Predicting the Retroreflectivity Degradation of Waterborne Paint Pavement Markings using Advanced Machine Learning Techniques

Abstract

Waterborne paint is the most common marking material used throughout the United States. Because of budget constraints, most transportation agencies repaint their markings based on a fixed schedule, which is questionable in relation to efficiency and economy. To overcome this problem, state agencies could evaluate the marking performance by utilizing measured retroreflectivity of waterborne paints applied in the National Transportation Product Evaluation Program (NTPEP) or by using retroreflectivity degradation models developed in previous studies. Generally, both options lack accuracy because of the high dimensionality and multi-collinearity of retroreflectivity data. Therefore, the objective of this study was to employ an advanced machine learning algorithm to develop performance prediction models for waterborne paints considering the variables that are believed to affect their performance. To achieve this objective, a total of 17,952 skip and wheel retroreflectivity measurements were collected from 10 test decks included in the NTPEP. Based on these data, two CatBoost models were developed with an acceptable level of accuracy which can predict the skip and wheel retroreflectivity of waterborne paints for up to 3 years using only the initial measured retroreflectivity and the anticipated project conditions over the intended prediction horizon, such as line color, traffic, air temperature, and so forth. These models could be used by transportation agencies throughout the United States to 1) compare between different products and select the best product for a specific project, and 2) determine the expected service life of a specific product based on a specified threshold retroreflectivity to plan for future restriping activities.

Probabilistic Service Life Model of Pavement Marking by Degradation Data

“…Table 1 summarizes relevant studies found in the literature ( 6 – 20 ) and presents the explanatory variables included in the model, the duration and location of each experiment, and the traffic volume. The explanatory variables for each model depend on experimental planning and data collection.…”

“…The explanatory variables for each model depend on experimental planning and data collection. As can be seen, there is no agreement on which explanatory variables best describe the retroreflectivity decrease, indicating that the degradation depends on several aspects of the experimental planning as well as the road characteristics ( 6 – 20 ).…”

“…Among the models found in the literature, the most common variable is the pavement marking age ( 6–8, 10–20 ). In addition, many authors have included the traffic volume as an explanatory variable for the pavement marking service life model and have correlated the traffic as a significant variable for retroreflectivity degradation ( 8, 10, 12–16, 19, 20 ).…”

“…Among the models found in the literature, the most common variable is the pavement marking age ( 6–8, 10–20 ). In addition, many authors have included the traffic volume as an explanatory variable for the pavement marking service life model and have correlated the traffic as a significant variable for retroreflectivity degradation ( 8, 10, 12–16, 19, 20 ). Road traffic and pavement marking age are important variables in the service life of pavement markings; therefore, some authors employ the variable of cumulated traffic passage (CTP) in their models because it depends on both traffic and age ( 8 , 13 , 15 ).…”