Data-Driven Traffic Engineering

Data-Driven Traffic Engineering

Understanding of Traffic and Applications Based on Three-Phase Traffic Theory

1st Edition - October 23, 2020

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  • Authors: Hubert Rehborn, Micha Koller, Stefan Kaufmann
  • Paperback ISBN: 9780128191385
  • eBook ISBN: 9780128191392

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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.

Key Features

  • 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


Researchers, engineers including traffic engineers, transportation engineers, traffic system designers, highway engineers, data analysts, information technology specialists, database specialists; traffic scientists

Table of Contents

  • 1. Introduction
    2. How Traffic Congestion can be measured
    3. Analysis of Congested Traffic Pattern Features on Freeways
    4. Analysis of Congested Traffic Patterns in Urban Areas
    5. Applications of Traffic in Transportation Science
    6. Future Directions
    7. Bibliography

Product details

  • No. of pages: 192
  • Language: English
  • Copyright: © Elsevier 2020
  • Published: October 23, 2020
  • Imprint: Elsevier
  • Paperback ISBN: 9780128191385
  • eBook ISBN: 9780128191392

About the Authors

Hubert Rehborn

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.

Affiliations and Expertise

Traffic Researcher, Daimler AG, Germany

Micha Koller

Micha Koller is a traffic researcher at Daimler AG responsible for the worldwide operation and development of traffic services. He has a doctorate in Computer Science from the University of Tübingen, holds more than 30 patents, and has published widely in the field.

Affiliations and Expertise

Traffic Researcher, Daimler AG

Stefan Kaufmann

Consultant, Germany

Affiliations and Expertise

Consultant, Germany

Ratings and Reviews

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  • RPS Mon May 03 2021

    Traffic Modeling and Data

    The book is a Must-Have for engineers and scientists dealing with congestion modeling and measurement problems. The authors deeply influenced by Kerner's 3-phases theory of traffic describing the evolution and availability of car-centric traffic data (probe data) to validate the theoretical skeleton of Kerner's theory and its powerful mechanism to measure congestion patterns on highways and surface roads. The book gives a number of great examples of how to utilize GPS-based probe data to identify traffic patterns and congestion patterns as well as traveltime measures. Another example to measure traffic behaviors is presented by using drones visualizing traffic flow and speed in time and space. On little feedback for the publishing house. The book would have benefited from a color print to get more out the great graphical representations of traffic. An overall great read. Highly recommended.