- Email: [email protected]
BALANCING THE AIR TRAFFIC CONTROL WORKLOAD THROUGH AIRSPACE COMPLEXITY FUNCTION
First IFAC Workshop on Multivehicle Systems
BALANCING THE AIR TRAFFIC CONTROL WORKLOAD THROUGH AIRSPACE COMPLEXITY FUNCTION Ítalo Romani de Oliveira, Renato Jorge Galvão Teixe
ira, Paulo Sérgio Cugnasca
Computer and Digital Systems Engineering Department Polytechnic School, University of São Paulo, Brazil
,
Abstract: The aircraft density in the airspace is g rowing, and this may increase the risk of accidents. In this sense, it is very important for air traffic controllers to work under safe traffic conditions, which include a good airspace d ivision. The goal of this paper is to study the use of a well known airspace complexity m easure, applied to evaluate air traffic controller workload in different organizations of t he airspace around São Paulo. The results obtained by simulating two different scenar ios show the relations between sector geometry, complexity and controller workload condit ions. Copyright © 2006 IFAC Keywords: Air Traffic Control, Human Factors, Safet Simulation
1. INTRODUCTION
y, Tasks, Complex Systems,
handle more aircraft, but safe communication and coordination still remain the system bottlenecks. Considering these limitations, this paper presents case study on airspace control organization, using he low level of automation, which is currently true in the most part of the airspaces through the world.
Between 1960 and 1999, the passenger air transportation made the number of passengers × kilometers increase 25 times. In the same period, t number of tons × kilometers of cargo air transportation increased 50 times. This increase represented, for the number of aircraft take-offs a nd landings, a 300% growth (ICAO, 2000).
a a
2. THE AIRSPACE SECTORIZATION
The airspace is divided into sectors in which human The air transportation demand increase expected for controllers are responsible for monitoring flights to the next years is substantial. Projecting the mean avoid conflicts and provide information to aircraft . value of the predicted increase rates (Eurocontrol, When designing the airspace sectors, not only the 2002; FAA, 2004; ICAO 2000), one can foresee for the year 2016 a traffic 45% greater than today. Wit hbalance of the number of aircraft per sector is the greater traffic activity, the aircraft density in important, but also the constraints following described (Trandac, Baptiste, Duong, 2003). airspace sectors will automatically increase. More and more controllers have to work with highly concentrated attention due to the greater number of managed aircraft, and to perform all the consequent control actions faster. This situation results in l ong periods of stressing high workload. There are two constraints for managing the high aircraft density in the airspace. The first one is the use of radio party line for communication between flight crew and ATCo. In this channel, one air traf fic controller cannot talk with a large number of pilot s at the same time. The second one is that, if one secto r is too small, the task of taking over and handing off the aircraft takes too high a proportion of the task execution time. Automation tools help the ATCo to
64
First IFAC Workshop on Multivehicle Systems
2.1 Convexity constraint The use of sectors with convexities that cross rout is not recommended. The aircraft route must not go through the same sector twice, as illustrated in Fig. 1.
This restriction aims to prevent the flight track f rom passing more than once through the same sector, to es avoid unnecessary workload. 3. SECTOR WORKLOAG BALANCING Besides the requirements for each individual sector the geometry of sectors and routes must enable a good workload distribution among the members of the Air Traffic Control team, avoiding keeping some operators unnecessarily more loaded of work than others.
Sector B Traffic line Sector A Sector boundary
Fig. 1. Convexity constraint.
,
Despite the difficulty in measuring the sector workload, some consistent studies have been 2.2 Minimum distance constraint performed to define reliable indicators for workloa d evaluation. An interesting overview of the existing There has to be a minimum distance between crossing the border of one sector and a route cross ingworkload measurement methods is available in the lines. The reason is that the controller must h ave(Farmer and Brownson, 2003). The present work time to resolve one possible conflict on this netwo rk adopted the calculation method of (NASA, 1996), which evaluates a number from several input node, as shown in Fig. 2. measures called complexity factors. Each complexity factor has an associated multiplier, which is appli ed Sector boundary in a formula to vary its weight. A brief descriptio n of the complexity factors is presented below. For more Traffic lines details on the complexity factors meaning and rationale, refer to (RAMS, 2006) and (NASA, 1996). Sector A Critical point (close to boundary) The complexity factors are: - Aircraft Count (ACT): This is a count of the Sector B number of aircraft within the boundaries of a secto r at an instant of time. Fig. 2. Minimum distance constraint. - Aircraft Density (DNS): Aircraft Density is the aircraft count divided by the usable amount of the 2.3 Minimum sector crossing time constraint sector area. - Airspace Structure (STR): It measures the A minimum permanence time (Tmin) of aircraft in conformance of the traffic flows with the geometry a sector has to be respected, in order to justify t he of the sector. radio frequency change and the sector transference - Climbing or Descending (CoD): This is a count of tasks. This constraint is shown in Fig. 3. the number of aircraft that are in climb or descent at an instant in time. Traffic line - Closest Points Approach (CPA): It weights the number of aircraft that are within a threshold separation of each other at any instant in time. Tmin Sector A - Aircraft Proximity to Sector Boundary (PRX): This is a count of the aircraft that are within a thresh old distance of a sector boundary at a given time insta nt. Sector B - Variance in Directions of Flight (VDF): This is a measure of the variability of heading of all of t he Fig. 3. Minimum sector crossing time constraint. aircraft in the sector at an instant in time. - Convergence Angle (ANG): This complexity factor 2.4 Connectivity constraint is a measurement of the severity of each conflict situation based on the conflict geometry. The sector must not be fragmented, but contiguous. - Conflict Near Sector Boundary (PRC): This is This is show in Fig. 4. a count of the predicted conflicts that will occur within a threshold distance of a sector boundary. - Aircraft Neighboring Conflict (NBR): This Sector A complexity factor is a count of other aircraft that are close to the area of the potential conflict. Sector B - Intent Knowledge (INT): The level of knowledge Sector A about the flight intents inside the sector. Fig. 4. Connectivity constraint.
65
First IFAC Workshop on Multivehicle Systems
The complexity function seems to keep a relation with the perceived workload by the Air Traffic Controller (ATCo), but there is no evidence that it keeps a direct relation with the time spent in cont tasks, so the control task load must be measured separately. Both of these measures are used in the case study of the next section.
Entry Clearance: For aircraft for which no flight strip is known, i.e., there is no previously known flight plan, a flight plan must be agreed. These aircraft may s. rolcome from external sectors or from internal airport ATCo, after verifying the non existence of conflict s, transmits a clearance for using the sector.
Flight Level Clearance: ATCo transmits an instruction to the aircraft, giving authorization t o use The complexity function is evaluated with a look a given altitude. ahead time, i.e., by predicting, at time t, the complexity function value for the time t + ∆. The complexity evaluation formula, in accordance with Horizontal Track Clearance: ATCo transmits an instruction to the aircraft, giving authorization t o (NASA, 1996), is: follow a given direction. 0.0172 x MAX(ACT(t),...,ACT(t+∆)) + 0.3280 x MAX(DNS(t),...,DNS(t+∆)) + Speed Clearance: ATCo transmits an instruction to 0.0498 x SUM(CPA(t),...,CPA(t+∆)) + the aircraft, ordering it to perform a given speed. 0.1070 x SUM(ANG(t),...,ANG(t+∆)) + 0.0426 x SUM(NBR(t),...,NBR(t+∆)) + Conflict Resolution: ATCo, after detecting a conflict, 0.0754 x SUM(PRX(t),...,PRX(t+∆)) + has to choose which aircraft will be penalized and 0.1134 x SUM(CoD(t),...,CoD(t+∆)) + which maneuver will be used: whether horizontal, 0.0709 x MAX(VDF(t),...,VDF(t+∆)) + vertical or just changing speed. 0.2000 x SUM(PRX(t),...,PRX(t+∆)) + 0.0676 x MAX(STR(t),...,STR(t+∆)) + Handoff: when an aircraft is leaving the sector, the 0.2564 x MAX(INT(t),...,INT(t+∆)) ATCo has to perform these tasks: instruct the pilot to where the expression STR(t),...,STR(t+∆), for instance, stands for a series of the complexity fac tor select another radio frequency, communicate the downstream sector to receive the aircraft. This values evaluated at intermediate steps between t and communication includes transmitting the flight stri p t+∆. manually or by some telecommunication line. A complementary view of workload measurement These are the common air traffic control tasks. was developed in the INTEGRA project (2003) Furthermore, many other tasks are required for the air traffic management, and the reader may refer to 4. AIR TRAFFIC CONTROLLER TASKS (Druart, Novales, 1997; Paternò, Santoro, Tahmassebi, 1998; Isaac, R, 1999) to find more Controlling air traffic requires the execution of information on this subject. several different tasks. Disregarding the use of automation tools, the most important tasks performe d In the air traffic control centers, there may be tw o by human operators are described as follows. controllers working in the same sector. One controller is called planning or strategic controll er, Handover: Receiving a flight into the sector involves who establishes medium term action plans to some sub-tasks for the ATCo. The first one is propitiate good conditions for the control tasks in receiving the flight strip (either on paper or his/her sector. The other controller is called exec utive electronically) when the aircraft is getting closer to or tactical controller, who communicates with the sector border, from the upstream sector, or fro m aircraft, transmits clearance instructions to the f light an internal airport. The flight strip gives to the ATCocrews, and manages the immediate situations. (Vink, the information necessary to be aware of the intend ed 1997). aircraft track inside the sector. When the neighbor ing sectors are managed by different control centers, t he receiver sector may be obliged to send an 5. CASE STUDY acknowledge message. Another task is to receive the pilot first call in that sector, to confirm that AT CoThe case study here described is an assessment of has assumed that aircraft. how the complexity function and weighted task execution counting can be used to guide the airspac e Conflict Search: ATCo verifies if the intended division and task distribution among air traffic trajectory of an aircraft is conflicting with anoth er controllers. The airspace around and inside the São one in his sector. Roughly saying, conflict is the Paulo Terminal Movement Area was used because it situation where two or more aircraft are horizontal ly is the main gateway city in Latin America. The less than 5 Nautical Miles and vertically less than simulation tool used was RAMS Plus version 5.24. 1,000 feet close to each other. This task is perfor med whenever an aircraft is entering the sector or has toThe goal is to study the airspace of Fig. 5 as a change its trajectory due to a prior detected confl ict. system, and to compare two distinct sector organizations for it. The simulated traffic was manually scheduled, trying to represent a reasonabl e
66
First IFAC Workshop on Multivehicle Systems
be aligned with new technical aspects of arrivals proportion of traffic contribution for each traffic zone present in Fig. 6, considering only flights un der management which can be found in (Grimaud et al., Instrument Flight Rules. This does not include flig hts 2005). that stay all the time inside the colored sectors o f The traffic of Fig. 6 was simulated by RAMS Plus in Fig. 5. this scenario, flowing through the white routes of En route sectors Fig. 7. São Paulo TMA B S1
BS2 SP4
CW3 N W
SP3
SP2
E S SP5
S P1
Fig. 5. Current Airspace sectors around São Paulo.
C W3
Fig. 7. Sector geometry of the baseline scenario. The complexity functions were evaluated along the time of a day for each sector, and they are plotted in Fig. 8 for the most complex sectors. In this figure , the vertical axis scale was restricted above the 1, 000 complexity function value, because this value was noted to be a kind of division between the critical sectors and the non-critical sectors. The most crit ical sectors were considered to be those which have many peaks far from the 1,000 line.
5.1 Baseline scenario definition and evaluation
In this section, the definition of the baseline sce nario is defined and evaluated. Firstly, the sector geome try is considered. The sector geometry and route structure assumed is depicted in Fig. 7. This secto r geometry is different from the actual geometry currently in practice, presented in Fig. 5. The geometry presented in Fig. 5 is designed in that fo rm because of, among other reasons, the use of groundbased navigation aids, such as VOR and DME. However, as the use of satellite navigation becomes more accurate and reliable, the sector geometry becomes less dependent on ground references, and can be better designed in order to conform to the traffic flows. In this configuration, there is one sector less than in the actual airspace division of Fig. 5 .
2500 BS1 BS2 CW 3
2000
complexity
Fig. 6. IFR flights per traffic zone in 24 hours.
CW 4 SP4 SP5
1500
1000
0
5
10
15
20
hours
Fig. 8. Complexity functions for the baseline The reasons for the new geometry in this scenario scenario (non-critical sectors are not shown). are: to lower the handover/handoff tasks for the flights in the Rio-São Paulo corridor, and for the The systemic complexity, which was evaluated by traffic departing from Guarulhos airport; and also to calculating the area under all curves of the graph, 67
First IFAC Workshop on Multivehicle Systems
The systemic complexity for this scenario, evaluate d including the non-critical sectors, resulted in the in the same way as in the baseline scenario, result ed value 58,253, which implies in a mean complexity in the value 45,361, which implies a mean value of 270.94 per hour for each sector (9 sectors overall), during the 24 hours of the day. Looking a tcomplexity value of 157.50 per hour for each sector y. Fig. 8, it is possible to identify that the most cr itical(12 sectors overall), during the 24 hours of the da Looking at Fig. 10, it is possible to see that ther e are sectors are BS1, CW3 and CW4. By the concept of the complexity function, these sectors will challenge t he less complexity peaks than in Fig. 8. Although the sector BS1b presented a higher peak around 19:00, the ATCo with the most difficult situations. peaks are less often than in the baseline scenario. 5.2 Reorganized scenario With more spaced peaks, it is possible to manage th e complexity with safe conditions, because the In the reorganized scenario, the critical sectors complexity function is evaluated, at least in the identified in the baseline scenario were divided simulation scenarios, with 40 minutes look ahead (resulting in BS1a, BS1b, CW3a, CW3b, CW4a and time. In this period, it is possible to comfortably take CW4b), obtaining the sector geometry shown in Fig. corrective actions to avoid the high complexity 9. The same traffic schedule was again simulated to situations, as it is done in the RAMS Plus software . obtain the complexity functions shown in Fig. 10. 5.3 Task load indicators The ATCo workload is not directly proportional to the complexity function. One reason for this is tha complexity function is evaluated with a 40-minute look ahead, which allows an inter-sector coordinato to manage the situation to deliver the traffic in l complex conditions for the tactical air traffic controller, reducing his/her workload. RAMS Plus software allows associating weights to each ATCo task and counting the task execution. The sum of th ATCo tasks for all the day, for the baseline and th reorganized scenarios, are shown in Figs. 11 and 12 respectively.
BS1a BS1b BS2 SP4
CW3b
SP3
SP2 SP5 SP1
CW3a
35000 30000 25000 Workload
CW4b CW4a
20000 15000 10000 5000
Fig. 9: Reorganized sector geometry.
BS1b CW 3a
SP_5
SP_4
SP_3
SP_2
SP_1
30000 Workload
CW 4b SP4 SP5
1500
CW_4
35000
CW 3b 2000
Tactical ATCo
Fig. 11: Total ATCo workload of baseline scenario.
BS2
2500
CW_3
Planning ATCo
3000
25000 20000 15000 10000 5000 SP_5
SP_4
SP_3
SP_2
SP_1
20
CW_4b
15
CW_4a
10
CW_3b
5
BS_2
0
CW_3a
1000
BS_1b
0 BS_1a
complexity
BS_2
BS_1
0
hours
Fig. 10: Complexity functions of the reorganized scenario.
Fig. 12: ATCo workload of reorganized scenario. The workload is counted by a weight attributed to each of the tasks described in section 4. Following 68
t r ess
e e ,
First IFAC Workshop on Multivehicle Systems
the default configuration of RAMS Plus, these weights keep a relation with the time (in seconds) spent to execute each task. In Fig. 11, one can not workload concentration in the airport sectors (SP4 and mostly in SP5). The airport sectors cannot be divided owing to their small size, so the ATCo has be very good to work in these sectors. Despite the high complexity evaluated for sectors CW3 and CW4, these sectors present the smallest workload in the system. This reason motivated not to employ planning controllers in the reorganized scenario.
GLOSSARY e aATCo FAA toDME ICAO IFR RAMS
Air Traffic Controller Federal Aviation Administration, U.S. Distance Measurement Equipment International Civil Aviation Organization Instrument Flight Rules Reorganized Mathematical ATC Simulator Terminal Movement Area VHF Omnidirectional Range
In the reorganized scenario, which has only tactica l TMA controllers, one can note that the number of VOR controllers is smaller (12 against 18 of the baseli ne scenario). However, the mean controller task load REFERENCES has raised, which is not good from the safety point of view. The baseline scenario resulted in a mean Druart, J.; Novales, L.: En route Control Standards workload per controller of 10,132, while the Mefisto Report WP1-4, November 1997. reorganized scenario resulted in 11,071. The higher Eurocontrol. Medium-Term Forecast of Annual workload seems to impact the traffic safety. Number of IFR Flights (2002 - 2009). Although no deep safety analysis was performed in Eurocontrol - Air Traffic Statistics and Forecasts this study, it was noted that only the reorganized (STATFOR), Volume 1, March 2002. scenario presented unresolved conflicts in the en FAA Aerospace Forecasts - Fiscal Years 2004-2015. route sectors BS1a and CW4b, when aircraft get closer U.S. Department of Transportation. Federal than 3 nautical miles from each other. The Aviation Administration, Office of Aviation unresolved conflict situation has been noticed in t he Policy & Plans, March 2004. TMA sectors for both scenarios, but only in the reorganized one this was registered for the en rout e Farmer, E. and A. Brownson. Review of Workload Measurement, Analysis and Interpretation sectors. Methods. CARE-Integra-TRS-130-02-WP2, Eurocontrol, 2003. Grimaud, I.; Hoffman, E.; Rognin, L.; Zeghal, K.: 6. FINAL REMARKS Spacing Instructions in Approach: Assessing Usability from the Air Traffic Controller The case study here presented allows deducing that Perspective. AIAA Guidance, Navigation and the division of sectors with high complexity functi on Control Conference, Austin, Texas, Aug. 2005. helps to lower the systemic complexity. This divisi on ICAO Circular 281. Outlook for Air Transport to the was attempted, together with task reassignment, by Year 2010. International Civil Aviation eliminating the planning controllers, and the numbe r Organization, 2000. of controllers was reduced from 18 to 12. However, some safety indicators, as the controller task load and Isaac, A., Ruitenber, B.: Air Traffic Control: Huma Performance Factors, 1999. the occurrence of conflicts, have shown that the NASA. "An Evaluation Of Air Traffic Control planning controllers have an important role in Complexity", report of NASA contract number conflict detection and resolution and, consequently , in the safety of the system. As a further study, it is NAS2-14284, Advanced Air Transportation Technology (AATT), Program, 31 October suggested to analyze the possibility of using a 1996. planner controller who would dynamically switch his Paternò, F.; Santoro, C.; Tahmassebi, S.: Formal presence between sectors as the sectors complexity Models for Cooperative Tasks: Concepts and an functions vary along time. Application for En-Route Air Traffic Control, 1998. RAMS Plus User Manual, Version 5.26, ISA ACKNOWLEDGEMENTS Software, March 2006. TranDac H.; Baptiste P.; Duong V.: The authors thank FDTE (Foundation for the Optimized Sectorization of Airspace with Technological Development in Engineering – São Constraints. Proceedings of the 5th Europe/USA Paulo) by the grant provided, and ISA Software for ATM R&D Seminar, Budapest, Hungary, conceding the University License of RAMS Plus for the University of São Paulo and also for the suppor t October 2003. on the use of the software. Vink, A. EATCHIP Medium Term Conflict Detection 1st USA / Europe ATM R&D Seminar. Disponível pela Web em http://atmseminar-97.eurocontrol.fr/vink.htm1997.
69
.
n
BALANCING THE AIR TRAFFIC CONTROL WORKLOAD THROUGH AIRSPACE COMPLEXITY FUNCTION Ítalo Romani de Oliveira, Renato Jorge Galvão Teixe
ira, Paulo Sérgio Cugnasca
Computer and Digital Systems Engineering Department Polytechnic School, University of São Paulo, Brazil
,
Abstract: The aircraft density in the airspace is g rowing, and this may increase the risk of accidents. In this sense, it is very important for air traffic controllers to work under safe traffic conditions, which include a good airspace d ivision. The goal of this paper is to study the use of a well known airspace complexity m easure, applied to evaluate air traffic controller workload in different organizations of t he airspace around São Paulo. The results obtained by simulating two different scenar ios show the relations between sector geometry, complexity and controller workload condit ions. Copyright © 2006 IFAC Keywords: Air Traffic Control, Human Factors, Safet Simulation
1. INTRODUCTION
y, Tasks, Complex Systems,
handle more aircraft, but safe communication and coordination still remain the system bottlenecks. Considering these limitations, this paper presents case study on airspace control organization, using he low level of automation, which is currently true in the most part of the airspaces through the world.
Between 1960 and 1999, the passenger air transportation made the number of passengers × kilometers increase 25 times. In the same period, t number of tons × kilometers of cargo air transportation increased 50 times. This increase represented, for the number of aircraft take-offs a nd landings, a 300% growth (ICAO, 2000).
a a
2. THE AIRSPACE SECTORIZATION
The airspace is divided into sectors in which human The air transportation demand increase expected for controllers are responsible for monitoring flights to the next years is substantial. Projecting the mean avoid conflicts and provide information to aircraft . value of the predicted increase rates (Eurocontrol, When designing the airspace sectors, not only the 2002; FAA, 2004; ICAO 2000), one can foresee for the year 2016 a traffic 45% greater than today. Wit hbalance of the number of aircraft per sector is the greater traffic activity, the aircraft density in important, but also the constraints following described (Trandac, Baptiste, Duong, 2003). airspace sectors will automatically increase. More and more controllers have to work with highly concentrated attention due to the greater number of managed aircraft, and to perform all the consequent control actions faster. This situation results in l ong periods of stressing high workload. There are two constraints for managing the high aircraft density in the airspace. The first one is the use of radio party line for communication between flight crew and ATCo. In this channel, one air traf fic controller cannot talk with a large number of pilot s at the same time. The second one is that, if one secto r is too small, the task of taking over and handing off the aircraft takes too high a proportion of the task execution time. Automation tools help the ATCo to
64
First IFAC Workshop on Multivehicle Systems
2.1 Convexity constraint The use of sectors with convexities that cross rout is not recommended. The aircraft route must not go through the same sector twice, as illustrated in Fig. 1.
This restriction aims to prevent the flight track f rom passing more than once through the same sector, to es avoid unnecessary workload. 3. SECTOR WORKLOAG BALANCING Besides the requirements for each individual sector the geometry of sectors and routes must enable a good workload distribution among the members of the Air Traffic Control team, avoiding keeping some operators unnecessarily more loaded of work than others.
Sector B Traffic line Sector A Sector boundary
Fig. 1. Convexity constraint.
,
Despite the difficulty in measuring the sector workload, some consistent studies have been 2.2 Minimum distance constraint performed to define reliable indicators for workloa d evaluation. An interesting overview of the existing There has to be a minimum distance between crossing the border of one sector and a route cross ingworkload measurement methods is available in the lines. The reason is that the controller must h ave(Farmer and Brownson, 2003). The present work time to resolve one possible conflict on this netwo rk adopted the calculation method of (NASA, 1996), which evaluates a number from several input node, as shown in Fig. 2. measures called complexity factors. Each complexity factor has an associated multiplier, which is appli ed Sector boundary in a formula to vary its weight. A brief descriptio n of the complexity factors is presented below. For more Traffic lines details on the complexity factors meaning and rationale, refer to (RAMS, 2006) and (NASA, 1996). Sector A Critical point (close to boundary) The complexity factors are: - Aircraft Count (ACT): This is a count of the Sector B number of aircraft within the boundaries of a secto r at an instant of time. Fig. 2. Minimum distance constraint. - Aircraft Density (DNS): Aircraft Density is the aircraft count divided by the usable amount of the 2.3 Minimum sector crossing time constraint sector area. - Airspace Structure (STR): It measures the A minimum permanence time (Tmin) of aircraft in conformance of the traffic flows with the geometry a sector has to be respected, in order to justify t he of the sector. radio frequency change and the sector transference - Climbing or Descending (CoD): This is a count of tasks. This constraint is shown in Fig. 3. the number of aircraft that are in climb or descent at an instant in time. Traffic line - Closest Points Approach (CPA): It weights the number of aircraft that are within a threshold separation of each other at any instant in time. Tmin Sector A - Aircraft Proximity to Sector Boundary (PRX): This is a count of the aircraft that are within a thresh old distance of a sector boundary at a given time insta nt. Sector B - Variance in Directions of Flight (VDF): This is a measure of the variability of heading of all of t he Fig. 3. Minimum sector crossing time constraint. aircraft in the sector at an instant in time. - Convergence Angle (ANG): This complexity factor 2.4 Connectivity constraint is a measurement of the severity of each conflict situation based on the conflict geometry. The sector must not be fragmented, but contiguous. - Conflict Near Sector Boundary (PRC): This is This is show in Fig. 4. a count of the predicted conflicts that will occur within a threshold distance of a sector boundary. - Aircraft Neighboring Conflict (NBR): This Sector A complexity factor is a count of other aircraft that are close to the area of the potential conflict. Sector B - Intent Knowledge (INT): The level of knowledge Sector A about the flight intents inside the sector. Fig. 4. Connectivity constraint.
65
First IFAC Workshop on Multivehicle Systems
The complexity function seems to keep a relation with the perceived workload by the Air Traffic Controller (ATCo), but there is no evidence that it keeps a direct relation with the time spent in cont tasks, so the control task load must be measured separately. Both of these measures are used in the case study of the next section.
Entry Clearance: For aircraft for which no flight strip is known, i.e., there is no previously known flight plan, a flight plan must be agreed. These aircraft may s. rolcome from external sectors or from internal airport ATCo, after verifying the non existence of conflict s, transmits a clearance for using the sector.
Flight Level Clearance: ATCo transmits an instruction to the aircraft, giving authorization t o use The complexity function is evaluated with a look a given altitude. ahead time, i.e., by predicting, at time t, the complexity function value for the time t + ∆. The complexity evaluation formula, in accordance with Horizontal Track Clearance: ATCo transmits an instruction to the aircraft, giving authorization t o (NASA, 1996), is: follow a given direction. 0.0172 x MAX(ACT(t),...,ACT(t+∆)) + 0.3280 x MAX(DNS(t),...,DNS(t+∆)) + Speed Clearance: ATCo transmits an instruction to 0.0498 x SUM(CPA(t),...,CPA(t+∆)) + the aircraft, ordering it to perform a given speed. 0.1070 x SUM(ANG(t),...,ANG(t+∆)) + 0.0426 x SUM(NBR(t),...,NBR(t+∆)) + Conflict Resolution: ATCo, after detecting a conflict, 0.0754 x SUM(PRX(t),...,PRX(t+∆)) + has to choose which aircraft will be penalized and 0.1134 x SUM(CoD(t),...,CoD(t+∆)) + which maneuver will be used: whether horizontal, 0.0709 x MAX(VDF(t),...,VDF(t+∆)) + vertical or just changing speed. 0.2000 x SUM(PRX(t),...,PRX(t+∆)) + 0.0676 x MAX(STR(t),...,STR(t+∆)) + Handoff: when an aircraft is leaving the sector, the 0.2564 x MAX(INT(t),...,INT(t+∆)) ATCo has to perform these tasks: instruct the pilot to where the expression STR(t),...,STR(t+∆), for instance, stands for a series of the complexity fac tor select another radio frequency, communicate the downstream sector to receive the aircraft. This values evaluated at intermediate steps between t and communication includes transmitting the flight stri p t+∆. manually or by some telecommunication line. A complementary view of workload measurement These are the common air traffic control tasks. was developed in the INTEGRA project (2003) Furthermore, many other tasks are required for the air traffic management, and the reader may refer to 4. AIR TRAFFIC CONTROLLER TASKS (Druart, Novales, 1997; Paternò, Santoro, Tahmassebi, 1998; Isaac, R, 1999) to find more Controlling air traffic requires the execution of information on this subject. several different tasks. Disregarding the use of automation tools, the most important tasks performe d In the air traffic control centers, there may be tw o by human operators are described as follows. controllers working in the same sector. One controller is called planning or strategic controll er, Handover: Receiving a flight into the sector involves who establishes medium term action plans to some sub-tasks for the ATCo. The first one is propitiate good conditions for the control tasks in receiving the flight strip (either on paper or his/her sector. The other controller is called exec utive electronically) when the aircraft is getting closer to or tactical controller, who communicates with the sector border, from the upstream sector, or fro m aircraft, transmits clearance instructions to the f light an internal airport. The flight strip gives to the ATCocrews, and manages the immediate situations. (Vink, the information necessary to be aware of the intend ed 1997). aircraft track inside the sector. When the neighbor ing sectors are managed by different control centers, t he receiver sector may be obliged to send an 5. CASE STUDY acknowledge message. Another task is to receive the pilot first call in that sector, to confirm that AT CoThe case study here described is an assessment of has assumed that aircraft. how the complexity function and weighted task execution counting can be used to guide the airspac e Conflict Search: ATCo verifies if the intended division and task distribution among air traffic trajectory of an aircraft is conflicting with anoth er controllers. The airspace around and inside the São one in his sector. Roughly saying, conflict is the Paulo Terminal Movement Area was used because it situation where two or more aircraft are horizontal ly is the main gateway city in Latin America. The less than 5 Nautical Miles and vertically less than simulation tool used was RAMS Plus version 5.24. 1,000 feet close to each other. This task is perfor med whenever an aircraft is entering the sector or has toThe goal is to study the airspace of Fig. 5 as a change its trajectory due to a prior detected confl ict. system, and to compare two distinct sector organizations for it. The simulated traffic was manually scheduled, trying to represent a reasonabl e
66
First IFAC Workshop on Multivehicle Systems
be aligned with new technical aspects of arrivals proportion of traffic contribution for each traffic zone present in Fig. 6, considering only flights un der management which can be found in (Grimaud et al., Instrument Flight Rules. This does not include flig hts 2005). that stay all the time inside the colored sectors o f The traffic of Fig. 6 was simulated by RAMS Plus in Fig. 5. this scenario, flowing through the white routes of En route sectors Fig. 7. São Paulo TMA B S1
BS2 SP4
CW3 N W
SP3
SP2
E S SP5
S P1
Fig. 5. Current Airspace sectors around São Paulo.
C W3
Fig. 7. Sector geometry of the baseline scenario. The complexity functions were evaluated along the time of a day for each sector, and they are plotted in Fig. 8 for the most complex sectors. In this figure , the vertical axis scale was restricted above the 1, 000 complexity function value, because this value was noted to be a kind of division between the critical sectors and the non-critical sectors. The most crit ical sectors were considered to be those which have many peaks far from the 1,000 line.
5.1 Baseline scenario definition and evaluation
In this section, the definition of the baseline sce nario is defined and evaluated. Firstly, the sector geome try is considered. The sector geometry and route structure assumed is depicted in Fig. 7. This secto r geometry is different from the actual geometry currently in practice, presented in Fig. 5. The geometry presented in Fig. 5 is designed in that fo rm because of, among other reasons, the use of groundbased navigation aids, such as VOR and DME. However, as the use of satellite navigation becomes more accurate and reliable, the sector geometry becomes less dependent on ground references, and can be better designed in order to conform to the traffic flows. In this configuration, there is one sector less than in the actual airspace division of Fig. 5 .
2500 BS1 BS2 CW 3
2000
complexity
Fig. 6. IFR flights per traffic zone in 24 hours.
CW 4 SP4 SP5
1500
1000
0
5
10
15
20
hours
Fig. 8. Complexity functions for the baseline The reasons for the new geometry in this scenario scenario (non-critical sectors are not shown). are: to lower the handover/handoff tasks for the flights in the Rio-São Paulo corridor, and for the The systemic complexity, which was evaluated by traffic departing from Guarulhos airport; and also to calculating the area under all curves of the graph, 67
First IFAC Workshop on Multivehicle Systems
The systemic complexity for this scenario, evaluate d including the non-critical sectors, resulted in the in the same way as in the baseline scenario, result ed value 58,253, which implies in a mean complexity in the value 45,361, which implies a mean value of 270.94 per hour for each sector (9 sectors overall), during the 24 hours of the day. Looking a tcomplexity value of 157.50 per hour for each sector y. Fig. 8, it is possible to identify that the most cr itical(12 sectors overall), during the 24 hours of the da Looking at Fig. 10, it is possible to see that ther e are sectors are BS1, CW3 and CW4. By the concept of the complexity function, these sectors will challenge t he less complexity peaks than in Fig. 8. Although the sector BS1b presented a higher peak around 19:00, the ATCo with the most difficult situations. peaks are less often than in the baseline scenario. 5.2 Reorganized scenario With more spaced peaks, it is possible to manage th e complexity with safe conditions, because the In the reorganized scenario, the critical sectors complexity function is evaluated, at least in the identified in the baseline scenario were divided simulation scenarios, with 40 minutes look ahead (resulting in BS1a, BS1b, CW3a, CW3b, CW4a and time. In this period, it is possible to comfortably take CW4b), obtaining the sector geometry shown in Fig. corrective actions to avoid the high complexity 9. The same traffic schedule was again simulated to situations, as it is done in the RAMS Plus software . obtain the complexity functions shown in Fig. 10. 5.3 Task load indicators The ATCo workload is not directly proportional to the complexity function. One reason for this is tha complexity function is evaluated with a 40-minute look ahead, which allows an inter-sector coordinato to manage the situation to deliver the traffic in l complex conditions for the tactical air traffic controller, reducing his/her workload. RAMS Plus software allows associating weights to each ATCo task and counting the task execution. The sum of th ATCo tasks for all the day, for the baseline and th reorganized scenarios, are shown in Figs. 11 and 12 respectively.
BS1a BS1b BS2 SP4
CW3b
SP3
SP2 SP5 SP1
CW3a
35000 30000 25000 Workload
CW4b CW4a
20000 15000 10000 5000
Fig. 9: Reorganized sector geometry.
BS1b CW 3a
SP_5
SP_4
SP_3
SP_2
SP_1
30000 Workload
CW 4b SP4 SP5
1500
CW_4
35000
CW 3b 2000
Tactical ATCo
Fig. 11: Total ATCo workload of baseline scenario.
BS2
2500
CW_3
Planning ATCo
3000
25000 20000 15000 10000 5000 SP_5
SP_4
SP_3
SP_2
SP_1
20
CW_4b
15
CW_4a
10
CW_3b
5
BS_2
0
CW_3a
1000
BS_1b
0 BS_1a
complexity
BS_2
BS_1
0
hours
Fig. 10: Complexity functions of the reorganized scenario.
Fig. 12: ATCo workload of reorganized scenario. The workload is counted by a weight attributed to each of the tasks described in section 4. Following 68
t r ess
e e ,
First IFAC Workshop on Multivehicle Systems
the default configuration of RAMS Plus, these weights keep a relation with the time (in seconds) spent to execute each task. In Fig. 11, one can not workload concentration in the airport sectors (SP4 and mostly in SP5). The airport sectors cannot be divided owing to their small size, so the ATCo has be very good to work in these sectors. Despite the high complexity evaluated for sectors CW3 and CW4, these sectors present the smallest workload in the system. This reason motivated not to employ planning controllers in the reorganized scenario.
GLOSSARY e aATCo FAA toDME ICAO IFR RAMS
Air Traffic Controller Federal Aviation Administration, U.S. Distance Measurement Equipment International Civil Aviation Organization Instrument Flight Rules Reorganized Mathematical ATC Simulator Terminal Movement Area VHF Omnidirectional Range
In the reorganized scenario, which has only tactica l TMA controllers, one can note that the number of VOR controllers is smaller (12 against 18 of the baseli ne scenario). However, the mean controller task load REFERENCES has raised, which is not good from the safety point of view. The baseline scenario resulted in a mean Druart, J.; Novales, L.: En route Control Standards workload per controller of 10,132, while the Mefisto Report WP1-4, November 1997. reorganized scenario resulted in 11,071. The higher Eurocontrol. Medium-Term Forecast of Annual workload seems to impact the traffic safety. Number of IFR Flights (2002 - 2009). Although no deep safety analysis was performed in Eurocontrol - Air Traffic Statistics and Forecasts this study, it was noted that only the reorganized (STATFOR), Volume 1, March 2002. scenario presented unresolved conflicts in the en FAA Aerospace Forecasts - Fiscal Years 2004-2015. route sectors BS1a and CW4b, when aircraft get closer U.S. Department of Transportation. Federal than 3 nautical miles from each other. The Aviation Administration, Office of Aviation unresolved conflict situation has been noticed in t he Policy & Plans, March 2004. TMA sectors for both scenarios, but only in the reorganized one this was registered for the en rout e Farmer, E. and A. Brownson. Review of Workload Measurement, Analysis and Interpretation sectors. Methods. CARE-Integra-TRS-130-02-WP2, Eurocontrol, 2003. Grimaud, I.; Hoffman, E.; Rognin, L.; Zeghal, K.: 6. FINAL REMARKS Spacing Instructions in Approach: Assessing Usability from the Air Traffic Controller The case study here presented allows deducing that Perspective. AIAA Guidance, Navigation and the division of sectors with high complexity functi on Control Conference, Austin, Texas, Aug. 2005. helps to lower the systemic complexity. This divisi on ICAO Circular 281. Outlook for Air Transport to the was attempted, together with task reassignment, by Year 2010. International Civil Aviation eliminating the planning controllers, and the numbe r Organization, 2000. of controllers was reduced from 18 to 12. However, some safety indicators, as the controller task load and Isaac, A., Ruitenber, B.: Air Traffic Control: Huma Performance Factors, 1999. the occurrence of conflicts, have shown that the NASA. "An Evaluation Of Air Traffic Control planning controllers have an important role in Complexity", report of NASA contract number conflict detection and resolution and, consequently , in the safety of the system. As a further study, it is NAS2-14284, Advanced Air Transportation Technology (AATT), Program, 31 October suggested to analyze the possibility of using a 1996. planner controller who would dynamically switch his Paternò, F.; Santoro, C.; Tahmassebi, S.: Formal presence between sectors as the sectors complexity Models for Cooperative Tasks: Concepts and an functions vary along time. Application for En-Route Air Traffic Control, 1998. RAMS Plus User Manual, Version 5.26, ISA ACKNOWLEDGEMENTS Software, March 2006. TranDac H.; Baptiste P.; Duong V.: The authors thank FDTE (Foundation for the Optimized Sectorization of Airspace with Technological Development in Engineering – São Constraints. Proceedings of the 5th Europe/USA Paulo) by the grant provided, and ISA Software for ATM R&D Seminar, Budapest, Hungary, conceding the University License of RAMS Plus for the University of São Paulo and also for the suppor t October 2003. on the use of the software. Vink, A. EATCHIP Medium Term Conflict Detection 1st USA / Europe ATM R&D Seminar. Disponível pela Web em http://atmseminar-97.eurocontrol.fr/vink.htm1997.
69
.
n