Yao, Q, Wang, H, Uttley, J orcid.org/0000-0002-8080-3473 et al. (1 more author) (2018) Illuminance Reconstruction of Road Lighting in Urban Areas for Efficient and Healthy Lighting Performance Evaluation. Applied Sciences, 8 (9). 9. p. 1646. ISSN 2076-3417
Abstract
Big lighting data are required for evaluation of lighting performance and impacts on human beings, environment, and ecology for smart urban lighting. However, traditional approaches of measuring road lighting cannot achieve this aim. We propose a rule-of-thumb model approach based on some feature points to reconstruct road lighting in urban areas. We validated the reconstructed illuminance with both software simulated and real road lighting scenes, and the average error is between 6 and 19%. This precision is acceptable in practical applications. Using this approach, we reconstructed the illuminance of three real road lighting environments in a block and further estimated the mesopic luminance and melanopic illuminance performance. In the future, by virtue of Geographic Information System technology, the approach may provide big lighting data for evaluation and analysis, and help build smarter urban lighting.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
Keywords: | luminance; mesopic luminance; non-visual biological effect; equivalent melanopic lux; big data |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) > ITS: Safety and Technology (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 07 Dec 2018 15:33 |
Last Modified: | 07 Dec 2018 15:33 |
Status: | Published |
Publisher: | Multidisciplinary Digital Publishing Institute |
Identification Number: | 10.3390/app8091646 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:139749 |