A Database Retrieval Method for the Prediction of Reduced Luminance Coefficient Tables of A Road Surface Based on Measurements in Situ. Issue 1 (2nd January 2022)

• Record Type: Journal Article
• Authors: Li, Wenyi; Hu, Yang; Ji, Yajing; Liu, Muqing; Shen, Haiping
• Is Part Of: Leukos. Volume 18:Issue 1(2022)
• Page Start: 21
• Page End: 29
• Publication Date: 2022-01-02
• Subjects: Road lighting -- road surface -- reduced luminance coefficient -- in-situ measurement -- database retrieval method; Electric lighting -- Periodicals; Electric lighting; Periodicals; 621.32
• DOI: 10.1080/15502724.2020.1813038
• Languages: English
• ISSNs: 1550-2724

Abstract

ABSTRACT: The in-situ measurements for r -tables have attracted special attention owing to their advantages, including increased speed, simplicity of use, and nondestructive road effects. This study presents a database retrieval method proposed for the construction of a full-scale r -table from an r -table database with 25 r coefficients used as input parameters. The proposed method was used in an in-situ, r -table measurement device as a data processing algorithm. To evaluate the accuracy of the proposed method, we performed a) a table-to-table comparison of Q0, S1, and r -tables constructed by different methods and b) comparisons of calculated luminance values ( Lave, U0, Ul ) for a typical road-lighting scenario based on the use of different r -tables. The table-to-table comparison results indicate that following the application of the proposed method, its calculated Q0, S1, and r -table values were very close to their actual values ( EQ0 = 2.1%, ES1 = 5.0%, Error = 2.3%). Moreover, the proposed method also exhibits an improved luminance response and achieves a 2.1% difference in Lave, a 3% difference in U0, and a 1.8% difference in Ul . This study proves that the proposed database retrieval method improves the model prediction accuracy.