Calculation Method for Load Capacity of Urban Rail Transit Station considering Cascading Failure

Abstract

The load capacity of urban rail transit station is of great significance to provide reference in station design and operation management. However, it is difficult to carry out quantitative calculation quickly and accurately due to the complex interaction among passenger behaviors, facility layout, and the limit capacity of single facility. In this paper, the association network of facilities is set up based on the analysis of passenger service chain in station. Then the concept of cascading failure is introduced to the dynamic calculation model of load capacity, which is established on the user-equilibrium allocation model. The solution algorithm is optimized with node attack strategy of complex network to effectively reduce the computational complexity. Finally, a case study of Lujiabang Road Station in Shanghai is carried out and compared with the simulation results of StaPass, verifying the feasibility of this approach. The proposed method can not only search for the bottleneck of capacity, but also help to trace the loading variation of facilities network in different scenarios, providing theoretical supports on passenger flow organization.

A Review and Prospect for the Complexity and Resilience of Urban Public Transit Network Based on Complex Network Theory

“…Subsequently, it has developed to consider the impact of the weights (station strength) and the adjacent stations (Du et al [101]) or define it based on the actual bus card passenger flow data fitting (He et al [102]). Additionally, the exponential type load definition control parameters are set to control the distribution of network initial load (Zhang et al [69], Zhang et al [32]), or the station initial load is defined by determining the passenger flow per unit area based on Fruin [105] service level (Huang et al [104]). Step number…”

“…The precise considerations of the definition of station initial load also bring a significant improvement in the definition of station capacity, because the station capacity is proportional to station initial load in most works based on the general assumption of loadcapacity model [107] (see the detailed introduction in Table 3, station capacity definition). It should be noted that the station capacity defined by Huang et al (the summation of the maximum service capacity, and the maximum queuing capacity of the facility nodes in a unit area) seems to provide a much better way that is close to the engineering application [104], the summation of the maximum service capacity, and the maximum queuing capacity of the facility nodes in a unit area seem to provide a much better way that is close to the engineering application. In terms of station state, the related studies are summarized into two states: the normal state and the failure state.…”

“…(ii) Initially, the rule of failure load dynamic redistribution involves only the dynamic update of station states and does not have the strict load redistribution process (Ma et al [99]); or the spreading process involves the failure load redistribution based on the proportion of adjacent station capacity (Zhang et al [69]), the shortest path redistribution (Zou et al [100], He et al [102]), but does not consider the congestion effect on edge (road section) and the conscious dynamic game process existing in path selection behavior. Subsequently, it has developed to introduce the congestion effect of road section based on the BPR road impedance function [103], so that the dynamic resilience model of single layered PTN based on user equilibrium evacuation is further established (Zhang et al [32], Huang et al [104]).…”

“…Complexity failures based microcosmic resilience model (Huang et al [104]). The ticket gates, escalators, platforms, and other facilities in a single urban rail transit station can be abstracted into a facility associated network; thus a cascading failures based resilience model for this network can be established.…”

Congestion Evaluation of Pedestrians in Metro Stations Based on Normal-Cloud Theory

“…ξ j is the crowding coefficient for different grades of service. Then, the principle of equal spacing, which was widely used in the study of urban traffic performance [4,5,9,15,21,22,27,43,44], was followed in this study to divide the ε into n grades. Finally, the GPC was calculated as follows:…”