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Performance Evaluation of GLOSA-Algorithms Under Realistic Traffic Conditions Using C2I-Communication

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 879)

Abstract

The aim of Green Light Optimized Speed Advisory (GLOSA) systems is to assist individual vehicles approaching an intersection with speed advices (either as single target speed or as complex speed-distance relation) in order to fulfill a given objective. Common objectives include the minimization of fuel usage, emissions and/or delay. The literature provides a wide selection of GLOSA-algorithms addressing different aspects of a real world application, like surrounding traffic, fixed time or actuated traffic lights and mode of communication. However, previous research usually addressed only a subset of possible aspects. Therefore, our goal is to investigate how the existing algorithms hold up in a scenario under largely realistic conditions. We measure the performance (in terms of overall fuel usage, carbon dioxide emissions and delay) of the different GLOSA-algorithms and identify potential shortcomings.

Keywords

  • GLOSA
  • Speed advisory system
  • Connected vehicles
  • ITS-G5

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Notes

  1. 1.

    https://www.synchrone-mobilitaet.de/en.html.

  2. 2.

    https://www.synchrone-mobilitaet.de/en/projects/syncar.html.

  3. 3.

    https://www.synchrone-mobilitaet.de/en/projects/harmonizedd.html.

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Acknowledgment

The authors are grateful for funding by the German Ministry of Transportation and Infrastructure (BMVI), project HarmonizeDD, and by the federal state of Saxony and “European Regional Development Fund” (EFRE), project SYNCAR. We also would like to thank Mario Krumnow and Anja Liebscher, both Chair of Traffic Control and Process Automation, Institute of Traffic Telematics, TU Dresden, 01069 Dresden, Germany, for preparing the simulation scenario.

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Correspondence to Michael Kloeppel or Jan Grimm .

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Kloeppel, M., Grimm, J., Strobl, S., Auerswald, R. (2019). Performance Evaluation of GLOSA-Algorithms Under Realistic Traffic Conditions Using C2I-Communication. In: Nathanail, E., Karakikes, I. (eds) Data Analytics: Paving the Way to Sustainable Urban Mobility. CSUM 2018. Advances in Intelligent Systems and Computing, vol 879. Springer, Cham. https://doi.org/10.1007/978-3-030-02305-8_6

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  • DOI: https://doi.org/10.1007/978-3-030-02305-8_6

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