scholarly journals Collective behavior in the North Rhine-Westphalia motorway network

2021 ◽  
Vol 2021 (12) ◽  
pp. 123401
Author(s):  
Shanshan Wang ◽  
Sebastian Gartzke ◽  
Michael Schreckenberg ◽  
Thomas Guhr
Keyword(s):  
Complex System ◽  
Complex Networks ◽  
Collective Behavior ◽  
Coherent Motion ◽  
Correlation Matrices ◽  
Phase Theory ◽  
The North ◽  
Three Phase ◽  
Eigenvalue And Eigenvector

Abstract To understand the dynamics on complex networks, measurement of correlations is indispensable. In a motorway network, it is not sufficient to collect information on fluxes and velocities on all individual links, i.e. parts of the freeways between ramps and highway crosses. The interdependencies and mutual connections are also of considerable interest. We analyze correlations in the complete motorway network in North Rhine-Westphalia, the most populous state in Germany. We view the motorway network as a complex system consisting of road sections which interact via the motion of vehicles, implying structures in the corresponding correlation matrices. In particular, we focus on collective behavior, i.e. coherent motion in the whole network or in large parts of it. To this end, we study the eigenvalue and eigenvector statistics and identify significant sections in the motorway network. We find collective behavior in these significant sections and further explore its causes. We show that collectivity throughout the network cannot directly be related to the traffic states (free, synchronous and congested) in Kerner’s three-phase theory. Hence, the degree of collectivity provides a new, complementary observable to characterize the motorway network.

Are all the word ranking methods the same?

2021 ◽  
pp. 2150144
Author(s):  
Elham Najafi ◽  
Alireza Valizadeh ◽  
Amir H. Darooneh
Keyword(s):  
Complex System ◽  
Time Series ◽  
Spatial Distribution ◽  
Time Series Analysis ◽  
Complex Networks ◽  
Rank Correlation ◽  
Ranking Methods ◽  
Series Analysis ◽  
Maximum Value

Text as a complex system is commonly studied by various methods, like complex networks or time series analysis, in order to discover its properties. One of the most important properties of each text is its keywords, which are extracted by word ranking methods. There are various methods to rank words of a text. Each method differently ranks words according to their frequency, spatial distribution or other word properties. Here, we aimed to explore how similar various word ranking methods are. For this purpose, we studied the rank correlation of some important word ranking methods for number of sample texts with different subjects and text sizes. We found that by increasing text size the correlation between ranking methods grows. It means that as the text size increases, the associated word ranks calculated by different ranking methods converge. Also, we found out that the rank correlations of word ranking methods approach their maximum value in the case of large enough texts.

scholarly journals Inferring propagation paths for sparsely observed perturbations on complex networks

2016 ◽  
Vol 2 (10) ◽  
pp. e1501638 ◽  
Author(s):  
Francesco Alessandro Massucci ◽  
Jonathan Wheeler ◽  
Raúl Beltrán-Debón ◽  
Jorge Joven ◽  
Marta Sales-Pardo ◽  
...  
Keyword(s):  
Social Sciences ◽  
Complex System ◽  
Complex Networks ◽  
Probabilistic Model ◽  
Belief Propagation ◽  
Shortest Paths ◽  
Single Observation ◽  
Perturbed System ◽  
The Social ◽  
Propagation Paths

In a complex system, perturbations propagate by following paths on the network of interactions among the system’s units. In contrast to what happens with the spreading of epidemics, observations of general perturbations are often very sparse in time (there is a single observation of the perturbed system) and in “space” (only a few perturbed and unperturbed units are observed). A major challenge in many areas, from biology to the social sciences, is to infer the propagation paths from observations of the effects of perturbation under these sparsity conditions. We address this problem and show that it is possible to go beyond the usual approach of using the shortest paths connecting the known perturbed nodes. Specifically, we show that a simple and general probabilistic model, which we solved using belief propagation, provides fast and accurate estimates of the probabilities of nodes being perturbed.

scholarly journals PROJECT PERFORMANCE AND MONITORING OF A REVETMENT, COCOS BAY, EAST TRINIDAD

2012 ◽  
Vol 1 (33) ◽  
pp. 38
Author(s):  
Charmaine O'Brien-Delpesh ◽  
Candice Gray-Bernard ◽  
Marisha Tang-Kai
Keyword(s):  
Structural Integrity ◽  
Oil And Gas ◽  
Wave Climate ◽  
Winter Period ◽  
Coastal Evolution ◽  
Suitable Material ◽  
The North ◽  
Three Phase ◽  
Severe Erosion ◽  
Actual Response

The eastern shoreline of Trinidad has been suffering extensively from erosion over the years. This coast is exposed to the Atlantic Ocean and is subjected to large swells especially during the North Atlantic winter period, storms and hurricanes. The coastal area of Cocos Bay located on the east coast of Trinidad between the Nariva and Ortoire Rivers has been undergoing severe erosion at a rate of approximately 0.5 m to 1.7 m per year. In 2005, the Ministry of Works and Transport, Drainage Division started the construction of a 2.3 km long rubble revetment (rip rap) which was completed in 2008. The function of the revetment was to control erosion and flooding thereby protecting the Manzanilla-Mayaro Road which is a major artery linking the oil and gas sector as well as several coastal villages. This revetment formed the emergency intervention phase of a three phase solution recommended along the South Cocos Bay. Conforming to the Certificate of Environmental Clearance (CEC), the Ministry of Works and Transport, Drainage Division has been monitoring the possible impacts of the structure on the beach environment from post construction to present and its structural integrity. During the last five years, the monitoring study has revealed that in the vicinity of the revetment, erosion and flooding have been mitigated; however there has been narrowing of the beach even though the gradient has remained the same. Flanking has also been observed immediately north and south of the revetment. In regards to the structural integrity of the revetment, the armour layer which was constructed using local rock (blue limestone), 200 - 300 kg is showing signs of weathering and shearing. This has resulted in voids in the armour layer which has exposed the filter and underlayers. It is recommended that in order to maintain the functionality and structural integrity over a design life of 25 years, the armour layer be repaired with a suitable material which has historically been sourced externally. The post documents the prevailing wave climate, previous coastal evolution modelling, and the pre and post construction profile records. The shoreline response to the revetment is predicted using GENESIS and compared to the actual response based on an analysis of the beach profile monitoring records are also presented.

On the Proper Description of Complex Network Dynamics

2018 ◽  
Author(s):  
Chun-Lin Yang ◽  
C. Steve Suh
Keyword(s):  
Complex Networks ◽  
Complex Network ◽  
Collective Behavior ◽  
Local Level ◽  
Network Dynamics ◽  
Global Dynamics ◽  
Statistical Entropy ◽  
Network Systems ◽  
Local Dynamics ◽  
The Difference

Real-world networks are dynamical complex network systems. The dynamics of a network system is a coupling of the local dynamics with the global dynamics. The local dynamics is the time-varying behaviors of ensembles at the local level. The global dynamics is the collective behavior of the ensembles following specific laws at the global level. These laws include basic physical principles and constraints. Complex networks have inherent resilience that offsets disturbance and maintains the state of the system. However, when disturbance is potent enough, network dynamics can be perturbed to a level that ensembles no longer follow the constraint conditions. As a result, the collective behavior of a complex network diminishes and the network collapses. The characteristic of a complex network is the response of the system which is time-dependent. Therefore, complex networks need to account for time-dependency and obey physical laws and constraints. Statistical mechanics is viable for the study of multi-body dynamic systems having uncertain states such as complex network systems. Statistical entropy can be used to define the distribution of the states of ensembles. The difference between the states of ensembles define the interaction between them. This interaction is known as the collective behavior. In other words statistical entropy defines the dynamics of a complex network. Variation of entropy corresponds to the variation of network dynamics and vice versa. Therefore, entropy can serve as an indicator of network dynamics. A stable network is characterized by a specific entropy while a network on the verge of collapse is characterized by another. As the collective behavior of a complex network can be described by entropy, the correlation between the statistical entropy and network dynamics is investigated.

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