Abstract
In this paper, the frequency of trains in the Munich subway network is analyzed. Using influence diagrams the stations and edges in the network that are most vulnerable to catastrophic attacks are determined. Upon obtaining the number of trains in each station at a certain moment in time, the most vulnerable stations will be automatically identified. This process is discrete in time, and various existing train schedules available to the general public are considered. Considering each schedule, the gain and the cost of destroying a station is calculated. Based on utility values for each station representing the difference between the gain and the cost, an influence diagram decides which stations are most vulnerable to attacks.
This is a preview of subscription content, log in via an institution to check access.
Notes
- 1.
There are 100 stations when four stations are doubly counted: these stations are not physically in the same location, but in close proximity.
References
Muenchner Verkehrsgesellschaft mbH (MVG) Muenchen, MVG in figures. http://www.mvg.de/dam/en/mvg/ueber/unternehmensprofil/mvg-in-figures-s. Accessed 13 Aug 2015
Muenchner Verkehrs- und Tarifverbund GmbH, Alle Informationen zu den Bahnhoefen im MVV (2015). http://www.mvv-muenchen.de/de/netz-bahnhoefe/bahnhofsinformation/index.html. Accessed 13 Aug 2015
Eisenfuehr, F., Langer, T., Weber, M.: Rational Decision Making. Springer, New York (2010)
Eriksen, S., Keller, L.R.: Decision Trees. Kluwer Academic Publishers, Boston (2001)
Shachter, R.D.: Probabilistic inference and influence diagrams. Oper. Res. 36, 589–604 (1988)
Von Neumann, J., Morgenstern, O.: Theory of Games and Economic Behavior. Princeton University Press, Princeton (1953)
Muenchner Verkehrs- und Tarifverbund GmbH, Netzplaene (2015). http://www.mvv-muenchen.de/de/netz-bahnhoefe/netzplaene/index.html. Accessed 13 Aug 2015
R Development Core Team, R: A Language and Environment for Statistical Computing, Vienna, Austria (2008). http://www.R-project.org. Accessed 13 Aug 2015
Acknowledgements
Research of author Marian Sorin Nistor, was funded by the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme FP7/2007-2013/ under REA Grant Agreement Number 317382.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing Switzerland
About this paper
Cite this paper
Nistor, M.S., Bein, D., Bein, W., Dehmer, M., Pickl, S. (2017). Time-Based Estimation of Vulnerable Points in the Munich Subway Network. In: Dörner, K., Ljubic, I., Pflug, G., Tragler, G. (eds) Operations Research Proceedings 2015. Operations Research Proceedings. Springer, Cham. https://doi.org/10.1007/978-3-319-42902-1_48
Download citation
DOI: https://doi.org/10.1007/978-3-319-42902-1_48
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-42901-4
Online ISBN: 978-3-319-42902-1
eBook Packages: Business and ManagementBusiness and Management (R0)