Wind effect analysis on air traffic congestion in terminal area via cellular automata
Abstract
The behavior of any traffic flow is sensitive to the speed pattern of the vehicles involved. The heavier the traffic, the more sensitive the behavior is to speed changes. Focusing on air traffic flow, weather condition has a major role in the deviations of aircraft operational speed from the desired speed and causes surplus delays. In this paper, the effects of wind on delays in a terminal area are analyzed using a Cellular Automaton (CA) model. Cellular automata are discrete models that are widely used for simulating complex emerging properties of dynamic systems. A one-dimensional cellular array is used to model the flow of the terminal traffic into a wind field. The proposed model, due to the quickness and acceptable level of accuracy, can be utilized online in the tactical phase of air traffic control processes and system-level decision-makings, where quick response and system behavior are needed. The modeled route is an RNAV STAR route to Atlanta International Airport. The model is verified by real traffic data in a non-delayed scenario. Based on simulation results, the proposed model exhibits an acceptable level of accuracy (3–15% accuracy drop), with worthy time and computational efficiency (about 2.9 seconds run time for a 2-hour operation).
Keyword : terminal area traffic, traffic flow management, wind effect, traffic modeling, cellular automata
How to Cite
Enayatollahi, F., & Amiri Atashgah, M. A. (2018). Wind effect analysis on air traffic congestion in terminal area via cellular automata. Aviation, 22(3), 102-114. https://doi.org/10.3846/aviation.2018.6252
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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He, Y., Cai, K., Li, Y., & Xiao, M. (2014). An improved cellular-automaton-based algorithm for real-time aircraft landing scheduling. Paper presented at the Seventh International Symposium on Computational Intelligence And Design (ISCID). Hangzhou, China. IEEE. https://doi.org/10.1109/ISCID.2014.243
Hoffman, E., Ivanescu, D., Shaw, C., & Zeghal, K. (2003, June). Analysis of constant time delay airborne spacing between aircraft of mixed types in varying wind conditions. Paper presented at the USA/Europe Air Traffic Management R&D Seminar, Budapest, Hungary.
Jacobsen, R. (2000). NASA airspace systems program. Paper presented at the Virtual Airspace Modeling and Simulation Project, Technical Interchange Meeting.
Kim, C., Abubaker, K., & Obah, O. (2005). Cellular automata modeling of en route and arrival self-spacing for autonomous aircrafts. Paper presented at the 50th Annual Meeting of Air Traffic Controllers Association.
Klein, A. (2010). Airport delay prediction using weather-impacted traffic index (WITI) model. Paper presented at the IEEE/AIAA 29th Digital Avionics Systems Conference (DASC), Salt Lake City, UT, USA. https://doi.org/10.1109/DASC.2010.5655493
Matsukidaira, J., & Nishinari, K. (2004). Euler–Lagrange correspondence of generalized burgers cellular automaton. International Journal of Modern Physics C, 15(04), 507-515. https://doi.org/10.1142/S0129183104005917
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Menon, P. K., Sweriduk, G. D., Lam, T., Diaz, G., & Bilimoria, K. D. (2006). Computer-aided eulerian air traffic flow modeling and predictive control. Journal Of Guidance, Control, and Dynamics, 29(1), 12-19. https://doi.org/10.2514/1.13496
Mori, R. (2010). Modeling of aircraft surface traffic flow at congested airport using cellular automata. Paper presented at the Proceedings of the 4th International Conference on Research in Air Transportation (ICRAT), Budapest, Hungary.
Mori, R. (2013). Aircraft ground-taxiing model for congested airport using cellular automata. IEEE Transactions on Intelligent Transportation Systems, 14(1), 180-188. https://doi.org/10.1109/TITS.2012.2208188
Nagel, K., & Schreckenberg, M. (1992). A cellular automaton model for freeway traffic. Journal De Physique I, 2(12), 2221-2229. https://doi.org/10.1051/jp1:1992277
NASA. (2016). Future air traffic management concepts evaluation tool (FACET). Retrieved from https://software.nasa.gov/software/arc-14653-1
Pasaoglu, C., Baspinar, B., Ure, N. K., & Inalhan, G. (2015). Hybrid systems modeling and automated air traffic control for three-dimensional separation assurance. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 230(9), 1788-1809. https://doi.org/10.1177/0954410015619443
Pradeep, P., & Wei, P. (2017). Predictability, variability and operational feasibility aspect of CDA. Paper presented at the IEEE Aerospace Conference. Big Sky, MT, USA. https://doi.org/10.1109/AERO.2017.7943728
Schumer, S. C. E. (2008). Your flight has been delayed again: flight delays cost passengers, airlines and the U.S. economy billions. Retrieved from https://www.jec.senate.gov/public/index.cfm/democrats/2008/5/your-flight-has-been-delayed-again_1539
Sun, D., Strub, I. S., & Bayen, A. M. (2007). Comparison of the Performance of Four Eulerian Network Flow Models for Strategic Air Traffic Management. Networks and Heterogeneous Media, 2(4), 569-595. https://doi.org/10.3934/nhm.2007.2.569
Wolfram, S. (2002). A new kind of science: Wolfram Media.
World Meteorogical Organization. (2017). Nowcasting. Retrieved from http://www.wmo.int/pages/prog/amp/pwsp/nowcasting.htm
Yang, B.-J., & Menon, P. (2015). Real-time air traffic flow estimation in the terminal area. Journal of Aircraft, 52(3), 778-791. https://doi.org/10.2514/1.C032701
Young, S.-Y., & Jerome, K. (2013). Optimal profile descent with 4-D trajectory. Paper presented at the Integrated Communications, Navigation And Surveillance Conference (ICNS). Herndon, VA, USA. https://doi.org/10.1109/ICNSurv.2013.6548601
Yu, S.-P., Cao, X.-B., & Zhang, J. (2011). A real-time schedule method for aircraft landing scheduling problem based on cellular automation. Applied Soft Computing, 11(4), 3485-3493. https://doi.org/10.1016/j.asoc.2011.01.022
Zeng, J.-W., Yang, X.-G., Qian, Y.-S., & Wei, X.-T. (2017). Research on three-phase traffic flow modeling based on interaction range. Modern Physics Letters B, 31(35), 1750328. https://doi.org/10.1142/S0217984917503286
Zeng, J., Qian, Y., Wang, M., & Yang, Y. (2017). On supervised graph laplacian embedding Ca Model & Kernel construction and its application. Journal of Experimental & Theoretical Artificial Intelligence, 29(1), 81-89. https://doi.org/10.1080/0952813X.2015.1132259
Zhang, H., Xu, Y., Yang, L., & Liu, H. (2014). Macroscopic model and simulation analysis of air traffic flow in airport terminal area. Discrete Dynamics in Nature and Society. Article ID 741654. https://doi.org/10.1155/2014/741654
Amor, S., Tran, D., & Bui, M. (2006, December). Investigating air traffic control dynamics using random cellular automata. Paper presented at the Eurocontrol Innovative Research Workshop & Exhibition. Bretigny-sur-Orge, France.
Amor, S. B., Dac, H. T., Bui, M., & Duong, V. (2007). Simulating dynamic ATM network effects using cellular automata'. Paper Presented at the Proceedings of the European Conference on Complex Systems. ECCS. Dresden. Germany.
Aviation Weather Center. (2017). Forecast winds and temps aloft. Retrieved from https://aviationweather.gov/windtemp
Bai, X., & Menon, P. (2013, August). Optimal terminal area flow control using eulerian traffic flow model. Paper presented at AIAA Guidance, Navigation, and Control (GNC) Conference. Boston, MA.
Barhydt, R., Eischeid, T. M., Palmer, M. T., & Wing, D. J. (2003). Regaining Lost Separation in a Piloted Simulation of Autonomous Aircraft Operation. Paper Presented at the 5th USA/Europe Air Traffic Management R&D Seminar. Budapest, Hungary.
Barker-Plummer, D. (2016). Turing machines. In E. N. Zalta (Ed.), The stanford encyclopedia of philosophy (Winter 2016 Ed.). Metaphysics Research Lab, Stanford University.
Bayen A. M., Raffard, R. L., & Tomlin, C. J. (2004). Eulerian network model of air traffic flow in congested areas. Paper presented at the Proceedings of the American Control Conference, Boston, MA. https://doi.org/10.23919/ACC.2004.1384733
Biham, O., Middleton, A. A., & Levine, D. (1992). Self-Organization and a dynamical transition in traffic-flow models. Physical Review A, 46(10), 3. https://doi.org/10.1103/PhysRevA.46.R6124
Bilimoria, K. D., Sridhar, B., Chatterji G. B., Sheth, K. S., & Grabbe S. R. (2001). FACET: Future ATM concepts evaluation tool. Air Traffic Control Quaterly, 9(1), 1-20. https://doi.org/10.2514/atcq.9.1.1
Bouarfa, S., Blom, H. A., Curran, R., & Everdij, M. H. (2013). Agent-based modeling and simulation of emergent behavior in air transportation. Complex Adaptive Systems Modeling, 1(1), 15. https://doi.org/10.1186/2194-3206-1-15
Callaham, M., Dearmon, J., Cooper, A., Goodfriend, J., Moch-Mooney, D., & Solomos, G. (2001, December). Assessing NAS performance: Normalizing for the effects of weather. Paper Presented At The 4th Usa/Europe Air Traffic Management R&D Symposium, Santa-Fe.
Chowdhury, D., Santen, L., & Schadschneider, A. (2000). Statistical physics of vehicular traffic and some related systems. Physics Reports, 329(4), 199-329. https://doi.org/10.1016/S0370-1573(99)00117-9
Daganzo, C. F. (1994). The cell transmission model: a dynamic representation of highway traffic consistent with the hydrodynamic theory. Transportation Research B, 28(4), 269-287. https://doi.org/10.1016/0191-2615(94)90002-7
Delgado, L., & Prats, X. (2013). Effect of wind on operating-cost-based cruise speed reduction for delay absorption. IEEE Transactions on Intelligent Transportation Systems, 14(2), 918-927. https://doi.org/10.1109/TITS.2013.2246864
Evans, J. E., & Ducot, E. R. (1994). The Integrated Terminal Weather System (ITWS). The Lincoln Laboratory Journal, 7(2), 449-474.
Federal Aviation Administration. (2017, August 29). Weather delay. NextGen. Retrieved from https://www.faa.gov/nextgen/programs/weather/faq/
Flightradar. (2018). Live air traffic. Retrieved from https://www.flightradar24.com/data/airports/atl/arrivals
Gardi, A., Marino, M., Ramasamy, S., Sabatini, R., & Kistan, T. (2016). 4-Dimensional trajectory optimisation algorithm for air traffic management systems. Paper presented at the IEEE/AIAA 35th Digital Avionics Systems Conference (DASC). https://doi.org/10.1109/DASC.2016.7778083
Gardi, A., Sabatini, R., Kistan, T., Lim, Y., & Ramasamy, S. (2015). 4 dimensional trajectory functionalities for air traffic management systems. Paper presented at the Integrated Communication, Navigation, And Surveillance Conference (ICNS), Herdon, VA, USA. https://doi.org/10.1109/ICNSURV.2015.7121246
Gomez, R. (2007). Next generation air traffic management, boeing's view.
Hauf, T., Hupe, P., Sauer, M., Rokitansky, C.-H., Lang, J., Sacher, D., . . . Sakiew, L. (2017). Aircraft route forecasting under adverse weather conditions. Meteorologische Zeitschrift, 26(2), 189-206. https://doi.org/10.1127/metz/2016/0786
He, Y., Cai, K., Li, Y., & Xiao, M. (2014). An improved cellular-automaton-based algorithm for real-time aircraft landing scheduling. Paper presented at the Seventh International Symposium on Computational Intelligence And Design (ISCID). Hangzhou, China. IEEE. https://doi.org/10.1109/ISCID.2014.243
Hoffman, E., Ivanescu, D., Shaw, C., & Zeghal, K. (2003, June). Analysis of constant time delay airborne spacing between aircraft of mixed types in varying wind conditions. Paper presented at the USA/Europe Air Traffic Management R&D Seminar, Budapest, Hungary.
Jacobsen, R. (2000). NASA airspace systems program. Paper presented at the Virtual Airspace Modeling and Simulation Project, Technical Interchange Meeting.
Kim, C., Abubaker, K., & Obah, O. (2005). Cellular automata modeling of en route and arrival self-spacing for autonomous aircrafts. Paper presented at the 50th Annual Meeting of Air Traffic Controllers Association.
Klein, A. (2010). Airport delay prediction using weather-impacted traffic index (WITI) model. Paper presented at the IEEE/AIAA 29th Digital Avionics Systems Conference (DASC), Salt Lake City, UT, USA. https://doi.org/10.1109/DASC.2010.5655493
Matsukidaira, J., & Nishinari, K. (2004). Euler–Lagrange correspondence of generalized burgers cellular automaton. International Journal of Modern Physics C, 15(04), 507-515. https://doi.org/10.1142/S0129183104005917
Menon, P. K., Sweriduk, G. D., & Bilimoria, K. D. (2004). New approach for modeling, analysis, and control of air traffic flow. Journal of Guidance, Control, and Dynamics, 27(5), 737-744. https://doi.org/10.2514/1.2556
Menon, P. K., Sweriduk, G. D., Lam, T., Diaz, G., & Bilimoria, K. D. (2006). Computer-aided eulerian air traffic flow modeling and predictive control. Journal Of Guidance, Control, and Dynamics, 29(1), 12-19. https://doi.org/10.2514/1.13496
Mori, R. (2010). Modeling of aircraft surface traffic flow at congested airport using cellular automata. Paper presented at the Proceedings of the 4th International Conference on Research in Air Transportation (ICRAT), Budapest, Hungary.
Mori, R. (2013). Aircraft ground-taxiing model for congested airport using cellular automata. IEEE Transactions on Intelligent Transportation Systems, 14(1), 180-188. https://doi.org/10.1109/TITS.2012.2208188
Nagel, K., & Schreckenberg, M. (1992). A cellular automaton model for freeway traffic. Journal De Physique I, 2(12), 2221-2229. https://doi.org/10.1051/jp1:1992277
NASA. (2016). Future air traffic management concepts evaluation tool (FACET). Retrieved from https://software.nasa.gov/software/arc-14653-1
Pasaoglu, C., Baspinar, B., Ure, N. K., & Inalhan, G. (2015). Hybrid systems modeling and automated air traffic control for three-dimensional separation assurance. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 230(9), 1788-1809. https://doi.org/10.1177/0954410015619443
Pradeep, P., & Wei, P. (2017). Predictability, variability and operational feasibility aspect of CDA. Paper presented at the IEEE Aerospace Conference. Big Sky, MT, USA. https://doi.org/10.1109/AERO.2017.7943728
Schumer, S. C. E. (2008). Your flight has been delayed again: flight delays cost passengers, airlines and the U.S. economy billions. Retrieved from https://www.jec.senate.gov/public/index.cfm/democrats/2008/5/your-flight-has-been-delayed-again_1539
Sun, D., Strub, I. S., & Bayen, A. M. (2007). Comparison of the Performance of Four Eulerian Network Flow Models for Strategic Air Traffic Management. Networks and Heterogeneous Media, 2(4), 569-595. https://doi.org/10.3934/nhm.2007.2.569
Wolfram, S. (2002). A new kind of science: Wolfram Media.
World Meteorogical Organization. (2017). Nowcasting. Retrieved from http://www.wmo.int/pages/prog/amp/pwsp/nowcasting.htm
Yang, B.-J., & Menon, P. (2015). Real-time air traffic flow estimation in the terminal area. Journal of Aircraft, 52(3), 778-791. https://doi.org/10.2514/1.C032701
Young, S.-Y., & Jerome, K. (2013). Optimal profile descent with 4-D trajectory. Paper presented at the Integrated Communications, Navigation And Surveillance Conference (ICNS). Herndon, VA, USA. https://doi.org/10.1109/ICNSurv.2013.6548601
Yu, S.-P., Cao, X.-B., & Zhang, J. (2011). A real-time schedule method for aircraft landing scheduling problem based on cellular automation. Applied Soft Computing, 11(4), 3485-3493. https://doi.org/10.1016/j.asoc.2011.01.022
Zeng, J.-W., Yang, X.-G., Qian, Y.-S., & Wei, X.-T. (2017). Research on three-phase traffic flow modeling based on interaction range. Modern Physics Letters B, 31(35), 1750328. https://doi.org/10.1142/S0217984917503286
Zeng, J., Qian, Y., Wang, M., & Yang, Y. (2017). On supervised graph laplacian embedding Ca Model & Kernel construction and its application. Journal of Experimental & Theoretical Artificial Intelligence, 29(1), 81-89. https://doi.org/10.1080/0952813X.2015.1132259
Zhang, H., Xu, Y., Yang, L., & Liu, H. (2014). Macroscopic model and simulation analysis of air traffic flow in airport terminal area. Discrete Dynamics in Nature and Society. Article ID 741654. https://doi.org/10.1155/2014/741654