A quantitatively controllable mesoscopic reliability model of an interdependent public transit network considering congestion, time-delay interaction and self-organization effects

This paper establishes a multi-parameter controlling cascading failures (CFs) model for measuring interdependent public transit network (PTN) reliability under mesoscopic perspective. First, we abstract the weighted PTN and geospatial PTN into a double-layered passenger flow system that considers the influences of the transportation infrastructure and the interaction between the passenger flow transited via the public transit mode and that transited via all other urban traffic modes. Second, for embedding the congestion, time-delay interaction and self-organization effects in the CFs model of an interdependent PTN, we propose the failure load dynamic redistribution (FLDR) considering the congestion effect in each sub-network, define the interaction way (another FLDR form) considering time-delay effect between two sub-networks and establish the CFs judging method considering the self-organization effect in the interdependent PTN. Furthermore, the multi-perspective measurement indicator system (i.e., (i) the global and local perspectives and (ii) the aggregated and discretized perspectives) is defined to provide a full viewing perspective for better understanding and capturing the CFs process. Finally, through a simulation analysis of Jinan’s PTN, the adaptability of the proposed CFs model is verified, and the significant nonlinearity is found, i.e., there is a threshold value of the station load tolerance parameter λ _C , making the cascading survivability of interdependent PTN significantly increase, when the actual station load tolerance parameter is larger than λ _C ; moreover, increasing the control parameter of the connected edge transit capacity (parameter θ ) can speed up the process of finding this λ _C of an interdependent PTN. This nonlinearity provides the simulation evidence of the CFs controllability in the interdependent PTN.