This guide is designed to provide direction on the monitoring of traffic characteristics. It begins with a discussion of the structure of traffic characteristics monitoring and traffic counting. The next two sections cover vehicle classification and truck weighing. The last section presents the coordinated record formats for station identification, traffic volume, vehicle classification, and truck weight data.

A nice night of October 2007, in Beijing, during the XV World Conference on ITS a number of colleagues met informally for a dinner party that spontaneously became a vivid discussion on the importance of traffic data for all types of p- poses. Researchers can hardly do any progress in modeling, developing, and te- ing theories without suitable data, and what practitioners can do in real life is limited not only by technology but also by the availability of the required data. Quite frequently, the data and not the technologies are what determine how far we can go. Any discussion about traffic data leads in a natural way to a discussion on the variety of traffic data sources, formats, levels of aggregation, accuracies, and so on. Consequently, we moved to talk on the initiative that Kuwahara had undertaken in his traffic laboratory at the University of Tokyo, known as the International Traffic Data Base, and thus smoothly but inexorably we came to agree that it would be convenient to organize a workshop to continue our discussion at a more formal level, share our points of view with other colleagues, listen what they had to say and, if possible, d- seminate the findings in our professional and academic communities.

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

Introduction -- Information sharing for traffic incident management -- Implications and challenges -- Conclusions and recommendations -- References -- Appendixes.

In every part of the world information and technology are changing society and challenging the structures, roles, and management of traditional government institutions. At the same time, universal needs for human and social development, environmental protection, commercial and financial stability, and scientific and technological advancement demand governmental attention. In this complex and changing environment, governments are still expected to provide for the public good through legal and political processes, and public programs and services. Digital transformation, electronic government, government 2.0, and electronic governance are just some of the labels used to characterize the ideas and actions that underlie adaptation, transformation, and reform efforts. This book contributes to the ongoing dialog within the digital government research and practice community by addressing leadership and management challenges through the interplay of five interconnected themes: management, policy, technology, data, and context. These themes are evident in a wide range of topics including policy informatics, smart cities, cross-boundary information sharing, service delivery, and open government, among others. Accordingly, it includes chapters that explore these themes conceptually and empirically and that emphasize the importance of context, the need for cross‐boundary thinking and action, a public value approach to performance, and the multi‐dimensional capabilities necessary to succeed in a dynamic, multi‐stakeholder environment. The chapters in this book were originally published as a special issue of the journal, Public Management Review.