Modeling and evolution characteristics of urban rail transit network resistance under the impact of unbalanced large passenger flows

MA Fei; JIANG Jinfeng; AO Yuyun; MA Zhuanglin; LIU Qing

doi:10.16511/j.cnki.qhdxxb.2024.26.045

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Journal of Tsinghua University(Science and Technology) ›› 2024, Vol. 64 ›› Issue (10) : 1717-1733. DOI: 10.16511/j.cnki.qhdxxb.2024.26.045

SPECIAL SECTION: BIG DATA ANALYTICS

Modeling and evolution characteristics of urban rail transit network resistance under the impact of unbalanced large passenger flows

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Abstract

[Objective] Despite unbalanced large passenger flows, urban rail transit network (URTN) frequently encounter the dual pressures of structural and functional resistance. This can result in cascade failure, potentially leading to partial or even total collapse of the URTN. To ensure the normal operation of these networks and understand the characteristics of their disaster resistance evolution, this study explores how an unbalanced large passenger flow affects the disaster resistance of URTN. [Methods] This study initially examines the effect of unbalanced large passenger flows on the URTN cascade failure from two perspectives: transport efficiency and passenger service. Subsequently, a passenger-flow weighting network is constructed to calculate the passenger-flow intensity. Herein, the weights of different nodes represent the proportion of the passenger flow per unit of time at different track stations during periods of unbalanced large passenger flows. This allows the measurement of a track station's importance level based on node number, node betweenness, and passenger flow intensity. Moreover, this study adapts the coupled map lattice (CML) model, building upon cascade failure theory and chaos dynamics, to obtain more accurate values for the failure node ratio and network strength entropy. In the modified CML model, the sudden disturbance level is defined according to the breakdown degree and influence range. The initial state value is determined by the saturation degree of the passenger flow at the track station, thus addressing the sensitive dependence of the spatiotemporal chaotic system on the initial state value. Subsequently, the dynamic evolution characteristics of the URTN structural and functional resilience levels are explored under different conditions of fault propagation and passenger flow strength. These analyses were based on failure node ratio and network strength entropy metrics. Finally, a case study was conducted using the Xi'an subway as an example. [Results] The results showed the following: (1) During a URTN cascade failure, the disaster resistance evolution trends of structure and function aligned, changed, and failed simultaneously. (2) Critical values existed for the inter-station coupling coefficient ε and sudden event disturbance R, which were ε=0.3 and R=1, respectively. Below these thresholds, the URTN cascade failure effect did not occur. However, when ε＞0.3 and R＞1, the failure time of the URTN's structural and functional disaster resistance decreased as ε and R increased. (3) The intensity of the passenger flow negatively affected the structural and functional resilience of the URTN. When disturbed, stations with high passenger flow intensity were more likely to trigger a URTN cascade failure. (4) Stations with large interconnectors and high passenger flow intensity exhibited lower structural and functional vulnerability after a sudden disturbance than stations with larger degrees. [Conclusions] This study has important theoretical and practical implications. Theoretically, it helps uncover the factors affecting cascade failure and the evolution characteristics of the URTN's disaster resistance under the impact of an unbalanced large passenger flows. In practice, this study provides a crucial foundation for decision-making regarding the enhancement of safety management in rail transit when faced with challenges posed by unbalanced large passenger flows.

Key words

urban rail transit / unbalanced large passenger flows / coupled map lattice / cascade failure / resistance

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MA Fei, JIANG Jinfeng, AO Yuyun, MA Zhuanglin, LIU Qing. Modeling and evolution characteristics of urban rail transit network resistance under the impact of unbalanced large passenger flows[J]. Journal of Tsinghua University(Science and Technology). 2024, 64(10): 1717-1733 https://doi.org/10.16511/j.cnki.qhdxxb.2024.26.045

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