Abstract— The en-route conflict resolution remains a major concern for Air Traffic Management (ATM), especially in core European airspace where the current Air Traffic Control (ATC) system is approaching its capacity limits. In this paper we discuss the emphasis of the coordination of conflict resolution actions.
Indeed, coordination of conflict resolution is required to reach a global solution for clusters involving many aircraft. A number of such models have already been proposed and some of them applied in practice, but there has been no cohesive discussion or comparative evaluation of these approaches. This paper presents a summary of coordination of conflict resolution approaches.
Index Terms—conflict detection and resolution, coordination of conflict resolution.
I. INTRODUCTION
he purpose of Air Traffic Management (ATM) is to enable airspace users to meet their schedules according to their preferred flight profiles without compromising safety levels.
To provide safe and efficient aircraft movements, the current approach comprises two main activities: Air Traffic Control (ATC) and Air Traffic Flow Management (ATFM). Both ATC and ATFM are ground based services. The ATC provides tactical, safe separation between aircraft and between aircraft and obstacles. The main goal of ATC is to guarantee security and to give aircraft optimal trajectories to fly from one airport to an other. The Air Traffic Flow Management deals with the allocation of scarce capacity resources such as routes and terminal operations time slots.
In the USA, airport capacity is the main problem. This problem exists also in Europe on the biggest airport. But in Europe, and mainly in France, En Route capacity is the critical point. There is also a problem linked with controller workload such as monitoring workload (the monitoring of the aircraft in the controller’s sector), resolution workload (the resolution of conflict) and coordination workload (a task that each controller must perform when a aircraft enters or leaves its sector). Thereby, the tools for air traffic control system, in particular, for conflict management as well as for ground- based Collaborative Decision Making (CDM) are necessary to optimize conflict resolution solutions. In other words, it aims at increasing capacity of controller.
Huy-Hoang Nguyen is with the Heudiasyc Laboratory, UMR CNRS 6599, University of Technology of Compiègne, Centre de Recherches de Royallieu, BP 20 529, F-60205 Compiègne cedex, France and EUROCONTROL Experimental Centre, Centre de Bois des Bordes, BP15, F-91222 Brétigny sur Orge cedex, France. (e-mail: [email protected]).
Additionally, the steady growth of traffic in core European airspace could lead to complex situations where separation standards may be infringed by several aircraft in a transitive configuration, called clusters of potential conflicts. It is necessary to treat large cluster of conflicts without inducing too much the costs of maneuvering to aircraft. Costs typically include fuel and time. Consequently, solutions to large cluster of potential conflicts are needed. Accordingly, coordination of conflict resolution is required to reach a global solution for clusters involving many aircraft.
This paper provides a summary and evaluation of the approaches that have been used to perform coordination of conflict resolution. The objective of the paper is to point out the advantages and disadvantages of each method.
The paper is organized as follows: Section 2 recalls quickly conflict detection and resolution. In Section 3, we review the approaches that have been proposed for the coordination problem and we discuss some of the most relevant methods.
II. CONFLICT DETECTION &RESOLUTION (CD&R) The conflict detection and resolution has been a major topic in ATM research. The air traffic conflict detection and resolution process consists of several tasks to ensure separation or avoid collisions depending on the scope of the system. Firstly, based on the information available, future position can then be estimated and potential conflicts can be predicted.
Conflict detection is based on the estimation of future vehicle position and through the application of predefined metrics on the situation in order to decide whether or not a conflict is present. This metric may include a sole parameter (e.g., distance) or may be a combination of several parameters (e.g., distance, time and maneuvering cost). After the detection of a conflict, a conflict resolution phase requires appropriate maneuver action and information distribution to all aircraft involved in the conflict.
Following the literature research, an important number of different modeling approaches (more than 60 methods [1]) have been applied in the past for conflict detection and resolution in aerospace. These models include a wide variety of techniques from varying viewpoints, but are all intended to provide an analytical basis for designing and evaluating conflict detection and resolution systems.
A. Conflict Detection
In order to ensure safety of aircraft traffic operations,
Huy-Hoang Nguyen
Survey of Coordination of En Route Air Traffic Conflicts Resolution Modelling Methods
T
adequate separation must be maintained. A conflict occurs when an aircraft’s protected zone1 is violated. The protected zone is currently defined by en route ATC standards as a circular zone of 5 nautical mile radius and a height of 2000 ft altitude (-1000 ft to +1000ft). In other words, a conflict between two aircraft is called effective if at some instant of time, the minimal distance between these two aircraft called
Closest Point of Approach (CPA) is inferior to the minimal separation standard (see Figure 1).
The conflict detection phase, permits to detect conflicts only with aircraft for which an intrusion of the protected zone takes place in the near future, which is defined by using a fixed look-ahead time for T minutes [2]. A new conflict is detected when an intrusion of the protected zone is predicted, and the time of this intrusion is within the look-ahead time. The conflict detection uses the current state (position and altitude) and trend vector (ground speed, track and vertical speed) to
detect conflicts.
For a global resolution of case of more than two aircraft simultaneously in conflicts, clusters of aircraft involved in these conflicts will be determined and identified during the look-ahead time. Recall that a cluster [3] is the transitive closure on all aircraft pairs involved in a conflict during the look-ahead time; that mean if A conflicts with B, and B conflicts with both A and C, then the cluster consists of A, B and C (see Figure 2).
B. Conflict Resolution
Once a conflict is detected it must also be resolved.
Generally, a conflict situation will be resolved by maneuvering horizontally (heading change) or maneuvering vertically (altitude change) or speed change of aircraft. Over the years, various methods for resolving conflict situations have been proposed. Some methods use force field techniques, others use genetic algorithms, rule-based methods, or optimization techniques. Kuchar and Yang [1] have also given an overview of various approaches to conflict detection and resolution problem.
Force field approaches model each aircraft as a changed
1 The protected zone is also often referred to as Protected Airspace Zone, which is a definition that originates from Radio Technical Commission for Aeronautics (RTCA)
particle and use modified electrostatic equations to determine resolution maneuvers. The repulsive forces between aircraft are used to define the maneuver each performs to avoid a collision [4], [7].
Optimized conflict resolution can involve a rule-based decision [5], [6] or determining which of several avoidance options minimizes a given cost function. The Traffic Alert and Collision Avoidance System (TCAS), for example, searches through a set of potential climb or descend maneuvers and chooses the least-aggressive maneuver that still provides adequate protection. Algorithms for resolving three- dimensional conflicts involving multiple aircraft are presented in [21]. These algorithms are based on trajectory optimization methods and provide resolution actions that minimize a certain cost function. Krozel et al. [14] have used an approach based on optimal control theory (OCT). They have developed an algorithm for the resolution of conflicts involving two aircraft.
This algorithm is based on the maximization of the inter- aircraft distance at the Point of Closest Approach. Game theory (Game) is used for conflict resolution by Tomlin et al.
[18]. Recently, Nicolas Durand [3] describes a mathematical programming model using a heuristic method based on genetic algorithms (GA) to optimally resolve conflict whereby the global optimization function aims at minimizing the overall cost incurred.
III. THE COORDINATION PROBLEM
En Route capacity is a problem mainly in Europe. Airspace is divided in control sectors, each sector being managed by two air traffic controllers. However, the capacity of a sector is limited. A controller can not handle more than a certain number of aircraft in its sector. Additionally, as air traffic keeps increasing, a controller must be able to manage clusters of conflicts. And then, a global solution to clusters involving many aircraft in conflicts, more than two aircraft, is needed.
Consequently, the coordination problem of conflict resolution appears and must be solved.
A
B
C
Figure 2. Cluster of three aircraft involving in conflicts {A, B, C}
CPA: Closest Point of Approach Point of crossing
Convergence angle
Aircraft 1 Aircraft 2
Figure 1. A Conflict
TABLE I
COORDINATION OF EN ROUTE CONFLICT RESOLUTION APPROACHES
Model Resolution Coordination Multi-aircraft
Eby [16] F A G
Kosecka et al. [7] F A G
Andrews [17] O O P
Wangermann &
Stengel [15] O(Rule) O G
Zeghal [12] F O G
Durand, Granger &
Alliot [3],[8] O (GA) A G
Tomlin et al. [18] O (Game) O G
Krozel et al. [14] O (OCT) O P
Love [19] O(Rule) O P
TCAS [20] O(Rule) O P
Schild & Kuchar
[22] O(Rule, GA) A G
[23],[24],[25],[26] DAI(Multi-
agent) A G
Coordination of conflict resolution has two benefits. First, the required costs of maneuvering are reduced. Costs typically include fuel and time, but also cover workload or safety.
Second, coordination helps ensure that all aircraft do not maneuver in a direction, which could prolong or intensify the conflict.
Over the years, different methods for coordination of conflict resolution have been proposed. Some methods use rule-based methods (VFR, EFR…), others use an order of priority based on code number (TCAS) or token allocation strategy, optimization techniques, disjunctive scheduling method or theory of multi-agent systems in Distributed Artificial Intelligence (DAI).
Table 1 shows the coordination of conflict resolution capabilities of different methods. These models do not represent an exhaustive list, but are believed to cover the major approaches to the problem. The “Resolution” column indicates the models for conflict resolution. Three categories are included here: Force field (F), Optimized (O) and Distributed Artificial Intelligence (DAI). The “Coordination”
column shows where the model accepts some form of coordination solution between aircraft. This includes:
Assumed coordination (A), in which all aircraft are assumed to coordinate their actions; and Optional coordination (O), in which the model may be used in both Assumed and Non- coordinative cases. The “Multi-aircraft” column describes how the model handles more than two aircraft simultaneously. This can take two forms: Pairwise (P), in which multiple conflicts are addressed sequentially in pairs; and Global (G), in which the entire traffic situation is examined simultaneously to determine the resolution maneuver.
Rule-Based Methods
Under certain operational conditions, rules systems are used for the resolution maneuver coordination. These rules define priorities to aircraft involved in the conflict and suggest a corresponding resolution maneuver.
For maneuver coordination based on rules, information of position or state, velocity vector, emergency status, target altitude and next waypoint can be used. This information is available in an Automatic Dependence Surveillance – Broadcast message (ADS-B).
The most basic information about conflicting aircraft are their positions (either absolute or relative). Priority determination in simple coordination rules such as Visual Flight Rules (VFR) is entirely based on the other vehicle’s relative position.
The velocity vector could also be used to determine priority based on the vertical rates of the aircraft involved.
Further state information, the velocity vector and additional information (priority categories such as emergencies or ambulance flights) can be used to determine the flight phase of aircraft and to assign priority. The Extended Flight Rules (EFR), as result of EUROCONTROL’s FREER project [5], [6], define some simple rules to apply to the resolution of conflicts in autonomous airborne separation. The Extended Flight Rules are an extension of VFR, taking advantage of the surveillance data available in the cockpit to better consider the economics of flight operation, as well as the freedom of the pilots in making manoeuvres avoiding collisions. They permit the assignment of priority to aircraft in conflict situations in the context of autonomous aircraft ATC to identify which aircraft should give way or maneuver to avoid a collision. The strategy of EFR for priority assignment is based on three principal parameters:
The maneuverability of the aircraft involved in the conflict, then
The availability of the aircraft against their current flight phase, then
The distance to the conflict (or the speed) of each aircraft.
This strategy allows the application of the same rules to conflicts involving more than two aircraft (see [5] for further details).
Schild and Kuchar [22] have conjoined existing rules systems (VFR), suggested systems found in the literature (EFR…) or logical combinations of priority determination and resolution maneuver options for different meta-rules. A meta- rule consists of different rules, which are combined forming a decision tree. Several rule inputs are connected to a rule output. An evaluation value function is used as the objective function for the genetic algorithm optimizing parameters of meta-rules combinations for comparability. Eleven meta-rules, four for priority determination and seven for conflict resolution, were defined and evaluated. Their simulation results show that the use of additional information such as the differentiation between flight phases in the priority determination process does not necessarily lead to better results in terms of efficiency. An increase in efficiency
through higher rule complexity occurs if the velocity vector is used for priority determination and maneuvers in the vertical direction are allowed.
Additionally, an order relation of priority must be transitive and be not symmetrical. For example, the Visual Flight Rule that gives priority to the aircraft coming from the right does not define a global order if there are more than two aircraft simultaneously in conflict. In Figure 3, this priority resolution rule cannot give an order of priority to solve conflict involving three aircraft because transitivity is not ensured.
The complexity of maneuver coordination rule systems depends on the operational environment and its characteristics.
The use of too complex rule systems holds the potential for human misinterpretation while they might not necessarily increase operational system performance. Additionally, the increase of rule complexity follows the law of diminishing returns, meaning that the additional gain through more input information and higher rule complexity is getting smaller.
Increasing rule complexity may make it more difficult for human operators to understand the basis behind conflict decision, potentially resulting in non-conformance and distrust of the system.
Multi-Agent System Model and Distributed Artificial Intelligence (DAI)
A methodology based on the theory of multi-agent systems in Distributed Artificial Intelligence (DAI) have been proposed [23], [24], [25], [26] for a multiple aircraft strategic conflict avoidance system in which aircraft share the costs involved in the conflict resolution. A set of proximate aircraft operating in free flight2 airspace is cast as the multi-agent
2 Two definitions of free flight:
a. In RTCA [20], free flight – A safe and efficient flight operating capability under instrument flight rule (IFR) in which the operators have the freedom to select their path and speed in real time. Air traffic restrictions are only imposed to ensure separation, to preclude exceeding airport capacity, to prevent unauthorized flight through special use airspace, and to ensure safety of flight. Restrictions are limited in extent and duration to correct the identified problem. Any activity, which removes restrictions, represents a move towards free flight.
b. EUROCONTROL’s definition, free flight – Freedom for the users to exercise the responsibility of separation from other traffic and to effect any trajectory change in any dimension.
system. These aircraft are modelled as intelligent agents having Joint Responsibility [24] to establish a defined Joint Goal [24] of separation assurance. This Joint Goal is achieved through a Joint Solution or common conflict resolution plan.
Upon detection of a predicted conflict by one or more aircraft, a team of the conflicting aircraft is formed with the purpose of resolving the conflict.
A set of rules (conventions and social conventions [24]) establishes the foundation for a communication protocol that allows the coordination and negotiation of resolution plans [25].
A dynamic programming algorithm [26] has been developed for airborne centralised conflict avoidance planning. The algorithm enables the agent aircraft conflict scenario to compute resolution actions consisting of a set of airspeed controls for each aircraft involved in the conflict.
The use of speed control actions as a strategic conflict resolution technique in free flight is justified by the fact that conflicts can be solved considering cost savings without changing the aircraft’s preferred paths.
The conflict avoidance manoeuvres considered in [26] are speed control actions. Therefore, the existence of a global optimal avoidance strategy is not guaranteed and depends on the loss functional that is being minimised. However, solutions can be found by increasing the weight of the safety parameters in the loss functional. This method will develop algorithm for three-dimensional conflict resolution and for coordination resolution that includes both horizontal and vertical manoeuvres.
Transponder Code and Token Allocation Strategy
The TCAS system uses the transponder code to decide which aircraft has to maneuver; giving resolution priorities to aircraft is a way often adopted for solving the coordination problem. Each pair of conflicts is examined and solved sequentially. If a conflict solution produces a new conflict, the original solution may be modified until a conflict-free solution is found.
Recently, Granger, Durand and Alliot [8] build a token allocation strategy whereby a global resolution order is given.
Using the simple order based on transponder number, they build a global resolution order with the following strategy:
1. First, every aircraft send its predicted trajectory to its neighbours. Each aircraft is then able to know whether it is conflicting with another aircraft or not for the next five (minute look-ahead time).
2. Each aircraft receives a token from every conflicting aircraft, which has a higher priority in its detection zone. Aircraft that are not in conflict never receive any token.
3. Then, each conflicting aircraft with no token solves conflict with every aircraft in its detection zone that
A1
A3 A2
Figure 3. Three aircraft conflict
have no token. It does not take into account aircraft that have one or more tokens.
4. When this trajectory has been computed, the aircraft broadcast its new trajectory; all aircraft, which have received a token from this aircraft, take this new trajectory into account, and cancel the token received from this aircraft.
5. Step 3 and 4 are repeated until no token remains.
This allocation-resolution method can not lead to situations where all aircraft have at least one token or situations where two aircraft without any token solve conflict simultaneously because it is guaranteed by the use of a total priority order on aircraft. However, there are cases in which aircraft remaining unsolved conflicts. These conflicts are appeared and unsolved because the order of priority of aircraft is not well chosen.
Disjunctive Scheduling Method
In a recent paper [9], we have proposed a new model for assigning priorities of aircraft in en-route conflicts based on disjunctive scheduling technique. In this model, a conflict is represent by a pair of disjunctions between the events E1,2 and
E2,1 in a PERT graph. E1,2corresponds to the event: "aircraft 1 goes through crossing point"; E2,1 corresponds to the event:
"aircraft 2 goes through crossing point". From the scheduling point of view, it will be necessary either that the event E1,2 precedes event E2,1, or event E2,1 precedes event E1,2. The two events have to be separated at least by a duration that must ensure the separation of aircraft throughout the look-ahead time.
We approach the resolution of this graph through a scheduling that optimizes the assignment of aircraft priority in order to minimize the total delay. It appears that the combinatorics nature of the optimization problem does not increase too much with the size of the cluster when using the
Branch and Bound technique, a matching technique. So the solutions (schedules or orders of actions) can be enumerated very rapidly. These schedules can be then compared between
themselves more accurately with the use of simulation software in order to select the best one.
This model permits efficient solution for priority assignment in case of multiple conflicts and thus facilitates the coordination of action. Figure 4 gives an example of order of priority of 3 aircraft for optimally solving en route conflicts.
In [9], a range of scenarios of tests for clusters ranging from
3 to 15 aircraft involving in conflicts (see Figure 5) with two methods, disjunctive scheduling method and method using Extended Flight Rules gives a comparison in term of the mean delays (see Figure 6). This comparison shows the good performances of the disjunctive scheduling method.
IV. CONCLUSION
In this paper, we have discussed a summary of coordination of conflict resolution approaches. It is clear from this survey of models that there are few various approaches to the coordination problem.
It looks extremely difficult to device an approach that would find the best possible order of priority without seriously increasing the complexity of the global algorithm and the necessary of the communication medium.
ACKNOWLEDGMENT
The author wishes to thank his professors Jacques Carlier, Vu Duong and Dritan Nace for reviewing this paper.
p 1,1
0 0 0
Eb1
E b E b2
Eb3
E1,2
p2,1 E2,3
p3,1 E3,2
E 1,3
E 2,1
r2,1
Ee1 p 1,3 p 1,2
Ee2
E3,1 E e3 E e 0 0
0 p2,3
p3,2 p2,2
r3,2 r3,1 p 3,3
Figure 4. Order of priority of 3 aircraft for optimally solving conflicts.
Order of priority: Aircraft 3 > Aircraft 2 > Aircraft 1 Aircraft 3 flies normally and does not change its trajectory.
Aircraft 2 must change its trajectory for solving the conflict with aircraft 3.
Aircraft 1 must change its trajectory for solving the conflict with aircraft 3 and aircraft 2.
Figure 5. A cluster involving 15 aircraft
Figure 6. Mean delay per aircraft in cluster
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TERMINOLOGY AND ABBREVIATIONS
ADS-B Automatic Dependent Surveillance - Broadcast ATC Air Traffic Control
ATM Air Traffic Management ATFM Air Traffic Flow Management CDM Collaborative Decision Making CD&R Conflict Detection and Resolution CPA Closest Point of Approach DAI Distributed Artificial Intelligence EFR Extended Flight Rules
FREER Free-Route Experimental Encounter Resolution GA Genetic Algorithms
OCT Optimal Control Theory
RTCA Radio Technical Commission for Aeronautics TCAS Traffic Alert and Collision Avoidance System VFR Visual Flight Rules
BIOGRAPHY
Huy-Hoang Nguyen obtained an Engineering degree in Computer Science from the Polytechnic University of Ho Chi Minh City, a Master degree in Computer Science from the Institut de la Francophonie pour l’Informatique, and is currently a Ph.D. candidate at the University of Technology of Compiègne, France. His interests include operations research, and in particular, scheduling and optimization problems.
