Spatial decisions and processes are fundamental to the understanding of spatial structure. In the earlier stages of spatial analysis proposed explanations were typically on the aggregate, phenomenological level. Now a new field has evolved and matured which calls for a deeper understanding of spatial structure with a particular emphasis on spatial decisions and processes. The present volume serves as a guide and as an anthology by discussing a wide variety of new modelling approaches, techniques and issues related to spatial decision and processes and drawing these seemingly disparate, but interlocking parts together.

The classical optimal control theory deals with the determination of an optimal control that optimizes the criterion subjects to the dynamic constraint expressing the evolution of the system state under the influence of control variables. If this is extended to the case of multiple controllers (also called players) with different and sometimes conflicting optimization criteria (payoff function) it is possible to begin to explore differential games. Zero-sum differential games, also called differential games of pursuit, constitute the most developed part of differential games and are rigorously investigated. In this book, the full theory of differential games of pursuit with complete and partial information is developed. Numerous concrete pursuit-evasion games are solved (?life-line? games, simple pursuit games, etc.), and new time-consistent optimality principles in the n-person differential game theory are introduced and investigated.

Schedule-Based Dynamic Transit Modeling: Theory and Applications outlines the new schedule-based dynamic approach to mass transit modeling. In the last ten years the schedule-based dynamic approach has been developed and applied especially for operational planning. It allows time evolution of on-board loads and travel times for each run of each line to be obtained, and uses behavioral hypotheses strictly related to transit systems and user characteristics. It allows us to open new frontiers in transit modelling to support network design, timetable setting, investigation of congestion effects, as well as the assessment of new technologies introduction, such as information to users (ITS technologies). The contributors and editors of the book are leading researchers in the field of transportation, and in this volume they build a solid foundation for developing still more sophisticated models. These future models of mass transit systems will continue to add higher levels of accuracy and sensitivity desired in forecasting the performance of public transport systems.

Dynamics and Stochasticity in Transportation Systems: Solutions for Transportation Network Modeling breaks new ground on the topics, providing consistent and comprehensive coverage of steady state equilibrium and dynamic assignment within a common strategy. The book details the most recent advances in network assignment, including day-to-day and within-day dynamics, providing a solid foundation to help transportation planners solve transient overload and other problems. Users will find a book that fills the gap in knowledge with its description on how to use and employ the latest dynamic network models for evaluation of traffic and transport demand interventions. This book demystifies the many different dynamic traffic assignment approaches and requires no previous knowledge on the part of the reader. All results are fully described and proven, thus eliminating the need to seek out other references. The skills described will appeal to transportation professionals, researchers and graduate students alike. - Presents a consistent and comprehensive theory on steady state equilibrium assignment and day-to-day dynamic assignment models within a common framework - Describes and solves modeling calculations in detail, with no need to reference other sources - Includes numerical and graphical examples, text boxes and summaries at the end of each chapter to help readers better understand theoretical components - Includes primary mathematical tools necessary for each dynamic model, easing comprehension

The application areas of uncertainty are numerous and diverse, including all fields of engineering, computer science, systems control and finance. Determining appropriate ways and methods of dealing with uncertainty has been a constant challenge. The theme for this book is better understanding and the application of uncertainty theories. This book, with invited chapters, deals with the uncertainty phenomena in diverse fields. The book is an outgrowth of the Fourth International Symposium on Uncertainty Modeling and Analysis (ISUMA), which was held at the center of Adult Education, College Park, Maryland, in September 2003. All of the chapters have been carefully edited, following a review process in which the editorial committee scrutinized each chapter. The contents of the book are reported in twenty-three chapters, covering more than . . ... pages. This book is divided into six main sections. Part I (Chapters 1-4) presents the philosophical and theoretical foundation of uncertainty, new computational directions in neural networks, and some theoretical foundation of fuzzy systems. Part I1 (Chapters 5-8) reports on biomedical and chemical engineering applications. The sections looks at noise reduction techniques using hidden Markov models, evaluation of biomedical signals using neural networks, and changes in medical image detection using Markov Random Field and Mean Field theory. One of the chapters reports on optimization in chemical engineering processes.