Retroreflectivity is the primary metric that controls the visibility of pavement markings during nighttime and in adverse weather conditions. Maintaining the minimum level of retroreflectivity as specified by Federal Highway Administration (FHWA) is crucial to ensure safety for motorists. The key objective of this study was to develop robust retroreflectivity prediction models that can be used by transportation agencies to reliably predict the retroreflectivity of their pavement markings utilizing the initially measured retroreflectivity and other key project conditions. A total of 49,632 transverse skip retroreflectivity measurements of seven types of marking materials were retrieved from the eight most recent test decks covered under the National Transportation Product Evaluation Program (NTPEP). Decision Tree (DT) and Artificial Neural Network (ANN) algorithms were considered for developing performance prediction models to estimate retroreflectivity at different prediction horizons for up to three years. The models were trained with randomly selected 80% data points and tested with the remaining 20% data points. Sequential ANN models exhibited better performance with the testing data than the sequential DT models. The training and testing R2 ranges of the sequential ANN models were from 0.76 to 0.96 and 0.55 to 0.94, respectively, which were significantly higher than the R2 range (0.14 to 0.75) from the regression models proposed in past studies. Initial or predicted retroreflectivity, snowfall, and traffic were found to be the most important inputs to model predictions.

中文翻译：

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使用先进的机器学习算法对美国各地路面标记的逆反射率退化进行建模

逆反射率是控制夜间和恶劣天气条件下路面标记可见度的主要指标。保持联邦公路管理局 (FHWA) 规定的最低逆反射率水平对于确保驾车者的安全至关重要。本研究的主要目标是开发强大的逆向反射率预测模型，交通机构可以使用该模型利用最初测量的逆向反射率和其他关键项目条件来可靠地预测其路面标记的逆向反射率。从国家交通产品评估计划 (NTPEP) 涵盖的八个最新测试平台中检索到了七种类型标记材料的总共 49,632 个横向跳跃逆反射率测量结果。考虑使用决策树 (DT) 和人工神经网络 (ANN) 算法来开发性能预测模型，以估计长达三年的不同预测范围内的逆反射率。使用随机选择的 80% 数据点对模型进行训练，并使用其余 20% 的数据点进行测试。序列 ANN 模型在测试数据上表现出比序列 DT 模型更好的性能。序列 ANN 模型的训练和测试 R2 范围分别为 0.76 至 0.96 和 0.55 至 0.94，显着高于过去研究中提出的回归模型的 R2 范围（0.14 至 0.75）。研究发现，初始或预测的逆反射率、降雪量和交通量是模型预测的最重要输入。