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Data Analytics for Intelligent Transportation Systems provides in-depth coverage of data-enabled methods for analyzing intelligent transportation systems (ITS), including 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. It presents extensive coverage of existing and forthcoming intelligent transportation systems and data analytics technologies. All fundamentals/concepts presented in this book are explained in the context of ITS. Users will learn everything from the basics of different ITS data types and characteristics to how to evaluate alternative data analytics for different ITS applications. They will discover 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. Data Analytics for Intelligent Transportation Systems will prepare an educated ITS workforce and tool builders to make the vision for safe, reliable, and environmentally sustainable intelligent transportation systems a reality. It serves as a primary or supplemental textbook for upper-level undergraduate and graduate ITS courses and a valuable reference for ITS practitioners. - Utilizes real ITS examples to facilitate a quicker grasp of materials presented - Contains contributors from both leading academic and commercial domains - Explains how to design effective data visualizations, tactics on the planning process, and how to evaluate alternative data analytics for different connected transportation applications - Includes exercise problems in each chapter to help readers apply and master the learned fundamentals, concepts, and techniques - New to the second edition: Two new chapters on Quantum Computing in Data Analytics and Society and Environment in ITS Data Analytics
Transportation-related challenges exist all over the world, with all countries struggling to develop efficient, effective and user-friendly transportation systems. Today, policy agencies and financing institutions are keen to invest heavily for a potentially good transport systems, as good mobility is pertinent to social growth and a sustainable environment. Intelligent Transportation Systems (ITS) have become a global area of growth in recent times because of increasing demand for mobility, rampant urbanization, and depleting energy reserves. Existing conventional transport infrastructure fails to meet the ever-increasing demand; building additional transportation infrastructure is cumbersome, as it is time-consuming and capital-intensive and available land space is very limited. Therefore, there is a pressing need for innovative and locally relevant systems that can be built rapidly with less investment by leveraging advances in technology. Good ITS enable informed decision-making for all stakeholders. This book presents the ingredients of good ITS, not from a technology perspective, but from a business administration, management, and policy perspective. The emphasis is on practice-oriented, impactful and context relevant systems. Short, real-life case studies are presented for each topic, to keep the discerning transportation enthusiast engaged.
Contains summaries of current U.S. intelligent transportation systems projects.
This new volume considers the use of smart technologies in commercial and hazardous vehicles, looking at the challenges and solutions to transportation issues that can be solved with such intelligent applications as artificial intelligence, Internet of Things, neural networks, blockchain, machine learning, big data, etc. The book illustrates the use these smart technologies for vehicle pedestrian detection, in the planning of smart cities for traffic patterns, for the improvement of transportation power stations, for smart railway cargo management systems, and more.
This report concludes a one-year. project conducted for the Texas Department of Transportation (TxDOT) to develop a statewide strategic plan for commercial vehicle operations (CVO). The report is divided into seven chapters which address the tasks that were defined at the outset of the project. The first chapter provides background information regarding the need for the strategic plan and its foundation. The second chapter presents the summary of a comprehensive literature review concerning strategic planning for CVO and other related topics. Chapter 3 describes the trucking activity in Texas in terms of fleet characteristics, the distribution of trucking activity, and commodity movements. Chapter 4 provides an overview of truck size and weight (TS&W) regulations and safety regulations, as well as current enforcement practices in the state. The next chapter presents an overview of current practices by government agencies and by motor carriers regarding administrative procedures in the state. Chapter 6 describes current advances in information and transportation technology. The proposed strategic plan for Texas is presented in Chapter 7. This plan includes the mission statement, long- and short-term goals and objectives, and the specific milestones, responsibilities, and funding opportunities for Texas.
TRB's National Cooperative Highway Research Program (NCHRP) Synthesis 314: Strategies for Managing Increasing Truck Traffic documents recent efforts by transportation organizations that construct, operate, and manage the transportation system and identifies truck-related challenges, planning activities for goods movement being undertaken, truck management strategies being considered, factors that have influenced the selection of particular strategies, and benefits expected from selected strategies.