Tech Brief

Programs of the Federal Motor Carrier

Safety Administration (FMCSA)

encompass a range of issues and

disciplines, related to motor carrier

safety and security. FMCSA’s Office of

Analysis, Research and Technology

defines a “research program” as any

systematic study directed toward fuller

scientific discovery, knowledge, or

understanding that will improve safety,

and reduce the number and severity of

commercial motor vehicle crashes.

Similarly, a “technology program” is a

program that adopts, develops, tests,

and/or deploys innovative driver and/or

vehicle best safety practices and

technologies that will improve safety

and reduce the number and severity of

commercial motor vehicle crashes. An

“analysis program” is defined as

economic and environmental analyses

done for agency rulemakings, as well

as program effectiveness studies,

state-reported data quality initiatives,

and special crash and other motor

carrier safety performance-related

analyses. A “large truck” is any truck

with a Gross Vehicle Weight rating or

Gross Combination Weight rating of

10,001 pounds or greater. 

Currently, the FMCSA Office of

Analysis, Research and Technology is

conducting programs in order to

produce safer drivers, improve safety

of commercial motor vehicles, produce

safer carriers, advance safety through

information-based initiatives, and

improve security through safety

initiatives. The study described in this

Tech Brief was designed and

developed to support the strategic

objective to produce safer drivers. The

primary goals of this initiative are to

ensure that commercial drivers are

physically qualified, trained to perform

safely, and mentally alert. 

FEDERAL MOTOR CARRIER SAFETY ADMINISTRATION

Office of Analysis, Research and

Technology

1200 New Jersey Ave. SE 

Washington, DC 20590

FMCSA Contact: Olu Ajayi, MC-RRR, (202) 366-0440

Evaluating the Safety Benefits of a 

Low-Cost Driving Behavior Management System

in Commercial Vehicle Operations

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. Behavioral safety programs are advantageous

because they are easy to implement, easy to teach, and may be implemented

successfully in the setting where the problem occurs to increase safety-related

work behaviors. Almost all prior behavioral safety research has been applied in

work settings where employees can systematically observe the safe versus atrisk behaviors of their coworkers. In contrast, commercial truck and bus drivers

typically work alone and in relative isolation and thus require alternative

strategies. Until recently, the primary problem with implementing behaviorbased programs has been getting 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 safetyrelated 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 monitoring 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

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 commercial drivers.

Research Design

This quasi-experiment used a simple A4B13 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, revenue-producing 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).

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 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/short-haul 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 informed

consent form 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).

Data Collection Process

A product vendor, DriveCam, was responsible for all data collection. The event recorder captured

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 seconds of

video (i.e., 8 seconds prior to the criterion being met or surpassed and 4 seconds 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 requested to be reviewed with the driver.

Event Scores in the current study ranged from 0 to 11. Typically, an Event Score greater than or

equal to 5 was marked to be reviewed by the safety manager with the driver present; however, it

was ultimately up to the safety manager which safety-related events were reviewed with the driver.

Once on the server, the authors and safety managers, only during the intervention phase had access

to the data via proprietary software (which was accessible via the Internet). 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, 58 safety-related events were captured by event

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

These drivers drove a total of 291,869 miles during the baseline phase. A rate was calculated to

account for exposure (i.e., frequency of safety-related 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 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 = 1.7, (35)

p = 0.046). Thus, the 38.1 percent reduction in the mean rate of safety-related events/10,000 VMT

from the baseline to intervention phases was significant.

Carrier B — During the 4-week intervention phase, 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. These 41 drivers drove a total of 162,492 miles during the baseline phase. As indicated

above, a mean rate of safety-related events/10,000 VMT 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, a total of 117 valid safety-related events were captured by

the event recorder (2 collisions and 115 risky driving events). 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. A paired sample t test found that 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 = 1.88, (40) p = 0.03). Thus, the 52.2 percent reduction in the mean rate of

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

Additional analyses are presented in the main body of the current report. These include

examination of a second hypothesis (to wit: there will be a significant reduction in the mean rate of

severe safety-related events from baseline to intervention, which was not supported at Carrier A or

Carrier B), analyses of questionnaire data, severe safety-related events, and post-hoc analyses of

drivers who did and did not receive feedback.

Conclusions

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

significant decreases than 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 safety-related

events/10,000 VMT). For example, Carriers A and B likely experienced different safety-related

environmental conditions due to the predominant roads driven. 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 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. 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.

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,

although 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.

Full report title:

Evaluating the Safety Benefits of a

Low-Cost Driving Behavior

Management System in

Commercial Vehicle Operations

(Doc. No. FMCSA-RRR-10-033)

Performed by:

Center for Truck and Bus Safety

Virginia Tech Transportation

Institute

3500 Transportation Research

Plaza (0536)

Blacksburg, VA 24061

Authored by:

Jeffrey S. Hickman and 

Richard J. Hanowski

Key Words:

behavioral safety, carrier safety,

CMV ,commercial motor vehicle,

driver behavior, low-cost driving

behavior management system,

onboard safety monitoring, truck

Notice:

Tech Briefs are disseminated under

the sponsorship of the U.S.

Department of Transportation

(USDOT) in the interest of

information exchange. Tech Briefs

provide a synopsis of the study’s

final publication. Tech Briefs do not

establish policies or regulations, nor

do they imply USDOT endorsement

of the conclusions or

recommendations. The U.S.

Government assumes no liability

for its contents or their use.

Web Site:

All FMCSA Tech and Analysis

Briefs may be accessed at

www.fmcsa.dot.gov.

April 2010

Publication No. FMCSA-RRR-10-032