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Prediction of Moving Bottleneck and Associated Traffic Phenomena for Automated Driving

In: Traffic and Granular Flow '17

Author

Listed:
  • Dominik Wegerle

    (University of Duisburg-Essen, Physics of Transport and Traffic)

  • Boris S. Kerner

    (University of Duisburg-Essen, Physics of Transport and Traffic)

  • Sergey L. Klenov

    (Moscow Institute of Physics and Technology, Department of Physics)

  • Michael Schreckenberg

    (University of Duisburg-Essen, Physics of Transport and Traffic)

Abstract

A slow driving vehicle within traffic flow is considered as a moving bottleneck (MB). In this paper, we present simulations made with a microscopic stochastic flow model with a moving bottleneck in the framework of the three-phase theory by Kerner. The goal is to predict traffic phenomena that may occur if traffic breakdown is realized at the moving bottleneck. Considered is a traffic flow in which different percentages of probe vehicles are randomly distributed, which send their position and their speed each second (simFCD). We investigate what percentage of probe vehicles is necessary to reliably detect a moving bottleneck and predict its motion.

Suggested Citation

  • Dominik Wegerle & Boris S. Kerner & Sergey L. Klenov & Michael Schreckenberg, 2019. "Prediction of Moving Bottleneck and Associated Traffic Phenomena for Automated Driving," Springer Books, in: Samer H. Hamdar (ed.), Traffic and Granular Flow '17, pages 61-69, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-11440-4_8
    DOI: 10.1007/978-3-030-11440-4_8
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