By:
James Hedlund
Highway Safety North, Ithaca, NY
Daniel Blower
Center for National Truck and Bus Statistics, University of Michigan
Contact:
Ralph Craft, Ph.D.
Ralph.Craft@fmcsa.dot.gov
(202) 366-0324
Office of Information Management
January 2006
Publication #: FMCSA-RI-05-037
The goal of the Federal Motor Carrier Safety Administration (FMCSA) is to reduce the number and severity of large truck- and bus-involved crashes through more commercial motor vehicle and operator inspections and compliance reviews, stronger enforcement measures against violators, expedited completion of rulemaking proceedings, scientifically sound research, and effective CDL testing, recordkeeping, and sanctions.
The Office of Information Management develops and maintains systems for collecting and analyzing motor carrier data, and disseminates information on the motor carrier industry.
This Analysis Brief was produced by the Analysis Division in FMCSA's Office of Information Management. The division analyzes motor carrier data pertaining to crashes, inspections, compliance reviews, and drug and alcohol testing, and supports research on the effectiveness of FMCSA inspections and compliance review programs.
Table of Contents
Summary
Introduction and Purpose
Strengths and Weaknesses of the LTCCS Database
Critical Issues Related to Large Truck Safety
Issue 1. Problem Identification
Issue 2. Driver Fatigue and Hours of Service
Issue 3. Vehicle Maintenance and Inspections
Issue 4. Relative Roles of Cars and Large Trucks
Issue 5. Driver Working Environment
Issue 6. Role of Environmental Factors
Issue 7. Truck Driver Performance
Issue 8. Vehicle Design and Load
Issue 9. Truck Driver Licensing and Monitoring
Issue 10. Truck Driver Training and Experience
Examples
Table 1 - FACT Data: Inspection Results for 407 Large Trucks
Table 2 - FACT Data: Driver Factors Recorded
Table 3 - FACT Data: Crash Involvement and Driver Fatigue by Motor Carrier Type
Figure 1 - FACT Data: Critical Events in Large Truck Fatal Crashes
Table 4 - FACT Data: Large Truck Brake Violations in Braking-Critical Crashes
Conclusions
Acknowledgments
Summary
The Large Truck Crash Causation Study (LTCCS) was undertaken
jointly by the Federal Motor Carrier Safety Administration
(FMCSA) and the National Highway Traffic Safety Administration
(NHTSA). The LTCCS is based on a nationally representative
sample of nearly 1,000 injury and fatal crashes involving large
trucks that occurred between April 2001 and December 2003.
The data collected provide a detailed description of the physical
events of each crash, along with an unprecedented amount of
information about all the vehicles and drivers, weather and roadway
conditions, and trucking companies involved in the crashes.
This analysis brief discusses how statistical analyses of the
LTCCS database can be used to investigate crash causes and
contributing factors. It defines 10 critical issues for large
truck safety, outlines the information needed to address each,
assesses how well the LTCCS database fills those needs, and
briefly discusses other data that could be used for questions
where LTCCS data are not adequate. Analytic techniques that
could be applied to the LTCCS data are illustrated by examples
using data from an earlier study of fatal commercial motor
vehicle crashes in Michigan.
The principal conclusions:
- The LTCCS is a general-purpose data file designed primarily for
problem identification. It contains more than 1,000 data variables
describing all aspects of the drivers, vehicles, and environment in
large truck crashes. Because it is based on a representative sample
of large truck crashes, it can be used to estimate unbiased national
frequencies.
- The LTCCS database can be used to investigate crash risk using relative
risk methods. With the LTCCS database, these methods apply to
many vehicle features, some driver features, and few environmental
features. Their usefulness depends on whether there is a suitable
control group of crashes in which the feature being examined has no
effect.
- The 963-case sample size limits some statistical conclusions from
the LTCCS data. Analyses and national estimates of relatively
infrequent situations will have large uncertainties and will be able
to distinguish only large differences.
- Data accuracy and completeness may limit some conclusions from
the data. Directly observable variables are likely to be quite accurate
and complete. Variables that depend on interviews may be less accurate
and complete, even if investigators have checked other sources
to confirm the interview reports.
- While LTCCS is designed as a statistical data file, its individual case
reports will be useful for investigative analyses based on in-depth
crash reconstructions.
- Additional data from experimental settings almost certainly will be
needed to develop specific interventions for reducing the risks of
large truck crashes.
[BACK TO CONTENTS]
Introduction and Purpose
The Motor Carrier Safety Improvement Act of 1999
(Public Law 106-159), which established the FMCSA,
requires the Agency to "conduct a comprehensive
study to determine the causes of, and contributing
factors to, crashes that involve commercial motor
vehicles." To fulfill that requirement, FMCSA joined
with NHTSA to design and operate the LTCCS. The
study investigated a nationally representative sample
of 963 large truck crashes at 24 data collection sites
within NHTSA's National Automotive Sampling
System (NASS). Trained NASS crash investigators
and State truck inspectors collected more than 1,000
individual data elements for each crash. After pilot
testing, full data collection began in April 2001 and
concluded in December 2003. The final LTCCS data
file will be released to the public in 2006.
In the first report of this LTCCS Analysis Series, Blower
and Campbell discussed the LTCCS methodology in
some detail [1]. They described two basic uses of the
LTCCS data file: (1) "investigative" or "clinical" analyses,
in which crash reconstruction experts can review
individual crash reports to investigate factors that
may have influenced or could have prevented specific
crashes; and (2) statistical analyses of the full database,
in which investigators can examine the frequencies of
various factors and their associations with crash risks.
This report discusses in greater detail how the LTCCS
data can be used for statistical analyses to explore
crash risk and measures to prevent or reduce crashes.
It begins with a general discussion of the data file's
strengths and weaknesses for statistical analyses. It
then lists 10 critical issues related to the safety of large
trucks, outlines the specific information that ideally
would be available to address each issue, and assesses
how well the LTCCS database fills those information
needs. Where appropriate, it discusses how other data
sources could be used to complement the LTCCS
data. Finally, it gives two examples of the types of
analyses that the LTCCS will support, using similar
data from a file of fatal truck crashes in Michigan.
Throughout this report, the terms "truck" or "large
truck" refer to all vehicles within the LTCCS scope—that
is, trucks with a gross vehicle weight rating
(GVWR) greater than 10,000 pounds. The term "passenger
vehicle" refers to all other vehicles (cars, pickup
trucks, vans, and sport utility vehicles). An "intervention"
is any measure intended to prevent or reduce
crashes (other authors may use the terms "countermeasure"
or "treatment"). Finally, as discussed below,
the term "cause" is used broadly to refer to any factor
that may increase the risk of a crash occurrence. To
emphasize this point, the phrase "cause or contributing
factor" is also used.
[BACK TO CONTENTS]
Strengths and Weaknesses of the LTCCS Database
As discussed by Blower and Campbell [1], the "cause"
of a crash can be defined in two ways: as a "necessary
factor" (had the factor not been present in the crash
sequence, the crash would not have occurred); or as a
"risk-increasing factor" (the factor increases the risk,
or probability, of a crash). This report uses the second
definition.
Using the risk-increasing factor definition of a crash
cause has several important consequences. First, it recognizes
that a crash does not have a single cause but is
influenced by many factors. Second, it renders the concept
of "fault" irrelevant. Third, the factors considered
are those that can be described by the LTCCS field data.
They do not depend on inferences made after the fact by
crash reconstruction experts—that is the role of investigative
analysis, as discussed in detail by McKnight [2].
Finally, the whole question of crash cause in a sense
misses the main point: the fundamental objective is to
prevent crashes, and so the true goal of the LTCCS is
to serve as a database for exploring possible interventions
that could reduce the risk of truck crashes. One
way to accomplish that goal is by looking for factors
that increase crash risk. Another is by examining conditions
that are common to many crashes and considering
whether changes in those conditions could
reduce crash risk.
Statistical and investigative analyses complement
each other. Statistical analyses of the full LTCCS
database and investigative analyses of individual
LTCCS cases serve different and complementary roles.
Statistical analyses can determine that specific factors
increase crash risk and can estimate how often the factors
occur on a national level. Investigative analyses can
dig further into specific causal mechanisms and suggest
interventions. Statistical analyses can then suggest
ways of extrapolating potential interventions back to a
national scale and estimating their costs and benefits.
Exposure data for large truck crashes are crude, and
most crash risk analyses of the LTCCS database will
require the use of induced exposure techniques.
Crash risk is defined as crashes per some measure of
exposure, or opportunity: typically crashes per mile
of travel, or crashes per hour, in appropriate circumstances.
For example, to examine the role of brake violations,
one could compare crashes per mile of travel
for trucks with brake violations against crashes per
mile of travel for trucks without brake violations (or
perhaps crashes per mile of travel on wet roads or in
other circumstances for trucks with and without brake
violations). Alternatively, one could use a case-control
study design, in which vehicles that have crashed (the
cases) are matched with vehicles that have not crashed
but that are similar on a number of other variables
(the controls—same vehicle type, driving on the same
road, at the same time of day and day of week, etc.).
Because the LTCCS did not use a case-control study
design, other exposure data are required for case-control
studies using the LTCCS database.
Exposure data on large truck travel are crude.
Registration data are of little use, because the spread of
annual miles traveled by different trucks is very large.
The available data on vehicle miles of travel (VMT)
are not especially accurate, and they make only gross
distinctions among truck and road types. Data on such
critical issues as driver fatigue and vehicle maintenance
may be available from inspection stations, but they are
difficult to extrapolate to travel estimates.
Induced exposure is a general technique that uses
crash data to estimate relative exposure for a specific
factor being examined. It is based on the assumption
that the factor can affect only some crashes. The
factor's presence in crashes that it cannot affect serves
as a measure of its presence on the road (its exposure);
the relative risk of the factor is the ratio of its
presence in crashes that it may affect to its presence
in crashes that it cannot affect (an example is given
in the final section of this report). Induced exposure
methods are standard techniques in crash data analyses
and are appropriate for the LTCCS database.
Sample sizes will limit statistical conclusions from
the LTCCS. The LTCCS data file contains 963 crashes.
This is a large file for investigative analyses and
should provide a wide variety of crash circumstances,
but it is small for statistical analyses. As an everyday
example, national single-issue polls (for example,
to estimate support for two competing presidential
candidates) typically use a sample of about 1,000 and
have a possible error of about 3 percent. The error
increases when the sample is not random or when
responses may be biased in some way.
The LTCCS is a complex multi-stage sample. As a
result, estimating variances is considerably more complicated
than in a simple random sample. The complexity
increases the variance. This means that if the
LTCCS file is used to estimate the national incidence
of any single parameter that is measured objectively
for all crashes, such as the proportion of large truck
crashes that occur during daylight hours, then the 95-percent
confidence error will be greater than 3 percent.
Many interesting and useful analytic questions will
go beyond simple estimates of a single objectively
recorded parameter. Some questions will apply only to
a subset of the LTCCS crashes, such as questions about
crashes involving multi-unit trucks. Other questions
may involve more than one parameter: for example,
does the proportion of crashes occurring in daylight
hours differ for single-unit and multi-unit trucks? As
the questions become more specific in either of these
ways, the size of the possible error increases. Some
questions must rely on more subjective data, such as a
driver's report on his hours of sleep the previous night.
The possibilities of inaccurate data are obvious.
The LTCCS file of 963 cases will serve to estimate
first-order effects (the proportion of something in all
crashes) fairly accurately (to within about 3 percent,
assuming that the data themselves are accurate and
complete). Comparisons of proportions in two types
of crashes will not be able to distinguish differences
smaller than about 10 percent. Any analysis of a relatively
infrequent situation—something that occurred
in fewer than 10 percent of the crashes (or fewer than
100 cases) in the LTCCS database—can distinguish
only large differences, on the order of 30 percent or
more (see Hedlund [3] for further examples).
Data accuracy and completeness will limit some
conclusions from LTCCS analyses. Incomplete data
both limit the size of the dataset for any analysis and
also probably introduce bias, because data seldom are
incomplete at random. Inaccurate data clearly bias
the conclusions. The authors have not had the opportunity
to examine data completeness and accuracy
in the final LTCCS file. In general, it is expected that
variables directly observed by LTCCS investigators
will be quite accurate and complete, including most
vehicle data and non-transitory environmental data.
Variables for which the LTCCS investigators had to
rely on second-hand information will be less accurate
and complete. Sensitive variables, such as whether or
not the truck driver was in violation of the FMCSA
Hours-of-Service (HOS) rules, are likely to be both
incomplete and highly biased. The following discussions
illustrate these points.
[BACK TO CONTENTS]
Critical Issues Related to Large Truck Safety
To provide a structure for discussing LTCCS analyses,
the authors selected 10 high-priority issues in large
truck safety and policy, developed problem statements
for each issue, and assessed how useful LTCCS data
would be in addressing the problem statements. The
issues were selected using the following criteria:
- Relevance (issues involved in enough truck crashes to be worthy of attention)
- Current interest and knowledge (issues actively being investigated)
- Opportunity for intervention (issues that may suggest measures to reduce crashes)
- Feasibility (the relative ease of potential interventions, including costs, timeframes, and implementation requirements)
- Jurisdiction (issues that FMCSA may be able to influence)
- Political priority (issues that FMCSA cannot afford to ignore)
The 10 issues and specific problem statements associated
with each are detailed below. The issues are listed
in the approximate order of their priority and are followed
by brief assessments of whether, and how well,
the LTCCS data could be used to address them.
[BACK TO CONTENTS]
Issue 1. Problem Identification
Problem statement: Identify factors involved in a
substantial number of crashes and factors that significantly
increase crash risk. This information is critical
for determining the most important targets to
be searched in order to formulate large truck safety
countermeasures and to estimate the potential benefits
of such countermeasures.
Assessment: To address the issue, it will be necessary
to estimate the number of crashes nationally that
involve various factors, and how the factors increase
crash risk. LTCCS data are well suited to assess both
issues across a wide range of potential causal or
contributing factors related to drivers, vehicles, the
environment, and motor carrier companies. Driver
factors include: data on fatigue (hours driving before
the crash, time and length of last sleep period, possible
causal links between driver fatigue and crashes);
driver license status, including crash and violation
history; driver experience and training; driver performance
during the crash, including any performance
errors; and driver working environment, including
wages, pay basis, schedule, and company safety
record. Vehicle factors include: maintenance status,
including any defects in brakes, tires, steering, or
other critical vehicle components; and vehicle size,
weight, load, and design. (An example of examining
brakes as a factor is presented in Table 4 in the
last section of this report.) Environmental factors
include: roadway geometry, surface conditions, lighting,
and traffic controls. Motor carrier factors, in
addition to those involving the driver, include: size
and type of carrier, carrier operations, and carrier
safety history.
The potential limitations are data completeness and
accuracy, especially for subjective data on driver factors.
Data file size should not be a limitation. If a
factor occurs infrequently enough that it cannot be
studied with LTCCS data, then it cannot affect a substantial
number of large truck crashes and, almost by
definition, is unlikely to be a major crash causation
issue for large trucks from an absolute point of view
(although it could be a major issue from a political,
regulatory, or relative risk point of view).
[BACK TO CONTENTS]
Issue 2. Driver Fatigue and Hours of Service
Problem statement: Determine effective regulatory
methods to reduce driver fatigue and increase alertness.
Driver fatigue has been identified as an important
crash cause. It is known that many drivers drive while
fatigued, but accurate estimates are not available.
HOS regulations that attempt to reduce fatigue are
highly controversial and widely violated.
Assessment: The necessary data to investigate the
role of fatigue and alertness in crashes include objective
measures of the driver's hours of driving before
the crash, his immediately previous hours of rest and
sleep, and his longer-term sleep and driving schedule.
Ideally there would be a measure of the driver's
fatigue and alertness before the crash. This would
require in-vehicle real-time monitoring of eye movements,
brain function, or the like, which would be
impossible without instrumenting all trucks. Next,
data are needed on HOS compliance, both reported
and actual, in order to determine the size of the problem.
To determine crash risk, similar data are needed
either for truck drivers not involved in crashes or,
using relative risk methods, for drivers in crashes not
involving fatigue or alertness. Then, data are needed
on the roles of fatigue and alertness in causing or
contributing to the crash: Did the driver fail to recognize
or interpret a dangerous situation? Did he fail to
take appropriate action that he might have taken if he
had been more alert?
LTCCS collects the proper data on driver sleep history,
driving hours, and fatigue and also on crash event
variables that relate to driver alertness, such as inattention
and distraction. Most of the data come from driver
interviews, however, and it is likely that they will
be considered suspect unless they can be confirmed
by other evidence. Additional driver data are collected
during Level 1 inspections of vehicles and drivers. If
the data are accurate they can be analyzed to address
the key issues of driver fatigue and hours of service.
[BACK TO CONTENTS]
Issue 3. Vehicle Maintenance and Inspections
Problem statement: Evaluate the role of vehicle maintenance
and defects in crash causation and the value of
the FMCSA Motor Carrier Safety Assistance Program
(MCSAP) truck inspection efforts in reducing defects.
Defective brakes and other components are frequently
cited as crash causes and contributing factors. FMCSA
spends more than $100 million annually on MCSAP-funded
truck and bus inspections.
Assessment: Data are needed on the status of major
vehicle components at the time of the crash, measured
against inspection standards. Components
should include brakes, tires, and steering. To estimate
relative crash risk, similar data are needed for
trucks in crashes that do not involve these components.
Next, data are needed on the role that these
components played in the crash. The effectiveness
of the MCSAP truck inspection program can be
approached in several ways. For example, maintenance
issues or defects shown to increase crash risk
could be compared with MCSAP inspection procedures
to see whether the inspections are looking at
the right things, and to see how frequently the maintenance
defects that cause or contribute to crashes are
observed in inspections.
LTCCS data include the results of a North American
Standard Level 1 inspection, the most rigorous
inspection in the MCSAP program. The data should
serve well to examine the role of vehicle maintenance
issues in crashes. Detailed study of MCSAP effectiveness
will also require data from MCSAP.
[BACK TO CONTENTS]
Issue 4. Relative Roles of Cars and Large Trucks
Problem statement: Estimate how many large truck
crashes result from actions by passenger vehicles (cars
or light trucks) and how many large truck crashes are
unlikely to be affected by measures directed at large
trucks and their drivers. This information will help
FMCSA and NHTSA explore whether interventions
directed at passenger vehicles are needed to reduce
large truck crashes.
Assessment: Statistical analyses of LTCCS data cannot
determine a single crash cause or assign a crash
cause to one vehicle or another. Statistical analyses
can determine contributing factors, assign them to
large trucks or to other vehicles, and estimate how
they increase crash risk. The question is really one of
problem identification and relative risk, a special case
of Issue 1. As with Issue 1, the potential limitations
are data completeness and accuracy and the size of
the LTCCS data file. Because the LTCCS protocol collects
the same data on all vehicles in a crash, it is well
suited to identifying risk factors for passenger vehicles
involved in serious crashes with large trucks.
[BACK TO CONTENTS]
Issue 5. Driver Working Environment
Problem statement: Determine the influence of driver
working conditions (wages, work schedule, company
structure) on large truck crashes. This information is
needed to investigate whether working conditions
should be monitored or regulated to improve safety.
Assessment: The key variables describing driver
working conditions are wages, pay method (by mile,
hour, or job), schedule, and employer type, as well
as the data describing fatigue (see Issue 2, above).
LTCCS collects the relevant data, largely through
driver interviews. If the data are reasonably complete
and accurate, then LTCCS can be used for simple
comparisons of working environment variables, such
as wage structure and driver scheduling practices.
Because of LTCCS sample size limitations and issues of
data completeness and accuracy, more detailed analyses
will require other data sources, probably at the
motor carrier level. For example, comparing the crash
records of drivers from motor carriers of similar types,
pay methods, and scheduling practices but different
wage levels would yield immediate information on
whether wage levels by themselves influence crash risk.
[BACK TO CONTENTS]
Issue 6. Role of Environmental Factors
Problem statement: Investigate whether changes in
roadway environmental design or operation, such as
exit ramp designs, truck-free lanes, or different speed
limits for trucks, would improve large truck safety.
Assessment: The LTCCS database is less useful for
investigating environmental issues than for driver or
vehicle issues. Many, if not most, environmental issues
are best analyzed from a road section point of view,
while the LTCCS data are suited to a truck or driver
point of view. For example, an investigation of the
effects of truck lane restrictions would compare crash
risk between otherwise similar roads with and without
truck lane restrictions, or compare crash rates before
and after lane restrictions were introduced. The data
needed include traffic volumes for both large trucks
and passenger vehicles in the lane restriction and
unrestricted roads, which are not available in LTCCS.
More detailed study requires substantial engineering
data, such as lane widths and analyses of how large
truck and passenger vehicle traffic enters and exits
the roadway. The best that LTCCS can do for most
environmental issues is estimate overall frequencies of
crashes involving specific environmental conditions
and provide individual cases for investigative analysis.
[BACK TO CONTENTS]
Issue 7. Truck Driver Performance
Problem statement: Determine the role of truck driver
performance in crashes, as measured by features such as
truck speed, danger recognition, and driver actions, and
identify areas where reasonable improvements in driver
performance could reduce the risk of large truck crashes.
Assessment: Driver performance is inherently more
difficult to assess than are the other issues discussed in
this report. Without in-vehicle data recorders or video
cameras, driver performance in crash situations must
be inferred after the fact from interview data, crash
reconstructions, and expert judgment. The LTCCS
collects information on the driver's attention, vision,
judgments, and actions during the crash sequence both
from interviews and from crash reconstructions. The
data should provide an initial estimate of the overall
contribution of driver performance errors to crashes,
begin to distinguish the relative importance of different
types of errors, and link specific crash types to specific
errors. Other data sources will be needed to address
how driver performance could be improved.
[BACK TO CONTENTS]
Issue 8. Vehicle Design and Load
Problem statement: Determine the number and types
of crashes in which truck design and/or load features
are contributing factors. Typical features include
truck conspicuity, truck driver blind spots, and load
shifts. This information could be used to explore
potential interventions.
Assessment: The LTCCS data describe truck design
and load features in great detail. The data are objective,
can be observed at the crash site, and should be recorded
completely and accurately. These data can be used to
address many truck design and load issues quite well,
up to the limits imposed by the LTCCS sample size.
[BACK TO CONTENTS]
Issue 9. Truck Driver Licensing and Monitoring
Problem statement: Determine the contribution of
improperly licensed or problem truck drivers to crash
causation. This information could be used to explore
voluntary or regulatory measures to improve licensing
and monitoring of drivers.
Assessment: The LTCCS data include driver license
status and driver history data from interviews, police
accident reports, and Motor Vehicle Department
files. If the data are accurate, they can be used to
estimate the contribution of improperly licensed or
problem drivers to crashes and may suggest specific
crash circumstances where these drivers are especially
involved. Additional data sources will be required to
explore interventions.
[BACK TO CONTENTS]
Issue 10. Truck Driver Training and Experience
Problem statement: Evaluate the effects of truck driver
training and experience in reducing crashes. This information
could be useful in considering methods to
improve training or increase experience if appropriate.
Assessment: The LTCCS data record only the number
of years driving a truck, the number of years driving
the class of vehicle involved in the crash, and the
date and type of driver training. These data will support
only crude comparisons of crash rates and crash
types for drivers at different experience levels or who
received different types of training. Detailed data on
training and driving experience will be needed for any
further investigations.
[BACK TO CONTENTS]
Examples
The following examples of analytical techniques that
could be used successfully with the LTCCS data are
based on a similar data collection. From 1996 to 2001,
the Michigan State Police Motor Carrier Enforcement
Division (MCD) sponsored the Fatal Accident
Complaint Team (FACT) program to collect data on
fatal commercial motor vehicle (CMV) crashes in
Michigan. The FACT approach was similar to that of
the LTCCS, with some important differences. First,
because the MCD has primary responsibility for
enforcement of CMV regulations, the FACT program
focused on truck data and collected relatively little
data on other vehicles in the crashes. Second, although
the crash type and critical event variables in the FACT
database are similar to those in the LTCCS, critical
reason was not coded. Third, the LTCCS data provide
significantly more information on associated factors.
Finally, the FACT program was restricted to traffic
crashes in which at least one fatality occurred. Despite
the differences, the FACT data provide useful examples
of the range of analyses that LTCCS data can support.
Distributions of events and factors. Table 1 summarizes
the vehicle inspection data from FACT. There are
records for 503 trucks in the FACT data, and inspections
were completed on 407. Just as in the LTCCS,
each truck was subject to a North American Standard
Level 1 inspection by an FMCSA-trained inspector.
The FACT inspection data are much more thorough
and reliable than the vehicle condition data in virtually
any other crash file. Inspectors recorded the condition
of the vehicle before the crash, to the extent that
it could be determined, excluding crash damage.
As shown in Table 1, more than one-third of the 407
trucks inspected by the Michigan FACT team had
maintenance defects that would have placed them
out-of-service (OOS) if they had been inspected
before the crash. Brake problems were found in 32.7
percent of the trucks, and violations of light/marker/signal regulations were found in 23.1 percent. Brake-related
inspection items are aggregated here; the
FACT file contains more detail about the nature of
the violation and the unit of the combination truck
for which the violation was noted.
Table 1 - FACT Data: Inspection Results for 407 Large Trucks
Inspection Item
|
Percent of Large Trucks with Pre-crash Violations
|
All log violations
|
12.3%
|
All hours-of-service (HOS) violations
|
2.2%
|
All other driver violations
|
16.2%
|
All brake problems
|
32.7%
|
All light/marker/signal violations
|
23.1%
|
All air pressure/hose violations
|
9.6%
|
All tire violations
|
14.5%
|
All steering axle violations, including brakes
|
14.0%
|
All suspension violations
|
9.6%
|
Any violation
|
66.1%
|
Any out-of-service (OOS) item
|
35.1%
|
Source: Michigan State Police, FACT data (1996-2001).
[BACK TO CONTENTS]
Table 2 shows the prevalence in the FACT data of several
driver factors that have been identified as risk factors
in large truck crashes. The LTCCS data provide
national estimates of these and other factors that are,
at least for items like fatigue, substantially better than
any currently available data.
Table 2 - FACT Data: Driver Factors Recorded
Driver Factor
|
Percent of Large Truck Drivers with Factor Recorded
|
Alcohol
|
1.0%
|
Illegal drugs
|
1.8%
|
Fatigue
|
2.6%
|
Unfamiliar with area
|
3.4%
|
Driver inexperience
|
2.2%
|
Source: Michigan State Police, FACT data (1996-2001).
[BACK TO CONTENTS]
It has been hypothesized that truckload carriers, at
least small truckload carriers, have a relatively high
incidence of fatigue-related crashes because of their
irregular and unpredictable operating schedules. The
only crash databases currently available that record
carrier type are FACT and LTCCS. Table 3 shows the
distribution of carrier type in the FACT data. More
than 42 percent of the motor carriers in FACT crashes
were for-hire, truckload carriers. Only 6.4 percent
were less-than-truckload (LTL) carriers.
Table 3 - FACT Data: Crash Involvement and Driver Fatigue by Motor Carrier Type
Carrier Type
|
Percent of Total Crash Involvements
|
Percent of Drivers Fatigued
|
For hire, less-than-truckload
|
6.4%
|
14.4%
|
For hire, truckload
|
42.2%
|
3.6%
|
Private
|
40.1%
|
0.0%
|
Other
|
4.6%
|
0.0%
|
Unknown
|
6.7%
|
0.0%
|
Source: Michigan State Police, FACT data (1996-2001).
[BACK TO CONTENTS]
Only about 2.6 percent of large truck drivers in the
FACT data showed evidence of fatigue, but fatigued
drivers were distributed unevenly across carrier
types. No driver for private carriers and fewer than 4
percent of drivers for truckload carriers were judged
to be fatigued at the time of the crash, but fatigue
was recorded for 14.4 percent of the drivers for LTL
firms involved in FACT crashes. The FACT sample
size is too small to allow general conclusions on
the relationship between carrier type and fatigue.
Further study with more data, as the LTCCS provides,
and ideally some measure of exposure, would
be useful to explore the relationship between carrier
type and fatigue.
Like the LTCCS, the FACT database includes data on
"critical events." Figure 1 shows the distribution of
broad critical event categories recorded in FACT. These
descriptive statistics provide immediate insight and
suggest where to look for countermeasures to reduce
the incidence of truck crashes. For example, 58.8 percent
of the critical events resulted from the action of
another vehicle, 6.0 percent from the action of a pedestrian
or pedalcyclist, 20.9 percent from the action of a
truck driver, and 6.0 percent from loss of control of a
large truck. These data directly address Issue 4, above.
Figure 1 - FACT Data: Critical Events in Large Truck Fatal Crashes
Source: Michigan State Police, FACT data (1996-2001).
[BACK TO CONTENTS]
Involvement ratios and relative risk. The most interesting
use of the FACT and LTCCS data is for testing
hypotheses using conditional probabilities. A primary
goal of the LTCCS methodology is to establish a relatively
detailed picture of what physically happened in
the crash. By incorporating that detail into statistical
analyses, it is possible to test hypotheses that certain
factors are associated with increased risk. Most of the
factors of interest operate through particular mechanisms.
Thus, they are more likely to be found in some
crash types than in others. Using the LTCCS data, one
can calculate conditional probabilities to measure the
relative risk of involvement of drivers or vehicles with
certain factors in crashes where those factors may
pose additional risks, as compared with other drivers
or vehicles without those factors.
Take, for example, HOS violations. HOS violations
themselves do not cause crashes, just as night or even
excessive alcohol use does not cause crashes. Rather,
we hypothesize that each increases the risk of crash
involvement. The LTCCS data provide detail about
what happened in a crash. Appropriately designed
analyses can then test for over-involvement of HOS
violations in that part of the crash population where
they are expected.
The FACT data provide an example of a relative risk
analysis of brake violations. To test for an association
between brake violations and large truck crashes,
specific crashes were identified in which the truck's
brakes were the primary crash avoidance mechanism:
rear-end crashes, crashes in which the vehicles were on
intersecting paths, and crashes in which one or more
of the vehicles involved were changing trafficways
(that is, intersection crashes where the vehicles were on
different roadways or one was turning onto a different
roadway) and the other vehicle had the right-of-way.
Braking is the primary collision-avoidance method at
intersections, just as it is in rear-end crashes.
In Table 4, truck crashes from the FACT database are
divided into two categories: (1) those in which the
truck's brakes were critical to avoiding the crash (the
truck was the striking vehicle in a rear-end crash or
went through a traffic light or stop sign in an intersection
crash); and (2) those in which the truck's
brakes were not critical. For the cases in which the
other vehicle needed to brake to avoid the crash (for
example, where the truck was struck in the rear by
the other vehicle), the condition of the truck's brakes
would not have affected the crash outcome. In crashes
where stopping the truck was the primary means
of avoiding the crash (for example, where the truck
struck another vehicle in the rear), the condition of
the truck's brakes was critical.
Table 4 - FACT Data: Large Truck Brake Violations in Braking-Critical Crashes
Brake Violations
|
Truck Braking Critical
|
Truck Braking Not Critical
|
Total
|
Number of Fatal Crashes
|
None
|
42
|
82
|
124
|
One or more
|
35
|
35
|
70
|
Total
|
77
|
117
|
194
|
Percent of Total Crashes
|
None
|
54.5%
|
70.1%
|
63.9%
|
One or more
|
45.5%
|
29.9%
|
36.1%
|
Total
|
100.0%
|
100.0%
|
100.0%
|
Note: The results of a standard Chi-square test for association of the variables in the table (Chi-square = 4.86, 1 degree of freedom, probability = 0.027) indicates only a 2.7-percent chance that there is no association between truck brake violations and involvement in fatal crashes where the truck's braking is critical. That is, trucks with brake violations are much more likely to be involved in braking-critical crashes than trucks without brake violations.
Source: Michigan State Police, FACT data (1996-2001).
[BACK TO CONTENTS]
The results of this relative risk analysis indicate that
large trucks involved in a crash where the braking
capacity of the truck was critical were 50 percent
more likely to have a brake violation than were
trucks involved in crashes where the truck's braking
capacity was not critical. Of the trucks involved in
brake-critical crashes, 45.5 percent had brake violations,
compared with 29.9 percent of trucks involved
in crashes of the same type but where their braking
was not relevant.
One explanation for this result could be that the
striking trucks are poorly operated and maintained,
and therefore the association of brakes and the
truck's role in the crashes reflects poor operations
rather than the hypothesized mechanical association.
However, there was no association with either overall
violations or out-of-service condition. Nor did
any other physical system on the truck, other than
lights/markers, show a statistically significant association
with violating the right-of-way in "brake-relevant"
crashes. The association with lights/markers
falls short of statistical significance at the 0.05
level, but it is in the opposite direction from that for
brakes (trucks with light/marker violations are more
likely to be the vehicle with the right-of-way), suggesting
that conspicuity may play a role.
Brake violations are statistically associated with
being the striking vehicle in crashes where braking is
important. The association is statistically significant,
of significant magnitude, and supported by a physical
mechanism. The FACT data are the first data
with which it is possible to examine statistically the
link between vehicle condition and crashes for large
trucks. The much richer LTCCS data support precisely
this type of analysis. With about twice as many
cases and much greater detail about all aspects of the
crashes, it should be possible to examine many more
plausible contributing factors with LTCCS data than
can be done with the FACT data.
[BACK TO CONTENTS]
Conclusions
The LTCCS is a general-purpose data file designed
primarily for problem identification: to estimate the
number of large truck crashes involving a particular
factor and the contribution of this factor to crash
risk. Because it is nationally representative, it can
estimate national frequencies. Because it collects
more than 1,000 data variables describing all aspects
of a crash's drivers, vehicles, and environment, its
estimates will be quite comprehensive. In addition,
while LTCCS is designed as a statistical data file, its
individual case reports will be useful for investigative
analyses.
The ability to use the LTCCS data to investigate crash
risk is based on estimating relative risk using induced
exposure techniques. These techniques will apply to
many vehicle features, some driver features, and few
environmental features. Their usefulness for vehicle
and driver features depends on whether there is a
suitable control group of crashes in which the feature
being examined has no effect.
The main limitations on statistical analyses of the
LTCCS database will be data accuracy and completeness
and overall sample size. Variables that investigators
observe directly, such as environmental features
and vehicle inspection data, are likely to be quite
accurate and complete. Variables that are more subjective,
obtained from interviews or from secondary
data sources, may well be less accurate and complete
even if the investigators have checked other sources to
confirm the data. The 963-crash sample size will limit
the statistical conclusions. Analyses of relatively rare
situations can distinguish only large differences.
By the very nature of its design, the LTCCS database
will be most useful for identifying and estimating
the significance of an issue and comparing different
issues with each other. The data may help to describe
the physical and behavioral phenomena involved that
must be understood to investigate, develop, and test
interventions for an issue, but data from experimental
settings almost certainly will be needed as well. If an
intervention is in place, the usefulness of the LTCCS
data in evaluating its effectiveness will be similar to its
usefulness in estimating the significance of the issue.
[BACK TO CONTENTS]
Acknowledgments
Much of the material in this paper is derived from Blower [4] and Hedlund [3], working
papers prepared for the Transportation Research Board's Committee for Review
of the Federal Motor Carrier Safety Administration's Large Truck Crash Causation
Study. The policy questions, priorities, assessments of LTCCS usefulness, and all other
opinions and conclusions are those of the authors and do not necessarily reflect
the views of their institutions or of the Federal Motor Carrier Safety Administration.
[BACK TO CONTENTS]
References
-
D. Blower and K.L. Campbell, Methodology of the Large Truck Crash Causation Study,
FMCSA-RI-05-035 (Federal Motor Carrier Safety Administration, Washington, DC,
February 2004), Web site
http://www.fmcsa.dot.gov/facts-research/research-technology/report/FMCSA-RI-05-035.htm.
-
J. McKnight, Investigative Analysis of Large Truck Accident Causation (Committee for
Review of the Federal Motor Carrier Safety Administration's Truck Crash Causation
Study, Letter Report of September 4, 2003, pp. 50-66), Web site
http://gulliver.trb.org/publications/reports/tccs_sept_2003.pdf.
-
J. Hedlund, Statistical Analyses of Large Truck Crash Causation Study Data (Committee
for Review of the Federal Motor Carrier Safety Administration's Truck Crash
Causation Study, Letter Report of September 4, 2003, pp. 16-49), Web site
http://gulliver.trb.org/publications/reports/tccs_sept_2003.pdf.
-
D. Blower, The Large Truck Crash Causation Study (Committee for Review of the
Federal Motor Carrier Safety Administration's Truck Crash Causation Study,
Letter Report of December 4, 2001, Appendix B), Web site
http://gulliver.trb.org/publications/reports/tccs_dec_2001.pdf.
[BACK TO CONTENTS]
For more information, contact the Analysis Division at (202) 366-1861, or visit our Web sites at:
www.fmcsa.dot.gov
ai.fmcsa.dot.gov
JANUARY 2006
Publication #: FMCSA-RI-05-037
|