# Artificial Intelligence-Enhanced Colorimetric Assessment of Self-Cleaning Road Marking Points
###### Abstract  
Road markings (RMs) typically consist of a paint layer and a retroreflective layer. They play a crucial role in road safety by offering visibility and guidance to drivers. Over their lifetime, dirt particles, oils, and greases are adsorbed on the RM surface, reducing their visibility and service life. A self-cleaning ability has been widely studied in several substrates. However, for RMs, this represents a breakthrough and a sustainable advance, while having the potential to increase their service life and enhance road safety. In this context, nanotechnology can be a strong ally through the application of semiconductor materials, such as TiO\({}_{2}\), to develop the self-cleaning ability. In addition to this novelty in RMs, quantifying this ability in terms of pollutant removal efficiency is also a challenge. In this sense, artificial intelligence (AI) and colorimetry can be combined to achieve improved results. The aims of the work herein reported were to assess the self-cleaning capability in an RM paint through the mass incorporation of semiconductors, evaluate their photocatalytic efficiency using traditional (spectrophotometric) and modern (AI-enhanced) colorimetry techniques, and compare the results obtained using both techniques. To this end, a water-based acrylic RM paint was modified through the mass incorporation of 0.5%, 1%, 2%, and 3% of nano-TiO\({}_{2}\), and a pollutant model widely used, Rhodamine B, was applied onto their surface. The samples were irradiated with a light source that simulates sunlight for 0, 3, 6, 12, 24, and 48 h. Visual analysis and spectrophotometric and artificial intelligence-enhanced colorimetry techniques were used and compared to evaluate the pollutant removal. The results confirm that RM paints with 2% and 3% nano-TiO\({}_{2}\) incorporated have a significantly higher pollutant removal ability and that both colorimetric techniques used are suitable for this assessment. R 2024  
The optical properties of RMs, luminance and retroreflectance, are related to daytime and nighttime visibility, respectively, and are key aspects in evaluating their remaining durability. Consequently, initial research into this subject was concentrated on enhancing these optical properties, specifically retroreflectance [3; 4; 5]. However, the practical use of RM materials faces significant challenges related to durability, maintenance costs, and environmental impact. The typical service life of RMs is approximately two years after application [6], although certain types can last between two and four years depending on the materials used [4; 7; 8]. Various factors affect the performance and durability of RMs, such as the traffic load and type, winter maintenance, environmental conditions, surface dirt accumulation, road surface quality, line location, and lane width [9]. The maintenance of RMs incurs high costs and has environmental impacts. The wear and degradation of these materials, whether due to natural use or removal for renewals, contribute to microplastic pollution, with RMs estimated to account for about 5% to 10% of the total microplastic pollution in the environment [10; 11]. Therefore, extending the life service of RMs by improving their durability could not only reduce maintenance costs by minimizing the frequency of reapplication but also significantly decrease their environmental impact. In this regard, recent research has prompted the exploration of smart materials, such as semiconductor nanoparticles, thermocapsules, photoluminescence materials, and other cutting-edge technologies, in various fields of road engineering to achieve new abilities [12; 13], which are designed to improve the visibility of RMs and extend their service life. Semiconductor materials, such as TiO\({}_{2}\), when exposed to UV light and humidity, produce superoxide ions that interact with contaminants such as dirt, organic compounds (e.g., oils, greases), and inorganic substances.