Prediction of Service Life of Thermoplastic Road Markings on Expressways

Luhua Zhao, Haonan Ding (), Junjing Sun, Guangna Wu, Huiyao Xing, Wei Wang and Jie Song ()
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Luhua Zhao: College of Transportation, Shandong University of Science and Technology, Qingdao 266590, China
Haonan Ding: College of Transportation, Shandong University of Science and Technology, Qingdao 266590, China
Junjing Sun: College of Transportation, Shandong University of Science and Technology, Qingdao 266590, China
Guangna Wu: College of Transportation, Shandong University of Science and Technology, Qingdao 266590, China
Huiyao Xing: College of Transportation, Shandong University of Science and Technology, Qingdao 266590, China
Wei Wang: Shandong High Speed Maintenance Group Co., Ltd., Jinan 250032, China
Jie Song: Institute of High Performance Computing (IHPC), Agency for Science Technology and Research (A*STAR), 3 Fusionopolis Way, #16-16 Connexis, Singapore 138632, Singapore

Sustainability, 2023, vol. 15, issue 21, 1-20

Abstract: Currently, historical data and on-site surveys—particularly in the context of China—are heavily relied upon to determine the best time to maintain expressway road markings. This study aims to determine what influences the service life of thermoplastic road markings on expressways in Shandong Province, China, while considering both those motorways’ unique characteristics and the local environment. Additionally, a scientific evaluation of the road markings’ retroreflective coefficient’s decay pattern will be undertaken. We collected the retroreflective data for twelve consecutive months regarding the thermoplastic road markings on five expressways and potential influencing factors such as age of marking and annual average daily traffic. The service life of the markings was forecast using a multiple linear regression. Dominance analysis was used to quantitatively analyze each explanatory factor’s impact on the service life of the markings, and statistically significant variables were also found. Using LightGBM, a machine learning technique, a nonparametric prediction model was also created based on examining the relevance of influencing elements. The modeling results show that LightGBM generates an R 2 of 0.942, implying that it offers better interpretability and higher accuracy than the regression-based approach. Additionally, LightGBM outperforms MLR according to final validation accuracies, with a score of 95.02% or more than 8% that of MLR. The results are useful for expressway marking upkeep and for driving safety.

Keywords: thermoplastic road markings; analysis of influencing factors; retroreflectivity; service life; regression model; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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