Developing Measures for Node Importance in Critical Transportation Networks - An Illustration to the Analysis of Switches at Finnish Railway Stations

Estimates on the importance of nodes in transportation networks provide guidance on the allocation of resources to preventive maintenance. As a rule, those nodes whose disruptions would most affect the level of service provided by the network are particularly important and, as a consequence, merit more attention in maintenance planning. Conventionally, measures of node importance have been generated based on structural properties (e.g. degree centrality, betweenness centrality). However, these properties do not account for the extent to which the network is capable of providing its intended level of service, such as enabling the planned traffic volume between terminal pairs that are defined by relevant origins and destinations in the network. In this setting, we extend well-known measures of probabilistic risk importance to prioritize nodes in support of preventive maintenance planning. Specifically, we adapt the risk achievement worth (RAW) measure to assess how much the planned traffic volume between terminal pairs of the network would be compromised due to disruptions at different nodes.
To support the applicability of our methodological advances, we develop a systematic, data-driven, semi-automated approach that makes but modest assumptions about the quality of underlying data. We illustrate this approach with a case study on the prioritization of switches at a representative railway station in Finland, based on an analysis of the reliability of the connections that this station offers to the adjacent railway track segments. We also compare the proposed RAWmeasure with structural importance measures and elaborate on how these two kinds of measures can be jointly examined to produce complementary sources of information.

Keywords: Importance measure, Probabilistic risk importance measure, Transportation networks, Critical infrastructures, Railways industry, Risk assessment, Risk management, System reliability, Probabilistic risk assessment.