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

Department of Applied Computer Science, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Kraków, Poland
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Sustainability 2018, 10(11), 3850; https://doi.org/10.3390/su10113850
Received: 21 September 2018 / Revised: 14 October 2018 / Accepted: 16 October 2018 / Published: 24 October 2018
(This article belongs to the Special Issue Sustainable Lighting and Energy Saving)
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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. View Full-Text
Keywords: street lighting; smart cities; graph transformations; Geographic Information System (GIS); geospatial data; lighting standards street lighting; smart cities; graph transformations; Geographic Information System (GIS); geospatial data; lighting standards
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Ernst, S.; Łabuz, M.; Środa, K.; Kotulski, L. Graph-Based Spatial Data Processing and Analysis for More Efficient Road Lighting Design. Sustainability 2018, 10, 3850.

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