基于随机森林的沥青路面性能预测  

Performance Prediction of Asphalt Pavement Based on Random Forest

作　　者：张金喜 郭旺达 宋波[2] 禚永昌 张阳光 ZHANG Jinxi;GUO Wangda;SONG Bo;ZHUO Yongchang;ZHANG Yangguang(Beijing Key Laboratory of Traffic Engineering,College of Metropolitan Transportation,Beijing University of Technology,Beijing 100124,China;Beijing Board Engineering Quality Supervision Station,Beijing 100076,China)

机构地区：[1]北京工业大学城市交通学院交通工程北京市重点实验室,北京100124 [2]北京市道路工程质量监督站,北京100076

出　　处：《北京工业大学学报》2021年第11期1256-1263,共8页Journal of Beijing University of Technology

基　　金：国家自然科学基金资助项目(51778027);北京市交通行业科技项目(2018-kjc-01-213)。

摘　　要：为弥补路面性能预测问题中传统预测方法效率差、预测精度低等缺陷,针对普通公路沥青路面状况指数(pavement condition index,PCI)的预测问题,利用北京市9个县区包括路面性能指标、路面结构、交通参数及气象资料在内的1249组观测值,提出了一种基于随机森林算法的PCI预测模型,并与神经网络、支持向量机模型预测结果进行对比.研究结果表明:通过对比分析不同模型的3个定量评价指标(均方根误差、平均绝对误差和决定系数)以及可视化散点图,证明采用随机森林算法的PCI预测模型的鲁棒性、准确性要优于神经网络和支持向量机模型,验证了该模型的有效性和优越性,可以为后续公路养护预算申请和决策方案制定提供科学依据,对于提高公路养护的经济效益具有重要意义.To make up the poor efficiency and low prediction accuracy of traditional prediction methods in the pavement performance prediction problem,this study aimed at the prediction of the pavement condition index(PCI)of the ordinary highway asphalt pavement,and then proposed a PCI prediction model based on random forest algorithm by using the performance indexes,pavement structure,traffic parameters and meteorological data of 1249 group observations of 9 counties in Beijing.The prediction results were compared with the neural network and support vector machine models.Results show that by comparing and analyzing three quantitative evaluation indexes(root mean square error,mean absolute error and r-square)of different models and visual scatter plots,the robustness and accuracy of the PCI prediction model using random forest algorithm are superior to the neural network and the support vector machine models.It can provide scientific basis for subsequent highway maintenance budget application and decision-making plan formulation,which is of great significance for improving the economic benefits of highway maintenance.

关 键 词：道路工程 沥青路面 路面性能预测 路面状况指数(PCI) 机器学习 随机森林(RF) 

分 类 号：U418.6[交通运输工程—道路与铁道工程]