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

Dynamic evolution algorithm designing and control parameters quantitatively grading for the cascading failures based reliability model of an interdependent public transit network

“…However, the existing works have the following limitations: (i) user behavior habits and transportation engineering characteristics (game relation between trac supply and trac demand) are not considered enough, while it is limited in the study perspective of network physical topology and system science; (ii) the interdependent relationship between two sub-networks is not further characterized as the detailed interaction mechanism; (iii) the station state is just characterized as a binary performance (either in normal state or failure state), but it actually lacks consideration of a transition state: it is an abnormal state but not so serious as to be complete failure; (iv) the existing CFs measurement indicator is so single that it cannot capture the full dynamics evolution. It should be noted that the aforementioned limitations have been well improved in our previous work [36], which establishes a quantitatively controllable CFs model of an interdependent PTN considering congestion, time-delay interaction and station self-organization eects (expressed as the CFs model). The novel CFs model provides a novel understanding perspective for measuring the interdependent PTN reliability, and it is considered to achieve the objective of being much closer to actual PTN operation in that paper.…”

“…(a) One main limitation is that although the multi-perspective measurement indicator system (covering i) the global and local perspectives, ii) the aggregated https://doi.org/10.1088/1742-5468/ab363b and discretized perspectives) has been proposed in our previous work [36], the dynamics simulation analysis is performed on only one CFs control parameter (i.e. control parameter of the connected edge transit capacity θ), resulting in the obtained dynamics analysis sample being too small to eectively measure the reliability of each CFs measurement indicator.…”

“…The CFs model which closely matches the actual PTN operation and can be quantitatively controlled, is the basis for understanding the dynamics evolution of PTN CFs. Thus, the proposed CFs model in our previous work [36] considers the congestion, time-delay interaction and station self-organization eects for expressing itself as a quantitatively controllable CFs model. For making this paper concise and easy to understand, all necessary introductions relating to the basic description of novel CFs model are provided in the designing process of CFs model evolution algorithm in section 2.2.…”

“…For making this paper concise and easy to understand, all necessary introductions relating to the basic description of novel CFs model are provided in the designing process of CFs model evolution algorithm in section 2.2. Moreover, a more complete modeling process can be seen in our previous work [36].…”

Cascading failure with preferential redistribution on bus–subway coupled network

“…Moreover, Schafe et al [15] proposed a proactive measure to enhance the robustness of heterogeneously traffic loaded networks against cascading failure, based on load-dependent weights. Zhang et al [16,17,18] studied cascading failure of a weighted public transit network with a two-layer (including a physical and a logical layers) structure. They proposed a cascading-failure-based mesoscopic reliability model that takes congestion effects and user equilibrium evacuation into consideration.…”