Tactical and post-tactical Air Traffic Control methods

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1 Tactical and post-tactical Air Traffic Control methods A generic testing structure Jérémy Omer and Thomas Chaboud DPRS Onera Toulouse, France Jeremy.Omer@onera.fr Thomas.Chaboud@onera.fr Abstract With a view to improving Air Traffic Management efficiency we describe a two-stage Air Traffic Control taking advantage of a higher interaction between short-term and midterm control in a future highly automated environment. As a first step towards implementation, we developed an automated conflict resolution system based on linear programming, which is involved in every step of the two-stage model. We finally tested the process on simple but unrealistic test cases to study the impact of such a traffic control. Keywords- air traffic control,tactical, conflict resolution, linear programming, complexity I. INTRODUCTION Is air traffic in the European airspace on a 20 years doubling trend, as hypothesized by the SESAR program, or will its growth be hampered by energy costs and financial crises? Either way, European Air Traffic Management (ATM) faces the same challenge to optimize the traffic trajectories in terms of efficiency and cost while improving the current safety levels. The constraints may be different in each scenario, but the goal to maximize the capacity and exploit it as efficiently as possible is common to both. Along with system-wide information sharing to improve the systems reactivity and predictability, an increasing of automation, through computer-aided methods or more autonomous software in the longer term, is accepted as the most promising means towards these objectives. Our ongoing research on conflict prediction and conflict solving algorithms focuses on cost and efficiency optimization; we designed a simple, generic, functional structure meant to test several, tactical and post-tactical, automated or humansupervised traffic control methods. We will present this formalisation and one of its application embedding linear programming algorithms. II. CONTEXT A. Evolution of ATM and traffic predictability There is little arguing that the future Air Traffic System (ATS) will benefit from higher levels of automation than the current one; automated tools will assist and in some cases take full responsibility of the human tasks in the system: piloting, traffic monitoring and conflict-solving (e.g. [1], [5]), airground communication, etc. Increased sharing of information, both between airspace users and ATM and within ATM itself, advocated e.g. by SESAR SWIM 1 and NIMS 2 projects (cf. [2]) and advances in CDM 3 are planned to improve the ATS efficiency. The airline operators and ATM will collaborate to design and then respect the aircraft s Business Trajectory (BT) as a contract on the 4D route it will follow. Necessary tools as 4D-capable Fligh Management System (FMS), advanced data-link and standardized geopositioning equipment are underway towards operational implementation. Improvements in weather now- and forecast belong to the SESAR programme as well, in part to help the aircraft respect its agreed trajectory. All these trends point towards better trajectory predictability, which in turn, in our area of interest, opens the way for longer-term conflict management and trajectory optimisation. Further gains in predictability can be made if and when airspace users give the ATM all the information relevant to the aircraft performance; ideally, the aircraft and Air Traffic Control (ATC) trajectory predictor should be the same, i.e., have the same performance model and the same parameters, updated in real-time. However, traffic prediction uncertainties increase with time; even with aircraft able to remain within 4D-bubbles 1 System-Wide Information Management 2 Network Information Management System 3 Collaborative Decision Making Experimental Centre Brétigny-sur-Orge, France 11

2 around their planned trajectories, this will remain true due e.g. to the necessary take-off time windows or unexpected weather. B. Multi-layered systems to manage uncertainties In the current ATC, the short-term (10-15 minutes) and medium-term time windows are dealt with through different methods and/or by different systems: ATC Operators (ATCOs) are responsible for their sector s traffic monitoring and conflict solving, but also ease the work of their downstream colleagues e.g. through conforming to commonly agreed practices, voice coordination, or aeronautical information publication (AIP) with neighbouring area control centres (ACC). Studies and experiments (Multi Sector Planning/Tactical Load Smoother in PHARE [3], Erasmus [5] ) taking advantage of the increasing traffic predictability and information sharing, have proposed methods and tools to use post-tactical complexity indicators that trigger immediate (tactical) ATC actions. A generic formalisation of these methods naturally defines a two-stage structure, comprising: - a tactical time frame in which the aircraft s speeds and positions are known accurately enough for the ATC system to give local rerouting instructions to solve conflicts, and - a post-tactical one, in which the traffic prediction is less accurate due to uncertainties (and thus whose forecast conflicts take lower priority over the short-term ones), but over which one can compute complexity thresholds relevant to their traffic manageability; it must be far enough ahead for smoother ATC actions to be efficient. III. FORMAL STRUCTURE A. Formal structure, Modules Starting from the set of predicted individual trajectories, the system simulates the necessary conflict solving in the shortterm by a call to the ATC conflict solver, and computes the leeway left to improve the post-tactical traffic situation. Within these constraints, a second stage tries to minimize the posttactical complexity (Figure 1.. The results of this minimization must be expressed in terms directly usable by the tactical stage, and strictly respect the short-term priority. The computation must be fast, so as not to impede the tactical actions, and because earlier nudges to an aircraft speed or trajectory are the more efficient. Figure 1. Formal structure of a two-stage control B. Conflict resolution system Given the context described above our work is situated in a highly automated environment. Therefore, we assume the conflict resolution is performed by a computerized solver and resulting actions are proposed to ATCOs for confirmation, or directly transmitted to pilots. Although systems for automated conflict resolution have not been successfully developed for operational use yet, several such systems were studied in past research. In [6] the automated conflict resolution system focuses on finding conflict free trajectories which could be operationally flown, without looking for global optimality. On the other hand, several algorithms were proposed for optimal conflict resolution ([7], [8], [9]) with a predefined set of resolution manoeuvres. The primary goal of automated conflict resolution is to reduce ATCOs' workload, but we consider that it should also aim at optimizing global traffic cost once traffic security is guaranteed. It is only natural that sectors should collaborate to ensure 4D trajectory compliance (minimizing the difference over ETA on given path points) in a 4D-contract oriented ATS. Added benefits of this practice are better traffic predictability and efficiency/capacity in terminal areas. Experimental Centre Brétigny-sur-Orge, France 12

3 Nowadays, only the pilot and FMS optimize the aircraft fuel consumption in flight; simple aircraft performance and weather models would allow the post-tactical ground segment to find the most economical speed/route length combination available. Other environmental factors, as e.g. noise impact or contrails formation, can be taken into account, though they would require advanced physical models. C. Acknowledging for uncertainties A number of factors may lead the post-tactical traffic situation to depart from the post-tactical prediction, either quantitatively (e.g. numerical errors on the traffic speeds and positions) or qualitatively (wrong conflict or non-conflict forecasts). The main sources of en-route unpredictability can be regrouped as Some aircraft will not (pilot choice, reaction time) or cannot (discrepancy with the ATC performance model, weather, incident/failure) comply with their assigned trajectory. As we mentioned, uncertainties still increase with time in such a system. In order to take them into account during the complexity optimization, choices were made to avoid undetected conflicts and give preference to short term: application of different safety margins than the tactical one; no request from the post-tactical evaluation unless the situation appears unmanageable (some conflicts cannot be solved in the simulation); common sense not to create a certain tactical conflict in order to solve a more hypothetical posttactical one. D. Functional model The functional model of the two-stage control is presented on Figure 2. Some downstream ATC system chooses other options than the predicted ones to deal with the traffic in its responsibility area. As discussed above, the severity of these disturbances is lessened in an ATS favouring predictability: information sharing, 4D contracts, with a high degree of automation based on standardized equipment. If the uncertainties are too high, complexity measures based on ATC workload analysis of space-time traffic density, traffic in evolution, traffic mix (see [10]), on an intrinsic characterization of the traffic configuration [13], or even on a simple instantaneous aircraft count are the reasonable choice. It is useless and perhaps counter-productive to try and predict (simulate) traffic situations too different from that which will occur. Complexity thresholds are then used, generally as a proxy of the traffic manageability by the ATC. In such a case, the second stage can implement a complexity indicator, based for instance on instantaneous aircraft counts in the downstream sectors, and return requests of rerouting or speed modifications to smooth the traffic peaks. However, in the hypothesis of a 4D contracts oriented- ATM, in which the tactical control in other sectors is itself predictable enough thanks to standardized conflict-solving automation, the traffic forecast becomes much more accurate: both the aircraft and ATC being in agreement over commonly designed 4D trajectories, the traffic situation and its evolution will not stray too far from a prediction made a few minutes ahead. With a good knowledge of future traffic, an interesting complexity measure is obtained by trying to solve explicitly all predicted conflicts (see [11] and [12]). The structure we present here relies on a good traffic prediction on the post-tactical time frame. The post-tactical traffic complexity is thus measured by calling the conflict solver. Figure 2. Detailed architecture of the two-stage control The TacSim part optimises the resolution of local conflicts; CompSim s role is to provide TacSim with constraints and/or goals such that its instructions to the aircrafts are optimal with respect to chosen criteria. In more detail, the two-stage control is called with the a traffic prediction in the tactical time frame (noted [T ini,t tac ]) and works as below. Experimental Centre Brétigny-sur-Orge, France 13

4 1) TacSim The traffic prediction input into TacSim reflects the trajectory predictions sent by aircraft to ACC or directly performed in the ACC without taking into account probable ATC requests. Thus, TacSim calls the automatic conflict resolution system for [T ini,t tac ] and computes the traffic prediction with ATC requests. It also computes the margins left for additional speed modifications and lateral deviations during [T ini,t tac ], which are available for post-tactical control. Those margins are sent to CompSim as the set of the allowed states at T tac for each aircraft. A state is allowed when it does not create any new conflict on [T ini,t tac ]. 2) Traffic prediction The traffic prediction without control request on the posttactical time frame (noted [T tac,t end ]) is computed by considering that, after they reach the state predicted by TacSim at time T tac, the aircraft will try and go back to their BT or just stick to their BT when they're already in conformity with it. 3) CompSim CompSim optimizes a complexity measure of the predicted traffic for [T tac,t end ] by exploring the sets of allowed states at T tac computed by TacSim. 10 automation [4]) or suggests conflict solving paths to human ATCO. E. Continuous control process The TacSim-CompSim model described above gives a solution for the air traffic separation problem predicted at a fixed initial time. The traffic safety is then insured by repeated calls to the algorithm (Figure 3.. Each call will benefit from the last known location of all aircrafts and may propose different actions for those flights which did not follow the expected trajectory. Once these proposed actions are accepted by ATCOs, the instructions are sent to the pilots who start manoeuvring as soon as possible. The control loop starts at the TacSim-CompSim call and ends with the beginning of manoeuvres. It is vital, for the continuous control process to stick to the operational situation, that each loop be as short as possible; its duration is computing_time + ATC_decision_delay + pilot_reaction_time. In a fully automated ATC context, ATC_decision_delay is limited to short data transfer delays; so is pilot_reaction_time if the ground decision is datalinked to an automated pilot. We choose to measure complexity by computing explicitly a conflict resolution on the traffic prediction made for [T tac,t end ], only with a bigger required separation distance. Complexity is then composed with three terms: the number of unsolved conflicts, which are given a very high cost; the impact on tactical control: additional requests to tactical control are penalized so that a posttactical separation is always preferred for a posttactical conflict; the conflict resolution cost (which follows the criteria given in III.B). 4) End of the control loop The optimal states at T tac may then be directly sent to ATC, if it is able to add them as constraints when calling the conflict resolution system or a new call is done to TacSim with constant aircraft states at T tac. Note that, depending on the airspace organization envisioned (definition of the ATC responsibility areas), CompSim computations regard either traffic in the next time window and in different sectors (multi-sector planning), or traffic in the next time window but in the same area (super-sector or even ATS-wide tactical management). Moreover, the modelling differs depending on whether the TacSim software has full traffic separation responsibility (level F. ATC operations context Figure 3. Continuous control process 1) Centralised ATC management In a centralised ATC environment, one big instance of TacSim and CompSim may be responsible for the whole traffic. 2) Sectorised ATC environment In a sectorised ATC environment, several instances of both TacSim and CompSim are networked. Each instance of TacSim takes care of one or a group of sectors; same for CompSim. Each TacSim computes a preliminary conflictsolving plan and provides the relevant CompSim instances with traffic entry conditions at time T tac. Each CompSim then replies with constraints, typically time offsets on each aircraft s entry Experimental Centre Brétigny-sur-Orge, France 14

5 time, needed to keep its area s complexity below an acceptable threshold. Eventually gets back to its forecast 3D trajectory. An illustration of such a manoeuvre is given by Figure 4. IV. THE CONFLICT RESOLUTION SYSTEM In order to explore the control loop feasibility, a traffic and conflicts resolution simulation is needed at every stage of our process. TacSim has to simulate conflicts resolution in order to predict the traffic evolution during the tactical time frame and provide ATCOs with control actions. CompSim also explicitly solves conflicts on the post-tactical time frame to measure complexity. As discussed in E, computational performance is crucial; besides, optimal solutions with respect to the initial hypotheses is desirable to compare different tactical/post-tactical ATC strategies fairly. If the problem can be formulated as the minimization of a linear form under inequality constraints (i.e. a linear program), the simplex method (see [14]) is likely to solve the problem quickly to optimality. Thus linear programming was considered a good candidate for our first model. Figure 4. Lateral deviation maoeuvre A. Conflict resolution through linear programming Such a model was proposed in [9] to maintain separation between aircrafts by speed and flight level manoeuvres. The aircraft states are characterized by their temporal deviation from the BT and a minimum temporal is computed so that separation between pairs of aircraft is insured when this threshold is overcome. Additional temporal deviations are achieved by speed changes only, which in most cases is not sufficient to keep aircrafts separated. In such bad cases, the algorithm branches over the solutions tree to spread the traffic over different flight levels. However, this technique is both expensive in terms of flight efficiency, and rarely applied by ATCOs who tend to prefer geographical separation. Level separation is usually performed at air route design and/or Air Traffic Flow Management (ATFM) stages. Furthermore, it is computationally costly as it introduces heavy combinatorics. 1) Conflic resolution manoeuvres Linear programming does not allow an optimization without restrictions on the possible types of avoidance manoeuvres. Compared to the research presented in [9], we also separate aircraft by speed control but we did not include flight level separation. On the other, we included a class of lateral deviation manoeuvres which we were able to express as linear constraints. When performing such a manoeuvre, an aircraft: Follows a new heading defined by an angle θ with its original direction on a given deviation length. This manoeuvre elongates the aircraft's trajectory and moves the conflict point initially forecast thus introducing a temporal deviation at the conflict point. Retrieves its initial heading until the conflict is overcome. 2) Discretization of air traffic The air traffic is represented by a network whose nodes are remarkable points on the aircrafts trajectories: entry in approach areas, flight level/direction/speed modifications, potential conflicts with other aircraft, and merging with other aircraft s trajectories (ref. Figure 5. ). Figure 5. Air traffic network with all possible nodes 3) Working hypotheses This model involves various restrictions and hypotheses for both modelling and performance reasons: The direction followed during a deviation manoeuvre has to be computed before the optimization. No linear model accepts both the deviations lengths and angles to be variables. Nevertheless, good deviations might be computed before resolution for each pair of aircrafts. The arrival orders of pairs of aircraft at the network's nodes cannot be reversed during optimization without adding great computation complexity. Experimental Centre Brétigny-sur-Orge, France 15

6 Aircraft are only in conflict with one trail of aircraft at each conflict node. Consecutive conflicts met by aircraft are supposed to be sufficiently far away from each other (10-20 NM); such constraints would have to be forced at ATFM level. Cost functions are piecewise linear. Trajectories are approximated by segments and aircraft speeds are considered constant on each arc of the network (the constant value is the mean speed). 4) Full model Under the above assumptions, the conflict resolution is simulated by manipulating two sets of variables on each arc: The first set describes the speed modifications with respect to the speed planned in the BT The second one describes the lateral deviation manoeuvres performed on each arc; the absolute value of the deviation angle is set before the call to the solver but it can be either positive or negative In order to minimize the sum of: The separation rupture between pairs of aircrafts (large penalty) The additional fuel consumption cost The 4D trajectory compliance gap (both temporal and lateral deviations) Subject to the following constraints: The temporal deviation between each pair of aircraft in potential conflicts meets the minimum temporal deviation computed before the optimization for this pair of aircraft The temporal deviations due to speed changes stay inside intervals defined by the aircrafts performances (e.g. minimum and maximum speeds) Lateral deviations lengths must fit into the arcs where they are performed A lateral deviation action performed on an arc must either be positive or negative (otherwise multiple lateral deviations could be requested only to elongate the trajectory, which is very rarely done by ATCOs) The temporal deviation of an aircraft at a given node is obtained from its deviation at the previous node by adding the temporal deviations due to speed modifications and lateral deviations performed on the arc connecting the two nodes 5) TacSim specificities TacSim simulates the air traffic to come in the next tactical time frame. This time frame starts at the moment T ini when the simulation is called but no action will be performed before the end of the control loop (cf. III.E). Therefore, the values of temporal and lateral deviations are initialized with their forecast values at T ini + loop_duration. These initial deviations are not variable. When TacSim is called before CompSim, the final values (at T tac ) for lateral and temporal deviations are variable. However, they are considered as hard constraints when TacSim is called after CompSim, as they represent a set of tactical actions required by the post-tactical stage. 6) CompSim specificities The temporal and lateral deviations of the post-tactical time frame are the final deviations of the tactical time frame. But in CompSim these initial deviations are variables as they give the control actions requested by the post-tactical control to the tactical control. Nevertheless initial deviations are required to quantify the actions requested by the post-tactical control. V. IMPLEMENTATION A. Implemented model So as to reach an optimal solution with regards to our hypotheses without repeating the whole TacSim/COmpSim loop, which is likely to generate additional computation time, the two simulations are merged into one global model. The variables and the air traffic network remain unchanged by the merging. As a consequence, it is still easy to identify the two tactical and post-tactical sub-problems. The relative independence between the two time frames is kept by weighing the tactical and post-tactical costs (C T and C PT ) differently in the overall cost. The tactical costs are given a much bigger weight (e.g. C = 10 C T + C PT ), thus: the priority is always given to tactical conflicts which are much more penalized; post-tactical actions are always preferred to tactical actions with regards to solving post-tactical conflicts; and tactical actions are not requested if they only aim at minimizing post-tactical fuel consumption or 4D trajectory compliance gap, as these tactical deviations will be more expensive. B. Parameters settings The implementation realized until now only involved one type of aircraft whose characteristics are given in TABLE I. TABLE I. Parameter Cruise flight level Cruise speed AIRCRAFT PERFORMANCES Aircraft performances Value ft mach Maximum speed mach (+ 6%) Minimum speed mach (- 6%) Experimental Centre Brétigny-sur-Orge, France 16

7 The main parameters of the two-stage control are summarized in TABLE II. TABLE II. Parameter Minimum separation distance Total traffic duration Tactical time frame length Post-tactical time-frame length Control loop duration (computation + decision + reaction) TWO-STAGE CONTROL PARAMETERS Value 5 NM s 600 s 1200 s 120 s Deviation manoeuvres angle 30 C. Data sets The ATS described herein is designed to manage en-route traffic. Our aim with this first implementation is to study the feasibility and the potential impact of a two-stage ATC process. We did not try to represent real air traffic as the primary objective was to generate traffic quickly, with increasing both air traffic density and the number of effective conflicts to quantify the effect of post-tactical control. As a first set of test cases, we simulated an en-route traffic concentrated on one single flight level structured into a grid on which 8 departure airports are linked to 8 arrival airports as shown on Figure 6. Each departure (respectively arrival) airport is followed (respectively preceded) by a 50 NM long approach area in which no control action occurs, as it is supposed to be managed by the airports control centers. effective conflicts are modified by tuning these take-off frequencies and by changing the distances between airports. D. Results The operational goal of the ATC process is to keep every pair of aircraft separated according to the chosen separation norm. However, as we are still in the first step of our two-stage model validation, we pushed the model to its limits by forcing a very high and homogenous aircraft density over the network on Figure 6. which most of the times leads to unacceptable operational results. The results presented in TABLE III. focus on the aircraft separation distance and control manoeuvres for different air traffic configurations with three different control processes: we first let the aircraft fly with no interruption ("No control"); then we simulated traffic only controlled by our automatic conflict resolution system ("Tactical control"); finally we ran the twostage process on the traffic ("Two-stage control"). For each air traffic density, the results given on TABLE III. summarize five randomly generated instances and traffic density varies by changing the minimum time separation between two aircraft flying on the same trail (the maximum separation is constant, equal to 240 s). Traffic minimum take-off separation 120 s 120 s 120 s 90 s 90 s 90 s 60 s 60 s 60 s 46 s 46 s 46 s TABLE III. Control type No control Tactical Two-stage No control Tactical Two-stage No control Tactical Two-stage No control Tactical Two-stage IMPLEMENTATION RESULTS Total number of conflicts per separation distance 0NM- 3NM 3NM - 5NM Manoeuvres costs Mean speed actions Mean additional distance % 3.14 NM % 2.99 NM % 4.21 NM % 3.49 NM % 4.54 NM % 3.59 NM % 4.62 NM % 3.59 NM Figure 6. Implemented air traffic A minimum and a maximum delay between two take-offs are defined for each departure airports and the take-off time of each flight is set randomly from the last take-off in order to meet these requirements. The traffic density and the number of The linear program was solved with CPLEX. The longest computation time observed for one two-stage control loop was less than 30 s, although it had to solve more than 700 potential conflicts, which is a lot more than what is expected in an operational context. These few results give a clearer idea of the impact of the traffic configurations and densities we used here as, without Experimental Centre Brétigny-sur-Orge, France 17

8 control, 1000 to 1500 conflicts would be detected. We could also verify the efficiency of our automatic conflict resolution system on such air traffic as it was able to handle safely situations with up to 1000 conflicts. The discussion regarding the impact of the two-stage control is not as straightforward. In the worst cases, it helped removing around 50-80% of the most serious conflicts (separation 0-3 NM) and 10%-40% of other conflicts (3-5 NM) without ever managing to reach a conflict-free traffic when the tactical control alone was not able to do so. This could be due to the "pathological" aspect of our data sets: the homogenous and symmetric traffic density hardly leaves any room for posttactical complexity optimisation. It would also be of interest to consider other complexity measures or complexity thresholds to trigger post-tactical requests: we decided that tactical actions would only be requested in the situations where post-tactical conflicts are expected to be unsolvable while it might be better to consider options were least costly (fuel, BT compliance) situations are always preferred or where conflicts are always anticipated (as in [5]). VI. CONCLUSION In an environment where air traffic density is planned to increase significantly, a two-stage control looking at both short-term and mid-term forecasts is a potential solution towards ATC efficiency improvement. After exposing a formal structure for two-stage control, we proposed a model applicable in a highly automated and predictable environment. In this model, a simulation of the short-term conflict resolution leads to a mid-term traffic forecast before optimising a complexity measure which is obtained by running an automated conflict resolution on the mid-term traffic. In order to emulate the automated environment, we developed an automatic conflict resolution system based on linear programming. We were then able to test our two-stage control model on a simple traffic formed by flight trails between pairs of airports located on a grid. Our first results seem to confirm that a two-stage control could have a positive impact although it does not satisfy operational requirements yet. Therefore, we plan on designing new, more realistic test cases and keep on improving our resolution parameterization. Including uncertainty in traffic is an objective of this phase. In a second step we would like to explore other solutions for automated conflict resolution and complexity measure. Finally, it will be interesting to consider this ATC process as a part of an overall ATM improvement in which every level (route design, ATFM, ATC, ) and interactions between these levels are essential: how could we design an ATM where every level is built to optimise its effect on all higher and lower levels? ACKNOWLEDGMENT (HEADING 5) We would like to thank Gérard Verfaillie and Jean-Loup Farges (ONERA) and Daniel Delahaye (ENAC) for sharing their insight and giving valuable advices on our research conduction. ATC ATCO ATFM ATM ATS BT CDM FMS ACRONYMS Air Traffic Control Air Traffic Control Operator Air Traffic Flow Management Air Traffic Management Air Traffic System Business Trajectory Collaborative Decision making Flight Management System REFERENCES [1] P. Kopardekar, T. Prevot and M. 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