Uncertainty Management at the Airport Transit View

Álvaro Rodríguez-Sanz, Fernando Gómez Comendador, Rosa Arnaldo Valdés, Jose Cordero García, Margarita Bagamanova
2018 Aerospace (Basel)  
Air traffic networks, where airports are the nodes that interconnect the entire system, have a time-varying and stochastic nature. An incident in the airport environment may easily propagate through the network and generate system-level effects. This paper analyses the aircraft flow through the Airport Transit View framework, focusing on the airspace/airside integrated operations. In this analysis, we use a dynamic spatial boundary associated with the Extended Terminal Manoeuvring Area concept.
more » ... Aircraft operations are characterised by different temporal milestones, which arise from the combination of a Business Process Model for the aircraft flow and the Airport Collaborative Decision-Making methodology. Relationships between factors influencing aircraft processes are evaluated to create a probabilistic graphical model, using a Bayesian network approach. This model manages uncertainty and increases predictability, hence improving the system's robustness. The methodology is validated through a case study at the Adolfo Suárez Madrid-Barajas Airport, through the collection of nearly 34,000 turnaround operations. We present several lessons learned regarding delay propagation, time saturation, uncertainty precursors and system recovery. The contribution of the paper is two-fold: it presents a novel methodological approach for tackling uncertainty when linking inbound and outbound flights and it also provides insight on the interdependencies among factors driving performance. Aerospace 2018, 5, 59 2 of 31 The Airport Transit View (ATV) concept describes the "visit" of an aircraft to the airport [9]. This framework connects inbound and outbound flights, providing a tool to optimise airport operations and to enable more efficient and cost-effective deployment of operator resources. It integrates airside operations (landing, taxiing, turnaround and take-off) and surrounding airspace operations (holding, final approach and initial climb) [3, 10, 11] . Moreover, the Airport Operations Plan (AOP) guarantees a common, agreed operational strategy between local stakeholders, providing knowledge about the current situation and detecting deviations [9] . It aims to achieve early decision-making and efficient management of the aircraft processes. In this sense, the Airport Collaborative Decision-Making (A-CDM) concept ensures that common situation awareness is reached between stakeholders [9]. Moreover, the implementation of the 4D-trajectory operational concept in future Air Traffic Management (ATM) systems will impose the compliance of very accurate arrival times over designated points on aircraft, including Controlled Times of Arrival (CTAs) at airports [12] [13] [14]. Uncertainty of operational conditions (e.g., runway configuration, aircraft performance, air traffic regulations, airline business models, ground services, meteorological conditions) makes airspace/airside integrated operations a stochastic phenomenon [15] [16] [17] [18] [19] . It is therefore necessary to define methodological frameworks to improve predictability and reliability of the airspace/airside integrated operations. Hence, the objective of the study is two-fold: (a) to analyse and characterise the aircraft flow of processes in order to understand the uncertainty dynamics; and (b) to generate a causal probabilistic model in order to manage uncertainty in the ATV environment. Aerospace 2018, 5, 59 3 of 31 system reliability [6, 39] . A significant portion of delay generation occurs at airports, where aircraft connectivity acts as a key driver for delay propagation [31] . Therefore, uncertainty management and delay propagation affecting internal E-TMA and airport processes have received significant attention over previous years [4, 15, 24, 32, 40, 41] . The inherent complexity of the delay propagation problem (operational uncertainty) and the intrinsic challenges in predicting an entire system's behaviour explains the use of different modelling techniques, such as queuing theory [8, 42] , stochastic delay distributions [43], propagation trees [29,44,45], periodic patterns [46], chain effect analysis [47], random forest algorithms [30], statistical approaches [48], non-linear physics [49], phase changes [50] and dynamic analysis [31]. In this paper, delay propagation patterns and influence variables are characterised with a Bayesian network (BN) approach, including stochastic parameters to reflect the inherent uncertainty of the aircraft flow performance at the E-TMA. BNs are graphical probabilistic models used for reasoning under uncertainty [51,52]. This technique has proven to be an effective tool for risk assessment, resource allocation and decision analysis [53]. Moreover, BNs have unique strengths regarding cross inference and visualization [54] and have previously been used to tackle several air transport issues, such as the efficiency of air navigation service providers [55], wayfinding at airports [56], delay propagation [57-59], safety [60,61] and the improvement of the aviation supply chain [62]. Several studies [63-65] have demonstrated the utility of graph theory and BNs as methodologies for modelling the diffusion of events and incidents from the node level to the system level (interdependence of multiple factors). Moreover, Liu et al. [66], Liu and Wu [59] and Xu et al. [58] confirmed that BNs can explain how subsystem level causes propagate to provoke system level effects, specifically focusing on how delays at an origin airport propagate to create delays at a destination airport. We seek to manage uncertainty at the source through the development of a tool for better understanding of hidden dynamics in the airspace/airside integrated operations. Consequently, the main contribution of the paper to the existing literature in the topic is the proposal of a methodological approach to (a) understand relationships between procedures at the ATV stage (development of a process model); (b) identify factors influencing performance; (c) characterise uncertainty sources; and (d) appraise the cross-dependencies among variables (development of a causal model). Following this methodological approach, a test case is discussed to obtain tangible outcomes. Materials and Methods The analysis is divided into two steps. First, we develop a theoretical appraisal of the aircraft operation within the E-TMA, by characterising the processes and structuring the different timestamps. We generate a BPM, which is combined and quantified with the A-CDM milestone approach. This provides us with a conceptual framework for the practical analysis of the ATV flow (Section 3.1). The second part of the analysis is developed with a practical case study at Adolfo Suárez Madrid-Barajas (LEMD) Airport (Section 3.2). We characterise the uncertainty sources and assess the system's time-efficiency performance. This is achieved by statistically evaluating the processes that were previously recorded in the first step and by also appraising the behaviour of uncertainty drivers (Section 3.2.1). After that, a probabilistic causal model is assembled to consider the interactions between different uncertainty explanatory variables (Section 3.2.2). The operational relationships that shape this model arise from the previous ATV flow framework. Figure 1 shows the proposed methodological approach for uncertainty management at the ATV (airspace/airside integrated operations). It summarizes the relationships among the different analyses and models described in Section 3.
doi:10.3390/aerospace5020059 fatcat:uzd35nzo5rdohgoxfc5cqxrw6a