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

Sebastian Ernst, Marek Łabuz, Kamila Środa and Leszek Kotulski
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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

Sustainability, 2018, vol. 10, issue 11, 1-18

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.

Keywords: street lighting; smart cities; graph transformations; Geographic Information System (GIS); geospatial data; lighting standards (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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