Rail networks are real-life examples of complex networks and critical logistic and economic contributors to the wellbeing of society. Natural or human-caused hazards leading to the disruptions of rail network’s components can cause severe consequences including significant economic impacts. The network topology, defined as the arrangement and connectivity among components, can be clearly depicted, quantified, and assessed using complex network theory. Additionally, under the premise of ensuring network connectivity remains unchanged, the size of rail networks with an overly large number of components were reduced, successfully increasing the efficiency of analysis, and breaking through the current limitations on rail network analysis. In comparing the two analyzed networks in this study, the Class I rail network is more complicated, efficient, and developed than the Amtrak rail network.&nbsp;The vulnerability of rail networks is a critical factor in evaluating the impacts of node and link disruptions due to potential failures. The vulnerability of rail networks is measured to compare the impact of all possible failures on the networks. The reduction in the size of the Class I rail network ensures the analysis of the network vulnerability is effective and efficient. The critical areas can be clearly depicted and identified depending on the arrangement of the most critical nodes and links.&nbsp;This presentation examines a pre-failure topology enhancement strategy to reduce the impact of potential failures by adding hypothetical loop lines in critical areas to reduce network vulnerability. The vulnerability of most critical components can be reduced to almost zero, indicating that inserting a proper hypothetical loop line can significantly reduce the vulnerability of components in the critical areas. Furthermore, the results also reflect that in a network, such as the Amtrak rail network, adding loop lines in an area can effectively increase the entire network efficiency and reduce the network vulnerability. However, in some networks with overly complicated connectivity, such as the Class I rail network, adding loop lines in an area may not effectively enhance the entire network. Other hypothetical loop lines need to be examined in future work by adding them in several areas rather than the most critical area.&nbsp;Network efficiency assessment also provides a basis to measure the resilience index based on assumed recovery sequences. The resilience index is used to quantify the ability of rail networks to withstand and recover from the failure of components. In order to enhance network resilience due to the failure of the most critical node (i.e., station) and its connected links (i.e., rails), this study compares all possible recovery sequences and ranks them with respect to the corresponding resilience index values. The recovery sequence with the largest resilience index value represents the best recovery strategy. In this case, after a failure occurs, the overall loss of network efficiency can be effectively minimized to reduce impacts. It should be emphasized that the repair of nodes (i.e., stations) is essential in the sequential recovery strategy, which may significantly affect the restoration of network efficiency.&nbsp;Finally, the reduced Class I rail network is intended to select and analyze a subset of nodes and links in the network while keeping the network connectivity unchanged. For different analysis purposes, additional nodes need to be added to the subset and analyzed. The recovery strategy in this study is only for the disruption case of one node and links connected to it. The same recovery analysis process can be used to determine the recovery strategies for practical situations according to different disruption cases and assumptions involving several nodes concurrently.

Topological Analytics for Vulnerability and Recovery Enhancements After Disruption of Rail Networks

Paper Proceedings: https://asmedigitalcollection.asme.org/JRC/JRC2021/volume/84775

2021 Joint Rail Conference (JRC2021)

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