Complex Systems

Airports are complex systems. They are characterised by complex interdependencies between different parts of (e.g. check-in, security and retail areas) and different aspects of airport operations (e.g. landside operations and airside operations). Additionally, there are multiple stakeholders including airport and airliner operators, government agencies and retailers. Furthermore, there are a number of sometimes conflicting operational objectives including safety (and security in particular), efficiency and passenger experience. Altogether, the emergent behaviour of the airport system as a whole cannot be inferred from an examination of the individual components of the system alone [1].

The characteristics of airports often violate key assumptions required by traditional systems engineering and are therefore no longer suitable for these applications.  New approaches and models are required to allow for flexible identification and description of complex patterns, multi-scale processes, and to balance the multi-faceted demands of emergency response, security, efficiency and economic viability.

Research Aims

The aims of the research are twofold. First, it seeks to develop a holistic airport system modelling framework. Such a framework is used to develop airport models to aid airport planning and design, provide decision support for real-time airport operations and for the review of key performance indicators. Secondly, the research seeks to identify important characteristics of complex systems and the relationships between underlying components. It strives to generate an understanding of and tools to support the application of complexity science and models to airport systems.

In essence, the modelling framework being developed captures the research outcomes of each research stream as it incorporates spatial, temporal, business process, security and passenger activity elements for a number of airport scenarios (including normal, peak-hour and emergency scenarios). Consequently, the complex systems research program plays a crucial role in the integration of outcomes generated by the six other research streams in order to enhance the current and future operational capabilities of Australian and international airports.

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Research Approach

The Complex Systems program comprises three tasks:

  1. Modelling each component of the airport model as a complex system;
  2. Integrating these components into a comprehensive complex system; and
  3. Synthesising different complex systems approaches.

 

The first two of these tasks will be addressed using a variety of systems approaches including Business Process Models, System Dynamics, Agent-Based Modelling and Bayesian Networks (BN). Quantified conceptual BN models will be developed to depict a set of interacting factors (which could be complex sub-systems) and their various directed linkages, where the quantification is in the form of conditional probabilities for each node based on the nodes that feed into it. Techniques will be developed to provide structural and probabilistic inferences, to incorporate uncertainty in processes and decisions, and to facilitate complex scenario analyses from the BNs.

The third task will be addressed using a mixed qualitative/quantitative approach and a novel extension of an adaptive, dynamic object-oriented BN [2]. Such adaptive approaches for complex systems are now widely accepted in order to facilitate learning and decision-making [3].

 

 

Agent-based model example

Agent based model of passenger movements through the quarantine are of the airport terminal

Research Team

  • Professor Kerrie Mengersen (Chief Investigator)
  • Associate Professor Duncan Campbell
  • Dr. Sandra Johnson
  • Dr. Paul Wu
  • Charisse Farr (PhD student)
  • Jegar Pitchforth (PhD student)
  • Jack Grummitt (Undergraduate student)
  • Ying Chan (Undergraduate student)

References

  1. Y. Bar-Yam, Unifying Principles in Complex Systems, in M. C. Roco and W. S. Bainbridge, Eds. Converging Technology (NBIC) for Improving Human Performance, Kluwer: Dordrecht, The Netherlands, 2003.
  2. P. Weber and L. Jouffe, Complex system reliability modelling with Dynamic Object Oriented Bayesian Networks, Reliability Engineering & System Safety, vol. 91, no. 2, pp. 149-162, 2006.
  3. K. A. Cabana, et al., Enterprise Systems Engineering Theory and Practice, Vol. 9: Enterprise Research and Development, The MITRE Corporation, MP05B0000043, November 2005.


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