APPLICATION OF DIGITAL CELLULAR RADIO FOR MOBILE LOCATION ESTIMATION IIUM Engineering Journal, Vol. 24, No. 1, 2023 Medianto et al. https://doi.org/10.31436/iiumej.v24i1.2223 TERMINAL CONTROL AREA COMPLEXITY MEASUREMENT USING SIMULATION MODEL RULLY MEDIANTO1,2*, NAFLAH MUTIARA ADINDA1, YAZDI IBRAHIM JENIE1, HISAR MANONGAM PASARIBU1 AND HARI MUHAMMAD1 1 Faculty of Mechanical and Aerospace Engineering, Bandung Insitute of Technology, Bandung, Indonesia 2 Faculty of Aerospace Technology, Institut Teknologi Dirgantara Adisutjipto, Yogyakarta, Indonesia * Corresponding author: hmpasaribu@itb.ac.id (Received: 9th November 2021; Accepted: 18th June 2022; Published on-line 4th January 2023) ABSTRACT: Traffic density in the terminal control area will increase flight safety risks. One effort to reduce the risk is to minimize the controller’s workload when affected by air traffic complexity. This research uses a simulation model to measure air traffic complexity in terminal control areas. The aircraft performance model has been constructed from ADS- B data and represents the aircraft movement in the terminal control area of Soekarno-Hatta International Airport. The simulation model can detect and resolve conflicts to keep separations between aircraft at a specified minimum separation limit. Air traffic complexity measurement uses several indicators, i.e., aircraft density, number of climbing and descending aircraft, aircraft type mixing, conflict control, aircraft speed difference, and controller communication. The weighting factor for each indicator has been obtained from Jakarta Air Traffic Service Center (JATSC) controller perception using an analytic hierarchy process. The simulation results show that the variation of resolution type affects the complexity level significantly. The results of this study can be used as consideration for improving air traffic control procedures and air space structures. ABSTRAK: Kepadatan trafik di kawasan terminal kawalan bakal menyebabkan peningkatan risiko keselamatan penerbangan. Salah satu cara bagi mengurangkan risiko adalah dengan meminimumkan beban kerja pengawal yang terlibat dengan kesesakan trafik udara. Kajian ini menggunakan model simulasi bagi mengukur kesesakan trafik udara di kawasan terminal kawalan. Model pretasi pesawat telah dibina menggunakan data ADS-B dan ini mewakili pergerakan pesawat di terminal kawalan lapangan terbang antarabangsa Soekarno-Hatta. Model simulasi ini dapat mengesan konflik dan membuat resolusi bagi mengekalkan penjarakan antara pesawat mengikut had penjarakan minimum yang ditetapkan. Beberapa indikator telah digunakan bagi mengukur kerumitan trafik udara, iaitu: ketumpatan pesawat, bilangan pesawat mendaki dan menurun, jenis pesawat, kawalan konflik, perbezaan kelajuan pesawat dan pengawal komunikasi. Faktor pemberat bagi setiap indikator telah diperoleh daripada pengawal persepsi Pusat Servis Trafik Udara Jakarta (JATSC) menggunakan proses analisis hierarki. Keputusan simulasi menunjukkan pelbagai jenis resolusi mempengaruhi tahap kerumitan dengan ketara. Hasil kajian ini boleh digunakan bagi menambah baik prosedur kawalan trafik udara dan struktur ruang udara. KEYWORDS: terminal control; air traffic complexity; simulation model; analytic hierarchy process 199 IIUM Engineering Journal, Vol. 24, No. 1, 2023 Medianto et al. https://doi.org/10.31436/iiumej.v24i1.2223 1. INTRODUCTION The Terminal Control Area (TCA) is airspace with the most complex and dense system compared to other airspace sectors. Three modes of flight operations run simultaneously: arrival, departure, and cross-flight [1]. TCA is highly sensitive to changes in traffic, weather, flight procedures, runway used, and other unusual events. The assessment of the system's performance is important and is affected by the system's complexity [2]. The air traffic complexity rate can be very high due to the traffic intensity and patterns in mutual interactions between different traffic flows and individual aircraft in the TCA. The increasing complexity of the TCA will increase the complexity of controller tasks and result in increased workload [3]. The management of traffic flow and airspace can be carried out correctly to avoid excessive controller workload if the measurement and prediction of air traffic complexity can be carried out accurately [4]. Table 1: Air traffic complexity indicators summary Air Traffic Complexity Indicators Arad [8] Grossberg [9] Mogford et al. [10] Pawlak et al. [11] Laudeman et al. [12] Majumdar et al. [13] Chatterji et al. [14] Koros et al. [15] Diaconu et al. [4] Sector design/geometry √ √ √ Aircraft density/volume √ √ √ √ √ √ Aircraft speed difference √ √ √ √ √ Emergency operations √ Altitude change √ √ √ √ √ The horizontal distance between aircraft √ √ √ √ √ The vertical distance between aircraft √ √ √ √ Aircraft type mixing √ √ √ √ √ √ Frequency of ATCo's communication √ √ √ Flight entering and exiting the sector √ Potential conflict control √ √ √ √ Number of cruising aircraft √ √ √ Number of climbing/descending aircraft √ √ √ √ √ √ √ Weather condition √ √ √ Aircraft heading change/difference √ √ Special flight √ √ √ √ Radiofrequency congestion √ √ Number of intersecting airways √ Restricted airspace √ √ The proximity of sector boundary √ √ ATC's procedure √ √ It is necessary to consider the interactions between individual aircraft and their flight characteristics to determine complexity more precisely. The interactions involve possible conflicts and the tendency for aircraft movement to converge at one point. [5]. There are at 200 IIUM Engineering Journal, Vol. 24, No. 1, 2023 Medianto et al. https://doi.org/10.31436/iiumej.v24i1.2223 least nine references from Mogford et al., Diaconu et al. and Dervic and Rank (2 that mention various indicators of complexity [4,6,7]. Table 1 shows the summary of some complexity indicators. Air traffic complexity can be measured by experts, who have experience controlling air traffic under various conditions, or by the complexity indicator obtained from air traffic data and the number of interactions between aircraft in a particular sector [16]. Another method to determine air traffic complexity is using a dynamic weighted network model. The nodes represent aircraft, waypoints, and airways in the network model. The total weights of all network edges represent air traffic complexity [17]. Andrasi et al. used an artificial neural networks model to estimate air traffic complexity. The best configuration of artificial neural networks was determined by a genetic algorithm [18]. This research developed an ATM model simulation in the TCA using MATLAB to reflect aircraft movements and the air traffic control process. The simulation model is then used to analyze air traffic complexity in the TCA, specifically at Soekarno-Hatta International Airport, Jakarta. The simulation model has several advantages in describing the air traffic system and its complexity, i.e. the movement of the aircraft can be visualized to analyze and validate it [19]. The simulation parameters also can be modified easily to obtain various scenarios. 2. MODELLING 2.1 System Description The ATM system model represents arrival and departure operations for Runway 25R and 25L on Soekarno-Hatta International Airport - Jakarta (JAKARTA). The arriving aircraft will enter the TCA from the en-route airspace through the transition points. The aircraft then fly towards the runway following a specific trajectory profile defined by some waypoints. The arrival trajectory profile refers to Standard Terminal Arrival Routes (STAR). The departing aircraft enter the TCA from the aerodrome control area and fly to the airway following the Standard Instrument Departure trajectory profile (SID). The information about STAR and SID can be accessed in the Aeronautical Information Regulation and Control (AIRAC) as a supplement to the Aeronautical Information Publication (AIP) published by the Directorate General of Civil Aviation (DGCA) of Indonesia [20]. With Flight Management Computer (FMC) support, aircraft can automatically fly along with these profiles. Under certain circumstances, the aircraft must follow the instruction from the air traffic controller containing the aircraft's direction (heading), altitude, and speed changes, often referred to as vectoring. Arrival operations have more significant conflict potential than departures because aircraft have trajectories that converge, especially when entering a merging point. Aircraft speed on arrival will also experience a reduction so that the aircraft in front tends to be overtaken by the aircraft behind it. The departure model has a smaller potential for conflict than the arrival because the trajectory tends to be diverging. Potential conflicts with arriving aircraft are also minimal due to differences in altitude and flight path. There are six TCA sectors related to Runway 25R and 25L are modeled in this study: Jakarta Lower Control North (LN), Jakarta Lower Control Center (LC), Jakarta Lower Control East (LE), Jakarta Terminal West (TW), Jakarta Terminal East (TE), and Jakarta Terminal South (TS). Each sector has boundaries described by latitude-longitude, altitude/flight level, and radius from ATC head radar. Information about the boundaries can 201 IIUM Engineering Journal, Vol. 24, No. 1, 2023 Medianto et al. https://doi.org/10.31436/iiumej.v24i1.2223 be accessed in Standard Operating Procedures Air Traffic Services Approach Control Service published by Airnav Indonesia Branch of Jakarta Air Traffic Service Center (JATSC) [21]. The arrival traffic model has six trajectory profiles, and the departure traffic model has ten. Complete trajectory profiles and TCA sectors related to the Runway 25R and 25L air traffic model are shown in Fig . 1. Fig. 1: Trajectory profiles and TCA sectors related to Runway 25R and 25L. 2.2 Air Traffic Model The air traffic model was built using MATLAB software by combining discrete-event and agent-based models. The wind speed model was added as an environmental element influencing the system. The wind speed consists of wind velocity and direction represents the weather condition. Each aircraft has a fixed parameter that will not change during simulation: aircraft type and trajectory profile based on SID/STAR. There are also dynamic parameters that will change during simulation; these parameters are: ▪ Position: In the form of local NED (North, East, Down) coordinates with a reference point at the NOKTA waypoint (X, Y, Z); ▪ Airspeed: Airspeed in the local NED direction (Vx, Vy, and Vz); ▪ Waypoint: present the waypoint to which the aircraft is headed; ▪ Heading: Heading aircraft relative to local north; ▪ Distance to waypoint: the distance of the aircraft to the next waypoint; ▪ Right of Way: priority of aircraft when heading/being on the same track; ▪ Conflict status: free from conflict or not; ▪ Resolution: selected conflict resolution mode (vectoring, speed control, or altitude control); ▪ TAS and GS: aircraft true airspeed and ground speed; ▪ Vertical speed: vertical aircraft speed when climbing (+) or descent (-); and ▪ RADAR radius: radius from RADAR. Each trajectory has a unique profile based on STAR and SID published in Aeronautical Information Publication (AIP) [20]. Each aircraft will move along the trajectory with the 202 IIUM Engineering Journal, Vol. 24, No. 1, 2023 Medianto et al. https://doi.org/10.31436/iiumej.v24i1.2223 waypoint as profile guidance. The aircraft distance relative to the waypoint is obtained by the equation [22]: 𝑑𝑥𝑡 = 𝑋𝑤𝑝 − 𝑋𝑡 (1) 𝑑𝑦𝑡 = 𝑌𝑤𝑝 − 𝑌𝑡 (2) 𝑑𝑧𝑡 = 𝑍𝑤𝑝 − 𝑍𝑡 (3) 𝑑𝑠,𝑡 = √(𝑑𝑥𝑡 ) 2 + (𝑑𝑦𝑡 ) 2 + (𝑑𝑧𝑡 ) 2 (4) With, Xt, Yt, Zt: aircraft coordinates on each coordinate axis; Xwp, Ywp, Zwp: waypoint coordinates on each coordinate axis; dxt, dyt, dzt: distance to the waypoint on each coordinate axis; ds,t: aircraft distance relative to the waypoint. After getting the distance relative to the waypoint using the above equation, then the heading angle can be calculated relative to the waypoint (𝜃𝑡 ) apply the equation: 𝜃𝑡 = tan −1 𝑑𝑥𝑡 𝑑𝑦𝑡 (5) The aircraft heading angle relative to the waypoint is used to calculate the relative aircraft speed on each axis (Vx, Vy, Vz) using the equation below: 𝑉𝑥𝑡 = { = 𝑉𝑡 cos 𝜃𝑡 , 𝑑𝑧𝑡 = 0 ≠ (√𝑉𝑡 2 − 𝑉𝑧𝑡 2) cos 𝜃𝑡 𝑑𝑧𝑡 ≠ 0 (6) 𝑉𝑦𝑡 = { = 𝑉𝑡 sin 𝜃𝑡 , 𝑑𝑧𝑡 = 0 ≠ (√𝑉𝑡 2 − 𝑉𝑧𝑡 2) sin 𝜃𝑡 𝑑𝑧𝑡 ≠ 0 (7) 𝑉𝑧𝑡 = { = 0, 𝑑𝑧𝑡 = 0 ≠ 0 𝑑𝑧𝑡 ≠ 0 (8) Furthermore, it can be determined the position of the aircraft for each axis at a time (t + 1) through the equation: 𝑋𝑡+1 = (𝑉𝑥𝑡 ∗ 𝛿𝑡) + 𝑋𝑡 (9) 𝑌𝑡+1 = (𝑉𝑦𝑡 ∗ 𝛿𝑡) + 𝑌𝑡 (10) 𝑍𝑡+1 = (𝑉𝑧𝑡 ∗ 𝛿𝑡) + 𝑍𝑡 (11) The separation between aircraft is maintained by using conflict detection and resolution models. It is necessary to calculate horizontal (dhor) and vertical (dver) separations between aircraft (a and b) to check whether the separation between aircraft is still safe (does not exceed the minimum limit) using the following equation: 𝑑ℎ𝑜𝑟 = √(𝑋 𝑎 − 𝑋𝑏 )2 + (𝑌𝑎 − 𝑌𝑏 )2 (12) 𝑑𝑣𝑒𝑟 = √(𝑍 𝑏 − 𝑋𝑎 )2 (13) X, Y, and Z are the aircraft coordinates a and b on each coordinate axis. After the separation between aircraft is known, whether the separation is still safe or if there has been a potential conflict (smaller than the specified minimum separation buffer) can be checked. Conflicts at TCA more often occur when the plane is heading to the merging point. If several aircraft experience conflict, it will be determined which aircraft gets the Right of Way (ROW) based on the closest distance to the merging point. The aircraft that gets the first ROW continues to fly following the specific trajectory without resolving conflict. The other aircraft should make specific maneuvers as part of conflict resolution. 203 IIUM Engineering Journal, Vol. 24, No. 1, 2023 Medianto et al. https://doi.org/10.31436/iiumej.v24i1.2223 The flowchart of the aircraft's movement in the simulation model is shown in Fig. 2. The model has three conflict resolution modes: vectoring, airspeed control, and altitude control. Fig. 2: Flow chart of aircraft movements in the simulation model. 2.3 Flight Parameter Model Flight parameter models were extracted from ADS-B data provided by FlightRadar24. The ADS-B data was collected from more than 38800 flights that departed and landed at Soekarno-Hatta International Airport, Jakarta [23]. The models included five aircraft flight parameters: Airbus A320, Boeing B737, Airbus A330, Boeing B777, and Boeing B787. Machine learning was used to handle a large quantity of ADS-B data to identify the phase of the flight and the time when the aircraft flew across specific waypoints. One advantage of this technique is that ADS-B data can be efficiently and cost-effectively gathered over the internet. ADS-B data was combined with weather data from Aviation Meteorological Information System in Meteorological, Climatology, and Geophysical Agency (BMKG) to develop the flight parameter model [24]. The waypoints used for analysis were the waypoints flown by aircraft using Runway 25R and 25L for departing and arriving. The waypoint information was obtained from the Soekarno Hatta International Airport Terminal Chart published by the Indonesia DGCA [20]. The K-Nearest Neighbor algorithms processed flight parameters from ADS-B data when the aircraft had the nearest position to specific waypoints within a radius of 1 NM. The altitude and the vertical speed were processed straight from the ADS-Data. The true airspeed was generated from the ground speed and wind speed data from weather data. Three probability distribution functions (Normal Distribution, Beta Distribution, and Gamma Distribution) approached flight parameter models using maximum likelihood estimation. The best distribution was determined using Kolmogorov-Smirnov Test. The flight parameters for this research used the mean value from the models. Validation was carried out on previous research by comparing the estimated parameters with flight 204 IIUM Engineering Journal, Vol. 24, No. 1, 2023 Medianto et al. https://doi.org/10.31436/iiumej.v24i1.2223 parameters from the Eurocontrol Aircraft Performance Database [25] and the reference parameter from the JAKARTA (WIII) Terminal Chart issued by the Indonesia Directorate General of Civil Aviation [26]. Table 2 shows some of the aircraft's flight parameter models when flying by specific waypoints. Table 2: Flight parameter models for each type of aircraft when flying by waypoints 2.4 Air Traffic Complexity The calculation of air traffic complexity at TCA begins by determining the indicators affecting the complexity and relevance of the airspace sector. After that, the weight of each indicator needs to be determined using the analytic hierarchy process (AHP) method. The most mentioned indicators relevant to the TCA airspace sector can be selected based on Table 1. Hence, air traffic complexity measurement in this paper uses seven indicators: air traffic density (Nin), number of climbing (Nclb) and descending (Ndes) aircraft, aircraft type mixing (Ttyp), potential conflict control (crossing and overtaking conflict, Ttrf), aircraft speed difference (Tspd), and frequency of controller's coordination or communication (Tcom). The weight of each indicator was obtained using an analytic hierarchy process (AHP) method. Waypoint Altitude (feet) True Airspeed (knot) Vertical Speed (m/s) A320 B737 A330 B777 B787 A320 B737 A330 B777 B787 A320 B737 A330 B777 B787 ABILO 35834 35834 35834 35834 35834 436 436 436 436 436 836 836 836 836 836 AJUNA 7758 7339 7131 6492 7208 287 283 279 285 278 2524 2473 2484 2115 2283 ALAMO 38228 38228 38306 38228 38228 450 450 450 477 477 425 425 425 524 524 ARKAP 2227 2215 2368 2350 2300 190 180 187 190 183 216 239 263 366 305 BUNIK 22799 22771 21798 22370 22788 395 373 389 389 391 1838 1630 1619 1543 1562 CA 10107 10929 11161 11161 11161 455 454 454 455 455 777 696 1193 1193 1193 CARTA 14032 14032 14032 13618 14101 314 336 335 330 326 1558 1789 1465 1396 1407 CORIL 8482 9483 9483 9483 9483 464 458 458 458 16 890 1096 1096 1096 1096 DAPIK 1262 1310 1320 1381 1291 217 214 208 212 207 371 560 384 601 397 DENDY 24419 24419 24124 23850 24169 378 378 390 392 393 1259 1259 1389 1554 1511 DOLTA 27591 29814 28087 26436 32599 446 453 443 470 473 1007 1171 1377 1479 1918 ESALA 3316 3317 3371 3627 3489 288 286 285 286 282 1271 1256 1227 1141 1251 FRIDA 32125 33409 33409 33409 33409 463 456 463 463 463 776 731 776 776 776 GAPRI 6909 6682 6912 7103 7335 375 365 378 375 362 1699 1538 1494 1105 1119 GASPA 9155 9335 8314 9020 9056 449 384 430 429 431 1180 775 1714 1750 992 HLM 4093 4203 4363 4620 4620 354 361 20 361 374 1987 2263 2093 3375 3375 IMU 8813 8765 8964 9254 9254 425 423 424 438 438 1350 1450 1554 1823 1823 KURUS 9418 8932 9079 9273 9273 447 432 460 469 469 2046 1715 1961 2338 2338 LARAS 21015 21015 19140 22357 19524 397 397 402 434 405 1778 1778 1578 1968 1856 LEPAS 29868 29868 29868 34739 31644 457 457 457 480 483 874 874 874 211 1040 NABIL 4425 4217 4121 4298 4298 356 346 350 350 367 2111 2259 1924 2338 2338 NADIN 7942 7670 7944 8185 8185 405 394 401 399 399 1731 1554 1591 1362 1362 NOKTA 11931 12443 12207 12385 13008 290 284 290 295 295 1403 1185 1185 1136 1132 PRIOK 3537 3712 3395 3425 3309 218 216 215 212 211 643 802 582 648 663 PW 7538 7847 9144 9144 9144 433 423 423 437 437 1235 966 1738 1738 1738 RAMAL 17531 17660 16867 17489 17576 342 329 338 343 345 1636 1426 1543 1535 1559 RAMBU 6905 6998 6768 7241 7202 255 256 259 262 262 1050 1112 1005 1088 1186 RATIH 15998 15998 16475 18685 16634 377 377 373 400 379 2034 2034 1737 2071 1870 SIKAD 18000 17300 17675 17675 17675 358 362 414 414 414 2100 2300 2800 2800 2800 TEGAR 2122 2146 2110 2115 2031 247 249 245 247 246 902 1027 813 899 857 WETES 5062 4967 5088 5284 5043 331 327 333 330 320 1596 1511 1604 1453 1335 WINAR 6157 5757 5409 5016 5271 278 274 264 273 271 2635 2595 2535 2144 2429 205 IIUM Engineering Journal, Vol. 24, No. 1, 2023 Medianto et al. https://doi.org/10.31436/iiumej.v24i1.2223 The first step in the AHP method was to collect input data with pairwise comparisons of the indicators. Complexity indicators were paired with a rating that determined which indicator was more critical. This data was collected by a survey involving respondents from experts and practitioners. The questionnaire was created to assess the relative importance (weight value) of the target respondents, experts, and air traffic controllers from Airnav Indonesia, notably the Jakarta Air Traffic Services Center (JATSC). The questionnaire was filled out by respondents using online media. The second step was to average the input comparison values using the Row Geometric Mean Method (RGMM). The average ri value was determined using the following equation. 𝑟𝑖 = 𝑒𝑥𝑝 [ 1 𝑁 ∑ 𝑙𝑛(𝑎𝑖𝑗 ) 𝑁 𝑗=1 ] = (∏ 𝑎𝑖𝑗 𝑁 𝑖=1 ) 1 𝑁 (14) The comparison matrix A = aij with dimensions of N×N, N = 7 (number of indicators). The third step was calculating the first eigenvector of matrix A (Eigen 1 in the E1 matrix). The fourth step was calculating the second eigenvector (Eigen 2 in the E2 matrix). Then proceed to the fifth step, calculating the difference between E1 and E2. The sixth step was to assess the consistency of the respondent's answers in the following way. a. Calculate the Weighted Sum Vector (WSV) by multiplying the rows of matrix A by matrix E1. b. Divide each element of the WSV matrix by each element of the E1 matrix to obtain the Consistency Vector (CV). c. Calculate the lambda (λ) by averaging CV and calculating the Consistency Index (CI) using the following equation. 𝐶𝐼 = 𝜆−𝑁 𝑁−1 (15) d. Divide CI by the Random Consistency Index to get the Consistency Ratio (CR) (RI). 𝐶𝑅 = 𝐶𝐼 𝑅𝐼 (16) Table 3 shows the RI value for a given N value. Table 3: Random consistency index [4]. N 1 2 3 4 5 6 7 8 9 10 RI 0 0 0,58 0,9 1,12 1,24 1,32 1,41 1,45 1,49 After knowing the value of each indicator and its weight of importance, the general equation for the function of air traffic complexity at TCA can be calculated. 𝐹𝑐 = (𝑊𝑖𝑛 × 𝑁𝑖𝑛 ) + (𝑊𝑐𝑙𝑏 × 𝑁𝑐𝑙𝑏 ) + (𝑊𝑑𝑒𝑠 × 𝑁𝑑𝑒𝑠 ) + (𝑊𝑡𝑦𝑝 × 𝑇𝑡𝑦𝑝) + (𝑊𝑟𝑡𝑓 × 𝑇𝑟𝑡𝑓 ) + (𝑊𝑠𝑝𝑑 × 𝑇𝑠𝑝𝑑 ) + (𝑊𝑐𝑜𝑚 × 𝑇𝑐𝑜𝑚 ) (17) Air traffic complexity values were calculated for several scenarios. The scenarios were varied on the inter-arrival time (IAT) and the percentage of possible resolution types (speed control, altitude control, and vectoring). 3. RESULTS AND DISCUSSION The simulation animation is used to validate the model by observing the model's behavior while the simulation is running [27]. Some entities (aircraft) are observed moving from the time they enter the system to the time they leave the system to determine whether 206 IIUM Engineering Journal, Vol. 24, No. 1, 2023 Medianto et al. https://doi.org/10.31436/iiumej.v24i1.2223 the aircraft moves correctly according to the predetermined modeling concept and whether the conflict detection and resolution have been applied. The animation observations show that the entity followed its arrival and departure trajectory according to the predetermined trajectory route. The data validation and operational graphics results also show that the model correctly implemented the applied conflict detection and resolution. No aircraft violated the minimum separation rules when the simulation ran in normal conditions. The waypoint modeling for Soekarno-Hatta International Airport is based on local coordinates of NED with NOKTA as a reference point. The model is used to simulate the air traffic in the TCA, especially at JAKARTA TCA for Runway 25R and 25L. The model visualized 2-dimensional forms that moved for each unit of time. Flight parameters were estimated from ADS-B data for specific waypoints. The movement of the aircraft in the simulation were repreented by a green dot when there was no conflict, a yellow dot when there was a potential conflict, and a red dot when there was a conflict with other aircraft. The simulation had twelve entry points and ran for 7200 units or the equivalent of 7200 seconds. There were three scenarios, each was running for just one resolution mode choice to solve the traffic conflict. Air traffic complexity was measured by calculating seven complexity indicators recorded during the simulation. Visualization of the simulation model is shown in Fig. 3. A total of 119 questionnaire respondents from the JATSC controller provided information to determine which complexity indicator was more critical among the indicators that have been paired. The questionnaire results were then processed by AHP to obtain the weighting value for each indicator, as shown in Table 4. Fig. 3: Visualization of the simulation model with aircraft movement representation. The level of consistency (Consistency Ratio) of the AHP was 0.72% which means that the answers were consistent (CR < 10%). This CR value indicated that the weighting values that were obtained could be used in the simulation. As shown in the table, the indicator that most influences complexity is the potential conflict control for crossing and overtaking conflict, with a weighting value of 38.87%. The indicator with the smallest weighting value 207 IIUM Engineering Journal, Vol. 24, No. 1, 2023 Medianto et al. https://doi.org/10.31436/iiumej.v24i1.2223 is the number of climbing aircraft (8.99%). Thus, the general function for air traffic complexity at JAKARTA TCA can be written as follows. 𝐹𝑐 = (0.1218 × 𝑁𝑖𝑛 ) + (0.0899 × 𝑁𝑐𝑙𝑏 ) + (0.1078 × 𝑁𝑑𝑒𝑠) + (0.1015 × 𝑇𝑡𝑦𝑝) + (0.3887 × 𝑇𝑟𝑡𝑓 ) + (0.0924 × 𝑇𝑠𝑝𝑑 ) + (0.0980 × 𝑇𝑐𝑜𝑚 ) (18) Table 4: Weighting value of each complexity indicator Complexity Indicators Weighting Value Air Traffic Density (𝑁𝑖𝑛) 12.18% Number of Climbing Aircraft (𝑁𝑐𝑙𝑏 ) 8.99% Number of Descending Aircraft (𝑁𝑑𝑒𝑠) 10.78% Aircraft Types Mixing (𝑇𝑡𝑦𝑝) 10.15% Potential Conflict Control (𝑇𝑟𝑡𝑓) 38.87% Aircraft Speed Difference (𝑇𝑠𝑝𝑑 ) 9.24% Frequency of Air Traffic Controller's Coordination or Communication (𝑇𝑐𝑜𝑚) 9.80% Function (18) shows that the number of descending aircraft is more critical than the number of climbing aircraft in influencing air traffic complexity. It is also shown that the aircraft type mix is more important than the aircraft speed difference. This result is different from Diaconu et al. (2014) in which the climbing was more important than the descent, and the aircraft speed difference was more important than the aircraft type mix [4]. The controller's significant preference with the same indicator may vary for different air traffic service units. So it is necessary to analyze the weighting value of the complexity indicator for related air traffic service units before measuring the air traffic complexity in specific airspace sectors. Controller task load is represented by controller communication time in the function through the frequency of the controller's coordination or communication (𝑇𝑐𝑜𝑚 ). The complexity rate will increase the more frequently the controller coordinates or communicates with the pilot and another controller. The weighting of communication time in the complexity function is more critical than the number of climbing aircraft (𝑁𝑐𝑙𝑏 ) and Aircraft Types Mixing (𝑇𝑡𝑦𝑝). In addition to task load, other factors such as equipment capability, individual preferences, and cognitive controller strategies are required to obtain a complete picture of the correlation between complexity and workload [28]. The simulation run for ten repetitions with aircraft type mix is a 9:1 ratio for Medium type (Boeing B737 and Airbus A330) and Heavy type (Airbus A330, Boeing B777, and Boeing B787). When entering the arrival point, time separation is set to 4 minutes, and departure is about 6 minutes. This gives high traffic density to the model. The minimum separation is 5 NM with a buffer of 15 NM to solve the potential conflict. A graphic of complexity measurement from a simulation (altitude control mode only) for each TCA sector is shown in Fig. 4. From Fig. 4, Jakarta Lower Control North and Jakarta Terminal West have a high rate of complexity relative to the other sectors. Jakarta Terminal East has the lowest complexity rate compared with the others on Runway 25R and 25L operation. The model can show the complexity rate comparison between sectors. It can be used to assess what sector has a higher rate of complexity for a particular runway operation, and the management should take some action to balance it. 208 IIUM Engineering Journal, Vol. 24, No. 1, 2023 Medianto et al. https://doi.org/10.31436/iiumej.v24i1.2223 Fig. 4: Result of complexity measurement (altitude control mode only). Simulation results of air traffic complexity measurement and the number of potential conflicts for specific resolution mode scenarios in each sector are shown in Fig. 5 and 6. Fig. 5: Values of air traffic complexity on sectors for specific resolution mode's scenario. (a) Jakarta Lower Control North (b) Jakarta Lower Control Center (c) Jakarta Lower Control East (d) Jakarta Terminal West (e) Jakarta Terminal East (f) Jakarta Terminal South 209 IIUM Engineering Journal, Vol. 24, No. 1, 2023 Medianto et al. https://doi.org/10.31436/iiumej.v24i1.2223 Fig. 6: Number of potential conflicts on sectors for specific resolution mode's scenario. Figure 5 shows that the speed control mode assigns a higher complexity rate for all sectors except Jakarta Lower Control North. For lower rate complexity, vectoring mode gives the lowest complexity than the other resolution mode, as seen in Jakarta Lower Control North, Jakarta Lower Control Centre, and Jakarta Terminal South. Altitude control mode gives a higher rate of complexity and more potential conflict if applied to the sector with many departure trajectories like Jakarta Lower Control North. Fig. 5 and 6 elaborate that speed control to solve the conflict increases complexity and creates more potential conflict for almost all sectors. Vectoring mode gives the least potential conflict than the other modes except on Jakarta terminal West. The simulation result can compare the complexity between sectors in a TCA and its effect on potential conflicts. However, the model cannot yet determine how this complexity affects aviation safety risks. As we know, complexity will affect the controller's workload level [5]. Representation of the human factor is needed to measure the controller workload, and its effect on safety risk factors can be observed. Future research needs methods to represent the human factor in a simulation model, including adding a controller to the simulation (human in the loop simulation) [16] or developing a controller workload model [29]. 4. CONCLUSION The weighting value of seven air traffic complexity indicators has been calculated using the AHP method in this study. The indicator that most significantly affects the air traffic complexity is the potential conflict control. The consistency of respondents' answers is less than 10%, indicating that these results are consistent. It can determine the air traffic complexity rate in the JAKARTA TCA model. The simulation has been run to measure the air traffic complexity of JAKARTA TCA in a high-density situation. The simulation result explains that the resolution mode selection (g) Jakarta Lower Control North (h) Jakarta Lower Control Center (h) Jakarta Lower Control East (i) Jakarta Terminal West (j) Jakarta Terminal East (k) Jakarta Terminal South 210 IIUM Engineering Journal, Vol. 24, No. 1, 2023 Medianto et al. https://doi.org/10.31436/iiumej.v24i1.2223 influences the complexity rate and potential conflicts. The simulation model requires further development by representing the human factor in the model so the model can be used to analyze safety risks. ACKNOWLEDGEMENT The authors would like to acknowledge Jakarta Air Traffic Services Center (JATSC), AirNav Indonesia, for providing information and the Indonesian Endowment Fund for Education (LPDP Scholarship) for the financial support. REFERENCES [1] Bouarfa S. (2015) Agent-Based Modelling and Simulation of Safety and Resilience in Air Transportation. Doctoral Dissertation, Delft University of Technology. https://doi.org/10.4233/uuid:b676db6c-ed86-4b42-9940-9b90b94651f1 [2] Netjasov F, Janic M, Tosic V. (2009) The Future Air Transport System: Looking for Generic Metrics of Complexity for Terminal Airspace. 88th Transportation Research Board (TRB) Annual Meeting, Washington DC, USA. [3] Netjasov F, Janic M, Tosic V. (2011) Developing a Generic Metric of Terminal Airspace Traffic Complexity. Transportmetrica 7(5), 369-394. https://doi.org/10.1080/18128602.2010.505590 [4] Diaconu AG, Stanciu V, Pleter OT. (2014) Air Traffic Complexity Metric for En-Route and Terminal Areas. U.P.B. Sci. Bull., Series D, Vol. 76, Iss. 1. [5] Djokic J, Lorenz B, Fricke H. (2010) Air Traffic Control Complexity as Workload Driver. Transportation Research Part C Emerging Technologies 18(6):930-936. https://doi.org/10.1016/j.trc.2010.03.005 [6] Mogford RH, Guttman JA, Morrow SL, Kopardekar P. (1995) The Complexity Construct in Air Traffic Control: A Review and Synthesis of the Literature. Report N0. DOT/FAA/CT- TN95/22. U.S. Department of Transportation, Federal Aviation Administration, Office of Aviation Research, Washington, D.C. [7] Dervic A, Rank A. (2015) ATC complexity measures: Formulas measuring workload and complexity at Stockholm TMA. Department of Science and Technology, Linköping University, Sweden. [8] Arad BA, Golden BT, Grambart JE, Mayfield CE, Van Saun HR. (1963) Control Load, Control Capacity, and Optimal Sector Design (Report No. RD64-16). Federal Aviation Administration, Atlantic City, NJ. [9] Grossberg M. (1989) Relation of Sector Complexity to Operational Errors. Quarterly Report of the FAA Office of Air Traffic Evaluations and Analysis. [10] Mogford RH, Murphy ED, Yastrop G, Guttman JA, Roske-Hofstrand R. (1993) The Application of Research Techniques for Documenting Cognitive Processes in Air Traffic Control (Report No. DOT/FAA/CT-TN93/39). Federal Aviation Administration, Atlantic City, NJ. [11] Pawlak WS, Brinton CR, Crouch K, Lancaster KM. (1996) A Framework for the Evaluation of Air Traffic Control Complexity. American Institute of Aeronautics and Astronautics, Inc. [12] Laudeman I, Shelden S, Brannstrom R, Brasil C. (1998) Dynamic Density: An Air Traffic Management Metric. Ames Research Center. [13] Majumdar A, Ochieng WY. (2000) The Factor Affecting Air Traffic Controller Workload: A Multivariate Analysis Based upon Simulation Modelling of Controller Workload. Center for Transport Studies. [14] Chatterji GB, Sridhar B. (2001) Measures for Air Traffic Controller Workload Prediction. First AIAA Aircraft Technology, Integration and Operations Forum. https://doi.org/10.2514/6.2001-5242 211 IIUM Engineering Journal, Vol. 24, No. 1, 2023 Medianto et al. https://doi.org/10.31436/iiumej.v24i1.2223 [15] Koros A, Della Rocco PS, Panjwani G, Ingurgio V, D'Arcy JF. (2003) Complexity in Air Traffic Control Towers: A Field Study. Part 1: Complexity Factors. DOT/FAA/CT-TN03/14. Federal Aviation Agency, Atlantic City, NJ. [16] Radišić T, Andraši P, Novak D, Juričić B, Antulov-Fantulin B. (2020) Risk Assessment in Air Traffic Management. Edited by P. Castán and J. Alberto. IntechOpen, London. pp 56-83. [17] Wang H, Song Z, Wen R. (2018) Modeling Air Traffic Situation Complexity with a Dynamic Weighted Network Approach. Journal of Advanced Transportation, vol. 2018 vol. 2018, Article ID 5254289, 15 pages. https://doi.org/10.1155/2018/5254289 [18] Andraši P, Radišić T, D. Novak, and B. Juričić (2019) Subjective Air Traffic Complexity Estimation Using Artificial Neural Networks. Traffic & Transportation, 31(4): 377-386. https://doi.org/10.7307/ptt.v31i4.3018 [19] Medianto R, Pasaribu HM, Muhammad H. (2019) Development of Hybrid Simulation Model of Air Traffic Management in the Terminal Control Area. IOP Conf. Series: Materials Science and Engineering, 645: 012003. https://doi.org/10.1088/1757-899x/645/1/012003 [20] Directorate General of Civil Aviation (2019) Nr: The Establishment And Revision of Instrument Flight Procedures at Soekarno Hatta International Airport – Jakarta, AIRAC AIP Supplement 14/19 25 APR 19. Directorate of Air Navigation, Directorate General of Civil Aviation, Republic Of Indonesia. FlightRadar24 Data/History Flights. [21] Airnav Indonesia (2020) Standard Operating Procedures Air Traffic Services Approach Control Service. Airnav Indonesia Branch of Jakarta Air Traffic Service Center. [22] Horasio K. (2019) Air Traffic Conflict Resolution Modelling and Analysis in Controlled Airspace. Master's Thesis. Bandung Institute of Technology, Aerospace Engineering Department. [23] FlightRadar24 Flight Database [https://www.flightradar24.com/data/flights] [24] METAR/SPECI & Trend Forecast, Aviation Meteorological Information System in Meteorological, Climatology, and Geophysical Agency (BMKG) [http://aviation.bmkg.go.id/web/metar_speci.php] [25] Pasaribu HM, Medianto R, Jusuf J, Oktafianto R, Atiqah R (2021) ADS-B data processing to develop aircraft kinematics model parameters. AIP Conference Proceedings, 2366: 02001. https://doi.org/10.1063/5.0060611 [26] Medianto R, Jusuf J, Oktafianto R, Atiqah R, Sembiring J, Pasaribu HM, Jenie YI, Muhammad H. (2021) Stochastic modelling of aircraft flight parameters in terminal control area based on automatic dependent surveillance-broadcast (ADS-B) data. IOP Conference Series: Materials Science and Engineering, 1173: 012053. https://doi.org/10.1088/1757- 899X/1173/1/012053 [27] Sargent RG. (2005) Verification and Validation of Simulation Models, Proceedings of the 2005 Winter Simulation Conference, 130-143. https://doi.org/10.1109/wsc.1994.717077 [28] Mogford R, Guttman J, Morrow S, and Kopardekar P. (1995) The Complexity Construct in Air Traffic Control: A Review and Synthesis of the Literature, Federal Aviation Administration. [29] Li-na S, Li Z, Lei Z. (2015) The Sector Capacity Evaluation Considering the Controller's Workloads. International Journal of Control and Automation, 8: 307-324. https://doi.org/10.14257/ijca.2015.8.7.31 212 https://doi.org/10.1063/5.0060611