scholarly journals Spatio-temporal networks: reachability, centrality and robustness

2016 ◽  
Vol 3 (6) ◽  
pp. 160196 ◽  
Author(s):  
Matthew J. Williams ◽  
Mirco Musolesi
Keyword(s):  
Real World ◽  
Failure Modes ◽  
Urban Transport ◽  
Transport Systems ◽  
Centrality Measures ◽  
Complex Nature ◽  
Temporal Networks ◽  
World Systems ◽  
Temporal System ◽  
Spatio Temporal

Recent advances in spatial and temporal networks have enabled researchers to more-accurately describe many real-world systems such as urban transport networks. In this paper, we study the response of real-world spatio-temporal networks to random error and systematic attack, taking a unified view of their spatial and temporal performance. We propose a model of spatio-temporal paths in time-varying spatially embedded networks which captures the property that, as in many real-world systems, interaction between nodes is non-instantaneous and governed by the space in which they are embedded. Through numerical experiments on three real-world urban transport systems, we study the effect of node failure on a network's topological, temporal and spatial structure. We also demonstrate the broader applicability of this framework to three other classes of network. To identify weaknesses specific to the behaviour of a spatio-temporal system, we introduce centrality measures that evaluate the importance of a node as a structural bridge and its role in supporting spatio-temporally efficient flows through the network. This exposes the complex nature of fragility in a spatio-temporal system, showing that there is a variety of failure modes when a network is subject to systematic attacks.

scholarly journals Higher-order temporal network effects through triplet evolution

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Qing Yao ◽  
Bingsheng Chen ◽  
Tim S. Evans ◽  
Kim Christensen
Keyword(s):  
Real World ◽  
Link Prediction ◽  
Higher Order ◽  
Prediction Algorithm ◽  
Interaction Patterns ◽  
Temporal Networks ◽  
World Systems ◽  
Order Interaction ◽  
Space And Time ◽  
Pairwise Interactions

AbstractWe study the evolution of networks through ‘triplets’—three-node graphlets. We develop a method to compute a transition matrix to describe the evolution of triplets in temporal networks. To identify the importance of higher-order interactions in the evolution of networks, we compare both artificial and real-world data to a model based on pairwise interactions only. The significant differences between the computed matrix and the calculated matrix from the fitted parameters demonstrate that non-pairwise interactions exist for various real-world systems in space and time, such as our data sets. Furthermore, this also reveals that different patterns of higher-order interaction are involved in different real-world situations. To test our approach, we then use these transition matrices as the basis of a link prediction algorithm. We investigate our algorithm’s performance on four temporal networks, comparing our approach against ten other link prediction methods. Our results show that higher-order interactions in both space and time play a crucial role in the evolution of networks as we find our method, along with two other methods based on non-local interactions, give the best overall performance. The results also confirm the concept that the higher-order interaction patterns, i.e., triplet dynamics, can help us understand and predict the evolution of different real-world systems.

Identifying important nodes based on upstream and downstream time-respecting paths in temporal networks

2021 ◽  
pp. 2150403
Author(s):  
Liang Luo ◽  
Minghao Li ◽  
Zili Zhang ◽  
Li Tao
Keyword(s):  
Real World ◽  
Social Contact ◽  
Centrality Measures ◽  
Temporal Networks ◽  
Contact Networks ◽  
Node Importance ◽  
Comparison Results ◽  
Temporal Structures ◽  
Social Contact Networks ◽  
The One

Identifying the nodes that play significant roles in the epidemic spreading process has attracted extensive attention in recent years. Few centrality measures, such as temporal degree and temporal closeness centrality, have been proposed to quantify node importance based on the topological structure of social contact networks. Most methods estimate the importance of a node from a single aspect, e.g. a higher degree in time snapshot graphs, or shorter distances to other nodes along time-respecting paths. However, this may not be the case in the real world. On the one hand, a node with more nodes on its out streams (i.e. downstream) should be more important because it may affect more nodes along its time-stamped contacting paths once it is infected. On the other hand, a node with more nodes in its in streams (i.e. upstream) deserves closer attention, as it has a higher probability of infection by other nodes. We propose a new temporal centrality measure, upstream and downstream centrality (UD-centrality) with two forms of realizations, i.e. a linear UD-centrality (L-UD) and a product UD-centrality (P-UD) to estimate the importance of nodes based on the temporal structures of social contact networks. We compare our L-UD and P-UD to three classic temporal network centralities through simulations on 14 real-world temporal networks based on the susceptible-infected (SI) model. The comparison results show that UD-centrality can more accurately rank the importance of nodes than the baseline centrality measures.

scholarly journals Biking Acculturation Incidence on the Cameroon Urban Activity Fluxes: A Traffic Congestion Panacea or Palliative in Bamenda?

2020 ◽  
Vol 4 (1) ◽  
pp. 40
Author(s):  
Zephania N. Fogwe
Keyword(s):  
Traffic Congestion ◽  
Urban Transportation ◽  
Urban Transport ◽  
Transport Systems ◽  
City Centre ◽  
Vehicular Traffic ◽  
Urban Problem ◽  
Spatio Temporal ◽  
The City ◽  
Urban Activity

City activity fluxes are reminiscent of stakeholder mobility in the urban sphere service and functions. Such mobility affects income and work assiduity though often creating accidents, material damage and pollution of varied forms of urban transportation that has lately witnessed the adaption to motorbikes as an alternatively favoured means of movement to that car. Salvaging economic depression and affordability has given biking an edge of success of recent which raises the recurrent question of its spatio-temporal and technical sustainability in in the freight sector. This paper evaluates the commercialisation of bike riding despite levied negativism perceptions in providing an alternative to the major urban problem of traffic congestion and its contribution to peter the pitfalls of the congestion. To assess the urban congestion intensity, traffic flow count was conducted during the rainy and dry season of spatio-temporal of vehicles (from the city centre to the suburbs). Findings agree to the fact that vehicular traffic congestion with increasing number of bikes was found to be higher along the N-E than the N-W road axis in direct conformity with sampled urban congestion triggers for Bamenda related to road infrastructural traits and vehicular numbers. Though motor bikes initially were a panacea, the sum effect of congestion reveals the urgent need for urban mass transport systems that should be complemented by a holistic urban transport governance involving the creation of deviation routes and enforcing traffic congestion rules.

Phenomenological Modelling

2018 ◽  
Author(s):  
Ray Huffaker ◽  
Marco Bittelli ◽  
Rodolfo Rosa
Keyword(s):  
Nonlinear Dynamics ◽  
Time Series ◽  
Real World ◽  
Biological Diversity ◽  
False Positives ◽  
World Systems ◽  
Economic Activities ◽  
Essential Services ◽  
Low Dimension ◽  
Convergent Cross Mapping

Detecting causal interactions among climatic, environmental, and human forces in complex biophysical systems is essential for understanding how these systems function and how public policies can be devised that protect the flow of essential services to biological diversity, agriculture, and other core economic activities. Convergent Cross Mapping (CCM) detects causal networks in real-world systems diagnosed with deterministic, low-dimension, and nonlinear dynamics. If CCM detects correspondence between phase spaces reconstructed from observed time series variables, then the variables are determined to causally interact in the same dynamic system. CCM can give false positives by misconstruing synchronized variables as causally interactive. Extended (delayed) CCM screens for false positives among synchronized variables.

scholarly journals Smart Cities in Turkey: Approaches, Advances and Applications with Greater Consideration for Future Urban Transport Development

Energies ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 2308 ◽  
Author(s):  
Can Bıyık
Keyword(s):  
Urban Areas ◽  
Planning Process ◽  
Smart Cities ◽  
Radical Change ◽  
Urban Transport ◽  
Transport Systems ◽  
Structured Interviews ◽  
Policy Makers ◽  
Comprehensive Survey ◽  
Future Vision

The smart city transport concept is viewed as a future vision aiming to undertake investigations on the urban planning process and to construct policy-pathways for achieving future targets. Therefore, this paper sets out three visions for the year 2035 which bring about a radical change in the level of green transport systems (often called walking, cycling, and public transport) in Turkish urban areas. A participatory visioning technique was structured according to a three-stage technique: (i) Extensive online comprehensive survey, in which potential transport measures were researched for their relevance in promoting smart transport systems in future Turkish urban areas; (ii) semi-structured interviews, where transport strategy suggestions were developed in the context of the possible imaginary urban areas and their associated contextual description of the imaginary urban areas for each vision; (iii) participatory workshops, where an innovative method was developed to explore various creative future choices and alternatives. Overall, this paper indicates that the content of the future smart transport visions was reasonable, but such visions need a considerable degree of consensus and radical approaches for tackling them. The findings offer invaluable insights to researchers inquiring about the smart transport field, and policy-makers considering applying those into practice in their local urban areas.

The Pulse of Urban Transport: Exploring the Co-evolving Pattern for Spatio-temporal Forecasting

2021 ◽  
Vol 15 (6) ◽  
pp. 1-25
Author(s):  
Jinliang Deng ◽  
Xiusi Chen ◽  
Zipei Fan ◽  
Renhe Jiang ◽  
Xuan Song ◽  
...  
Keyword(s):  
Time Series Data ◽  
Demand Forecasting ◽  
Intrinsic Property ◽  
Urban Transport ◽  
Series Data ◽  
Transportation Demand ◽  
Demand Information ◽  
Spatio Temporal ◽  
Mode Of Transport ◽  
Pattern Information

Transportation demand forecasting is a topic of large practical value. However, the model that fits the demand of one transportation by only considering the historical data of its own could be vulnerable since random fluctuations could easily impact the modeling. On the other hand, common factors like time and region attribute, drive the evolution demand of different transportation, leading to a co-evolving intrinsic property between different kinds of transportation. In this work, we focus on exploring the co-evolution between different modes of transport, e.g., taxi demand and shared-bike demand. Two significant challenges impede the discovery of the co-evolving pattern: (1) diversity of the co-evolving correlation, which varies from region to region and time to time. (2) Multi-modal data fusion. Taxi demand and shared-bike demand are time-series data, which have different representations with the external factors. Moreover, the distribution of taxi demand and bike demand are not identical. To overcome these challenges, we propose a novel method, known as co-evolving spatial temporal neural network (CEST). CEST learns a multi-view demand representation for each mode of transport, extracts the co-evolving pattern, then predicts the demand for the target transportation based on multi-scale representation, which includes fine-scale demand information and coarse-scale pattern information. We conduct extensive experiments to validate the superiority of our model over the state-of-art models.

scholarly journals Asymmetry underlies stability in power grids

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ferenc Molnar ◽  
Takashi Nishikawa ◽  
Adilson E. Motter
Keyword(s):  
Symmetry Breaking ◽  
Real World ◽  
Power Grids ◽  
Stable Operation ◽  
World Systems ◽  
Symmetric State ◽  
Network Systems ◽  
Additional Improvement ◽  
The Stability ◽  
General Method

AbstractBehavioral homogeneity is often critical for the functioning of network systems of interacting entities. In power grids, whose stable operation requires generator frequencies to be synchronized—and thus homogeneous—across the network, previous work suggests that the stability of synchronous states can be improved by making the generators homogeneous. Here, we show that a substantial additional improvement is possible by instead making the generators suitably heterogeneous. We develop a general method for attributing this counterintuitive effect to converse symmetry breaking, a recently established phenomenon in which the system must be asymmetric to maintain a stable symmetric state. These findings constitute the first demonstration of converse symmetry breaking in real-world systems, and our method promises to enable identification of this phenomenon in other networks whose functions rely on behavioral homogeneity.

Spatio-Temporal Modeling on Village Land Feature Change

2012 ◽  
Vol 204-208 ◽  
pp. 2721-2725
Author(s):  
Hua Ji Zhu ◽  
Hua Rui Wu
Keyword(s):  
Real World ◽  
Temporal Change ◽  
End Users ◽  
Temporal Modeling ◽  
The Real ◽  
Change Type ◽  
Feature Change ◽  
Spatio Temporal ◽  
The Village ◽  
Land Changes

Village land continually changes in the real world. In order to keep the data up-to-date, data producers need update the data frequently. When the village land data are updated, the update information must be dispensed to the end-users to keep their client-databases current. In the real world, village land changes in many forms. Identifying the change type of village land (i.e. captures the semantics of change) and representing them in the data world can help end-users understand the change commonly and be convenient for end-users to integrate these change information into their databases. This work focuses on the model of the spatio-temporal change. A three-tuple model CAR for representing the spatio-temporal change is proposed based on the village land feature set before change and the village land feature set after change, change type and rules. In this model, the C denotes the change type. A denotes the attribute set; R denotes the judging rules of change type. The rule is described by the IF－THEN expressions. By the operations between R and A, the C is distinguished. This model overcomes the limitations of current methods. And more, the rules in this model can be easy realized in computer program.

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