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The problems of urban traffic in the industrially developed countries have been at the top of the priority list for a long time. While making a critical contribution to the economic well being of those countries, transportation systems in general and highway traffic in particular, also have detrimental effects which are evident in excessive congestion, high rates of accidents and severe pollution problems. Scientists from different disciplines have played an important role in the development and refinement of the tools needed for the planning, analysis, and control of urban traffic networks. In the past several years, there were particularly rapid advances in two areas that affect urban traffic: 1. Modeling of traffic flows in urban networks and the prediction of the resulting equilibrium conditions; 2. Technology for communication with the driver and the ability to guide him, by providing him with useful, relevant and updated information, to his desired destination.
Intelligent Vehicle-Highway Systems are providing a welcome stimulus to research on dynamic urban transportation network models. This book presents a new generation of models for solving dynamic travel choice problems including traveler's destination choice, mode choice, departure/arrival time choice and route choice. These models are expected to function as off-line travel forecasting and evaluation tools, and eventually as on-line prediction and control models in advanced traveler information and traffic management systems. In addition to a rich set of new formulations and solution algorithms, the book provides a summary of the necessary mathematical background and concludes with a discussion of the requirements for model implementation.
This book seeks to summarize our recent progress in dynamic trans portation network modeling. It concentrates on ideal dynamic network models based on actual travel times and their corresponding solution algorithms. In contrast, our first book DynamIc Urban Transportation Network Models - The ory and Implications for Intelligent Vehicle-Hzghway Systems (Springer-Verlag, 1994) focused on instantaneous dynamic network models. Comparing the two books, the major differences can be summarized as follows: 1. This book uses the variational inequality problem as the basic formulation approach and considers the optimal control problem as a subproblem for solution purposes. The former book used optimal control theory as the basic formulation approach, which caused critical problems in some circumstances. 2. This book focuses on ideal dynamic network models based on actual travel times. The former book focused on instantaneous dynamic network models based on currently prevailing travel times. 3. This book formulates a stochastic dynamic route choice model which can utilize any possible route choice distribution function instead of only the logit function. 4. This book reformulates the bilevel problem of combined departure time/ route choice as a one-level variational inequality. 5. Finally, a set of problems is provided for classroom use. In addition, this book offers comprehensive insights into the complexity and challenge of applying these dynamic network models to Intelligent Trans portation Systems (ITS). Nevertheless, the models in this text are not yet fully evaluated and are subject to revision based on future research.
This book arises from the NATO-Russia Workshop, and focuses on the creation of a risk-informed analysis capability for modeling and predicting the behavior of complex infrastructure networks; applying emerging technology to the problems of designing, constructing, monitoring, and operating critical infrastructure systems; and building an understanding of the social, economic, and environmental factors that affect, and are affected by, critical infrastructure.
The core of ths book presents a theory developed by the author to combine the recent insight into empirical data with mathematical models in freeway traffic research based on dynamical non-linear processes.
This book gathers the peer-reviewed proceedings of the 13th Annual Meeting of the Bulgarian Section of the Society for Industrial and Applied Mathematics, BGSIAM'18, held in Sofia, Bulgaria. The general theme of BGSIAM'18 was industrial and applied mathematics with particular focus on: mathematical physics, numerical analysis, high performance computing, optimization and control, mathematical biology, stochastic modeling, machine learning, digitization and imaging, advanced computing in environmental, biomedical and engineering applications.
One aspect of the new economy is a transition to a networked society, and the emergence of a highly interconnected, interdependent and complex system of networks to move people, goods and information. An example of this is the in creasing reliance of networked systems (e. g. , air transportation networks, electric power grid, maritime transport, etc. ) on telecommunications and information in frastructure. Many of the networks that evolved today have an added complexity in that they have both a spatial structure – i. e. , they are located in physical space but also an a spatial dimension brought on largely by their dependence on infor mation technology. They are also often just one component of a larger system of geographically integrated and overlapping networks operating at different spatial levels. An understanding of these complexities is imperative for the design of plans and policies that can be used to optimize the efficiency, performance and safety of transportation, telecommunications and other networked systems. In one sense, technological advances along with economic forces that encourage the clustering of activities in space to reduce transaction costs have led to more efficient network structures. At the same time the very properties that make these networks more ef ficient have also put them at a greater risk for becoming disconnected or signifi cantly disruptedwh en super connected nodes are removed either intentionally or through a targeted attack.