal-setting training. 

The authors served as independent evaluators, and the purpose of the study was to assess a 

commercially available DBMS; no attempt was made to revise the carriers’ existing safety 

program. Moreover, assessment of drivers’ goal-setting behavior in the current study would have 

introduced an element not present in the existing safety program, thereby providing an inaccurate 

assessment of the this DBMS program. This aspect might be addressed in future studies 

assessing the efficacy of DBMSs. 

Seventh, the “safety climate” at each carrier was different and these differences likely influenced 

drivers’ perceptions of the DBMS (as shown in the questionnaires and the differences in 

implementing the project). While both carriers had similar scores in FMCSA’s Safety Status 

Measurement System (an analysis system that combines current and historical safety 

performance data to measure the relative safety fitness of interstate commercial motor carriers), 

thus similar safety cultures, the difference in each safety manager’s rapport and trust with drivers 

was evident. While “safety culture” refers to the underlying beliefs and values of workers in 

relation to safety (Glendon, Clarke, & McKenna, 2006), “safety climate” refers to workers’ 

perceptions of how safety is managed in the workplace (Cox & Cheyne, 2000). It is likely that 

drivers at Carrier B were not ready for the DBMS and initial efforts might have focused on 

improving the relationship between drivers and the new safety manager. The authors recommend 

that carriers considering implementing a DBMS evaluate their safety culture prior to 

implementing any safety intervention, especially one that monitors driver behavior via video 

cameras. Drivers may view this as an invasion of their privacy and/or a “blame the driver” 

program. 

36 
Eighth, only a portion of the trucks at each carrier were instrumented with event recorders. 

Safety managers decided a-priori which trucks were instrumented in event recorders. This (and 

the limited time frame) limits the ability to assess the potential “fleet-wide” safety benefits of the 

DBMS. 

Taking these eight considerations into account, the findings from this study are quite remarkable 

and the associated safety benefits are not typical of other safety technology interventions (Blanco 

et al., 2009; Fitch et al., 2008). The safety improvements found in this study, depending on the 

analysis, ranged from a 34.1 percent reduction to a 75.5 percent reduction in safety-related 

events. While in some analyses the power to assess statistical significance were absent, the 

practical significance of these favorable results should not be dismissed. Safety benefits on the 

scale found in this study highlight the potential for DBMSs to have a robust impact in reducing 

truck crashes on our nation’s highways. 

4.3 RECOMMENDATIONS 

The goal of the current study was to assess the efficacy of a commercially available low-cost 

DBMS in an applied setting while normal, revenue-producing deliveries were made. Thus, no 

attempt was made to deviate significantly from the existing DBMS program. As prior research 

has found the combination of goal setting and feedback to be the optimal approach; the authors 

recommend that future studies assessing the efficacy of a DBMS consider the addition of goalsetting training and direct assessment of participants’ goals. The current study did not assess 

implicit goal setting; thus, variations in goal setting among drivers could have been the reason 

for differential behavior change among drivers. 

The current DBMS was successful in significantly reducing the mean rate of safety-related 

events/10,000 VMT (by 37 and 52.2 percent at Carriers A and B, respectively); however, carrier 

managers may be more inclined to adopt a DBMS that shows an advantageous return-oninvestment. The authors recommend that such an assessment include the costs associated with 

implementing and maintaining the DBMS as well as the direct (e.g., damage, health care, etc.) 

and indirect (e.g., legal fees, insurance costs, etc.) costs associated with reduced crashes and 

violations. 

The authors recommend that future studies assess the potential fleet-wide safety benefits of a 

DBMS. Data collection should last at least 1 year (possibly longer) and all participating carrier 

CMVs should be instrumented with data collection equipment. This will limit selection bias and 

allow an appropriate assessment of the safety benefits of the DBMS. 

37 
38 

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APPENDIX A. DRIVER DEMOGRAPHIC QUESTIONNAIRE 

Driver#______________ Date: _____________ 

Instructions: Thank you for taking the time to fill in this questionnaire! It should take you about 

10 minutes to complete. Please answer each of the following items as honestly as possible. 

THERE ARE NO RIGHT OR WRONG ANSWERS. Select your answers quickly and do not 

spend too much time thinking about your answers. If you change an answer, erase the first one 

well. The information you provide will be kept confidential and will NOT be shared with anyone 

outside the VTTI research team. 

1. What is your age?: ___________ years 

2. Gender (Check one):  Male  Female 

3. What is your highest level of education (please check one): 

 Did not complete High School  High School graduate or GED  Some College  2yr College degree 

 4yr College degree  Masters degree  Professional degree  Doctorate degree 

4. Do you wear contact lenses? (Check one)  No  Yes (Lens color:________________) 

5. Do you wear glasses when driving?  No  Yes 

6. Which of the following groups is most representative of your background? (Check one) 

 African/American  Asian/American  Caucasian/American  Pacific Islander 

 Hispanic/American  Native American  Middle Eastern  Other:_____________ 

7. Is English your language of preference for: Reading? (check one)  No  Yes 

Speaking? (check one)  No  Yes 

8. How long have you been driving commercial vehicles? _____years _____months 

9. How long have you been working for this company? _____years _____months 

10. How long did you work for your previous employer (your job before this one)? _____years _____months 

11. Are you a member of a union (Check one)?  No  Yes, which one? ___________ 

12. Type of license and endorsements held: License:________________ Endorsements: _______________ 

13. How many moving violations have you had in the: Last 12 months_____ Last 36 months_____ 

14. How many vehicular crashes (personal or work) have you been involved in during the: Last 12 months___ 

 Last 36 months___ 

15. How many of these crashes in Question 14 were considered “your fault” during the: Last 12 months_____ 

 Last 36 months_____ 

39 
40 

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APPENDIX B. IN-STUDY QUESTIONNAIRE 

Instructions: Thank you for taking the time to complete this survey! It should take you about five minutes to 

complete. Please leave blank any information you are not sure of or do not feel comfortable providing. The 

information you provide will be kept anonymous and will NOT be shared with any safety managers or other drivers. 

By completing and returning this questionnaire you will be entered into the bi-monthly raffle for $50. 

Driver Number:___________________________________ 

Date:___________ 

Event#__________ 

Description of 

Event:____________________________________________________________________________________

__________________________________________________________________________________________

__________________________________________________________________________________________ 

1. Did you review video from this event?  No  Yes 

2. Did you review any prior events (if any) during this training session?  No  Yes  N/A 

3. Was the event an example of risky or safe driving behavior?  Safe  Risky 

4. How clearly was the “root cause” of the event communicated to you (please circle)? 

1 2 3 4 5 6 7 8 9 

 Very Unclear Neutral Very Clear 

5. Did you agree with the manager’s viewpoint?  No  Yes 

6. Did the manager identify ways the event could be prevented in the future?  No  Yes 

7. How likely are you to use the information discussed in the coaching session (please circle)? 

1 2 3 4 5 6 7 8 9 

 Very Unlikely Neutral Very Likely 

8. Did you feel like the coaching session was positive (please circle)? 

1 2 3 4 5 6 7 8 9 

 Very Negative Neutral Very Positive 

9. Did you have any comments regarding the event?  No  Yes 

a. If YES, briefly describe them:______________________________________________________ 

__________________________________________________________________________________________

__________________________________________________________________________________________

__________________________________________________________________________________________

__________________________________________________________________________________________

__________________________________________________________________________________________

__________________________________________________________________________________________ 

10. How long was the coaching session? _____minutes 

41 
42 

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APPENDIX C. POST-STUDY DRIVER QUESTIONNAIRE 

Instructions: Thank you for taking the time to complete this survey! It should take you about five minutes to 

complete. Please leave blank any information you are not sure of or do not feel comfortable providing. The 

information you provide will be kept anonymous and will NOT be shared with safety managers or other drivers. By 

completing and returning this questionnaire you will be entered into a raffle for $50. 

Driver Number:___________________________________ 

Date:___________ 

1. Did you ever review a video(s) from an event with your manager?  Yes  No (If No, Skip to Question 5) 

2. Overall, did you find the video(s) to be helpful in identifying risky driving behavior (on a scale of 1 to 9, please circle)? 

1 2 3 4 5 6 7 8 9 

 Extremely Unhelpful Neutral Extremely Helpful 

3. Overall, how much did you trust the accuracy of the video(s) (on a scale of 1 to 9, please circle)? 

1 2 3 4 5 6 7 8 9 

 Extremely Distrustful Neutral Extremely Trustful 

4. Overall, how clearly was the “root cause” of the event(s) communicated to you (on a scale of 1 to 9, please 

circle)? 

1 2 3 4 5 6 7 8 9 

 Extremely Unclear Neutral Extremely Clear 

5. Overall, do you feel like you are a safer driver after participating in this program (on a scale of 1 to 9, please 

circle)? 

1 2 3 4 5 6 7 8 9 

 Much Riskier No Change Much Safer 

6. Overall, what do you think about this safety process (on a scale of 1 to 9, please circle)? 

1 2 3 4 5 6 7 8 9 

 Extremely Negative Neutral Extremely Positive 

7. Overall, did you find this system useful in reducing some of your risky driving habits (on a scale of 1 to 9, 

please circle)? 

1 2 3 4 5 6 7 8 9 

Extremely Useless Neutral Extremely Useful 

43 
8. What improvements would you make to the technology used to capture events (i.e., video)? 

_______________________________________________________________________________________

_______________________________________________________________________________________

_______________________________________________________________________________________

_______________________________________________________________________________________

_______________________________________________________________________________________

_______________________________________________________________________________________ 

9. What improvements would you make to the coaching process (if 

applicable)?_____________________________________________________________________________

_______________________________________________________________________________________

_______________________________________________________________________________________

_______________________________________________________________________________________

_______________________________________________________________________________________

_______________________________________________________________________________________

_______________________________________________________________________________________ 

10. What, if anything, would you change about this safety process? 

_______________________________________________________________________________________

_______________________________________________________________________________________

_______________________________________________________________________________________

_______________________________________________________________________________________

_______________________________________________________________________________________

_______________________________________________________________________________________

_______________________________________________________________________________________

_______________________________________________________________________________________ 

11. Any general 

comments?______________________________________________________________________________

_______________________________________________________________________________________

_______________________________________________________________________________________

_______________________________________________________________________________________

_______________________________________________________________________________________

_______________________________________________________________________________________

_______________________________________________________________________________________

_______________________________________________________________________________________ 

Thank you for your time. This will count as one 

entry in the raffle. 

44 
APPENDIX D. SAFETY MANAGER DEMOGRAPHIC 

QUESTIONNAIRE 

Date: _____________ 

Instructions: Thank you for taking the time to fill in this questionnaire! It should take you about 

10 minutes to complete. Please answer each of the following items as honestly as possible. 

THERE ARE NO RIGHT OR WRONG ANSWERS. Select your answers quickly and do not 

spend too much time thinking about your answers. If you change an answer, erase the first one 

well. The information you provide will be kept confidential and will NOT be shared with anyone 

outside the VTTI research team. 

1. What is your age?: ___________ years 

2. Gender (Check one):  Male  Female 

3. What is your highest level of education (please check one): 

 Did not complete High School  High School graduate or GED  Some College  2yr College degree 

 4yr College degree  Masters degree  Professional degree  Doctorate degree 

4. Which of the following groups is most representative of your background? (Check one) 

 African/American  Asian/American  Caucasian/American  Pacific Islander 

 Hispanic/American  Native American  Middle Eastern  Other:_____________ 

5. Is English your language of preference for: Reading? (check one)  No  Yes 

Speaking? (check one)  No  Yes 

6. How long have you been a manager in commercial vehicle operations? _____years _____months 

7. How long have you been working for this company? _____years _____months 

8. How many moving violations have you had in the: Last 12 months_____ Last 36 months_____ 

9. How many vehicular crashes (personal or work) have you been involved in during the: Last 12 months___ 

 Last 36 months___ 

10. How many of these crashes in Question 9 were considered “your fault” during the: Last 12 months_____ 

 Last 36 months_____ 

45 
46 

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APPENDIX E. POST-STUDY SAFETY MANAGER 

QUESTIONNAIRE 

Instructions: Thank you for taking the time to complete this survey! It should take you about five minutes to 

complete. Please leave blank any information you are not sure of or do not feel comfortable providing. The 

information you provide will be kept anonymous and will NOT be shared with any managers or drivers. You will 

be paid a flat fee of $25 for completing this questionnaire

Date:___________ 

1. Overall, how time consuming did you find coaching drivers (please circle)? 

1 2 3 4 5 6 7 8 9 

 Not Time Consuming Moderate Very Time Consuming 

2. Overall, how would you describe your interactions with drivers while reviewing the video events (please 

circle)? 

1 2 3 4 5 6 7 8 9 

 Very Negative Neutral Very Positive 

3. Overall, do you feel drivers were interested in reviewing the video events (please circle)? 

1 2 3 4 5 6 7 8 9 

 Very Indifferent Neutral Very Interested 

4. Overall, did you find it difficult to communicate the “root cause” of the event to drivers (please circle)? 

1 2 3 4 5 6 7 8 9 

 Very Easy Neutral Very Difficult 

5. Overall, do you feel like you’ve improved in your management of drivers after participating in this study 

(please circle)? 

1 2 3 4 5 6 7 8 9 

 Much Worse Neutral Much Improved 

6. Would you recommend this safety process to other fleet safety managers?  No  Yes 

a. If “No or “Yes”, 

why?__________________________________________________________________________

_______________________________________________________________________________

_______________________________________________________________________________

_______________________________________________________________________________

_______________________________________________________________________________

_______________________________________________________________________________ 

7. Overall, do you believe drivers who participated in this project have become safer drivers? 

1 2 3 4 5 6 7 8 9 

 Much Riskier Neutral Much Safer 

8. Overall, what do you think about this safety process (please circle)? 

1 2 3 4 5 6 7 8 9 

 Very Negative Neutral Very Positive 

9. Overall, did you find this system useful in identifying drivers’ risky driving habits (please circle)? 

1 2 3 4 5 6 7 8 9 

 Completely Useless Neutral Extremely Useful 

47 
48 

10. What improvements would you make to the technology used to capture events (i.e., video)? 

______________________________________________________________________________________

______________________________________________________________________________________

______________________________________________________________________________________

______________________________________________________________________________________

______________________________________________________________________________________

______________________________________________________________________________________ 

11. What improvements would you make to the software used to review video events with drivers? 

______________________________________________________________________________________

______________________________________________________________________________________

______________________________________________________________________________________

______________________________________________________________________________________

______________________________________________________________________________________

______________________________________________________________________________________ 

12. What improvements would you make to the coaching process? 

______________________________________________________________________________________

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13. What, if anything, would you change about this safety process? 

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14. General comments? 

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Thank you for your time 
ACKNOWLEDGEMENTS 

This project would not have been possible without the participation of the trucking fleets and 

drivers that participated in the current study. 

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