An evolutionary computational framework for capacity-safety trade-off in an air transportation network
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摘要
Airspace safety and airport capacity are two key challenges to sustain the growth in Air Transportation. In this paper, we model the Air Transportation Network as two sub-networks of airspace and airports, such that the safety and capacity of the overall Air Transportation network emerge from the interaction between the two. We propose a safety-capacity trade-off approach,using a computational framework, where the two networks can inter-act and the trade-off between capacity and safety in an Air Transport Network can be established. The framework comprise of an evolutionary computation based air traffic scenario generation using a flow capacity estimation module(for capacity), Collision risk estimation module(for safety) and an air traffic simulation module(for evaluation). The proposed methodology to evolve air traffic scenarios such that it minimizes collision risk for given capacity estimation was tested on two different air transport network topologies(random and small-world) with the same number of airports. Experimental results indicate that though airspace collision risk increases almost linearly with the increasing flow(flow intensity) in the corresponding airport network, the critical flow depend on the underlying network configuration. It was also found that, in general, the capacity upper bound depends not only on the connectivity among airports and their individual performances but also the configuration of waypoints and mid-air interactions among conflicts. Results also show that airport network can accommodate more traffic in terms of capacity but the corresponding airspace network cannot accommodate the resulting traffic flow due to the bounds on collision risk.
Airspace safety and airport capacity are two key challenges to sustain the growth in Air Transportation. In this paper, we model the Air Transportation Network as two sub-networks of airspace and airports, such that the safety and capacity of the overall Air Transportation network emerge from the interaction between the two. We propose a safety-capacity trade-off approach,using a computational framework, where the two networks can inter-act and the trade-off between capacity and safety in an Air Transport Network can be established. The framework comprise of an evolutionary computation based air traffic scenario generation using a flow capacity estimation module(for capacity), Collision risk estimation module(for safety) and an air traffic simulation module(for evaluation). The proposed methodology to evolve air traffic scenarios such that it minimizes collision risk for given capacity estimation was tested on two different air transport network topologies(random and small-world) with the same number of airports. Experimental results indicate that though airspace collision risk increases almost linearly with the increasing flow(flow intensity) in the corresponding airport network, the critical flow depend on the underlying network configuration. It was also found that, in general, the capacity upper bound depends not only on the connectivity among airports and their individual performances but also the configuration of waypoints and mid-air interactions among conflicts. Results also show that airport network can accommodate more traffic in terms of capacity but the corresponding airspace network cannot accommodate the resulting traffic flow due to the bounds on collision risk.
Airspace safety and airport capacity are two key challenges to sustain the growth in Air Transportation. In this paper, we model the Air Transportation Network as two sub-networks of airspace and airports, such that the safety and capacity of the overall Air Transportation network emerge from the interaction between the two. We propose a safety-capacity trade-off approach,using a computational framework, where the two networks can inter-act and the trade-off between capacity and safety in an Air Transport Network can be established. The framework comprise of an evolutionary computation based air traffic scenario generation using a flow capacity estimation module(for capacity), Collision risk estimation module(for safety) and an air traffic simulation module(for evaluation). The proposed methodology to evolve air traffic scenarios such that it minimizes collision risk for given capacity estimation was tested on two different air transport network topologies(random and small-world) with the same number of airports. Experimental results indicate that though airspace collision risk increases almost linearly with the increasing flow(flow intensity) in the corresponding airport network, the critical flow depend on the underlying network configuration. It was also found that, in general, the capacity upper bound depends not only on the connectivity among airports and their individual performances but also the configuration of waypoints and mid-air interactions among conflicts. Results also show that airport network can accommodate more traffic in terms of capacity but the corresponding airspace network cannot accommodate the resulting traffic flow due to the bounds on collision risk.
引文
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5.Chen Y,Zhang D.Dynamic airspace configuration method based on a weighted graph model.Chinese Journal of Aeronautics 2014;27(4):903-12.
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9.Wenjing Z,Richard C,Abbass H.Dynamic capacity-demand balance in air transport systems using co-evolutionary computational red teaming.Proceeding of the 5th international air transport and operations symposium;2015.p.1-11.
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11.Jacquillat A,Odoni AR.A new airport demand management approach based on targeted scheduling interventions.Journal of Transport Economics and Policy 2007;51(2):115-38.
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13.Ahmed MS,Alam S.An evolutionary optimization approach to maximize runway through-put capacity for hub and spoke airports.Australasian conference on artificial life and computational intelligence;2016.p.313-23.
14.Alam S,Hossain MM,Al-Alawi F,Al-Thawadi F.Optimizing lateral airway offset for collision risk mitigation using differential evolution.Air Traffic Control Quarterly 2015;23(4):301-24.
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19.Du W B,Zhou XL,Lordan O,Wang Z,Zhao C,Zhu YB.Analysis of the Chinese airline network as multi-layer networks.Transportation Research Part E:Logistics and Transportation Review 2016;89:108-116.
20.Bojia YE,Hu M,Shortle JF.Collision risk-capacity tradeoff analysis of an en-route corridor model.Chinese Journal of Aeronautics 2014;27(1):124-135.
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27.Alam S,Shafi K,Abbass HA,Barlow M.An ensemble approach for conflict detection in free flight by data mining.Transportation Research Part C:Emerging T echnologies 2009;17(3):298-317.
28.Zhang X,Guan X,Zhu Y,Lei J.Strategic flight assignment approach based on multi-objective parallel evolution algorithm with dynamic migration interval.Chinese Journal of Aeronautics2015;28(2):556-563.
29.Price K,Storn RM,Lampinen JA.Differential evolution:a practical approach to global optimization.Berlin:Springer Science&Business Media;2006.
30.Alam S,Abbass H,Barlow M.ATOMS:Air traffic operations and management simulator.IEEE Transactions on Intelligent Transportation Systems 2008;9(2):209-25.
31.Hossain M,Alam S,Rees T,Abbass H.Australian airport network robustness analysis:a complex network approach.The 36th Australasian transport research forum(ATRF);2013.p.1-21.
32.Symon F,Alam S,Hossain MM,Blom H.Airspace network characterization for effect of intermediate waypoints on collision risk assessment.Proceedings of the 6th international conference on research in air transportation;2014.p.1-8.
33.Cai K Q,Z hang J,Du WB,Cao X B.Analysis of the Chinese air route network as a complex network.Chinese Physics B 2012;21(2):028903.
34.Wang Y,Xu X,Hu M,Zhan J.The structure and dynamics of the multilayer air transport system.The 12th USA/Europe air traffic management R&D seminar;2017.
35.Cong W,Hu M,Dong B,Wang Y,Feng C.Empirical analysis of airport network and critical airports.Chinese Journal of Aeronautics 2016;29(2):512-9.
36.Mehadhebi K.A methodology for the design of a route network.Proceedings of the 3rd Air traffic management R&D seminar ATM-2000;2000.p.8-69.
37.Bourke P.Efficient triangulation algorithm suitable for terrain modelling.Pan pacific computer conference;1989.
38.Erdos P,Renyi A.On the evolution of random graphs.Publication of the Mathematical Institute of the Hungarian Academy of Sciences 1960;5:17-61.
39.Watts DJ,Strogatz SH.Collective dynamics of small-world networks.Nature 1998;393:440-442.
40.Lin X,Fulton N,Westcott M.Target level of safety measures in air transportation-review,validation and recommendations.Proceedings of the IASTED International Conference;2009.
41.Hsu D.The evaluation of aircraft collision probabilities at intersecting air routes.Journal of Navigation 1981;34(1):78-102.
42.Donohue GL.A macroscopic air transportation capacity model:Metrics and delay correlation.New concepts and methods in air traffic management;2001.p.45-62.
43.Alam S,Lokan C,Aldis G,Barry S,Butcher R,Abbass H.Systemic identification of airspace collision risk tipping points using an evolutionary multi-objective scenario-based methodology.Transportation Research Part C:Emerging Technologies 2013;35(9):57-84.
44.ICAO.Review of the general concept of separation panel.Working group A meeting:Summary of discussion and conclusions;Montreal,Canada:ICAO;1995.
2.Netjasov F.Framework for airspace planning and design based on conflict risk assessment:Part 1:Conflict risk assessment model for airspace strategic planning.Transportation Research Part C:Emerging Technologies 2012;24:190-212.
3.Graham R,Youn D.Preparing an initial assessment of the SESAR concept of operations EP3:Single European sky implementation support through validation.Bre′tigny-sur-Orge:Eurocontrol Experimental Centre;2006.Technical Report DLT-0612-222-02-00.
4.Joint Planning and Development Office.Concept of operations for the next generation air transportation system.Washington,D.C.:Joint Planning and Development Office 2006.
5.Chen Y,Zhang D.Dynamic airspace configuration method based on a weighted graph model.Chinese Journal of Aeronautics 2014;27(4):903-12.
6.Chen Y,Zhang D.Dynamic airspace configuration method based on a weighted graph model.Chinese Journal of Aeronautics 2014;27(4):903-12.
7.Perrotte J.Performance based navigation.Integrated communications,navigation and surveillance conference;2017.p.1-9.
8.EUROCONTROL.Indicators and analysis of the ATM network operations performance.Brussels:Central Flow Management Unit;2010;Network Operations Report 2010.
9.Wenjing Z,Richard C,Abbass H.Dynamic capacity-demand balance in air transport systems using co-evolutionary computational red teaming.Proceeding of the 5th international air transport and operations symposium;2015.p.1-11.
10.ICAO.Unified framework for collision risk modelling in support of the manual on airspace planning methodology with further applications,Circular 319 AN/181.Montreal,Canada:International Civil Aviation Organization;2008.
11.Jacquillat A,Odoni AR.A new airport demand management approach based on targeted scheduling interventions.Journal of Transport Economics and Policy 2007;51(2):115-38.
12.Pyrgiotis N,Odoni A.On the impact of scheduling limits:A case study at Newark liberty international airport.Transportation Science 2015;50:150-65.
13.Ahmed MS,Alam S.An evolutionary optimization approach to maximize runway through-put capacity for hub and spoke airports.Australasian conference on artificial life and computational intelligence;2016.p.313-23.
14.Alam S,Hossain MM,Al-Alawi F,Al-Thawadi F.Optimizing lateral airway offset for collision risk mitigation using differential evolution.Air Traffic Control Quarterly 2015;23(4):301-24.
15.Kopardekar P,Bilimoria KD,Sridhar B.Airspace configuration concepts for the next generation air transportation system.Air Traffic Control Quarterly 2008;16(4):313-6.Yousefi A,Xie R.Safety-capacity trade-off and phase transition analysis of automated separation assurance concepts.The 30th IEEE/AIAA digital avionics systems conference;2011 October 16-20;Seattle,WA.Piscataway:IEEE Press;2011.
16.Kopardekar P,Bilimoria KD,Sridhar B.Airspace configuration concepts for the next generation air transportation system.Air Traffic Control Quarterly 2008;16(4):313-6.
17.Haynie RC.An investigation of capacity and safety in nearterminal airspace for guiding information technology adoption.Washington D.C.:George Mason University;2002.
18.Haynie RC.An investigation of capacity and safety in nearterminal airspace for guiding information technology adoption.Washington D.C.:George Mason University;2002.
19.Du W B,Zhou XL,Lordan O,Wang Z,Zhao C,Zhu YB.Analysis of the Chinese airline network as multi-layer networks.Transportation Research Part E:Logistics and Transportation Review 2016;89:108-116.
20.Bojia YE,Hu M,Shortle JF.Collision risk-capacity tradeoff analysis of an en-route corridor model.Chinese Journal of Aeronautics 2014;27(1):124-135.
21.Kopardekar P,Rhodes J,Schwartz A,Magyarits S,Willems B.Relationship of maxi-mum manageable air traffic control complexity and sector capacity.26th International congress of the aeronautical sciences;2008.p.15-19.
22.Pesic B,Durand N,Alliot JM.Aircraft ground traffic optimisation using a genetic algorithm.Proceedings of the 3rd Genetic and evolutionary computation conference.2001.p.1397-404.
23.Stroeve SH,Blom HA,Bakker GB.Systemic accident risk assessment in air traffic by monte carlo simulation.Safety Science2009;47(2):238-49.
24.Donohue GL.A simplified air transportation system capacity model.Journal of Air Traffic Control 1999;41(1):8-15.
25.Gilbo EP.Airport capacity:Representation,estimation,optimization.IEEE Transactions on Control Systems Technology 1993;1(3):144-154.
26.Hossain M,Alam S,Abbass H.A dynamic multi-commodity flow optimization algorithm for estimating airport network capacity.International Workshop on ATM/CNS;2017;Springer Tokyo;2017.p.205-220.
27.Alam S,Shafi K,Abbass HA,Barlow M.An ensemble approach for conflict detection in free flight by data mining.Transportation Research Part C:Emerging T echnologies 2009;17(3):298-317.
28.Zhang X,Guan X,Zhu Y,Lei J.Strategic flight assignment approach based on multi-objective parallel evolution algorithm with dynamic migration interval.Chinese Journal of Aeronautics2015;28(2):556-563.
29.Price K,Storn RM,Lampinen JA.Differential evolution:a practical approach to global optimization.Berlin:Springer Science&Business Media;2006.
30.Alam S,Abbass H,Barlow M.ATOMS:Air traffic operations and management simulator.IEEE Transactions on Intelligent Transportation Systems 2008;9(2):209-25.
31.Hossain M,Alam S,Rees T,Abbass H.Australian airport network robustness analysis:a complex network approach.The 36th Australasian transport research forum(ATRF);2013.p.1-21.
32.Symon F,Alam S,Hossain MM,Blom H.Airspace network characterization for effect of intermediate waypoints on collision risk assessment.Proceedings of the 6th international conference on research in air transportation;2014.p.1-8.
33.Cai K Q,Z hang J,Du WB,Cao X B.Analysis of the Chinese air route network as a complex network.Chinese Physics B 2012;21(2):028903.
34.Wang Y,Xu X,Hu M,Zhan J.The structure and dynamics of the multilayer air transport system.The 12th USA/Europe air traffic management R&D seminar;2017.
35.Cong W,Hu M,Dong B,Wang Y,Feng C.Empirical analysis of airport network and critical airports.Chinese Journal of Aeronautics 2016;29(2):512-9.
36.Mehadhebi K.A methodology for the design of a route network.Proceedings of the 3rd Air traffic management R&D seminar ATM-2000;2000.p.8-69.
37.Bourke P.Efficient triangulation algorithm suitable for terrain modelling.Pan pacific computer conference;1989.
38.Erdos P,Renyi A.On the evolution of random graphs.Publication of the Mathematical Institute of the Hungarian Academy of Sciences 1960;5:17-61.
39.Watts DJ,Strogatz SH.Collective dynamics of small-world networks.Nature 1998;393:440-442.
40.Lin X,Fulton N,Westcott M.Target level of safety measures in air transportation-review,validation and recommendations.Proceedings of the IASTED International Conference;2009.
41.Hsu D.The evaluation of aircraft collision probabilities at intersecting air routes.Journal of Navigation 1981;34(1):78-102.
42.Donohue GL.A macroscopic air transportation capacity model:Metrics and delay correlation.New concepts and methods in air traffic management;2001.p.45-62.
43.Alam S,Lokan C,Aldis G,Barry S,Butcher R,Abbass H.Systemic identification of airspace collision risk tipping points using an evolutionary multi-objective scenario-based methodology.Transportation Research Part C:Emerging Technologies 2013;35(9):57-84.
44.ICAO.Review of the general concept of separation panel.Working group A meeting:Summary of discussion and conclusions;Montreal,Canada:ICAO;1995.