Date Published: April 18, 2017
Publisher: Public Library of Science
Author(s): Christian Bongiorno, Salvatore Miccichè, Rosario N. Mantegna, Wen-Bo Du.
We present an agent based model of the Air Traffic Management socio-technical complex system aiming at modeling the interactions between aircraft and air traffic controllers at a tactical level. The core of the model is given by the conflict detection and resolution module and by the directs module. Directs are flight shortcuts that are given by air controllers to speed up the passage of an aircraft within a certain airspace and therefore to facilitate airline operations. Conflicts between flight trajectories can occur for two main reasons: either the planning of the flight trajectory was not sufficiently detailed to rule out all potential conflicts or unforeseen events during the flight require modifications of the flight plan that can conflict with other flight trajectories. Our model performs a local conflict detection and resolution procedure. Once a flight trajectory has been made conflict-free, the model searches for possible improvements of the system efficiency by issuing directs. We give an example of model calibration based on real data. We then provide an illustration of the capability of our model in generating scenario simulations able to give insights about the air traffic management system. We show that the calibrated model is able to reproduce the existence of a geographical localization of air traffic controllers’ operations. Finally, we use the model to investigate the relationship between directs and conflict resolutions (i) in the presence of perfect forecast ability of controllers, and (ii) in the presence of some degree of uncertainty in flight trajectory forecast.
According to the majority of predictions the air traffic demand will increase for both business and leisure flights during the next years. This increase will bring the current air traffic management (ATM) system close to its capacity limits. As a consequence an overall improvement of the air transportation system productivity with respect to, for example, traffic flows, air traffic controllers workload and operational efficiency it is urgently needed [1–4]. Within this major change ATM efficiency, safety and resilience standards should be drastically enhanced.
Original data are collected by Eurocontrol, the European public institution that coordinates and plans air traffic control for all Europe. Data were obtained as part of the SESAR Joint Undertaking WP-E research project ELSA “Empirically grounded agent based model for the future ATM scenario”. Data can be accessed by asking permission to the owner (Eurocontrol). Eurocontrol databases include all flights occurring in the enlarged European Civil Aviation Conference (ECAC) airspace even if they departed and/or landed in airports external to the enlarged ECAC airspace. Countries in the enlarged ECAC space are: Iceland (BI), Kosovo (BK), Belgium (EB), Germany-civil (ED), Estonia (EE), Finland (EF), UK (EG), Netherlands (EH), Ireland (EI), Denmark (EK), Luxembourg (EL), Norway (EN), Poland (EP), Sweden (ES), Germany-military (ET), Latvia (EV), Lithuania (EY), Albania (LA), Bulgaria (LB), Cyprus (LC), Croatia (LD), Spain (LE), France (LF), Greece (LG), Hungary (LH), Italy (LI), Slovenia (LJ), Czech Republic (LK), Malta (LM), Monaco (LN), Austria (LO), Portugal (LP), Bosnia-Herzegovina (LQ), Romania (LR), Switzerland (LS), Turkey (LT), Moldova (LU), Macedonia (LW), Gibraltar (LX), Serbia-Montenegro (LY), Slovakia (LZ), Armenia (UD), Georgia (UG), Ukraine (UK).
In our agent-based model we have two types of agents: (i) aircraft/pilots that are active within an ACC of the European airspace, and (ii) air traffic controllers (ATCOs) that manage the flight trajectories in the different sectors of the ACC. The pilots are passive agents, in fact they follow the flight plan or the instructions of the ATCOs if they were different. The ATCOs monitor the execution of the flight plan. The type of actions decided by controllers depends by the current workload of their own sector and by the workload of the neighboring sectors as well as by the details of the flight plans. Our ABM simulates realizations of the en-route phase of the flight trajectories originally planned.
In this section we want to discuss the calibration activities that have to be performed in order to use our model.
In this section we give some examples of the simulation outputs of our model obtained with the parameters of the calibration procedure of section 4 for the evolution of the planned flight trajectories of the LIRR ACC (Rome, Italy) of the AIRAC 334.
Finally, we report on how our model performs under parameters different from the ones chosen for calibration. Specifically, we evaluate the performances of our model with respect to model decisions concerning directs and conflict resolutions as a function of procedures followed by air traffic controllers and air traffic conditions of sectors.
In this work we have presented an agent-based model of the ATM system that aims at modeling the interactions between aircraft and air traffic controllers at a tactical level. We have presented in detail the different modules of the model whose core is given by the conflict detection and resolution module of section 3.5 and by the directs module of section 3.7.