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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-