Robustness of urban railway networks against the cascading failures induced by the fluctuation of passenger flow

•An urban railway network is constructed to examine network robustness.•A linear threshold model is developed to imitate cascading failure propagation.•The robustness of temporal urban railway network is found to vary over time.•Network robustness is affected by cascading failure induced by passenge...

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Bibliographic Details
Published in:Reliability engineering & system safety 2022-03, Vol.219, p.108227, Article 108227
Main Authors: Zhang, Yifan, Ng, S. Thomas
Format: Article
Language:English
Subjects:
Cascading failure
Dynamic robustness
Failure
Failure modes
Linear threshold model
Network topologies
Passenger flow
Passengers
Propagation modes
Rail transportation
Railway construction
Railway engineering
Railway networks
Railways
Reliability engineering
Robustness
Topology
Transportation networks
Urban railway network
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Summary:•An urban railway network is constructed to examine network robustness.•A linear threshold model is developed to imitate cascading failure propagation.•The robustness of temporal urban railway network is found to vary over time.•Network robustness is affected by cascading failure induced by passenger flow. This paper constructs an urban railway network (URN) as a directed weighted network at different times and studies the dynamic network robustness against the fluctuation of passenger flow-induced cascading failures under different failure modes. The propagation of cascading failure is then imitated through the linear threshold (LT) model, where the influence parameter of edges is defined. In the light of network topology and functionality, two robustness indices, which include the change of edge size in the most connected component (RGCSe) and operational efficiency (ROEt) are employed. By coalescing these two indices, a synthetic operator Rt is proposed to quantify the dynamic TURN robustness comprehensively. The simulation results show that the TURN robustness varies over time. Besides, an increase in the volume of passenger flow can exacerbate the sizes of cascading failure and the impacts on network robustness under different scenarios. Consequently, it is imperative to examine the impacts of time-varying cascading failure on URN robustness. The findings of this research are widely applicable to other networked systems.
ISSN:0951-8320
1879-0836
DOI:10.1016/j.ress.2021.108227