Quantifying bus route service disruptions under interdependent cascading failures of a multimodal public transit system based on an improved coupled map lattice model

Lin Zhang, Min Xu and Shuaian Wang

Reliability Engineering and System Safety, 2023, vol. 235, issue C

Abstract: The coupled map lattice (CML) model can present rich spatiotemporal dynamic behaviors of complex systems, e.g., it has been observed to have good adaptability in the cascading failure modeling of subway networks (SNs). However, most studies rarely considered an interdependency between an SN and a bus network (BN), which reveals an open problem concerning the CML model's adaptability to a multimodal public transit system (MPTS). This paper develops an improved CML model to simulate interdependent cascading failures of an MPTS and quantify bus route service disruptions for amplifying severe consequences of failures. Remarkably, an interdependency integrating operational and nonlinear geographical interdependencies is proposed to modify the node state evolutionary function and model cascading failures across layers. Moreover, an estimation method for the node initial state is proposed to accommodate the initial value-sensitive dependence due to a logistic chaotic map involved in the evolutionary function. Finally, a case is simulated to verify the model's adaptability and enlighten operation management. Results indicate that (i) the neglect of interdependency causes an overestimation of the severity of cascading failures of an SN while a severe underestimation of those of a BN; (ii) the management and engineering measures to adjust transfers both have a phase transition point on the controllability of cascading failures, and the latter exhibits more a direct and comprehensive effect.

Keywords: Cascading failures; Coupled map lattice model; Bus route network; Interdependency; Complex networks (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:235:y:2023:i:c:s0951832023001655

DOI: 10.1016/j.ress.2023.109250

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