Modeling Estimation And Control Of Traffic

Road Traffic Modeling and Management

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

Traffic Modeling, Estimation and Control for Large-scale Congested Urban Networks

Advances in Control, Communication Networks, and Transportation Systems

This unified volume is a collection of invited articles on topics presented at the Symposium on Systems, Control, and Networks, held in Berkeley June 5–7, 2005, in honor of Pravin Varaiya on his 65th birthday. Varaiya is an eminent faculty member of the University of California at Berkeley, widely known for his seminal contributions in areas as diverse as stochastic systems, nonlinear and hybrid systems, distributed systems, communication networks, transportation systems, power networks, economics, optimization, and systems education. The book will serve as an excellent resource for practicing and research engineers, applied mathematicians, and graduate students working in such areas as communication networks, sensor networks, transportation systems, control theory, hybrid systems, and applications.