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Graph-Based Spatial Data Processing and Analysis for More Efficient Road Lighting Design

Author

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  • Sebastian Ernst

    (Department of Applied Computer Science, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Kraków, Poland)

  • Marek Łabuz

    (Department of Applied Computer Science, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Kraków, Poland)

  • Kamila Środa

    (Department of Applied Computer Science, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Kraków, Poland)

  • Leszek Kotulski

    (Department of Applied Computer Science, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Kraków, Poland)

Abstract

The efficiency and affordability of modern street lighting equipment are improving quickly, but systems used to manage and design lighting installations seem to lag behind. One of their problems is the lack of consistent methods to integrate all relevant data. Tools used to manage lighting infrastructure are not aware of the geographic characteristics of the lit areas, and photometric calculation software requires a lot of manual editing by the designer, who needs to assess the characteristics of roads, define the segments, and assign the lighting classes according to standards. In this paper, we propose a graph-based method to integrate geospatial data from various sources to support the process of data preparation for photometric calculations. The method uses graph transformations to define segments and assign lighting classes. A prototype system was developed to conduct experiments using real-world data. The proposed approach is compared to results obtained by professional designers in a case study; the method was also applied to several European cities to assess its efficiency. The obtained results are much more fine-grained than those yielded by the traditional approach; as a result, the lighting is more adequate, especially when used in conjunction with automated optimisation tools.

Suggested Citation

  • Sebastian Ernst & Marek Łabuz & Kamila Środa & Leszek Kotulski, 2018. "Graph-Based Spatial Data Processing and Analysis for More Efficient Road Lighting Design," Sustainability, MDPI, vol. 10(11), pages 1-18, October.
  • Handle: RePEc:gam:jsusta:v:10:y:2018:i:11:p:3850-:d:177877
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    References listed on IDEAS

    as
    1. Igor Wojnicki & Leszek Kotulski, 2018. "Improving Control Efficiency of Dynamic Street Lighting by Utilizing the Dual Graph Grammar Concept," Energies, MDPI, vol. 11(2), pages 1-15, February.
    2. Igor Wojnicki & Leszek Kotulski, 2018. "Empirical Study of How Traffic Intensity Detector Parameters Influence Dynamic Street Lighting Energy Consumption: A Case Study in Krakow, Poland," Sustainability, MDPI, vol. 10(4), pages 1-16, April.
    3. Beccali, Marco & Bonomolo, Marina & Ciulla, Giuseppina & Galatioto, Alessandra & Lo Brano, Valerio, 2015. "Improvement of energy efficiency and quality of street lighting in South Italy as an action of Sustainable Energy Action Plans. The case study of Comiso (RG)," Energy, Elsevier, vol. 92(P3), pages 394-408.
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    Cited by:

    1. Davidovic, M. & Kostic, M., 2022. "Comparison of energy efficiency and costs related to conventional and LED road lighting installations," Energy, Elsevier, vol. 254(PB).
    2. Michelangelo Scorpio & Roberta Laffi & Massimiliano Masullo & Giovanni Ciampi & Antonio Rosato & Luigi Maffei & Sergio Sibilio, 2020. "Virtual Reality for Smart Urban Lighting Design: Review, Applications and Opportunities," Energies, MDPI, vol. 13(15), pages 1-26, July.
    3. Igor Wojnicki & Konrad Komnata & Leszek Kotulski, 2019. "Comparative Study of Road Lighting Efficiency in the Context of CEN/TR 13201 2004 and 2014 Lighting Standards and Dynamic Control," Energies, MDPI, vol. 12(8), pages 1-14, April.

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