Measuring the resilience of an airport network

Abstract Resilience is the ability of a system to withstand and stay operational in the face of an unexpected disturbance or unpredicted changes. Recent studies on air transport system resilience focus on topology characteristics after the disturbance and measure the robustness of the network with respect to connectivity. The dynamic processes occurring at the node and link levels are often ignored. Here we analyze airport network resilience by considering both structural and dynamical aspects. We develop a simulation model to study the operational performance of the air transport system when airports operate at degraded capacity rather than completely shutting down. Our analyses show that the system deteriorates soon after disruptive events occur but returns to an acceptable level after a period of time. Static resilience of the airport network is captured by a phase transition in which a small change to airport capacity will result in a sharp change in system punctuality. After the phase transition point, decreasing airport capacity has little impact on system performance. Critical airports which have significant influence on the performance of whole system are identified, and we find that some of these cannot be detected based on the analysis of network structural indicators alone. Our work shows that air transport system’s resilience can be well understood by combining network science and operational dynamics.

[1]  José J. Ramasco,et al.  Systemic delay propagation in the US airport network , 2013, Scientific Reports.

[2]  Kash Barker,et al.  A review of definitions and measures of system resilience , 2016, Reliab. Eng. Syst. Saf..

[3]  Charles Perrings,et al.  Resilience in the Dynamics of Economy-Environment Systems , 1998 .

[4]  Albert-László Barabási,et al.  Universal resilience patterns in complex networks , 2016, Nature.

[5]  Sameer Alam,et al.  A complex network approach towards modeling and analysis of the Australian Airport Network , 2017 .

[6]  Carmen Elena Patiño Rodriguez,et al.  Analysis of transportation networks subject to natural hazards - Insights from a Colombian case , 2016, Reliab. Eng. Syst. Saf..

[7]  Elise Miller-Hooks,et al.  Resilience: An Indicator of Recovery Capability in Intermodal Freight Transport , 2012, Transp. Sci..

[8]  Daniel Delahaye,et al.  Integrated sequencing and merging aircraft to parallel runways with automated conflict resolution and advanced avionics capabilities , 2017 .

[9]  Chen Zhao,et al.  Analysis of the Chinese Airline Network as multi-layer networks , 2016 .

[10]  Sara Meerow,et al.  Defining urban resilience: A review , 2016 .

[11]  C. Alessio,et al.  Review on resilience in literature and standards for critical built-infrastructure , 2014 .

[12]  J. E. Hirsch,et al.  An index to quantify an individual's scientific research output , 2005, Proc. Natl. Acad. Sci. USA.

[13]  Luis E C Rocha,et al.  Dynamics of Air Transport Networks: A Review from a Complex Systems Perspective , 2016, 1605.04872.

[14]  Cheng Feng,et al.  Empirical analysis of airport network and critical airports , 2016 .

[15]  Amir Bashan,et al.  Articulation points in complex networks , 2016, Nature Communications.

[16]  Alan O'Connor,et al.  Resilience of traffic networks: From perturbation to recovery via a dynamic restricted equilibrium model , 2016, Reliab. Eng. Syst. Saf..

[17]  Oriol Lordan,et al.  Robustness of the air transport network , 2014 .

[18]  Gang Yan,et al.  Identifying vital edges in Chinese air route network via memetic algorithm , 2016, ArXiv.

[19]  S. Cristobal,et al.  Measuring the cost of resilience , 2016 .

[20]  Yong Qin,et al.  Modeling cascade dynamics of railway networks under inclement weather , 2015 .

[21]  Amedeo R. Odoni,et al.  Modelling delay propagation within an airport network , 2013 .

[22]  Tao Zhou,et al.  The H-index of a network node and its relation to degree and coreness , 2016, Nature Communications.

[23]  Xavier Prats,et al.  Operating cost sensitivity to required time of arrival commands to ensure separation in optimal aircraft 4D trajectories , 2015 .

[24]  Daniel B. Work,et al.  Using coarse GPS data to quantify city-scale transportation system resilience to extreme events , 2015, ArXiv.

[25]  Giovanni Sansavini,et al.  A quantitative method for assessing resilience of interdependent infrastructures , 2017, Reliab. Eng. Syst. Saf..

[26]  Yi Liu,et al.  Incorporating Predictability Into Cost Optimization for Ground Delay Programs , 2016, Transp. Sci..

[27]  Augusto Voltes-Dorta,et al.  Vulnerability of the European air transport network to major airport closures from the perspective of passenger delays: Ranking the most critical airports , 2017 .

[28]  R. May Thresholds and breakpoints in ecosystems with a multiplicity of stable states , 1977, Nature.

[29]  Kpotissan Adjetey-Bahun,et al.  A model to quantify the resilience of mass railway transportation systems , 2016, Reliab. Eng. Syst. Saf..

[30]  Jian Li,et al.  Connectivity reliability and topological controllability of infrastructure networks: A comparative assessment , 2016, Reliab. Eng. Syst. Saf..

[31]  Lishuai Li,et al.  Characterizing air traffic networks via large-scale aircraft tracking data: A comparison between China and the US networks , 2018 .

[32]  Yi Liu,et al.  Ground Delay Program Decision-making using Multiple Criteria: A Single Airport Case , 2013 .

[33]  Lu Hao,et al.  Block time reliability and scheduled block time setting , 2014 .

[34]  Jie Liu,et al.  Online four dimensional trajectory prediction method based on aircraft intent updating , 2018, Aerospace Science and Technology.