scholarly journals Identifying Key Bus Stations Based on Complex Network Theory considering the Hybrid Influence and Passenger Flow: A Case Study of Beijing, China

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Jianlin Jia ◽  
Yanyan Chen ◽  
Ning Chen ◽  
Hui Yao ◽  
Yongxing Li ◽  
...  
Keyword(s):  
Complex Network ◽  
Shortest Paths ◽  
Response Speed ◽  
New Method ◽  
Transfer Lines ◽  
Complex Network Theory ◽  
Passenger Flow ◽  
Network Methods ◽  
Bus Network ◽  
Beijing China

In the bus network, key bus station failure can interrupt transfer lines, which leads to the low effectiveness of the whole network, especially during peak hours. Thus, identifying key stations in the bus network before the emergency occurs has a great significance to improve the response speed. In this paper, we proposed a new method considering station hybrid influence and passenger flow to identify key stations in the whole bus network. This method aims to measure the influence of bus stations while combining the topological structure of the bus network and dynamic bus stations passenger flow. The influence of bus stations was calculated based on the local structure of the network, which refines from finding the shortest paths with high computational complexity. To evaluate the performance of the method, we used the efficiency of the network and vehicle average speed at the station to examine the accuracy. The results show that the new method can rank the influence of bus stations more accurately and more efficiently than other complex network methods such as degree, H-index, and betweenness. On this basis, the key stations of the bus network of Beijing in China are identified out and the distribution characteristics of the key bus stations are analyzed.

scholarly journals Urban Transit Network Properties Evaluation and Optimization Based on Complex Network Theory

2019 ◽  
Vol 11 (7) ◽  
pp. 2007 ◽  
Author(s):  
Guo-Ling Jia ◽  
Rong-Guo Ma ◽  
Zhi-Hua Hu
Keyword(s):  
Complex Network ◽  
Network Theory ◽  
Public Transportation ◽  
Path Length ◽  
Transportation Network ◽  
Average Path Length ◽  
Theory And Practice ◽  
Complex Network Theory ◽  
Public Transportation Network ◽  
Bus Network

Urban public transportation contributes greatly to sustainable urban development. An urban public transportation network is a complex system. It is meaningful for theory and practice to analyze the topological structure of an urban public transportation network and explore the spatial structure of an urban transportation network so as to mitigate and prevent traffic congestion and achieve sustainability. By examining the Xi’an bus network, the degree distribution, average path length, aggregation coefficient, and betweenness centrality of the bus station network were computed using models in complex network theory. The results show that the node degrees of the Xi’an bus network are unevenly distributed and present a polarization diagram with long average path length and high aggregation. A model based on betweenness and its solution method was developed to improve the public transportation network’s sustainability and discuss the possibility of optimizing the sustainability by network analyzing methods.

scholarly journals Analysis of Complex Transportation Network and Its Tourism Utilization Potential: A Case Study of Guizhou Expressways

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-22 ◽  
Author(s):  
Sihong Chen ◽  
Jianchao Xi ◽  
Menghao Liu ◽  
Tao Li
Keyword(s):  
Spatial Structure ◽  
Complex Network ◽  
Transportation Networks ◽  
Transportation Network ◽  
Tourism Development ◽  
Scientific Basis ◽  
Complex Network Theory ◽  
Passenger Flow ◽  
Service Areas ◽  
Utilization Potential

Transportation is an example of a typical, open, fluid complex network system. Expressways are one form of complex transportation networks, and expressway service areas serve as infrastructure nodes in the expressway transportation network; hence, their construction has a significant impact on tourism development and utilization. Domestic and foreign studies on complex transportation networks have mostly been conducted from the perspective of railways, air transport, and urban transportation but seldom on expressway transportation networks. This study employed complex network theory, social network analysis, kernel density analysis, and bivariate autocorrelation to characterize the spatial structure of expressway transport networks in terms of geographical centrality. By innovating the coupling of geographical centrality and passenger flow centrality in clustering, the study also quantitatively analyzed the differences between the geographical advantage and actual passenger flow advantage of China’s Guizhou expressway transportation network to analyze the tourism utilization potential of expressway service areas. We found that (1) the geographical centrality of the Guizhou expressway transportation network ranged from −1.28 to 3.33, and its distribution shows a single-core, polyconcentric dispersed spatial structure; (2) the passenger-car flow rate ranged from 15,000 to 3.66 million, and its distribution showed a dual-core, polycentric dispersed structure that is weakly concentric; and (3) there was a positive correlation of 0.22 between the geographical centrality and passenger flow of the Guizhou expressway transportation network, which showed seven cluster types—“high-high,” “moderately high-high,” “low-high,” “moderately low-high,” “high-low,” “moderately high-low,” and “low-low”—for which seven corresponding models of tourism development were proposed. This study broadens the practical application of traffic network complexity research and provides a scientific basis for upgrading and transforming the Guizhou expressway transportation network as well as for developing composite tourism uses for expressway service areas.

scholarly journals A Novel Algorithm of Identification Theory of Complex Network for Public Transportation Network Cascading Failure

2021 ◽  
Vol 15 ◽  
pp. 236-247
Author(s):  
Xu Xu
Keyword(s):  
Complex Network ◽  
Network Theory ◽  
Public Transportation ◽  
Transportation Network ◽  
Network Evolution ◽  
Cascading Failure ◽  
Complex Network Theory ◽  
Node Importance ◽  
Public Transportation Network ◽  
Bus Network

With the development of complex network theory and the gradual application of the traffic field, the problem of cascading failure has caused great attention of researchers. This paper tries to propose a new method based on complex network theory to measure the importance of nodes in the network. Based on complex network theory, this paper first discusses the network evolution mechanism of three main contents, define the importance of nodes in the network, and the design of the network center and the evaluation of the importance of node algorithm. In the end, a critical section identification method considering the failure probability and the failure consequence is designed, and the method for calculating the node importance based on the cascading failure is proposed. Using complex network theory, a quantitative assessment of the center of public transportation network and node importance model is designed. The bus network center, for the study of node importance analysis of bus network survivability has important significance. Help guide the optimization of public transport network service. Improve transport capacity of public transportation system.

scholarly journals Structural Analysis of Bus Networks Using Indicators of Graph Theory and Complex Network Theory

2017 ◽  
Vol 11 (1) ◽  
pp. 92-100 ◽  
Author(s):  
Hui Zhang
Keyword(s):  
Graph Theory ◽  
Complex Network ◽  
Adjacency Matrix ◽  
Network Theory ◽  
Weighted Average ◽  
Power Law Distribution ◽  
Complex Network Theory ◽  
Path Distance ◽  
Bus Network ◽  
Bus Networks

The structure of bus network is very significant for bus system. To evaluate the performance of the structure of bus network, indicators basing on graph theory and complex network theory are proposed. Three forms of matrices comprising line-station matrix, weighted adjacency matrix and adjacency matrix under space P are used to represent the bus network. The paper proposes a shift power law distribution which is related average degree of network to fit the degree distribution and a method to calculate the average transfer time between any two stations using adjacency matrix under P space. Moreover, this paper proposes weighted average shortest path distance and transfer efficiency to evaluate the bus network. The results show that the indicators that we introduce, effectively reflect properties of bus network.

Deep learning and complex network theory based analysis on socialized manufacturing resources utilisations and an application case study

2021 ◽  
pp. 1063293X2110031
Author(s):  
Maolin Yang ◽  
Auwal H Abubakar ◽  
Pingyu Jiang
Keyword(s):  
Deep Learning ◽  
Complex Network ◽  
Network Models ◽  
Neural Network Models ◽  
Complex Network Theory ◽  
New Type ◽  
Manufacturing Resource ◽  
Manufacturing Resources ◽  
Resource Characteristics

Social manufacturing is characterized by its capability of utilizing socialized manufacturing resources to achieve value adding. Recently, a new type of social manufacturing pattern emerges and shows potential for core factories to improve their limited manufacturing capabilities by utilizing the resources from outside socialized manufacturing resource communities. However, the core factories need to analyze the resource characteristics of the socialized resource communities before making operation plans, and this is challenging due to the unaffiliated and self-driven characteristics of the resource providers in socialized resource communities. In this paper, a deep learning and complex network based approach is established to address this challenge by using socialized designer community for demonstration. Firstly, convolutional neural network models are trained to identify the design resource characteristics of each socialized designer in designer community according to the interaction texts posted by the socialized designer on internet platforms. During the process, an iterative dataset labelling method is established to reduce the time cost for training set labelling. Secondly, complex networks are used to model the design resource characteristics of the community according to the resource characteristics of all the socialized designers in the community. Two real communities from RepRap 3D printer project are used as case study.

scholarly journals Ecological Network Optimization in Urban Central District Based on Complex Network Theory: A Case Study with the Urban Central District of Harbin

2021 ◽  
Vol 18 (4) ◽  
pp. 1427
Author(s):  
Shuang Song ◽  
Dawei Xu ◽  
Shanshan Hu ◽  
Mengxi Shi
Keyword(s):  
Complex Network ◽  
Network Optimization ◽  
Network Theory ◽  
Habitat Destruction ◽  
Landscape Patterns ◽  
Ecological Network ◽  
Complex Network Theory ◽  
Ecological Corridors ◽  
Low Degree ◽  
Central District

Habitat destruction and declining ecosystem service levels caused by urban expansion have led to increased ecological risks in cities, and ecological network optimization has become the main way to resolve this contradiction. Here, we used landscape patterns, meteorological and hydrological data as data sources, applied the complex network theory, landscape ecology, and spatial analysis technology, a quantitative analysis of the current state of landscape pattern characteristics in the central district of Harbin was conducted. The minimum cumulative resistance was used to extract the ecological network of the study area. Optimized the ecological network by edge-adding of the complex network theory, compared the optimizing effects of different edge-adding strategies by using robustness analysis, and put forward an effective way to optimize the ecological network of the study area. The results demonstrate that: The ecological patches of Daowai, Xiangfang, Nangang, and other old districts in the study area are small in size, fewer in number, strongly fragmented, with a single external morphology, and high internal porosity. While the ecological patches in the new districts of Songbei, Hulan, and Acheng have a relatively good foundation. And ecological network connectivity in the study area is generally poor, the ecological corridors are relatively sparse and scattered, the connections between various ecological sources of the corridors are not close. Comparing different edge-adding strategies of complex network theory, the low-degree-first strategy has the most outstanding performance in the robustness test. The low-degree-first strategy was used to optimize the ecological network of the study area, 43 ecological corridors are added. After the optimization, the large and the small ecological corridors are evenly distributed to form a complete network, the optimized ecological network will be significantly more connected, resilient, and resistant to interference, the ecological flow transmission will be more efficient.

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