Time-Based Estimation of Vulnerable Points in the Munich Subway Network

Marian Sorin Nistor (), Doina Bein (), Wolfgang Bein (), Matthias Dehmer () and Stefan Pickl ()
Additional contact information
Marian Sorin Nistor: Universität der Bundeswehr München
Doina Bein: California State University
Wolfgang Bein: University of Nevada
Matthias Dehmer: The Health and Life Sciences University
Stefan Pickl: Universität der Bundeswehr München

A chapter in Operations Research Proceedings 2015, 2017, pp 355-360 from Springer

Abstract: 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.

Keywords: Adjacency Matrix; Directed Acyclic Graph; Decision Alternative; Gain Function; Maximize Expect Utility (search for similar items in EconPapers)
Date: 2017
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:oprchp:978-3-319-42902-1_48

Ordering information: This item can be ordered from
http://www.springer.com/9783319429021

DOI: 10.1007/978-3-319-42902-1_48

Access Statistics for this chapter

More chapters in Operations Research Proceedings from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().