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Intelligent Transport Systems. 6th EAI International Conference, INTSYS 2022, Lisbon, Portugal, December 15-16, 2022, Proceedings

Research Article

Adaptive Dimming of Highway Lights Using Recurrent Neural Networks

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BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-30855-0_8,
        author={Angelos Kolaitis and Georgios Alexandridis and Panagiota Adam and Petros Alexandridis and Grigoris Chasanis and Fabrice Breton},
        title={Adaptive Dimming of Highway Lights Using Recurrent Neural Networks},
        proceedings={Intelligent Transport Systems. 6th EAI International Conference, INTSYS 2022, Lisbon, Portugal, December 15-16, 2022, Proceedings},
        proceedings_a={INTSYS},
        year={2023},
        month={4},
        keywords={Traffic load prediction Highway Lighting LED Lighting Intelligent Traffic Control Systems Recurrent Neural Networks Long Short-Term Memory},
        doi={10.1007/978-3-031-30855-0_8}
    }
    
  • Angelos Kolaitis
    Georgios Alexandridis
    Panagiota Adam
    Petros Alexandridis
    Grigoris Chasanis
    Fabrice Breton
    Year: 2023
    Adaptive Dimming of Highway Lights Using Recurrent Neural Networks
    INTSYS
    Springer
    DOI: 10.1007/978-3-031-30855-0_8
Angelos Kolaitis1, Georgios Alexandridis1,*, Panagiota Adam2, Petros Alexandridis2, Grigoris Chasanis3, Fabrice Breton3
  • 1: Artificial Intelligence and Learning Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens
  • 2: Extrabit L.P., 97 Nikolaou Plastira str
  • 3: Olympia Odos S.A., 4 Rizareiou str
*Contact email: gealexandri@islab.ntua.gr

Abstract

Highway operators are in a constant search of techniques and methodologies that can reduce their energy footprint. In this respect, the installation of dimmable light-emitting diode lights on the open road section of highways appears to be a promising solution, due to the reduced energy consumption (compared to high pressure sodium lamps) and the ability to adjust their brightness at various levels, based on the road’s traffic load. However, setting the desired level of light intensity cannot be performed instantly, due to safety and contractual reasons that a highway operator must follow. For this reason, an adaptive and intelligent system is proposed in this work, that models traffic load and is able to predict its future trend, based on current load and light intensity measurements. In this way, the unnecessary use of the lighting equipment is avoided, as brightness is dropped to a minimum level when traffic load is predicted to be low. The proposed model is based on recurrent neural networks and more specifically on long short-term memory cells that are able to model complex dependencies in data with temporal correlations, like traffic load measurements. The overall approach is evaluated on relevant data provided by Olympia Odos S.A. that operates the Elefsina-Korinthos-Patra highway in Greece, with promising results.

Keywords
Traffic load prediction Highway Lighting LED Lighting Intelligent Traffic Control Systems Recurrent Neural Networks Long Short-Term Memory
Published
2023-04-28
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-30855-0_8
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