Development and analysis of 2D flight planning search engine considering fusion of swim data

Michael Hius Sentoso, Neno Ruseno

Abstract

Flight planning is one of the essential factors of the airline operation. The selection of routes will determine the economic value of the flight. However, some conditions may prevent the flight to use the most optimum route due to airspace restriction or weather condition. The research aims to develop a search engine program that uses dynamic flight parameters that considers fusion of System Wide Information Management (SWIM) data including weather data and NOTAM to produce the most optimum route in 2D flight planning. The Dijkstra’s pathfinding is implemented in Python programming language to produce the flight plan. The navigation data used is enroute airway in Indonesian FIR regions. The scenario used is a flight from Jakarta to Makassar with duration of 2 hours flight with considering the effect of restricted airspace and weather blockage during in-flight. The study also uses the optimum route produced by the algorithm to be compared with the possible alternate routes to define how optimum the route is. Adding a restricted airspace parameter will result in a new optimum flight plan that able avoids the airspace and the most minimum distance. The effect of external wind parameter could influence the optimum route which may vary depends on the speed of the wind.

Keywords

flight planning, SWIM data, Dijkstra’s path finding, optimum route, restricted airspace.

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References

D. Ferro, V. Roan, P. Haas, and F. Rollet, “Method and device for creating an aircraft flight plan.” Google Patents, 2015.

M. J. Gibbs, D. Van Omen, M. B. Adams, K. L. Chase, D. E. Lewis, and D. E. McCrobie, “Method and system for entering data within a flight plan entry field.” Google Patents, 2005.

F. Coulmeau, “Method of creating and updating an ATC flight plan in real time to take account of flight directives and implementation device.” Google Patents, 2014.

L. Agam and D. Agam, “System for producing a flight plan.” Google Patents, 2009.

H. Badli and R. Ghosh, “Method and system for managing flight plan data.” Google Patents, 2010.

D. McNally et al., “Dynamic weather routes: two years of operational testing at American Airlines,” Air Traffic Control Q., vol. 23, no. 1, pp. 55–81, 2015.

A. Klein, L. Cook, B. Wood, and D. Simenauer, “Airspace capacity estimation using flows and weather-impacted traffic index,” in 2008 Integrated Communications, Navigation and Surveillance Conference, 2008, pp. 1–12.

W. Shijin, C. A. O. Xi, L. I. Haiyun, L. I. Qingyun, H. Xu, and W. Yanjun, “Air route network optimization in fragmented airspace based on cellular automata,” Chinese J. Aeronaut., vol. 30, no. 3, pp. 1184–1195, 2017.

L. Wang, W. Wang, F. Wei, and Y. Hu, “Research on the Classification of Air Route Intersections in the Airspace of China,” Transp. Res. Rec., vol. 2673, no. 2, pp. 243–251, 2019.

A. Akgunduz, B. Jaumard, and G. Moeini, “Deconflicted air-traffic planning with speed-dependent fuel-consumption formulation,” IEEE Trans. Intell. Transp. Syst., vol. 19, no. 6, pp. 1890–1901, 2017.

A. Bombelli, L. Soler, E. Trumbauer, and K. D. Mease, “Strategic Air Traffic Planning with Fréchet distance aggregation and rerouting,” J. Guid. Control. Dyn., vol. 40, no. 5, pp. 1117–1129, 2017.

C. Bongiorno, G. Gurtner, F. Lillo, R. N. Mantegna, and S. Miccichè, “Statistical characterization of deviations from planned flight trajectories in air traffic management,” J. Air Transp. Manag., vol. 58, pp. 152–163, 2017.

M. Y. Pusadan, J. L. Buliali, and R. V. H. Ginardi, “K optima Clustering as Determination of Optimum Flight Route,” in 2018 International Conference on Computer Engineering, Network and Intelligent Multimedia (CENIM), 2018, pp. 299–304.

M. Y. Pusadan, J. L. Buliali, and R. V. H. Ginardi, “Optimum partition in flight route anomaly detection,” Indones. J. Electr. Eng. Comput. Sci., vol. 14, no. 3, pp. 1315–1329, 2019.

J. Kang, K. Choi, Y. Kim, and H. Yang, “A Method of Integrating Information for SWIM,” Proc. - 2017 IEEE 13th Int. Symp. Auton. Decentralized Syst. ISADS 2017, pp. 195–198, 2017, doi: 10.1109/ISADS.2017.30.

S. Ayhan and P. Comitz, “Swim interoperability with flight object mediation service,” in 2009 IEEE/AIAA 28th Digital Avionics Systems Conference, 2009, pp. 6--D.

B. Neumayr, E. Gringinger, C. G. Schuetz, M. Schrefl, S. Wilson, and A. Vennesland, “Semantic data containers for realizing the full potential of system wide information management,” AIAA/IEEE Digit. Avion. Syst. Conf. - Proc., vol. 2017-Septe, 2017, doi: 10.1109/DASC.2017.8102002.

X. Lu and T. Koga, “SWIM concept-oriented information integration for air traffic surviellance,” in 2017 IEEE 6th Global Conference on Consumer Electronics (GCCE), 2017, pp. 1–2.

J. C. H. Cheung, “Flight planning: node-based trajectory prediction and turbulence avoidance,” Meteorol. Appl., vol. 25, no. 1, pp. 78–85, 2018.

““Wayback Machine.” https://web.archive.org/web/20140725005129/http://www.boeing.com/assets/pdf/commercial/startup/pdf/737ng_perf.pdf (accessed Aug. 13, 2020).

“Pertamina Aviation.” https://www.pertamina.com/aviation/News.aspx?p=price (accessed Aug. 14, 2020).

Flightradar24, “Live Flight Tracker - Real-Time Flight Tracker Map.” https://www.flightradar24.com/data/flights/ga616 (accessed Aug. 14, 2020).

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