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18 Hazardous Materials Transportation Incident Data for Root Cause Analysis It is argued that accident databases can be reliably analyzed using HFACS and, in doing so, objec- tive, data-driven intervention strategies can be identified. The authors (Shappell and Wiegmann 2000) state that application of HFACS has been proven effective and the approach is now being utilized by multiple military and civilian organizations. 2.2.7 "Human Factors Root Cause Analysis of Accidents/Incidents Involving Remote Control Locomotive Operations" "Human Factors Root Cause Analysis of Accidents/Incidents Involving Remote Control Loco- motive Operations" (FRA 2005) documents a human factors root cause analysis (RCA) of six train accidents/incidents involving remote-control locomotive (RCL) operations in U.S. railroad switch- ing yards that occurred in 2004. RCA used a modified version of the Human Factors Analysis and Classification System (HFACS), in which operator impacts, preconditions for operator acts, super- visory factors, organizational factors, and outside factors were defined as concentric category influ- ences. Data collection and analysis tools included information gathered from participating railroads, interviews and surveys, travel to the accident/incident site, and the development of decision trees designed around the HFACS taxonomy. A total of 36 probable contributing factors were identified among the 6 case studies, from which several key safety issues emerged. 2.2.8 Large Truck Crash Causation Study (LTCCS) Analysis Series: Using LTCCS Data for Statistical Analyses of Crash Risk The Large Truck Crash Causation Study (LTCCS) was undertaken jointly by FMCSA and NHTSA, utilizing a representative sample of nearly 1,000 injury and fatal crashes involving large trucks that occurred between April 2001 and December 2003. This report (Hedlund and Blower 2006) focuses on how statistical analyses of the LTCCS database can be used to investigate crash causes and contributing factors. Within this context, data limitations are discussed. These include issues involving data accu- racy and completeness. The authors conclude that variables that are directly observable by inves- tigators are likely to be more accurate and complete, such as most vehicle and non-transitory environmental data. By contrast, variables that depend on interviews are more suspect in terms of accuracy and completeness (even if investigators have checked other sources to confirm the inter- view reports). An example of this latter consideration is whether the truck driver was in violation of the federal hours-of-service rules at the time of the crash. 2.2.9 Highway Safety: Further Opportunities Exist to Improve Data on Crashes Involving Commercial Motor Vehicles The process for collecting, entering, and processing commercial motor vehicle crash data to meet federal reporting requirements involves several steps. Crash data initially are collected by local law enforcement then sent to the state for processing before being uploaded by the state into FMCSA's data system. The objective of this study (GAO 2005) was two-fold: to examine what is known about the quality of commercial motor vehicle crash data and what states are doing to improve it, and to evaluate the results of FMCSA's efforts to facilitate the improvement of the quality of commercial motor vehicle crash data submitted to the agency. Sources of information utilized in the study included data reported by FMCSA; previous studies on the quality of commercial motor vehicle crash data; interviews with FMCSA officials, developers of FMCSA crash data tools, commercial vehicle industry researchers, and public interest organizations; grant documentation for 34 states that participated in FMCSA's safety data improvement program in fiscal year 2004; case studies of six states that participated in that program; and interviews with states that had not participated or were no longer participating in the safety data improvement program.