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Publications online

Research interests
  • Mathematical and computational modeling and analysis of safety, security, and resilience of complex sociotechnical systems in aviation.
  • Application of multiagent systems and artificial intelligence techniques in aviation.
  • Exploration and development of analysis methods (for example, model checking techniques) and tools for complex sociotechnical systems.
  • Development of tools and techniques for simulation of sociotechnical systems.

Participation in projects
  • Topconsortia for Knowledge and Innovation grant (topsector High Tech Systems and Materials) on airport security (the period of involvement: 2015 - 2020)
  • XFRIS project "Safety and risk modelling and analysis of very low level RPAS operations" (the period of involvement: 2016 - 2020)
  • A personal Veni grant from The Netherlands Organisation for Scientific Research (NWO) for developing novel mathematical and computational techniques for safety analysis of modern organisations, for example in air traffic (the period of involvement: 02/2012 - 02/2015)
  • SESAR WP-E research project "Mathematical Approach towards Resilience Engineering in ATM" (MAREA) (the period of involvement: 03/2011 - 09/2013)
  • KNAW project "Emergence of Aviation Safety Culture in Complex Operational Practices"(the period of involvement: 10/2012 - 8/2013)
  • Safety modeling and analysis of organisational processes in air traffic (the period of involvement: 01/2007 - 01/2010)
  • SOCIONICAL a project funded under European Seventh Framework Programme (FP7), aiming to develop Complexity Science based modelling, prediction and simulation methods for large scale socio-technical systems (the period of involvement: 02/2009 - 02/2013)
  • Distributed Engine for Advanced Logistics (DEAL) (the period of involvement: 02/2004 - 04/2007)
  • Cybernetic Incident Management (CIM) (the period of involvement: 02/2004 - 12/2007)
  • Automated Support for Management of Requirements for Knowledge-intensive Compositional Systems (the period of involvement: 10/2005 - 10/2006)

Research awards
  • Recipient of a personal VENI grant for innovative research on aviation safety from The Netherlands Organisation for Scientific Research (NWO)
  • Best Paper Award at ICCCI 2010 for the paper "Abstraction Relations Between Internal and Behavioural Agent Models for Collective Decision Making"
  • Best Paper Award at AAMAS 2010 for the paper "Can We Predict Safety Culture?"
  • Best Presented Paper Award at MSVVEIS 2008 for the paper "Formal Goal-based Modeling of Organizations"
  • Best Paper Award at Virt 2001

Active research lines


Multiagent, distributed control of air transport operations

Within this research line, novel, distributed control architectures are studied for efficient and safe management of air traffic in the air and on the ground. The main topics are: multiagent planning and scheduling under uncertainty, multiagent coordination, multiagent learning. Distributed control provides a promising way forward for building highly scalable, flexible, and resilient air transport systems.

A multiagent system model and a tool were built in collaboration with company To70 for efficient distributed control of airport surface movement operations at a major airport.

Distributed architecture

A distributed control architecture was also realized in a multiagent system model for airport luggage logistics, developed in collaboration with company Vanderlande for their FLEET concept.

Distributed luggage logistics

For more information please refer to:

[1] Timo Noortman (2018). Agent-Based Modelling of an Airport's Ground Surface Movement Operation: Understanding the principles and mechanisms of decentralised control. MSc thesis, TU Delft.
[2] Heiko Udluft (2017). Decentralization in Air Transportation. PhD thesis, TU Delft.



Agent-based modelling and simulation of security, efficiency, and resilience of airport terminal processes

In this research, a holistic approach toward the airport is pursued, taking into account all different aspects that make up the airport ecosystem. Both passengers and airport employees are modelled by agents, using theories from behavioural sciences. Different aspects of the airport performance are studied in relation to each other. Furthermore, trade-offs between particular performance dimensions, such as efficiency and security, are quantified. Decision making tools are developed to support airport managers in their operational decisions. For this study, Rotterdam-the Hague Airport is used as a living lab.


For more information please refer to:

[1] Janssen, S., Sharpanskykh, A., & Curran, R. (2019). Agent-based modelling and analysis of security and efficiency in airport terminals. Transportation Research Part C: Emerging Technologies, 100, 142-160.
[2] Blok, A. N., Sharpanskykh, A., & Vert, M. (2018). Formal and computational modeling of anticipation mechanisms of resilience in the complex sociotechnical air transport system. Complex Adaptive Systems Modeling, 6(1), 7.
[3] Knol, A., Sharpanskykh, A., & Janssen, S. (2019). Analyzing airport security checkpoint performance using cognitive agent models. Journal of Air Transport Management, 75, 39-50.


Agent-based safety risk modelling and analysis of air transport operations

Within this line, a methodology has been developed for quantitative safety risk assessment and management of air transport systems, represented by agent-based, sociotechnical, stochastic system models. Safety analysis of both existing air transport operations (e.g., runway incursion scenarios, airline ground service operations, mid air collision scenarios) and future concepts of operation (such as, UAS operations).


Safety risk analysis cycle


For more information please refer to:

[1] Sharpanskykh, A., & Haest, R. (2016). An agent-based model to study compliance with safety regulations at an airline ground service organization. Applied Intelligence, 45(3), 881-903.
[2] Rattanagraikanakorn, B., Sharpanskykh, A., Schuurman, M. J., Gransden, D., Blom, H., & Wagter, C. D. (2018). Characterizing UAS collision consequences in future UTM. In 2018 Aviation Technology, Integration, and Operations Conference (p. 3031).
[3] Stroeve, S. H., Blom, H. A., & Bakker, G. B. (2013). Contrasting safety assessments of a runway incursion scenario: Event sequence analysis versus multi-agent dynamic risk modelling. Reliability Engineering & System Safety, 109, 133-149.

Analysis of delay propagation in networks of airports under disruption

Mechanisms and patterns of delay propagation are studied using state-of-the-art statistical AI methods (machine learning, pattern recognition), Complex Networks methods, and Dynamical Systems Theory. An important challenge is to establish relations between the characteristics of the network structure and its dynamics in terms of spread of delays.

Delay propagation Delay analysis

For more information please refer to:

[1] Richard Termaat (2018). Modeling the Dynamics of Propagated Flight Delay: A case study of the United States National Aviation System. MSc thesis, TU Delft.
[2] Klemens Kostler (2018). Analysis of Delay Propagation in Networks of U.S. Airports. BSc Honours project report, TU Delft.