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Examining Unstable Approach Predictors Using Flight Data Monitoring Information

PI David Carroll

The approach and landing phase of flight is statistically the most dangerous part of flying. While it only accounts for 4% of flight time, it represents 49% of commercial jet mishaps. One key to mitigating the risks involved in this flight segment is the stabilized approach. A stabilized approach requires meeting rigorous standards for many flight parameters as the aircraft nears landing. Exceeding any of these parameters results in an unstable approach (UA). The energy management (EM) accomplished by the flight crew, represented by the EM variables in the study, influences the execution of a stabilized approach.

While EM is a critical element of executing a stabilized approach, there appears to be a lack of studies that identify specific EM variables that contribute to UA probability. Additionally, several possible moderating variables (MVs) may affect the probability of a UA. Fortunately, modern jet transport aircraft have Flight Data Monitoring (FDM) systems that capture a wealth of information that enable the analysis of these EM variables. This study used FDM data to answer the questions about what influence a set of EM variables has on the probability of a UA event. The analysis also determined what impact a set of possible MVs, not directly related to EM, has on these EM variables influence.

The analysis used logistic regression (LR) to investigate FDM information. The LR provided estimations of odds ratios for each of the variables and the interaction factors for the MVs. These statistics defined a model to evaluate the influences of the EM and MVs, providing answers to the research questions posed. The results determined the model was a good fit to the data but had poor discrimination. The model supported three of the original seven EM hypotheses and none of the 28 MV hypotheses.

The study identified three specific EM variables that significantly influenced the probability of a UA event. Of the MVs, only one significant influence was revealed but was opposite that hypothesized. Identifying the EM variables, and examining their impacts, shows their importance in preventing UAs. Further, the results help prevent future UAs by informing the design of training programs. Additionally, the current effort fills gaps in the current body of knowledge, as there appears to be a lack of studies in the areas investigated.

A gap in the body of knowledge filled by investigating an area of limited research and the results provide practical application in the analysis of EM-related events. Aviation safety practitioners now have additional information to identify trend issues that may lead to the increased probability of a UA event. Finally, this study was one of very few granted access to actual operational FDM information by an air carrier. The data were crucial in evaluating the proposed model against real-world flight operations, comparing theory to reality. Without access to such closely held information, the research for this dissertation would not have been possible.

Research Dates

to 10/01/2020


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    Department of Graduate Studies
    Ph.D., M.A.S., Embry-Riddle Aeronautical University
    B.A.S., University of Minnesota-Twin Cities
  • David A. Esser
    Aeronautical Science Department
    Ph.D., Capella University
    M.A.S., B.S., B.S., A.S., A.S., A.S., Embry-Riddle Aeronautical University

Tags: Ph.D. in Aviation Program dissertation unstable approach Flight Data Monitoring emergency management

Categories: Graduate