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

This textbook provides a comprehensive and instructive coverage of vehicular traffic flow dynamics and modeling. It makes this fascinating interdisciplinary topic, which to date was only documented in parts by specialized monographs, accessible to a broad readership. Numerous figures and problems with solutions help the reader to quickly understand and practice the presented concepts. This book is targeted at students of physics and traffic engineering and, more generally, also at students and professionals in computer science, mathematics, and interdisciplinary topics. It also offers material for project work in programming and simulation at college and university level. The main part, after presenting different categories of traffic data, is devoted to a mathematical description of the dynamics of traffic flow, covering macroscopic models which describe traffic in terms of density, as well as microscopic many-particle models in which each particle corresponds to a vehicle and its driver. Focus chapters on traffic instabilities and model calibration/validation present these topics in a novel and systematic way. Finally, the theoretical framework is shown at work in selected applications such as traffic-state and travel-time estimation, intelligent transportation systems, traffic operations management, and a detailed physics-based model for fuel consumption and emissions.

Traffic Operations at Intersections: Learning and Applying the Models and Methods of the Highway Capacity Manual Chapters on all-way stop-controlled intersections, two-way stop-controlled intersections, and signalized intersections Designed for practicing transportation engineers and university seniors and graduate students 11 simplified scenarios to open-up your understanding of the HCM 43 example calculations that are fully worked out and explained in detail 7 computational engines that allow you to see inside and then apply the models 138 figures to clearly illustrate concepts Additional problems online The models of the Highway Capacity Manual (HCM) are often the engineer's choice to analyze intersection performance. These models are complex, and nearly all transportation engineers use software implementations of these models to conduct their analyses. Software applications are powerful tools that help engineers solve problems. But these applications also serve as barriers to the understanding of the complex models embedded in the software. Our major objective in writing this book is to transform the "black box" of the HCM intersection models, and their software implementations, into a "clear box" that allows the engineer to better understand how these models work. We do this through the idea of the "simplified scenario." The eleven scenarios that we present are based on conditions greatly simplified from what you would normally see in the field. By focusing on one concept at a time, in the context of these simplified conditions, you will better understand the fundamentals of the HCM intersection models. You will then be able to apply these models to more complex intersections with skill, confidence, and insight.

Intended to assist agencies responsible for incident management activities on public roadways to improve their programs and operations.Organized into three major sections: Introduction to incident management; organizing, planning, designing and implementing an incident management program; operational and technical approaches to improving the incident management process.