Test of a risk judgments model in Air Traffic Control Stankovic, S., Rantanen, E.M., & Loft, S. The University of Queensland (Australia), Rochester Institute of Technology, (USA), University of Western Australia (Australia) Introduction General Hypotheses We expected that the revised model provide a better explanation of risk judgments than the initial model because it takes into account more traffic situation components. As reported in previous studies on expert judgments, we expect to identify a “linear fan” pattern which indicates the use of a multiplicative rule for judgment making. In this study, we tested the relevance of two models of conflict judgments in air traffic control (ATC). Both models aim to explain air traffic controllers (ATCos) performances when completing a conflict detection task. Understanding how ATCos detect conflict has gained a regain of interest since future Air Traffic Management systems required automatic conflict detection tools (Wickens, Marvor, Parasuraman, & McGee,1998; Metzger & Parasuraman, 2005). Results The revised model accounted for 35% (vs. 34% by the initial model) of the variance in risk judgments by expert controllers. In the new model, regression analysis showed that Dt0 had no more contribution in risk judgments whereas the difference of altitude obtained a positive coefficient (t = 12,51, p < .001) and velocity difference obtained a negative one (t = -5,03, p < .001). Background In an initial model, proposed by Stankovic, Raufaste and Averty (2008), three horizontal distances determined risk judgments about conflict between two aircraft (Figure 1): Dt0 corresponds to the distance between the crossing of the aircraft trajectories and the first aircraft to reach that point; Dth is the distance between the two aircraft when they are horizontally closest and, Dtv corresponds to the horizontal distance between the two aircraft when their growing vertical distance reaches 1,000 feet . This initial risk judgments model (Stankovic, 2008) identified the role of these three distances in risk judgments but it failed for describing cognitive processes involved in judgment. The revised model aims to identify cognitive processed involved in expert ATCos’ risk judgments. Anderson (1996) approach was used to cope this objective. This approach permits to identify simple algebric rules that individual use in order to process information that will be integrated in judgment or decision making. Simple rules as addition reflect simple processes whereas more sophisticate rules as multiplication or average reflect a higher level of processes complexity. Figure 2. Pattern of risk judgments about conflict Conclusion Despite the fact that the revised model enabled to provide a significant improvement in the data explanation, the revised model permitted to define the conditions in which risk judgement increase. Moreover, the revised model still provided a satisfying account of the data. We argue that the Anderson method is useful for investigating the strategies involved in conflict judgment in ATC. These results has direct implications for developing user-friendly conflict detection devices in the context of future Air Traffic Management systems. Risk judgments Factors Betas SE p Dtv -0,32 0,01 < 0,001 Dth altitude 0,13 vitesse -0,05 Dt0 -0,01 0,221 Figure 1. The variables in the initial model from Stankovic et al. (2008). Method Table 1. Summary of the linear regression analysis for risk judgments Participants and procedure A total of 161 certified controllers provided risk judgments on a 8-point scale (where value 1 reflected “no risk” and value 8 “high risk”) about scenarios in which the model variables were manipulated*. The revised model had two additional traffic situation components, the differences of velocity and altitude between the two aircraft. * Data collected in 2005 by the DSNA/DTI (Toulouse, France) for the CREED project. Most importantly, there was a significant interaction between Dtv and Dth (F(6, 6861) = 4,11, p < .001, η² = 0.003). According to the functional theory of cognition (Anderson, 1996) and to our second hypothesis, the revised model indicated a linear fan pattern for expert risk judgments (Figure 2). This pattern type which indicates the use of a multiplicative rule for judgment is common in studies on experts judgments. Bibliography Anderson, N. H. (1996). A functional theory of cognition. New Jersey: Lawrence Erlbaum Associates. Metzger, U. & Parasuraman, R. (2005) Automation in future air traffic management: Effects of decision aid reliability on controller performance and mental workload. Human Factors, 47, 35-49. Stankovic, S., Raufaste, E., & Averty, P. (2008). Determinants of Conflict Detection: A Model of Risk Judgments in Air Traffic Control. Human Factors, 50,121-134. Wickens, C.D., Marvor, A, Parasuraman, R., & McGee, J. (1998) The future of air traffic control: Human operators and automation. Washington, DC: National Academy.