This unique resource gives you a hands-on understanding of the latest sensors, processors, and communication links for everything from vehicle counts to urban congestion measurement. Moreover, you learn statistical techniques for quantifying data accuracy and reducing uncertainty in both current system state assessments and future system state forecasts.

This report examines the quality of state crash information; the activities states undertook using 411 grant funds to improve their traffic safety data systems, and the progress they made using the grant funds; and NHTSA's oversight of the grant program

The objective of these AASHTO Guidelines is to improve the quality of the traffic information that supports decisions at all levels of the transportation profession. The Guidelines provide a reference for professional traffic monitoring and establish a process for adoption of national traffic nomitoring standards. They specifically address concerns of state transportation agencies.

Road Traffic Modeling and Management: Using Statistical Monitoring and Deep Learning provides a framework for understanding and enhancing road traffic monitoring and management. The book examines commonly used traffic analysis methodologies as well the emerging methods that use deep learning methods. Other sections discuss how to understand statistical models and machine learning algorithms and how to apply them to traffic modeling, estimation, forecasting and traffic congestion monitoring. Providing both a theoretical framework along with practical technical solutions, this book is ideal for researchers and practitioners who want to improve the performance of intelligent transportation systems. - Provides integrated, up-to-date and complete coverage of the key components for intelligent transportation systems: traffic modeling, forecasting, estimation and monitoring - Uses methods based on video and time series data for traffic modeling and forecasting - Includes case studies, key processes guidance and comparisons of different methodologies