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Data Analytics for Intelligent Transportation Systems provides in-depth coverage of data-enabled methods for analyzing intelligent transportation systems that includes detailed coverage of the tools needed to implement these methods using big data analytics and other computing techniques. The book examines the major characteristics of connected transportation systems, along with the fundamental concepts of how to analyze the data they produce. It explores collecting, archiving, processing, and distributing the data, designing data infrastructures, data management and delivery systems, and the required hardware and software technologies. Users will learn how to design effective data visualizations, tactics on the planning process, and how to evaluate alternative data analytics for different connected transportation applications, along with key safety and environmental applications for both commercial and passenger vehicles, data privacy and security issues, and the role of social media data in traffic planning. - Includes case studies in each chapter that illustrate the application of concepts covered - Presents extensive coverage of existing and forthcoming intelligent transportation systems and data analytics technologies - Contains contributors from both leading academic and commercial researchers - Explains how to design effective data visualizations, tactics on the planning process, and how to evaluate alternative data analytics for different connected transportation applications
"This book highlights entirely new and detailed spatial-temporal micro-simulation methodologies for human mobility and the emerging dynamics of our society, offering novel ideas grounded in big data from various data mining and transportation science sources"--
Mobility is fundamental to economic and social activities such as commuting, manufacturing, or supplying energy. Each movement has an origin, a potential set of intermediate locations, a destination, and a nature which is linked with geographical attributes. Transport systems composed of infrastructures, modes and terminals are so embedded in the socio-economic life of individuals, institutions and corporations that they are often invisible to the consumer. This is paradoxical as the perceived invisibility of transportation is derived from its efficiency. Understanding how mobility is linked with geography is main the purpose of this book. The third edition of The Geography of Transport Systems has been revised and updated to provide an overview of the spatial aspects of transportation. This text provides greater discussion of security, energy, green logistics, as well as new and updated case studies, a revised content structure, and new figures. Each chapter covers a specific conceptual dimension including networks, modes, terminals, freight transportation, urban transportation and environmental impacts. A final chapter contains core methodologies linked with transport geography such as accessibility, spatial interactions, graph theory and Geographic Information Systems for transportation (GIS-T). This book provides a comprehensive and accessible introduction to the field, with a broad overview of its concepts, methods, and areas of application. The accompanying website for this text contains a useful additional material, including digital maps, PowerPoint slides, databases, and links to further reading and websites. The website can be accessed at: http://people.hofstra.edu/geotrans This text is an essential resource for undergraduates studying transport geography, as well as those interest in economic and urban geography, transport planning and engineering.
Data-Driven Solutions to Transportation Problems explores the fundamental principle of analyzing different types of transportation-related data using methodologies such as the data fusion model, the big data mining approach, computer vision-enabled traffic sensing data analysis, and machine learning. The book examines the state-of-the-art in data-enabled methodologies, technologies and applications in transportation. Readers will learn how to solve problems relating to energy efficiency under connected vehicle environments, urban travel behavior, trajectory data-based travel pattern identification, public transportation analysis, traffic signal control efficiency, optimizing traffic networks network, and much more.
Provides users of transportation statistics with a comprehensive inventory of transportation data sources to effect easier accessibility and availability of information. Listed by agency, each profile contains the name and type of the data source, mode (area of transportation relating to the source), abstract, source of data, attributes, significant features or limitations, corresponding printed source, sponsoring organization, performing organization, availability, and contact for additional information. Indexed alphabetically and by mode.
Includes subject, agency, and budget indexes.
Includes subject, agency, and budget indexes.
This book presents papers based on the presentations and discussions at the international workshop on Big Data Smart Transportation Analytics held July 16 and 17, 2016 at Tongji University in Shanghai and chaired by Professors Ukkusuri and Yang. The book is intended to explore a multidisciplinary perspective to big data science in urban transportation, motivated by three critical observations: The rapid advances in the observability of assets, platforms for matching supply and demand, thereby allowing sharing networks previously unimaginable. The nearly universal agreement that data from multiple sources, such as cell phones, social media, taxis and transit systems can allow an understanding of infrastructure systems that is critically important to both quality of life and successful economic competition at the global, national, regional, and local levels. There is presently a lack of unifying principles and methodologies that approach big data urban systems. The workshop brought together varied perspectives from engineering, computational scientists, state and central government, social scientists, physicists, and network science experts to develop a unifying set of research challenges and methodologies that are likely to impact infrastructure systems with a particular focus on transportation issues. The book deals with the emerging topic of data science for cities, a central topic in the last five years that is expected to become critical in academia, industry, and the government in the future. There is currently limited literature for researchers to know the opportunities and state of the art in this emerging area, so this book fills a gap by synthesizing the state of the art from various scholars and help identify new research directions for further study.