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Each chapter in Equilibrium and Advanced Transportation Modelling develops a topic from basic concepts to the state-of-the-art, and beyond. All chapters relate to aspects of network equilibrium. Chapter One advocates the use of simulation models for the representation of traffic flow movements at the microscopic level. Chapter Two presents travel demand systems for generating trip matrices from activity-based models, taking into account the entire daily schedule of network users. Chapter Three examines equilibrium strategic choices adopted by the passengers of a congested transit system, carefully addressing line selection at boarding and transfer nodes. Chapter Four provides a critical appraisal of the traditional process that consists in sequentially performing the tasks of trip generation, trip distribution, mode split and assignment, and its impact on the practice of transportation planning. Chapter Five gives an insightful overview of stochastic assignment models, both in the static and dynamic cases. Chapters Six and Seven investigate the setting of tolls to improve traffic flow conditions in a congested transportation network. Chapter Eight provides a unifying framework for the analysis of multicriteria assignment models. In this chapter, available algorithms are summarized and an econometric perspective on the estimation of heterogeneous preferences is given. Chapter Nine surveys the use of hyperpaths in operations research and proposes a new paradigm of equilibrium in a capacitated network, with an application to transit assignment. Chapter Ten analyzes the transient states of a system moving towards equilibrium, using the mathematical framework of projected dynamical systems. Chapter Eleven discusses an in-depth survey of algorithms for solving shortest path problems, which are pervasive to any equilibrium algorithm. The chapter devotes special attention to the computation of dynamic shortest paths and to shortest hyperpaths. The final chapter considers operations research tools for reducing traffic congestion, in particular introducing an algorithm for solving a signal-setting problem formulated as a bilevel program.
This book provides a systematic analysis, modeling and evaluation of the performance of advanced transport systems. It offers an innovative approach by presenting a multidimensional examination of the performance of advanced transport systems and transport modes, useful for both theoretical and practical purposes. Advanced transport systems for the twenty-first century are characterized by the superiority of one or several of their infrastructural, technical/technological, operational, economic, environmental, social and policy performances as compared to their conventional counterparts. The advanced transport systems considered include: Bus Rapid Transit (BRT) and Personal Rapid Transit (PRT) systems in urban area(s), electric and fuel cell passenger cars, high speed tilting trains, High Speed Rail (HSR), Trans Rapid Maglev (TRM), Evacuated Tube Transport system (ETT), advanced commercial subsonic and Supersonic Transport Aircraft (STA), conventionally- and Liquid Hydrogen (LH2)-fuelled commercial air transportation, advanced Air Traffic Control (ATC) technologies and procedures for increasing the airport runway capacity, Underground Freight Transport (UFT) systems in urban area(s), Long Intermodal Freight Train(s) (LIFTs), road mega trucks, large advanced container ships and freight/cargo aircraft and advanced freight/goods collection distribution networks. This book is intended for postgraduates, researchers, professionals and policy makers working in the transport industry.
In this book, recent developments, the future outlook, and advanced and analytical modeling techniques of smart electric and hybrid vehicles are explained with examples backed by experimental and numerical data. It also discusses the integration of newer developments like digital twin, artificial intelligence, nature-inspired algorithms, Internet of Things, and the role of Industry 4.0 in advancements in vehicle engineering. It compiles overall aspects of advancements in smart electric and hybrid vehicles by bringing the latest research and development by comprehensive range of mathematical, numerical, and simulation modeling, and management techniques to strengthen the engineering science and technological developments for the future. Features: • This book focuses on contemporary aspects of smart electric and hybrid vehicles techniques for new means and models for green environment. • Discusses the role of artificial intelligence, machine learning, and machine vision tools in smart electric and hybrid vehicles. • Presents design and analysis of charging stations and their sustainability roadmap for smart electric vehicles. • Highlights the cyber and functional security of intelligent and hybrid vehicles. • Explains diagnostics, prognostics, reliability, and durability issues in smart electric and hybrid vehicles. • Covers the Internet of Things-based battery and charging management approach and effect of voltage drop in charging capacity of smart electric vehicles. It is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, and automotive engineering.
Transport systems are facing an impossible dilemma: satisfy an increasing demand for mobility of people and goods, while decreasing their fossil-energy requirements and preserving the environment. Additionally, transport has an opportunity to evolve in a changing world, with new services, technologies but also new requirements (fast delivery, reliability, improved accessibility). The subject of traffic is organized into two separate but complementary volumes: Volume 3 on Traffic Management and Volume 4 on Traffic Safety. Traffic Management, Volume 3 of the 'Research for Innovative Transports' Set, presents a collection of updated papers from the TRA 2014 Conference, highlighting the diversity of research in this field. Theoretical chapters and practical case studies address topics such as cooperative systems, the global approach in modeling, road and railway traffic management, information systems and impact assessment.
The 17 chapters in this book, which evolved from a conference on measuring the contributions of ITS sponsored by the California Department of Transportation in February 2002, examine the costs and benefits of ITS in an economic and business policy context. Section 1 examines the broad theme of how and what ITS contributes to the economy and how one makes a business case for ITS. Section 2 includes three chapters on ITS applications in mass transit. Section 3 explores ITS applications in the automobile/highway system. Section 4 considers integrative issues including how ITS is perceived and how it can be positioned to improve surface transportation. This volume will be especially useful to researchers and policy makers working in transportation, transportation engineering, and the economic analysis of transportation systems.
Due to the growing use of web applications and communication devices, the use of data has increased throughout various industries. It is necessary to develop new techniques for managing data in order to ensure adequate usage. Deep learning, a subset of artificial intelligence and machine learning, has been recognized in various real-world applications such as computer vision, image processing, and pattern recognition. The deep learning approach has opened new opportunities that can make such real-life applications and tasks easier and more efficient. Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications is a vital reference source that trends in data analytics and potential technologies that will facilitate insight in various domains of science, industry, business, and consumer applications. It also explores the latest concepts, algorithms, and techniques of deep learning and data mining and analysis. Highlighting a range of topics such as natural language processing, predictive analytics, and deep neural networks, this multi-volume book is ideally designed for computer engineers, software developers, IT professionals, academicians, researchers, and upper-level students seeking current research on the latest trends in the field of deep learning.
Innovative and smart mobility systems are expected to make transportation systems more sustainable, inclusive, and safe. Because of changing mobility paradigms, transport planning and design require different methodological approaches. Over twelve chapters, this book examines and analyzes Mobility as a Service (MaaS), travel behavior, traffic control, intelligent transportation system design, electric, connected, and automated vehicles, and much more.