Home | deutsch  | Legals | Sitemap | KIT

Agent based modelling for assessing Resilience of cities in the course of time

Agent based modelling for assessing Resilience of cities in the course of time

Dr. Sadeeb Ottenburger

project group:

Unfallfolgen (UNF)/ Accident Management Systems


The welfare of modern societies highly depends on the functioning of Critical Infrastructures (CIs) like power and water supply, transport and distribution systems.
Thus it is desirable to gain a profound understanding on how disruptions of CIs for example caused by extreme weather events or terrorist attacks affect the vulnerability of a city - today and in the future. Detecting hidden or a priori underestimated interdependencies could help to identify enhanced strategies for crisis management and design patterns concerning robust future CI developments.

For this purpose an Agent Based Modelling (ABM) approach, where CIs or CI components are modelled as agents, seems to be most promising.

Aims / Objectives

The main goal is to establish a simulation software based on ABM which supports decision making during a crisis as well as facilitates city planners or CI owners to design or redesign future or existing CIs resp. in an optimal way in the sense of robustness and mitigation.
Therefore this simulation software should comprise the following key features:

  • Flexible specification of disruption scenarios.
  • Flexible specification of model parameters in order to define different instances of certain future trends.
  • Visualization of urban resilience related quantities on the level of CIs, CI-complexes or the city as a whole.
  • Dynamic options for interaction: counter measures may be implemented and evaluated during run time.

In order to achieve the main goal the following intermediate steps have to be done:

  • CIs as they exist nowadays have to be modelled as agents – this also includes network and environment modelling.
  • The most promising future trends related to CIs have to be identified, evaluated and if applicable modelled e.g. smart grid technology or car sharing.
  • A holistic simulation framework with a genericity that allows dynamic model parametrization, dynamic specification of disruption scenarios, dynamic implementation of counter measures and a wide range of graphical tools for analysis has to be established.