Road Traffic Modeling and Management (eBook)

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

Dr. Fouzi Harrou received the Ph.D. degree in systems optimization and security from the University of Technology of Troyes (UTT), France. He was an Assistant Professor at UTT for one year and was an Assistant Professor at the Institute of Automotive and Transport Engineering, Nevers, France, for one-year. He was also a Post-Doctoral Research Associate at the Systems Modelling and Dependability Laboratory, UTT, for one year. He was a Research Scientist with the Chemical Engineering Department, Texas A&M University at Qatar, Doha, Qatar, for three years. Since 2015, he has been Post-Doctoral Fellow with the Division of Computer, Electrical, and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology. Dr. Fouzi Harrou is the author of more than 100 refereed journal and conference publications and book chapters. His current research interests include statistical decision theory and its applications, fault detection and diagnosis, and signal processing

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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