MODELLING & SIMULATION OF SOUTH CHINA SEA AIR TRAFFIC

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1 MODELLING & SIMULATION OF SOUTH CHINA SEA AIR TRAFFIC ISSUE 01, 2016 VLVT VVVV VHHK VTBB ZJSA RPHI VDPP VVTS WMFC WSJC WBFC

2 Records of Revision Issue Number Year Remarks / Revision Oct 2016 i P a g e

3 TABLE OF CONTENTS Modelling & Simulation of South China Sea Air Traffic 1. Introduction Modelling of SCS Region Data Sources Modelling & Simulation Introduction of SAAM Software SAAM Functionalities Sample of Statistics on Air Traffic in SCS Region Traffic on ATS Routes Traffic in Airports Theoretical Analysis & Forecasting of SCS Air Traffic Theoretical Capacity of ATS Routes Forecasting Conclusion References ANNEX A ii P a g e

4 LIST OF FIGURES Figure 1. FIRs in the SCS region... 1 Figure 2. ATS Route Network in SCS Region... 2 Figure 3. Traffic Simulation for SCS Region... 3 Figure 4. Traffic Density in SCS Region... 4 Figure 5. Entry Rate Curve... 4 Figure 6. Future Traffic Demand for A Figure 7. Future Traffic Demand for A Figure 8. Future Traffic Demand for A Figure 9. Future Traffic Demand for A Figure 10. Future Traffic Demand for B Figure 11. Future Traffic Demand for M Figure 12. Future Traffic Demand for M Figure 13. Future Traffic Demand for L Figure 14. Future Traffic Demand for M Figure 15. Future Traffic Demand for M LIST OF TABLES Table 1. Scenario Economy Analysis on SCS Region... 5 Table 2. Sample of Statistics for flights within SCS from 01 Dec 2015 to 07 Dec Table 3. Sample of Statistics of flight movements per airport on 04 December Table 4. Theoretical Capacity of ATS routes... 8 Table 5. Forecast of Traffic Demand for ATS routes... 9 iii P a g e

5 Modelling & Simulation of South China Sea Air Traffic 1. Introduction 1.1. This report presents the modelling and simulation carried out using the System for Traffic Assignment and Analysis at a Macroscopic level (SAAM) - EUROCONTROL tool on South China Sea (SCS) by ATMRI. Examples of the various types of analysis are included in this report This report consists of the following sections: 1. Introduction 2. Modelling of SCS Region 3. Data sources 4. Modelling & Simulation 4.1 Introduction of SAAM Software 4.2 SAAM Functionalities a) Visualization of Traffic b) Entry Rate Analysis c) Scenario Economy 5. Sample of Statistics on Air Traffic in SCS Region 5.1 Traffic on ATS Routes 5.2 Traffic in Airports 6. Theoretical Analysis & Forecasting of SCS Air Traffic 6.1 Theoretical Capacity of ATS Routes 6.2 Forecasting 2. Modelling of SCS Region FIRs have been modelled in SAAM to constitute the South China Sea airspace as shown in Figure 1 below. Figure 1. FIRs in the SCS region

6 2.2. Figure 2 shows the ATS route network in the SCS region. Figure 2. ATS Route Network in SCS Region 3. Data Sources 3.1. The data sources that have been used for the purpose of modelling and simulation are as follows: a) Air Traffic Data FlightGlobal INNOVATA* Traffic Period 01 Dec 2015 to 07 Dec 2015 (24 Hours Traffic for 7 days) b) Airspace Data Online Mapping and Aeronautical Charts Provider SkyVector c) Route Network Data Online Mapping and Aeronautical Charts Provider SkyVector d) Traffic Growth Rate Figures 6.5% CAGR for ASEAN Airbus Global Market Forecast 1 * Based on a random sample of 150 flights per day for 7 days, a comparison between INNOVATA and FlightStats was carried out. The percentage of flight cancellations per day was found to be approximately 2-3%. 2 P a g e

4. Modelling & Simulation 4.1 Introduction of SAAM Software 4.1.1. The modelling & simulation work was conducted with the use of SAAM.

7 4. Modelling & Simulation 4.1 Introduction of SAAM Software The modelling & simulation work was conducted with the use of SAAM. This tool was acquired from EUROCONTROL and they are currently using this as a tool for Modelling and Simulation SAAM is an integrated system for network-wide or local design, evaluation, analysis and display of air traffic, civil/military airspace and TMA scenarios SAAM is used for operational planning purposes to: a. Optimize strategic traffic flows; b. Design the route network and airspace; c. Analyze past and future traffic flows; and d. Display and compare projects. 4.2 SAAM Functionalities SAAM offers a set of functions that could be used for the analysis of South China Sea air traffic. This section describes those functions with examples. a) Visualization of Traffic The traffic within SCS region was simulated in SAAM using traffic data from INNOVATA. Figure 3 below shows the traffic simulation for SCS region. Figure 3. Traffic Simulation for SCS Region 3 P a g e

Traffic Density in SCS Region b) Entry Rate Analysis The Airspace Load Function provides the entry rate in any airspace and also gives an

8 SAAM provides the functionality to visualize traffic density. For example, Figure 4 shows the traffic density in SCS region. Such visualization functions depicts the utilization of routes in the SCS region. Figure 4. Traffic Density in SCS Region b) Entry Rate Analysis The Airspace Load Function provides the entry rate in any airspace and also gives an assessment of the amount of traffic within the specified airspace volumes. Figure 5 below provides an example of the hourly aircraft entry rate in the SCS airspace. Figure 5. Entry Rate Curve 4 P a g e

9 c) Scenario Economy The scenario economy function in SAAM can be used to compare length, time, fuel, CO2 and NOx emission of two traffic scenarios. This could be used in route optimization studies where a comparison between Before and After optimization can be made. The analysis results of route optimization potential could be studied in terms of: excess distance flown (NM), excess time flown (hours), excess fuel consumed (t), excess CO2 emitted (kg) and excess NOx emitted (kg) for each FIR in the SCS region. Table 1 shows an example of the Scenario Economy analysis on the SCS region. Total impacted flights Excess Distance (NM) Scenario Economy for SCS Region Excess Time (Hours) Excess Fuel (t) Excess CO 2 (kg) Excess NO X (kg) 7, , ,248 3,942,631 18,656 Table 1. Scenario Economy Analysis on SCS Region 5 P a g e

10 5. Sample of Statistics on Air Traffic in SCS Region 5.1 Traffic on ATS Routes The total number of flights per ATS route is calculated by SAAM using an advanced query function. It should be noted this calculation was based on the logic that any flights that enter the ATS route at any segment will be included to reflect the utilization of that route Table 2 depicts the number of flights for a sample of 15 ATS Routes from 01 December 2015 to 07 December No 01-Dec- 02-Dec- 03-Dec- 04-Dec- 05-Dec- 06-Dec- 07-Dec- Date ATS route Tues Wed Thurs Fri Sat Sun Mon 1 A A A A L L M M M M N W Y Y Y Table 2. Sample of Statistics for flights within SCS from 01 Dec 2015 to 07 Dec In addition, the total number of flights for 7 days within the entire SCS region was 56,429. The least traffic was on 01 December 2015 (Tuesday) with 7,937 flights, and the most traffic was on 04 December 2015 (Friday) with 8,186 flights. 6 P a g e

11 5.2 Traffic in Airports SAAM has the functionality to process consolidated traffic data to provide the total number of flight movements at each airport. A sample of the traffic movements for 20 airports, based on 04 December 2015, is provided in Table 3. Number of Airport Arrivals and Departures VHHH 1075 WSSS 1029 WMKK 999 VTBS 892 RPLL 708 VTBD 614 VVTS 531 ZJHK 399 VVNB 331 ZJSY 322 VTSP 259 WMSA 189 WMKP 184 VTCC 181 RPVM 178 WBKK 174 VVDN 136 WBGG 125 VDSR 110 WBGR 97 Table 3. Sample of Statistics of flight movements per airport on 04 December P a g e

12 6 Theoretical Analysis & Forecasting of SCS Air Traffic 6.1 Theoretical Capacity of ATS Routes Table 4 provides a theoretical calculation of the capacity of each ATS route based on the four different longitudinal separation. Route Length (nm) No of Flight Levels Flight Time (Hr) Longitudinal (based on 450kts) No of Aircraft Longitudinal Longitudinal Longitudinal 80nm 50nm 30nm 20nm (based on 450kts) (based on 450kts) (based on 450kts) (based on 450kts) (based on 450kts) (based on 450kts) No. of No. of No. of No of No of No of Aircraft Aircraft Aircraft Aircraft Aircraft Aircraft per hour per hour per hour (based on 450kts) No. of Aircraft per hour Table 4. Theoretical Capacity of ATS routes 8 P a g e

13 6.2 Forecasting The traffic demand for randomly selected 10 ATS routes in 2020, 2025 and 2030 have been forecasted with High, Base and Low scenarios. The High scenario assumes a Compounded Annual Growth Rate (CAGR) of 7.5%, the Base scenario assumes a CAGR of 6.5%, and the Low scenario assumes a CAGR of 5.5%. Refer to Annex A for the forecasted traffic demand and corresponding graphs. ATS 04 Dec 15 Low (5.5% Traffic Growth) Base (6.5% Traffic Growth) High (7.5% Traffic Growth) route A , ,278 A , ,151 A ,021 A B M M L M M Table 5. Forecast of Traffic Demand for ATS routes 9 P a g e

14 7 Conclusion 7.1. This report presents the capability of ATMRI using the SAAM tool to conduct various modelling and simulation of air traffic in SCS region. Such modelling and simulation enables a deeper analysis of the flow for current and future traffic. With and the availability of appropriate data, more comprehensive studies of the ATS routes in SCS could be undertaken to enhance airspace safety, efficiency and capacity in the region. References 1. Airbus, Impact of ASAM on Maintenance Organizations: An Airbus Perspective, Regional Conference on ASEAN Single Aviation Market, Bangkok, Thailand, Mar P a g e

15 Forecasting of Traffic Demand ANNEX A 1,400 Number of Flights per day 1,200 1, Year Low (5.5% Traffic Growth) High (7.5% Traffic Growth) Base (6.5% Traffic Growth) Figure 6. Future Traffic Demand for A457 Figure 7. Future Traffic Demand for A P a g e

16 Figure 8. Future Traffic Demand for A202 Figure 9. Future Traffic Demand for A1 12 P a g e

17 800 Number of Flights per day South China Sea Year Low (5.5% Traffic Growth) High (7.5% Traffic Growth) Base (6.5% Traffic Growth) Figure 10. Future Traffic Demand for B469 Figure 11. Future Traffic Demand for M P a g e

18 Figure 12. Future Traffic Demand for M758 Figure 13. Future Traffic Demand for L P a g e

19 400 Number of Flights per day South China Sea Year Low (5.5% Traffic Growth) High (7.5% Traffic Growth) Base (6.5% Traffic Growth) Figure 14. Future Traffic Demand for M771 Figure 15. Future Traffic Demand for M P a g e

20 ACKNOWLEDGEMENT This report was prepared by Mr. Francisco Thomas Galaura, Mr. Lee Yu Xuan, Ms. Shafirah Aneeka, Mr. Goh Jin Wen and Mr. Yeo Wen Bin from the Regional Airspace Capacity Enhancement (RACE) Project, reviewed by Mr. Mohamed Faisal Bin Mohamed Salleh and supported by Associate Professor Zhong Zhaowei. For any further information, please contact: 65 Nanyang Drive, N3.2 - B3M - 07, Singapore Telephone Fax : : atmri@ntu.edu.sg

F r o m B O B C AT t o C r o s s - B o r d e r AT F M

F r o m B O B C AT t o C r o s s - B o r d e r AT F M IATA-ICAO Cross-Border ATFM Workshop 3 4 September 2015 Delhi, India Piyawut Tantimekabut (Toon) Air Traffic Management Network Manager Network Operations