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In-Time Aviation Safety Management Challenges and Research for an Evolving Aviation System (2018) / Chapter Skim
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3 System Monitoring
Pages 29-35

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From page 29...
... This chapter identifies three key challenges and two high-priority research projects: • Challenges -- Data Completeness and Quality -- Data Fusion -- Collecting Data on the Performance of Human Operators • Research Projects -- Data Fusion, Completeness, and Quality -- Protecting Personally Identifiable Information 29
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From page 30...
... SWIM is intended to replace legacy point-to-point system interfaces with a modern service-oriented architecture, thereby providing a common set of connections and data components. As of September 2017, SWIM streams are available for traffic flow management data, terminal radar data, flight plan data, and airspace data.
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From page 31...
... For example, the SWIM Traffic Flow Management System provides a large set of flight data and flow information, including ­ flight planning data, aircraft positions, airport and route status, and predeparture flight status information. Flight position data come either from en route radar, which provides accurate and reliable data, or from oceanic position reports, which provide data that are less accurate, less reliable, and less consistent in terms of quality.
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From page 32...
... This risk is increased when data are stored and are made available for post-event analysis. Collecting Data on the Performance of Human Operators Challenge Summary Statement: Data regarding operator performance that are essential to achieving the full potential of the envisioned IASMS cannot be collected in a timely fashion or at all, in part because of privacy and related concerns.
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From page 33...
... or miscommunication, it is important to identify states and conditions that indicate an elevated risk state that involves human error and/or miscommunication. Key issues include how to identify those factors that contribute to elevated risk states related to human performance (e.g., high emotion, fatigue, or inattention, etc.)
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From page 34...
... Analyses of data quality requirements will determine which IASMS functions are feasible, and they will provide a basis for setting alerting thresholds that result in a high probability of detecting elevated risk states and a low probability of false alarms. Protecting Personally Identifiable Information Research Project Summary Statement: Develop methods of de-identifying and/or protecting sensitive data in a way that does not preclude effective data fusion.
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From page 35...
... This research project could assess the value to each stakeholder of collecting operator data so that all stakeholders understand the value of collecting and storing relevant data in terms of improving the safety of the NAS. For example, the research project could work with stakeholders to identify potentially unsafe conditions that could effectively be addressed using data on operator performance to identify data of particular interest and when it should be collected.
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Key Terms

  • aviation safety
  • elevated risk
  • flight data
  • safety management
  • risk states

  • flow management
  • traffic flow
  • commercial transports
  • safety data
  • data quality

  • input data
  • flight plan
  • iasms functions
  • regarding privacy
  • related concerns

  • personally identifiable
  • voice communications
  • challenge summary
  • collecting data
  • require data

  • specific data
  • position data
  • timely data
  • flight plans
  • identifiable information

  • aviation community
  • information related
  • develop methods
  • risk state
  • operators regarding


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