Analysis of Identification Methods of Key Nodes in Transportation Network

2022 ◽  
Author(s):  
Qiang Lai ◽  
Hong-hao Zhang
Keyword(s):  
Transportation Networks ◽  
Transportation Network ◽  
Practical Significance ◽  
Node Degree ◽  
Connected Subgraph ◽  
Network Efficiency ◽  
Identification Methods ◽  
Identification Method ◽  
Bus Networks ◽  
Key Nodes

Abstract The identification of key nodes plays an important role in improving the robustness of the transportation network. For different types of transportation networks, the effect of the same identification method may be different. It is of practical significance to study the key nodes identification methods corresponding to various types of transportation networks. Based on the knowledge of complex networks, the metro networks and the bus networks are selected as the objects, and the key nodes are identified by the node degree identification method, the neighbor node degree identification method, the weighted k-shell degree neighborhood identification method (KSD), the degree k-shell identification method (DKS), and the degree k-shell neighborhood identification method (DKSN). Take the network efficiency and the largest connected subgraph as the effective indicators. The results show that the KSD identification method that comprehensively considers the elements has the best recognition effect and has certain practical significance.

A transportation network evolution model based on link prediction

2021 ◽  
Author(s):  
Shuang Gu ◽  
Keping Li ◽  
Yan Liang ◽  
Dongyang Yan
Keyword(s):  
Network Structure ◽  
Link Prediction ◽  
Transportation Networks ◽  
Network Evolution ◽  
Evolution Model ◽  
Civil Aviation ◽  
Node Degree ◽  
Network Efficiency ◽  
Evolution Mechanism ◽  
Proposed Model

An effective and reliable evolution model can provide strong support for the planning and design of transportation networks. As a network evolution mechanism, link prediction plays an important role in the expansion of transportation networks. Most of the previous algorithms mainly took node degree or common neighbors into account in calculating link probability between two nodes, and the structure characteristics which can enhance global network efficiency are rarely considered. To address these issues, we propose a new evolution mechanism of transportation networks from the aspect of link prediction. Specifically, node degree, distance, path, expected network structure, relevance, population and GDP are comprehensively considered according to the characteristics and requirements of the transportation networks. Numerical experiments are done with China’s high-speed railway network, China’s highway network and China’s inland civil aviation network. We compare receiver operating characteristic curve and network efficiency in different models and explore the degree and hubs of networks generated by the proposed model. The results show that the proposed model has better prediction performance and can effectively optimize the network structure compared with other baseline link prediction methods.

Best Neighbor Heuristic Search for Finding Minimum Paths in Transportation Networks

1998 ◽  
Vol 1651 (1) ◽  
pp. 48-52 ◽  
Author(s):  
Jeffrey L. Adler
Keyword(s):  
Heuristic Search ◽  
Transportation Networks ◽  
Transportation Network ◽  
Search Problems ◽  
A Algorithm ◽  
Running Time ◽  
Search Effort ◽  
Wide Range ◽  
Path Search ◽  
Sparse Networks

For a wide range of transportation network path search problems, the A* heuristic significantly reduces both search effort and running time when compared to basic label-setting algorithms. The motivation for this research was to determine if additional savings could be attained by further experimenting with refinements to the A* approach. We propose a best neighbor heuristic improvement to the A* algorithm that yields additional benefits by significantly reducing the search effort on sparse networks. The level of reduction in running time improves as the average outdegree of the network decreases and the number of paths sought increases.

scholarly journals Small world and stability analysis of industrial coupling symbiosis network of ecological industrial park of oil and gas resource cities

2021 ◽  
pp. 014459872098361
Author(s):  
Yanqiu Wang ◽  
Zhengxin Sun ◽  
Pengtai Li ◽  
Zhiwei Zhu
Keyword(s):  
System Analysis ◽  
Oil And Gas ◽  
Small World ◽  
Industrial Park ◽  
Sharp Change ◽  
Connected Subgraph ◽  
Network Efficiency ◽  
Complex Network Theory ◽  
The Stability ◽  
Agglomeration Coefficient

This paper analyzes the small cosmopolitan and stability of the industrial coupling symbiotic network of eco-industrial parks of oil and gas resource-based cities. Taking Daqing A Ecological Industrial Park as an example, we constructed the characteristic index system and calculated the topological parameters such as the agglomeration coefficient and the average shortest path length of the industrial coupling symbiotic network. Based on the complex network theory we analyzed the characteristics of the scaled world, constructed the adjacency matrix of material and information transfers between enterprises, drew the network topology diagram. We simulated the system analysis and analyzed the stability of the industrial coupling symbiotic network of the eco-industrial park using the network efficiency and node load and maximum connected subgraph. The analysis results are as follows: the small world degree δ of Daqing A Eco-industrial Park is 0.891, which indicates that the industrial coupled symbiotic network has strong small world characteristics; the average path is 1.268, and the agglomeration coefficient is 0.631. The probability of edge connection between two nodes in a symbiotic network is 63.1%, which has a relatively high degree of aggregation, indicating that energy and material exchanges are frequent among all enterprises in the network, the degree of network aggregation is high, and the dependence between nodes is high; when the tolerance parameter is 0 to 0.3, the network efficiency and the maximum connected subgraphs show a sharp change trend, indicating that the topology of the industrial coupling symbiotic network of the eco-industrial park changes drastically when the network is subjected to deliberate attacks. It is easy to cause the breakage of material flow and energy flow in the industrial park, which leads to the decline of the stability of the industrial coupling symbiotic network of the eco-industrial park.

scholarly journals Identification and Quantification of Node Criticality through EWM–TOPSIS: A Study of Hong Kong’s MTR System

2021 ◽  
Author(s):  
Yifan Zhang ◽  
S. Thomas Ng
Keyword(s):  
Hong Kong ◽  
Public Transportation ◽  
Early Stage ◽  
Network Robustness ◽  
Global Network ◽  
Practical Significance ◽  
Topsis Method ◽  
Urban Resilience ◽  
Network Efficiency ◽  
Entropy Weight

AbstractPublic transport networks (PTNs) are critical in populated and rapidly densifying cities such as Hong Kong, Beijing, Shanghai, Mumbai, and Tokyo. Public transportation plays an indispensable role in urban resilience with an integrated, complex, and dynamically changeable network structure. Consequently, identifying and quantifying node criticality in complex PTNs is of great practical significance to improve network robustness from damage. Despite the proposition of various node criticality criteria to address this problem, few succeeded in more comprehensive aspects. Therefore, this paper presents an efficient and thorough ranking method, that is, entropy weight method (EWM)–technology for order preference by similarity to an ideal solution (TOPSIS), named EWM–TOPSIS, to evaluate node criticality by taking into account various node features in complex networks. Then we demonstrate it on the Mass Transit Railway (MTR) in Hong Kong by removing and recovering the top k critical nodes in descending order to compare the effectiveness of degree centrality (DC), betweenness centrality (BC), closeness centrality (CC), and the proposed EWM–TOPSIS method. Four evaluation indicators, that is, the frequency of nodes with the same ranking (F), the global network efficiency (E), the size of the largest connected component (LCC), and the average path length (APL), are computed to compare the performance of the four methods and measure network robustness under different designed attack and recovery strategies. The results demonstrate that the EWM–TOPSIS method has more obvious advantages than the others, especially in the early stage.

Network dynamics on the Chinese air transportation multilayer network

2021 ◽  
pp. 2150070
Author(s):  
Yong Yang ◽  
Kai-Jun Xu ◽  
Chen Hong
Keyword(s):  
Success Rate ◽  
Transportation Networks ◽  
Human Mobility ◽  
Transportation Network ◽  
Air Transportation ◽  
Transfer Number ◽  
Multilayer Network ◽  
Scheduling Model ◽  
Network Mechanism ◽  
Random Failures

Air transportation networks play important roles in human mobility. In this paper, from the perspective of multilayer network mechanism, the dynamics of the Chinese air transportation network are extensively investigated. A multilayer-based passengers re-scheduling model is introduced, and a multilayer cooperation (MC) approach is proposed to improve the efficiency of network traffic under random failures. We use two metrics: the success rate and the extra transfer number, to evaluate the efficiency of re-scheduling. It is found that a higher success rate of passengers re-scheduling can be obtained by MC, and MC is stronger for resisting the instability of the capacity of links. Furthermore, the explosion of the number of extra transfer can be well restrained by MC. Our work will highlight a better understanding of the dynamics and robustness of the Chinese air transportation network.

Face Recognition

2011 ◽  
pp. 163-254
Author(s):  
Daijin Kim ◽  
Jaewon Sung
Keyword(s):  
Face Recognition ◽  
Access Control ◽  
Individual Identification ◽  
Modern Life ◽  
Identification Methods ◽  
Personal Security ◽  
Identification Method ◽  
The Individual ◽  
Biometric Features

In the modern life, the need for personal security and access control is becoming an important issue. Biometrics is the technology which is expected to replace traditional authentication methods that are easily stolen, forgotten and duplicated. Fingerprints, face, iris, and voiceprints are commonly used biometric features. Among these features, face provides a more direct, friendly and convenient identification method and is more acceptable compared with the individual identification methods of other biometrics features. Thus, face recognition is one of the most important parts in biometrics.

scholarly journals Deep Autoencoder Neural Networks for Short-Term Traffic Congestion Prediction of Transportation Networks

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2229 ◽  
Author(s):  
Sen Zhang ◽  
Yong Yao ◽  
Jie Hu ◽  
Yong Zhao ◽  
Shaobo Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Large Scale ◽  
Transportation Networks ◽  
Transportation Network ◽  
Transportation Systems ◽  
Neural Network Models ◽  
Computation Efficiency ◽  
Congestion Prediction

Traffic congestion prediction is critical for implementing intelligent transportation systems for improving the efficiency and capacity of transportation networks. However, despite its importance, traffic congestion prediction is severely less investigated compared to traffic flow prediction, which is partially due to the severe lack of large-scale high-quality traffic congestion data and advanced algorithms. This paper proposes an accessible and general workflow to acquire large-scale traffic congestion data and to create traffic congestion datasets based on image analysis. With this workflow we create a dataset named Seattle Area Traffic Congestion Status (SATCS) based on traffic congestion map snapshots from a publicly available online traffic service provider Washington State Department of Transportation. We then propose a deep autoencoder-based neural network model with symmetrical layers for the encoder and the decoder to learn temporal correlations of a transportation network and predicting traffic congestion. Our experimental results on the SATCS dataset show that the proposed DCPN model can efficiently and effectively learn temporal relationships of congestion levels of the transportation network for traffic congestion forecasting. Our method outperforms two other state-of-the-art neural network models in prediction performance, generalization capability, and computation efficiency.

scholarly journals A Parameter Identification Method for Stewart Manipulator Based on Wavelet Transform

Mathematics ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 257
Author(s):  
Chenyang Zhang
Keyword(s):  
Wavelet Transform ◽  
Parameter Identification ◽  
Coulomb Friction ◽  
Velocity Analysis ◽  
Dynamical Parameter ◽  
New Approach ◽  
Identification Methods ◽  
Identification Method ◽  
True Value ◽  
Joint Velocity

Aiming at inertial and viscous parameter identification for the Stewart manipulator regardless of the influence of Coulomb friction, a simple and effective dynamical parameter identification method based on wavelet transform and joint velocity analysis is proposed in this paper. Compared with previously known identification methods, the advantages of the new approach are that (1) the excitation trajectory is easy to design, and (2) it can not only identify the inertial matrix, but also the viscous matrix accurately regardless of the influence of Coulomb friction. Comparison is made among the identification method proposed in this paper, another identification method proposed previously, and the true value calculated with a formula. The errors from results of different identification methods demonstrate that the method proposed in this paper shows great adaptability and accuracy.

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