Dynamic Network User Equilibrium


Book Description

This book presents advanced research in a relatively new field of scholarly inquiry that is usually referred to as dynamic network user equilibrium, now almost universally abbreviated as DUE. It provides the first synthesis of results obtained over the last decade from applying the differential variational inequality (DVI) formalism to study the DUE problem. In particular, it explores the intimately related problem of dynamic network loading, which determines the arc flows and effective travel delays (or generalized travel costs) arising from the expression of departure rates at the origins of commuter trips between the workplace and home. In particular, the authors show that dynamic network loading with spillback of queues into upstream arcs may be formulated as a differential algebraic equation system. They demonstrate how the dynamic network loading problem and the dynamic traffic user equilibrium problem may be solved simultaneously rather than sequentially, as well as how the first-in-first-out queue discipline may be maintained for each when Lighthill-Whitham-Richardson traffic flow theory is used. A number of recent and new extensions of the DVI-based theory of DUE and corresponding examples are presented and discussed. Relevant mathematical background material is provided to make the book as accessible as possible.




The Oxford Handbook of Analytical Sociology


Book Description

Analytical sociology is a strategy for understanding the social world. It is concerned with explaining important social facts such as network structures, patterns of residential segregation, typical beliefs, cultural tastes, and common ways of acting. It explains such facts by detailing in clear and precise ways the mechanisms through which the social facts were brought about. Making sense of the relationship between micro and macro thus is one of the central concerns of analytical sociology. The approach is a contemporary incarnation of Robert K. Merton's notion of middle-range theory and presents a vision of sociological theory as a tool-box of semi-general theories each of which is adequate for explaining certain types of phenomena. The Handbook brings together some of the most prominent sociologists in the world. Some of the chapters focus on action and interaction as the cogs and wheels of social processes, while others consider the dynamic social processes that these actions and interactions bring about.




Dynamical Systems on Networks


Book Description

This volume is a tutorial for the study of dynamical systems on networks. It discusses both methodology and models, including spreading models for social and biological contagions. The authors focus especially on “simple” situations that are analytically tractable, because they are insightful and provide useful springboards for the study of more complicated scenarios. This tutorial, which also includes key pointers to the literature, should be helpful for junior and senior undergraduate students, graduate students, and researchers from mathematics, physics, and engineering who seek to study dynamical systems on networks but who may not have prior experience with graph theory or networks. Mason A. Porter is Professor of Nonlinear and Complex Systems at the Oxford Centre for Industrial and Applied Mathematics, Mathematical Institute, University of Oxford, UK. He is also a member of the CABDyN Complexity Centre and a Tutorial Fellow of Somerville College. James P. Gleeson is Professor of Industrial and Applied Mathematics, and co-Director of MACSI, at the University of Limerick, Ireland.




Dynamic R&D Networks


Book Description




Dynamic Routing in Telecommunications Networks


Book Description

Dynamic routing techniques are the key to growth in every kind of telecommunications network. Here at last is the definitive guide that shows how to analyze, design, manage, and operate dynamic networks - written by one of the key originators of the technology. Based on actual implementation, this in-depth manual provides all the tools needed by network engineers and planners involved with any aspect of dynamic networks. The author's practical, A-to-Z treatment of the subject will also prove invaluable to telecommunications software designers, researchers, and students.




Networks, Topology and Dynamics


Book Description

There is convergent consensus among scientists that many social, economic and ?nancial phenomena can be described by a network of agents and their inter- tions. Surprisingly, even though the application ?elds are quite different, those n- works often show a common behaviour. Thus, their topological properties can give useful insights on how the network is structured, which are the most “important” nodes/agents, how the network reacts to new arrivals. Moreover the network, once included into a dynamic context, helps to model many phenomena. Among the t- ics in which topology and dynamics are the essential tools, we will focus on the diffusion of technologies and fads, the rise of industrial districts, the evolution of ?nancial markets, cooperation and competition, information ?ows, centrality and prestige. The volume, including recent contributions to the ?eld of network modelling, is based on the communications presented at NET 2006 (Verbania, Italy) and NET 2007 (Urbino, Italy); offers a wide range of recent advances, both theoretical and methodological, that will interest academics as well as practitioners. Theory and applications are nicely integrated: theoretical papers deal with graph theory, game theory, coalitions, dynamics, consumer behavior, segregation models and new contributions to the above mentioned area. The applications cover a wide range: airline transportation, ?nancial markets, work team organization, labour and credit market.




Advances in Dynamic Network Modeling in Complex Transportation Systems


Book Description

This edited book focuses on recent developments in Dynamic Network Modeling, including aspects of route guidance and traffic control as they relate to transportation systems and other complex infrastructure networks. Dynamic Network Modeling is generally understood to be the mathematical modeling of time-varying vehicular flows on networks in a fashion that is consistent with established traffic flow theory and travel demand theory. Dynamic Network Modeling as a field has grown over the last thirty years, with contributions from various scholars all over the field. The basic problem which many scholars in this area have focused on is related to the analysis and prediction of traffic flows satisfying notions of equilibrium when flows are changing over time. In addition, recent research has also focused on integrating dynamic equilibrium with traffic control and other mechanism designs such as congestion pricing and network design. Recently, advances in sensor deployment, availability of GPS-enabled vehicular data and social media data have rapidly contributed to better understanding and estimating the traffic network states and have contributed to new research problems which advance previous models in dynamic modeling. A recent National Science Foundation workshop on “Dynamic Route Guidance and Traffic Control” was organized in June 2010 at Rutgers University by Prof. Kaan Ozbay, Prof. Satish Ukkusuri , Prof. Hani Nassif, and Professor Pushkin Kachroo. This workshop brought together experts in this area from universities, industry and federal/state agencies to present recent findings in this area. Various topics were presented at the workshop including dynamic traffic assignment, traffic flow modeling, network control, complex systems, mobile sensor deployment, intelligent traffic systems and data collection issues. This book is motivated by the research presented at this workshop and the discussions that followed.




Endogenous Technology Cycles in Dynamic R&D Networks


Book Description

We study the coevolutionary dynamics of knowledge creation and diffusion with the formation of R&D collaboration networks. Differently to previous works, we do not treat knowledge as an abstract scalar variable, but rather represent it as a multidimensional portfolio of technologies. Over time the composition of this portfolio may change due innovations and knowledge spillovers between collaborating firms. The collaborations between firms, in turn, are dynamically adjusted based on the firms' expectations of learning a new technology from their collaboration partners. We show that the interplay between knowledge diffusion, network formation and competition across sectors can give rise to a cyclical pattern in the collaboration intensity, which can be described as a damped oscillation. This theoretical finding recapitulates the novel observation of oscillations in an empirical sample of a large R&D collaboration network over several decades. Finally, we apply our findings to describe how an effective R&D policy can balance subsidies for entrants as well as R&D collaborations between incumbent firms.




Dynamic Network Representation Based on Latent Factorization of Tensors


Book Description

A dynamic network is frequently encountered in various real industrial applications, such as the Internet of Things. It is composed of numerous nodes and large-scale dynamic real-time interactions among them, where each node indicates a specified entity, each directed link indicates a real-time interaction, and the strength of an interaction can be quantified as the weight of a link. As the involved nodes increase drastically, it becomes impossible to observe their full interactions at each time slot, making a resultant dynamic network High Dimensional and Incomplete (HDI). An HDI dynamic network with directed and weighted links, despite its HDI nature, contains rich knowledge regarding involved nodes’ various behavior patterns. Therefore, it is essential to study how to build efficient and effective representation learning models for acquiring useful knowledge. In this book, we first model a dynamic network into an HDI tensor and present the basic latent factorization of tensors (LFT) model. Then, we propose four representative LFT-based network representation methods. The first method integrates the short-time bias, long-time bias and preprocessing bias to precisely represent the volatility of network data. The second method utilizes a proportion-al-integral-derivative controller to construct an adjusted instance error to achieve a higher convergence rate. The third method considers the non-negativity of fluctuating network data by constraining latent features to be non-negative and incorporating the extended linear bias. The fourth method adopts an alternating direction method of multipliers framework to build a learning model for implementing representation to dynamic networks with high preciseness and efficiency.




Evolutionary Dynamics of Complex Communications Networks


Book Description

Until recently, most network design techniques employed a bottom-up approach with lower protocol layer mechanisms affecting the development of higher ones. This approach, however, has not yielded fascinating results in the case of wireless distributed networks. Addressing the emerging aspects of modern network analysis and design, Evolutionary Dyna