Identifying Opportunities of Tracking Major Human Factors Risks through Flight Data Monitoring
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It is widely believed that human factors risks contribute to more than half of the aviation accidents (Shappell et al., 2007). Thus, aviation safety risk identification, and in particular human factor risk identification, is one of the crucial components in today’s aviation safety management systems. There is a need to identify examples of major human factors risks in recent years in the industry and track the exposure of these risks in an individual airline’s own operation routinely. Flight Data Monitoring (FDM) is a systematic and proactive program (Civil Aviation Authority, 2013), which aims to improve aviation safety by collecting and analyzing digital flight data. Since the flight data is able to provide objective and up-to-date information of routine flight performance, this program has the potential to contribute to the identification of the existence and status of the some major human factors risks in airlines’ routine operations. However, current FDM data is not widely used to proactively monitor and track human factors issues. This thesis presents an initial analysis of the potential of using FDM data for identifying and tracking human factors risks. As a first step, in order to obtain insights into the current key human factors risks in the North American commercial aviation operations, the Human Factors Analysis and Classification System (HFACS) was used to categorize 267 accident and incident final reports from 2006 to 2010. Semi-structured interviews have also been conducted to identify and understand major and projected human factors issues from the airline operators’ perspectives. By combining the results obtained from two methods, examples of perceived major human factors risks in current operations are determined. The current top risks of concern include Standard Operational Procedures (SOPs) noncompliance, fatigue, distraction, communication issues, inadequate situation awareness, training issues, pressure, and high workload. In order to assess the potential opportunities of tracking these top human factors risks in airline operations through FDM, current FDM process, applications, best practices and recorded flight parameters were studied. A literature review, field observations, and interviews with experienced safety investigators and flight data analysts were conducted. Models of general FDM process, event setting process, and daily review workflow are presented and human performance related flight parameters are categorized into seven classes. Finally, opportunities and two potential approaches of using FDM to track some major human factors risks have been identified. These two approaches have the potential of being embedded into current FDM processes are 1) setting up new human factors events (HF events) and 2) conducting specific human factors focused studies (HF studies). Implementation examples demonstrating how these two approaches can be applied to track some major human factors, including automation confusion, high workload, and on time pressure are provided. For example, a proposed “automation mode confusion event” is recommended especially for new type of aircrafts (e.g., the Boeing 787), where new pilots are interacting with new operational environments. Applications of the potential approaches, recommendations to commercial airlines, and future work of this study are also discussed.