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Database Analysis 67
recorded in this field include gasoline, anhydrous ammonia, or jet fuel. More detail, such as the
Motor Carrier (MC) type of liquid or gas tank, is not considered.
In the 2005 data year, the amount of data recorded for hazardous material was both expanded
and restricted. Now, whether the truck was hauling hazardous cargo is recorded at the vehicle
level, not separately for each cargo body. In addition, TIFA no longer collects cargo weight. Con-
currently, more detail about the hazardous material is now recorded, including the hazmat class
and UN number.
4.4.5 Usefulness of the Data for Determining Root Causes
This section provides a discussion of the variables in the TIFA Crash file that relate to under-
standing the factors that contributed to the crash or affected the severity of its consequences.
Most of the variables are extracted from the FARS file and are added to the TIFA file without
change. Of the contributing factors identified in Table 4-18, the TIFA file provides the informa-
tion on truck configuration, cargo body, gross vehicle weight (GVW) (and gross cargo weight
[GCW]), and accident type.
Virtually all of the factors listed in Table 4-18 are captured in a single variable. For exam-
ple, roadway surface condition is described in one variable with several possible levels. Driver
condition, however, is captured across several variables. There is no single variable that con-
solidates the information about the driver's condition at the time of the crash. Instead there
are separate variables that identify whether the driver had been drinking or using illegal drugs.
Driver fatigue or illness is captured in a "driver-related-factors" variable that includes a vari-
ety of driver state, action, and other conditions.
In this discussion, it is useful to recognize that there are two types of parameters: conditions
present and conditions contributing to producing the crash. Both types can be contributing or
root causes of accidents. For example, the weather at the time of the accident is a condition and
it may or may not be a contributing or root cause of an accident. Clearly, if it is not captured, there
will be no information to implicate weather as a contributing or root cause. The parameters con-
tributing to producing a crash include parameters like driver condition pre-crash, conditions such
as a car cutting off a truck, forcing it to take evasive action to prevent the accident. The important
point is that to identify contributing and root causes of accidents, which parameters serve a spe-
cific function as to recognize that both types of parameters are needed. Descriptive information
Table 4-18. Contributing factors present in TIFA/FARS.
Vehicle Driver Packaging Infrastructure Situational
Configuration Age Package Type Road Surface Pre-Crash
Condition
Cargo Body Experience Quantity Shipped Road Condition Event Type
GVW Condition Quantity Lost Road Type Vehicle Speed
Valid License Age (Cargo Tank) Traffic Way Impact Location
Citation Issued Rollover Access Control Primary Reason
Protection Speed Limit Accident Type
Inspection History No. of Lanes Weather
Design Condition
Specification Light Condition
Time of Day
Key:
Factor obtained
Partially met
Not captured
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68 Hazardous Materials Transportation Incident Data for Root Cause Analysis
characterizes conditions at the time of the crash. These include items such as light condition, road-
way surface condition, time of day, and even road type. These pieces of data describe the circum-
stances and many can be related to changes in crash risk. That is, the condition may have con-
tributed to producing the crash, but coding the condition does not tell us whether it did, just that
it was present. For example, wet or icy roads increase the possibility that a vehicle will skid while
maneuvering, but the fact that the road was coded as wet does not imply a judgment that the lower
road friction contributed to producing the crash. However, the TIFA file also includes a set of data
elements that identify actions and conditions that are either identified as contributing to the crash
or that strongly imply they contributed to producing the crash. There are separate sets of road-
way, vehicle, and driver "related factors" that identify defects, conditions, and actions that con-
tributed to the crash. Charged traffic violations are also coded for the driver. These two types of
variables--conditions present and conditions producing--will be discussed separately, with the
producing factors discussed first.
There are four variables that directly identify factors that represent a judgment of contribu-
tion to a crash:
· Driver-related factors,
· Vehicle-related factors,
· Accident-related factors, and
· Violations charged.
Each of these factor variables are coded by the FARS analysts from the PAR or other investiga-
tive reports that the analysts may be able to obtain. That is, the analysts capture the recorded
judgment of the reporting officers, rather than applying their own judgment or inferring condi-
tions and actions from the available evidence. Accordingly, information coded in these factor
variables has been explicitly stated by the original crash investigators.
The variables for driver-related factors record driver conditions or actions that may have con-
tributed to producing the crash. Up to four responses may be recorded in these variables. The
variables for driver-related factors that can be coded are divided into five different subsections
that capture different types of influences on the driver. The first subsection includes driver con-
ditions, such as fatigue, asleep, ill, blackout, or some other condition that impaired the ability of
the driver to control the vehicle. The second subsection is called "miscellaneous factors" in the
coding manual, but really each is a driver error of some sort. Examples of these errors are run-
ning off the road, driving with a suspended license, speeding, following improperly, and failure
to yield. The next subsection of variables for driver-related factors identifies different conditions
that obscured the driver's vision, including reflected glare, fog, trees, other vehicles, and build-
ings. There also is a subsection for conditions or events that caused the vehicle to swerve or skid,
including wind, slippery road, objects or holes in the road, and swerving to avoid animals or
pedestrians. Finally, there is a subsection to capture the presence of possible distractions, includ-
ing cell phones, computers, and navigation systems.
With the exception of the codes that merely specify possible distractions, the information cap-
tured in the variables for driver-related factors record the original police investigator's judgment
on conditions influencing, or errors made by, the driver that helped to produce the crash. All the
others point to driving errors, unsafe driver conditions (e.g., fatigue), or conditions that con-
tribute to a high-risk situation, such as view obstructions or causes of skidding or swerving. Each
of these may be regarded as identifying a causal factor in the collision.
Overall, the variables appear to be both useful and reasonably consistent. Missing data rates
are low, with the variable left unknown in just over 1% of cases. Previous analysis has shown that
the coding is reasonable. In two-vehicle collisions, typically only one driver will be coded with a
factor, although there are some instances where both drivers are coded as having contributed to
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Database Analysis 69
the crash. This makes some intuitive sense, because we generally think of crashes being prima-
rily due to poor actions by one of the drivers. In addition, the codes have been shown to be con-
sistent with the general configuration of the crash. For more on this subject, see The Relative
Contribution of Truck Drivers and Passenger Car Drivers to Two-Vehicle, Truck-Car Traffic
Crashes (Blower 1998).
The variables for vehicle-related factors capture vehicle defects that may have contributed to the
crash. The FARS coding manual indicates that the variable is used to record pre-existing vehicle
defects (i.e., defects not caused by the crash itself). The FARS manual (NHTSA 2002, p 344) also
states that "The vehicle condition(s) noted only indicate the existence of the condition(s). They
may or may not have played a role in the accident." Thus, according to the manual, defects noted
may not have contributed to the crash. However, in light of how the data are collected, it is likely
that the defects played a role. The source for the data is ultimately the PAR or other crash investi-
gation document. Crash investigators typically do not routinely perform a vehicle inspection, but
primarily detect vehicle defects if they contributed to the crash. Reviews of the PARs of crashes in
which tire defects were recorded in the vehicle-related factors variable showed that in every case
the tire defect was mentioned as contributing to producing the crash.
Missing data rates for the vehicle-related factors variable are low, averaging just under 2%.
However, it is very likely, at least for trucks, that vehicle factors are greatly underreported. Typ-
ically, the vehicle-related factors record about 2% of the trucks in fatal accidents with brake prob-
lems, and much lower percentages with light system defects. However, roadside inspections of
trucks--not crash-involved trucks but just trucks operating on the road--show much higher
rates, with about 25% having one or more brake defects and similar percentages with light sys-
tem problems. For additional discussion of this topic, see "Vehicle Condition and Heavy Truck
Accident Involvement" (Blower 2002). It appears that the variables for vehicle-related factors
significantly underreport the true incidence of vehicle defects in crashes. This is not surprising,
since police officers are not trained to do vehicle inspections. Such underreporting, however,
limits the use of the data.
The variables for accident-related factors record conditions at the accident level that may have
contributed to the crash. These include roadway design problems and roadway defects such as
worn or missing pavement markings, inadequate warnings, and roadway washouts. Addition-
ally, the variable captures special circumstances that may have affected the crash, such as previ-
ous crashes nearby and police pursuits. Again, the variable is coded from the original PAR, with
the requirement that the coded items were specifically mentioned. They are coded as factors that
either existed or contributed to producing the crash.
Overall, missing data rates for the variables for accident-related factors are also low, and are
not coded in only 0.3% of cases. However, in more than 93% of the cases, no accident-related
factor is coded. Given the very slight incidence of roadway defects recorded in the variables, road
design and condition play a small role or the source of the information is unable to capture its
contribution to crashes.
Turning to the variables that capture conditions present or characteristics of the vehicle,
driver, or environment, each may contribute to a specific crash, but the coding of the variable
does not directly establish contribution. Instead, these factors are associated with crash risk--
they may, in a sense, establish the preconditions for a crash. Table 4-19 lists all of the contribut-
ing factors for each analytical level within an accident, and provides a brief comment character-
izing how the information is available in the TIFA database.
For the variables relating to infrastructure, all are taken from the FARS file and are coded in
formats (i.e., code levels) that match national standards. For example, the same code levels are
used in MCMIS, GES (General Estimates System), CDS (Crashworthiness Data System), and
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70 Hazardous Materials Transportation Incident Data for Root Cause Analysis
Table 4-19. Coverage of contributing factors in TIFA.
Level Variable Comment
Configuration Captures the exact configuration of the vehicle
Distinguishes 16 types of cargo bodies, including liquid, dry
Cargo Body
bulk, and gaseous tank types
Vehicle
Prior to 2005, captures both gross vehicle weight and gross
GVW combination weight; weight variables dropped for 2005 and
later
Age Captured
Experience Not captured
Captured in separate variables that identify alcohol use, drug
Condition use, and a set of multiple-response variables that code fatigue,
Driver
asleep, ill, emotional, distracted, etc.
Valid License Captured
Captured in a multiple-response variable; up to four citations
Citation Issued
may be recorded
Package Type Captured only to the level of cargo body type
Quantity Shipped Weight only, prior to 2005; not captured in 2005 and later
Quantity Lost Spill/no spill captured only, not quantity spilled
Packaging Tank Age Not captured
Rollover Protection Not captured
Inspection History Not captured
Design Specification Not captured
Road Surface Captured, in standard format
Road Condition Captured, in standard format
Road Type Captured, in standard format
Traffic Way Captured, in standard format
Infrastructure
Access Control Not captured directly; instead, inferred from roadway function
class
Speed Limit Captured, in standard format
No. of Lanes Captured, in standard format
Pre-Crash Condition Captured as pre-crash maneuver; standard format
Event Type Captured as first harmful event; standard format
Vehicle Speed Captured in standard format
Impact Location Captured as relation to roadway of the first harmful event;
standard format
Situational Primary Reason Inferred from the vehicle-, crash-, and driver-related factors
discussed above
Accident Type Coded in the TIFA survey to match similar variable in GES and
CDS
Weather Condition Captured in standard format
Light Condition Captured in standard format
Time of Day Captured in standard format
most state files. Characteristics of the vehicle are available in greater detail than other crash files,
but code levels could be aggregated to match other crash data files if necessary. With respect to
drivers, all of the information is available, except driver experience. The factors listed as "situa-
tional" also are available in formats that match national standards. Pre-crash condition identi-
fies the vehicle's maneuver prior to the initiation of the crash sequence, and the format used has
been widely adopted. The accident-type variable, which captures the relative motion and posi-
tion of the vehicles prior to the collision, is very useful in understanding how a crash occurred.
The format used in the TIFA file is also used in GES and CDS.
The primary area in which the TIFA file falls short relates to the details of packaging. TIFA
includes only the general cargo body type with no information about the design specification,
inspection history, or rollover protection. Moreover, quantity shipped is partially captured for 1999-
2004, but not captured after that. For all years prior to 2005, quantity shipped was partially captured
in the TIFA file as cargo weight, in pounds, for all cargo, but capturing this information was aban-
