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Suggested Citation:"Chapter 1 - Introduction." National Academies of Sciences, Engineering, and Medicine. 2009. Hazardous Materials Transportation Incident Data for Root Cause Analysis. Washington, DC: The National Academies Press. doi: 10.17226/14336.
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Page 10
Suggested Citation:"Chapter 1 - Introduction." National Academies of Sciences, Engineering, and Medicine. 2009. Hazardous Materials Transportation Incident Data for Root Cause Analysis. Washington, DC: The National Academies Press. doi: 10.17226/14336.
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Page 11
Suggested Citation:"Chapter 1 - Introduction." National Academies of Sciences, Engineering, and Medicine. 2009. Hazardous Materials Transportation Incident Data for Root Cause Analysis. Washington, DC: The National Academies Press. doi: 10.17226/14336.
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Page 11
Page 12
Suggested Citation:"Chapter 1 - Introduction." National Academies of Sciences, Engineering, and Medicine. 2009. Hazardous Materials Transportation Incident Data for Root Cause Analysis. Washington, DC: The National Academies Press. doi: 10.17226/14336.
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Page 12
Page 13
Suggested Citation:"Chapter 1 - Introduction." National Academies of Sciences, Engineering, and Medicine. 2009. Hazardous Materials Transportation Incident Data for Root Cause Analysis. Washington, DC: The National Academies Press. doi: 10.17226/14336.
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Page 13

Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

1.1 Project Purpose The objective of this project is to develop a set of potential measures that would enable offi- cials to use incident reporting databases more effectively to identify major contributors to haz- ardous materials (hazmat or HM) transport accidents for all modes of transport. The focus of this study is accidents, namely those incidents in which the vehicle was involved in a crash event (as opposed to a stationary release from a loose fitting). In the discussion to follow, the terms “accident” and “crash” are used interchangeably. The goal is to provide the proper data elements, accurately reported, such that root causes of accidents can be determined. For this project, the research team used the following definition of root cause: One or more contributing factors that lead to the occurrence of a transportation accident and/or affect the severity of its consequences. Inherent in this definition is the assumption that if a contributing factor were not present, then the accident would not have occurred and/or the consequences would not have been realized. However, in reality there is seldom one factor, but often there is a series of causal factors or a causal chain that leads to the accident or the impacts. Furthermore, by identifying several contributing factors to an accident, much can be learned when analyses show that one contributing factor is present in a large fraction of a particular type or class of hazmat accident. Table 1-1 provides an example of a sequence of questions associated with investigating the root cause. The research team recognized that in order to effectively determine the root cause of a haz- mat crash or a series of crashes, data on diverse parameters needed to be collected and analyzed. Hazardous Materials Serious Crash Analysis: Phase 2 (Battelle 2005), which is described in more detail in Section 2.2.16, developed a matrix listing the parameters believed to provide a more comprehensive understanding of the accident environment. These parameters are as follows: • Vehicle, • Driver, • Packaging, • Infrastructure, and • Situational. In some cases, an individual parameter could shed light on root cause but, in many cases, analyses of two or more parameters are needed. In effect, a systems analysis is required. For example, the root cause of a rollover hazmat tank truck crash resulting in a spill could be related to a vehicle problem manifested by faulty brakes, an inexperienced driver with inadequate training, a full load in an obsolete cargo tank with an inadequate inspection history, slick road conditions, and a precipitous lane change by another truck. A matrix showing the 9 C H A P T E R 1 Introduction

five major parameters with key variables under each is presented in Table 1-2, where the con- ditions described in this example are shaded. Note that, in addition to these major parameters, institutional characteristics, such as com- pany financial condition, organizational structure, and safety culture, can play an important role in contributing to accident potential. For the purposes of this study, it is assumed that these con- siderations are embedded in the likelihood that the major parameter variables emerge as causal factors. For example, an organization with a poor safety culture is more likely to utilize a young driver with little experience and an invalid license. Unfortunately, the science on the relation- ship of “company organization” or “safety culture” to safety is still sufficiently new that there is no well-defined set of variables capturing the salient characteristics of “company organization” and “safety culture” that could be implemented feasibly. 1.2 Research Approach For the project, the following research approach was used. 1.2.1 Literature Review The research team examined the literature related to hazmat crash databases, including those that may be dominated by non-hazmat crashes, to determine how the problem of identifying root causes had been addressed in the past. Part of the literature review was aimed at gaining insight into how root cause analysis should be conducted and lessons learned in other research 10 Hazardous Materials Transportation Incident Data for Root Cause Analysis Question Response Why did the truck run off the road? The driver fell asleep. Why did the driver fall asleep? Driver has sleep apnea. Why were you not aware of this disease? We did not have an up-to-date medical record. Why did no one check that he had an outdated medical record? Our written procedure did not require us to check if employees had not updated their medical records in the last year. Table 1-1. Root cause questioning. Vehicle Driver Packaging Infrastructure Situational Configuration Age Package Type Road Surface Pre-CrashCondition Cargo Body Experience Quantity Shipped Road Condition Dangerous Event GVW Condition Quantity Lost Road Type Vehicle Speed Vehicle Defect Valid License Age (Cargo Tank) Traffic Way Impact Location Vehicle Response Citation Issued RolloverProtection Access Control Primary Reason Driver Response Inspection History Speed Limit Accident Type Training Design Specification No. of Lanes Weather Condition Location Light Condition Time of Day Health Consequences Note: Shading reflects contributing factors to root cause of the hypothetical hazmat tank truck crash described in Section 1.1. Table 1-2. Accident parameters.

efforts. This included National Transportation Safety Board reports that have identified the major contributors and, in some cases, the root causes of severe accidents. For each report, the evaluation looked at the “why” questions that were asked and how the information needed to answer the “why” questions was obtained. A summary of the literature review is presented in Chapter 2 of this report. 1.2.2 Survey of Agencies, Shippers, and Carriers To learn what quality control measures are being utilized, the project team surveyed agencies that maintain accident databases. Shippers and carriers also were surveyed to gain an under- standing of their accident investigation and reporting activities. Agencies were interviewed and questioned concerning the checks that are made to ensure data accuracy and completeness. These interviews are discussed in Chapter 3. In parallel with the discussion with federal agencies, the researchers asked shippers and carriers to address their ability to identify information that would answer “why” questions as well as their willingness to report that information. The results of these interviews also are summarized in Chapter 3. 1.2.3 Analysis of Databases The team next examined the major crash databases and identified fields that might provide answers to any of the “why” questions associated with identifying root cause. The analysis also included an assessment of data quality—an aspect deemed critical to an understanding of the root causes of hazmat crashes. The consideration of data quality includes both accuracy and completeness. Unless the data is of high quality, any root causes, even if they were reported, could be difficult to uncover. High-quality data enables the analyst to more easily identify trends and relationships; for example, a group of similar accidents, perhaps very severe accidents, can be analyzed for the most common root causes. Even with high-quality data, the results may not be adequate if the pertinent fields are not included in the database. The following major databases were included in this assessment: • Motor Carrier Management Information System (MCMIS) managed by FMCSA. The data are compiled by the states from police accident reports (PARs) from serious crashes involving large trucks. • Hazardous Materials Incident Reporting System (HMIRS) managed by PHMSA. The data- base only covers shipments of hazardous materials and is self-reported by carriers for the various modes. • Trucks Involved in Fatal Accidents (TIFA) managed by the University of Michigan Trans- portation Research Institute (UMTRI). The crashes are culled from the Fatality Analysis Reporting System (FARS) and supplemental data on the crashes are collected by a survey. Only fatal, large truck crashes are included, but data quality is very high. • FARS managed by NHTSA. The database is designed to include fatal crashes involving any vehicle and is not restricted to trucks. • Railroad Accident/Information Reporting System (RAIRS) managed by FRA. The data are reported by the carrier and the focus is just rail, although intermodal hazmat shipments are also covered. • Marine Information for Safety and Law Enforcement (MISLE) managed by the Coast Guard. The dataset is limited to accidents involving an actual or potential violation of the law. Data are closely controlled by the Coast Guard. • Large Truck Crash Causation Study (LTCCS), a one-time, specific analysis managed by FMCSA and NHTSA. The database involved about 1,000 crashes and included Level I on-the- scene inspections. Introduction 11

In reviewing data quality, agencies were interviewed and questioned concerning the checks that are made to ensure data accuracy and completeness. These interviews are discussed in Chapter 3. In addition to using answers to questions administered to representatives of these agencies, the study team analyzed the databases to gather information on data quality. The most effective method employed was using one database to confirm the data quality of another. That is, the team compared two databases in order to identify missing or incomplete data or, in some cases, crashes that should have been included but were not part of the database. The project focused on addressing the following issues: • Availability of accident data for the transportation of hazardous materials, • Criteria for reporting the accidents, • Database format and availability (e.g., online, paper, other), • How data files can be coordinated or integrated, • How the data are used by each agency to analyze trends, • How the data are compiled for analysis, • Methods used to improve data quality, • Usefulness of the database for identifying root causes, • Techniques used to link the database to another database describing the same crash, and • Suggestions for enhancing the usefulness of the database for identifying root causes. 1.3 Effective Methods to Ensure High-Quality Data The research team relied heavily on the TIFA database for examining truck crashes. This data- base is recognized in the truck safety community as being both comprehensive and of high qual- ity. The accident records contained in TIFA are the result of careful data checking using police accident reports and an intensive program of additional data collection on a fatal truck crash. This process involves direct contact with key parties such as police, carriers, and tow truck drivers. The research team used TIFA as a benchmark to compare fatal truck accidents occur- ring in other databases with those in TIFA. A similar technique of data confirmation and augmentation was used for the Hazmat Serious Truck Crash Project conducted for FMCSA from 2002 to 2005. Police accident reports were checked against a crash description in MCMIS and carriers were interviewed by telephone to supplement the information in MCMIS. This process was followed for approximately 1,000 hazmat accidents identified in the MCMIS database and supplemented where possible with data from the HMIRS database. Supplementing the data provided key information that shed light on the root cause and major contributors to hazmat accidents. For example, one of the carriers interviewed reported that tank trucks, because of the higher center of gravity when full, are more difficult to handle; therefore, they are more prone to rollover. This suggests that driver experience might be important. Consequently, one of the questions asked during carrier interview was the number of years of driving experience the driver had at the time of the accident. The results from this question were significant—drivers with less than five years of experience had a larger fraction of cargo tank accidents resulting in rollovers than did more experienced drivers. The same analysis showed that rollovers were the most common precursor to a hazmat release in an accident. Based in part on lessons learned from the process of compiling TIFA and the Hazmat Serious Truck Crash Project databases, the research team looked for answers to the following questions: 1. Is it possible to have a high-quality database without follow-up correspondence (contacts) with the reporting entity? 2. How much correspondence is required and what type is most effective? 12 Hazardous Materials Transportation Incident Data for Root Cause Analysis

3. Could the techniques found effective for TIFA and the Hazmat Serious Truck Crash Project be applied to databases that include or focus on hazmat transport? These questions are considered throughout the analyses of the databases in Chapter 4 and in the potential measures and conclusions presented in Chapter 5. 1.4 Potential Measures to Enhance the Ability of Databases to Identify the Root Causes of Hazmat Crashes After the team performed the literature review, surveys, and database analyses, it identified potential measures for improving the ability of selected databases to identify the root causes of hazmat crashes. Included are specific measures for improving the quality of data, reducing underreporting, adding fields that will improve the identification of root causes, and linking descriptions of the same accident in different databases. Finally, a method is outlined to supple- ment the databases through additional data checks and collection of additional data from key sources. The measures are organized in a hierarchy based on whether the focus is on data improvement, institutional challenges, or the cost of implementing particular solutions. The potential measures are provided in Chapter 5. Introduction 13

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TRB’s Hazardous Materials Cooperative Research Program (HMCRP) Report 1: Hazardous Materials Transportation Incident Data for Root Cause Analysis examines potential technical improvements to hazardous materials accident databases that are collected and managed by various agencies. The report explores gaps and redundancies in reporting requirements and attempts to estimate the extent of the under-reporting of serious incidents.

Appendixes A through E to HMCRP 1 are available online.

Appendix A: Questionnaires

Appendix B: Questionnaire Results for Carriers and Database Administrators

Appendix C: Brief Summary of the 2005 MCMIS Crash Records

Appendix D: The Percent of Missing Data for Variables from TIFA/FARS, 1999–2004

Appendix E: Selected Analyses Performed with the Hazmat Accident Database

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