Data-Driven Traffic Engineering (eBook)

Data-Driven Traffic Engineering: Understanding of Traffic and Applications Based on Three-Phase Traffic Theory shifts the current focus from using modeling and simulation data for traffic measurements to the use of actual data. The book uses real-world, empirically-derived data from a large fleet of connected vehicles, local observations and aerial observation to shed light on key traffic phenomena. Readers will learn how to develop an understanding of the empirical features of vehicular traffic networks and how to consider these features in emerging, intelligent transport systems. Topics cover congestion patterns, fuel consumption, the influence of weather, and much more. This book offers a unique, data-driven analysis of vehicular traffic in traffic networks, also considering how to apply data-driven insights to the intelligent transport systems of the future. - Provides an empirically-driven analysis of traffic measurements/congestion based on real-world data collected from a global fleet of vehicles - Applies Kerner's three-phase traffic theory to empirical data - Offers a critical scientific understanding of the underlying concerns of traffic control in automated driving and intelligent transport systems

Hubert Rehborn is a traffic researcher at Daimler AG where he investigates the future of traffic as part of connected navigations systems. His doctorate in Electrical Engineering is from the University of Hagen, and he holds more than 135 patents, and has authored more than 60 papers in the field.

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Data-Driven Traffic Engineering: Understanding of Traffic and Applications Based on Three-Phase Traffic Theory shifts the current focus from using modeling and simulation data for traffic measurements to the use of actual data. The book uses real-world, empirically-derived data from a large fleet of connected vehicles, local observations and aerial observation to shed light on key traffic phenomena. Readers will learn how to develop an understanding of the empirical features of vehicular traffic networks and how to consider these features in emerging, intelligent transport systems. Topics cover congestion patterns, fuel consumption, the influence of weather, and much more. This book offers a unique, data-driven analysis of vehicular traffic in traffic networks, also considering how to apply data-driven insights to the intelligent transport systems of the future. - Provides an empirically-driven analysis of traffic measurements/congestion based on real-world data collected from a global fleet of vehicles- Applies Kerner's three-phase traffic theory to empirical data- Offers a critical scientific understanding of the underlying concerns of traffic control in automated driving and intelligent transport systems