Establishment and Analysis of the Supernetwork Model for Nanjing Metro Transportation System

Abstract

In recent years, many researchers have applied complex network theory to urban public transport network to construct complex network and analyze its network performance. The original analysis method generally uses the Space L and Space R model to establish a simple link between public sites but ignores the organic link between the overall network system and the line subsystem. As an important part of urban public transport system, subway plays an important role in alleviating traffic pressure. In this paper, a supernetwork model of Nanjing metro network is established by using the supernetwork method. Three parameters, node-hyperedge degree, hyperedge-node degree, and hyperedge degree, are proposed to describe the model. The model is compared with the traditional Space L and Space P models. The study on the supernetwork model of Nanjing metro complex network shows that the network density, network centrality, and network clustering coefficient are large, and the average network distance is small, which meets the requirements of traffic planning and design. In this study, the subway line is considered as a subsystem and further simplified as a node, so that the complex network analysis method can be applied to the new supernetwork model, expanding the thinking of complex network research.

Evaluation Indexes and Correlation Analysis of Origination–Destination Travel Time of Nanjing Metro Based on Complex Network Method

“…Yu W analyzed the evolution of the Nanjing metro network by using the complex network method, combined with the urban spatial pattern [41]. Wei Y proposed the concept and performance index of the supernetwork model based on the complex network model, and analyzed the performance analysis, combined with the Nanjing metro network [42].…”

“…Direction and stations of Nanjing metro lines 15,14,13,12,11,10,9,8,7,6,5,41,42,43,44,45,46,47,48,49,50,. 51, 52, 53, 54, 55] …”

Characterizing the hypergraph-of-entity and the structural impact of its extensions

“…While in graphs all edges are binary, leading to only one other node, in hypergraphs hyperedges are n-ary, leading to multiple nodes, and thus different degree statistics. While some authors use 'degree' to refer to node and hyperedge degrees (Yu andSun 2018, §4) (Klamt et al 2009, §Network Statistics in Hypergraphs), in this work we opted to use the 'degree' designation when referring to nodes and the 'cardinality' designation when referring to hyperedges. This is to avoid any confusion for instance between an "hyperedge-induced" node degree and a hyperedge cardinality.…”

Analysis of Space-Time Variation of Passenger Flow and Commuting Characteristics of Residents Using Smart Card Data of Nanjing Metro

“…Li J et al established a passenger flow fluctuation model to evaluate the impact of weather conditions on the fluctuation of single-line passenger flow of Nanjing metro in China [20]. Wei Y et al established the supernetwork model of Nanjing metro network, proposed new parameters to describe the model, and compared it with the traditional space L and space P models [21]. Yu W et al used the complex network method to analyze the space-time evolution of Nanjing metro network according to the opening sequence of 10 metro lines in Nanjing [22].…”