Stochastic Dynamic Traffic Assignment For Intermodal Transportation Networks With Consistent Information Supply Strategies

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.

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

This book shows how transit assignment models can be used to describe and predict the patterns of network patronage in public transport systems. It provides a fundamental technical tool that can be employed in the process of designing, implementing and evaluating measures and/or policies to improve the current state of transport systems within given financial, technical and social constraints. The book offers a unique methodological contribution to the field of transit assignment because, moving beyond “traditional” models, it describes more evolved variants that can reproduce:• intermodal networks with high- and low-frequency services;• realistic behavioural hypotheses underpinning route choice;• time dependency in frequency-based models; and• assumptions about the knowledge that users have of network conditionsthat are consistent with the present and future level of information that intelligent transport systems (ITS) can provide. The book also considers the practical perspective of practitioners and public transport operators who need to model and manage transit systems; for example, the role of ITS is explained with regard to their potential in data collection for modelling purposes and validation techniques, as well as with regard to the additional data on network patronage and passengers’ preferences that influences the network-management and control strategies implemented. In addition, it explains how the different aspects of network operations can be incorporated in traditional models and identifies the advantages and disadvantages of doing so. Lastly, the book provides practical information on state-of-the-art implementations of the different models and the commercial packages that are currently available for transit modelling. Showcasing original work done under the aegis of the COST Action TU1004 (TransITS), the book provides a broad readership, ranging from Master and PhD students to researchers and from policy makers to practitioners, with a comprehensive tool for understanding transit assignment models.

This thesis employs traditional and new approaches to analyze properties of transportation systems under uncertainty and their effect on the various stages to transportation management; i.e., strategic, tactical, operational and real-time. Numerous pertinent problems are presented or extended to account for dynamics and uncertainty such as the network design problem, dynamic traffic assignment, centralized guidance, and the online shortest path problem. Analytical formulations are developed to describe system properties, and solution algorithms are presented to model transport systems under the stated conditions and, ultimately, to improve the performance of the transportation network.