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Integrated Evaluation of C-ITS Services: Synergistic Effects of GLOSA and CACC on Traffic Efficiency and Sustainability

Author

Listed:
  • Manuel Walch

    (Department of Logistics, University of Applied Sciences Upper Austria, Wehrgrabengasse 1-3, 4400 Steyr, Austria)

  • Matthias Neubauer

    (Department of Logistics, University of Applied Sciences Upper Austria, Wehrgrabengasse 1-3, 4400 Steyr, Austria)

Abstract

Cooperative Intelligent Transport Systems (C-ITS) have emerged as a key enabler of more efficient, safer, and environmentally sustainable road traffic by allowing vehicles and infrastructure to exchange information and coordinate behavior. To evaluate their benefits, impact assessment studies are essential. However, most existing studies focus on individual C-ITS services in isolation, overlooking how combined deployments influence outcomes. This study addresses this gap by presenting the first systematic evaluation of individual and joint deployments of Cooperative Adaptive Cruise Control (CACC) and Green Light Optimal Speed Advisory (GLOSA) under diverse conditions. A dual-model simulation framework is applied, combining controlled artificial networks with calibrated real-world corridors in Upper Austria. This allows both statistical testing and validation of plausibility in real-world contexts. Key performance indicators include travel time and CO 2 emissions, evaluated across varying lane configurations, numbers of traffic lights, demand levels, and equipment rates. The results demonstrate that C-ITS effectiveness is strongly context-dependent: while CACC generally provides larger efficiency gains, GLOSA yields consistent emission reductions, and the combined deployment offers conditional synergies but may also diminish benefits at high demand. The study contributes a guideline for selecting service configurations based on site conditions, thereby providing practical recommendations for future C-ITS rollouts.

Suggested Citation

  • Manuel Walch & Matthias Neubauer, 2025. "Integrated Evaluation of C-ITS Services: Synergistic Effects of GLOSA and CACC on Traffic Efficiency and Sustainability," Sustainability, MDPI, vol. 17(19), pages 1-35, October.
  • Handle: RePEc:gam:jsusta:v:17:y:2025:i:19:p:8855-:d:1764400
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    References listed on IDEAS

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    1. Xin, Qi & Fu, Rui & Yuan, Wei & Liu, Qingling & Yu, Shaowei, 2018. "Predictive intelligent driver model for eco-driving using upcoming traffic signal information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 806-823.
    2. Edgar Talavera & Alberto Díaz-Álvarez & Felipe Jiménez & José E. Naranjo, 2018. "Impact on Congestion and Fuel Consumption of a Cooperative Adaptive Cruise Control System with Lane-Level Position Estimation," Energies, MDPI, vol. 11(1), pages 1-17, January.
    3. Georges M. Arnaout & Jean-Paul Arnaout, 2014. "Exploring the effects of cooperative adaptive cruise control on highway traffic flow using microscopic traffic simulation," Transportation Planning and Technology, Taylor & Francis Journals, vol. 37(2), pages 186-199, March.
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