---
_id: '61492'
abstract:
- lang: eng
  text: "This paper deals with the development and results of a prediction framework
    for traffic light control systems as well as the usage and benefits of such predictions
    in green light optimal speed advisory (GLOSA) scenarios.\r\nVarious machine learning
    methods like support vector machines, neural networks or reinforcement learning
    were evaluated for their applicability in the prediction context and compared
    based on their efficiency and most importantly accuracy. The resulting prediction
    framework uses decision tree ensemble models combined with certain model knowledge
    to forecast different control strategies. This method was chosen due to its best
    performance in various test scenarios. Very high accuracy and fidelity were achieved
    for standard control methods like fixed-time, time-of-day-based and 'ordinary'
    traffic-based programs. Only for the more sophisticated model predictive control
    which was tested lower accuracies were achieved.\r\nFor the upcoming GLOSA application
    the penetration of equipped vehicles was varied for different traffic scenarios
    and control strategies. Results showcase high potentials for enhancing urban mobility
    and reducing environmental impact by lower emissions and waiting times. However,
    it is also clear from the studies presented in this contribution that the coordination
    of the control strategy with the GLOSA vehicles is of enormous importance."
author:
- first_name: Kevin
  full_name: Malena, Kevin
  id: '36303'
  last_name: Malena
  orcid: 0000-0003-1183-4679
- first_name: Christopher
  full_name: Link, Christopher
  id: '38249'
  last_name: Link
- first_name: Sandra
  full_name: Gausemeier, Sandra
  id: '17793'
  last_name: Gausemeier
- first_name: Ansgar
  full_name: Trächtler, Ansgar
  id: '552'
  last_name: Trächtler
citation:
  ama: 'Malena K, Link C, Gausemeier S, Trächtler A. ML-based Prediction Framework
    for varying Traffic Signal Control Strategies and its GLOSA-application. In: <i>2025
    IEEE 28th International Conference on Intelligent Transportation Systems (ITSC)</i>.
    Vol 28. IEEE.'
  apa: Malena, K., Link, C., Gausemeier, S., &#38; Trächtler, A. (n.d.). ML-based
    Prediction Framework for varying Traffic Signal Control Strategies and its GLOSA-application.
    <i>2025 IEEE 28th International Conference on Intelligent Transportation Systems
    (ITSC)</i>, <i>28</i>.
  bibtex: '@inproceedings{Malena_Link_Gausemeier_Trächtler, title={ML-based Prediction
    Framework for varying Traffic Signal Control Strategies and its GLOSA-application},
    volume={28}, booktitle={2025 IEEE 28th International Conference on Intelligent
    Transportation Systems (ITSC)}, publisher={IEEE}, author={Malena, Kevin and Link,
    Christopher and Gausemeier, Sandra and Trächtler, Ansgar} }'
  chicago: Malena, Kevin, Christopher Link, Sandra Gausemeier, and Ansgar Trächtler.
    “ML-Based Prediction Framework for Varying Traffic Signal Control Strategies and
    Its GLOSA-Application.” In <i>2025 IEEE 28th International Conference on Intelligent
    Transportation Systems (ITSC)</i>, Vol. 28. IEEE, n.d.
  ieee: K. Malena, C. Link, S. Gausemeier, and A. Trächtler, “ML-based Prediction
    Framework for varying Traffic Signal Control Strategies and its GLOSA-application,”
    in <i>2025 IEEE 28th International Conference on Intelligent Transportation Systems
    (ITSC)</i>, Gold Coast (Australia), vol. 28.
  mla: Malena, Kevin, et al. “ML-Based Prediction Framework for Varying Traffic Signal
    Control Strategies and Its GLOSA-Application.” <i>2025 IEEE 28th International
    Conference on Intelligent Transportation Systems (ITSC)</i>, vol. 28, IEEE.
  short: 'K. Malena, C. Link, S. Gausemeier, A. Trächtler, in: 2025 IEEE 28th International
    Conference on Intelligent Transportation Systems (ITSC), IEEE, n.d.'
conference:
  end_date: 2025-11-21
  location: Gold Coast (Australia)
  name: 28th International Conference on Intelligent Transportation Systems (ITSC)
  start_date: 2025-11-18
date_created: 2025-10-01T11:20:34Z
date_updated: 2026-01-26T08:50:37Z
department:
- _id: '153'
intvolume: '        28'
keyword:
- ML
- Prediction
- Tree Ensembles
- GLOSA
language:
- iso: eng
publication: 2025 IEEE 28th International Conference on Intelligent Transportation
  Systems (ITSC)
publication_status: accepted
publisher: IEEE
quality_controlled: '1'
status: public
title: ML-based Prediction Framework for varying Traffic Signal Control Strategies
  and its GLOSA-application
type: conference
user_id: '36303'
volume: 28
year: '2026'
...