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The stable and efficient operation of rail transit networks (RTNs) is critical for the integrated development of metropolitan areas. However, numerous studies have indicated that RTNs are prone to large-scale cascading failures when subjected to disturbances. To address the limitations of traditional cascading failure models, this paper proposes an innovative cascading failure model for metropolitan areas RTNs, which incorporates nonlinear load fluctuations and the bounded rationality of passengers. This model aims to capture the cascading failure characteristics of RTNs with chaotic properties under 12 combination strategies. A single- and dual-parameter coupling analysis of chaotic evolution parameters and prospect theory parameters are conducted. Taking the RTN in the Chengdu metropolitan area as an example, both the static characteristics and cascading failure features of the network are analyzed. The findings reveal the following: (i) the RTN is a assortativity network and lacks small-world and scale-free properties. (ii) During network disturbances, a higher level of passenger familiarity with the network increases the likelihood of large-scale cascading failures. (iii) When passengers tend to avoid risks, stations with higher carrying capacity are more prone to failures. This study holds significant implications for ensuring the stable and reliable operation of rail transit systems within metropolitan areas., Author(s): Changfeng Zhu [1]; Zhaoxin Tang (corresponding author) [1]; Chun An [1]; Jinhao Fang [1]; Jie Wang [1]; Linna Cheng [1,2] 1. Introduction The rise of intelligent transportation systems has [...]

During peak passenger flow periods, congestion propagation directly affects the operational safety and efficiency of multi-mode rail transit interconnections. By analyzing the key factors affecting congestion propagation, such as the train stop schedule, and considering parameters such as the basic reproduction number and propagation threshold, this study proposes a multi-mode rail transit susceptible-infected-recovered-susceptible (MRT-SIRS) epidemic model to analyze passenger flow congestion propagation. Simulation experiments and sensitivity analyses using data from the multi-mode rail transit in Beijing, China, were conducted to examine the influence mechanism of key factors on congestion propagation. The degree of influence of each factor was investigated using Gray correlation analysis. Each key factor, including the propagation and recovery rates, influences congestion propagation differently. The results of this study may provide theoretical support for the efficient operation and management of multi-mode rail transit systems. [ABSTRACT FROM AUTHOR]