基于核密度估计的沥青路面状况动态分段方法

Method for dynamic segmentation of asphalt pavement performance based on kernel density estimation

针对现有路面静态分段方法不能充分利用基础检测数据以指导实际养护工作等问题,提出了一种基于核密度估计的沥青路面状况动态分段新方法。基于快速检测设备获取精细的路况数据,利用线要素核密度估计对路面破损率、国际平整度指数和车辙深度进行分析,得到以核密度值为指标的连续变化路况地图;在现有路况评价标准的基础上,建立以核密度值为指标的路况评价新标准,实现对沥青路面的动态分段和评价;通过重分类和组合,将基于核密度值的路面破损、平整度和车辙深度3个指标进行组合,进一步得到可以同时反映多个指标的组合分段方案。以晋江市和平南路为例,演示该方法在实践中的应用。最后,将该方法、静态路段划分方法、基于累积差异法的动态路段划分方法进行对照分析。研究结果表明:提出的方法可以考虑路况数据的空间自相关特性,可得到精细的路段划分结果,将道路划分成若干路段,使得路段内部的路况尽可能相似,而路段间路况差异尽可能大;能够从基于核密度值的路面破损、车辙深度、平整度3个单指标或者组合指标的不同角度评价路况,将路面状况的整体分布情况以及不同路段需要维修的紧急程度呈现出来,同时保留了基础检测数据的细节;能够与现有的路面管理系统紧密衔接,有利于动态分段方法的推广应用。

Aimed at the problem that the currently used static road segmentation method cannot make full use of the basic detection data to guide the actual maintenance work, a new dynamic segmentation method for asphalt pavement based on kernel density estimation was proposed. Based on the fine pavement condition data acquired by rapid detection equipment, the pavement damage rate, international roughness index and rut depth data were analyzed by a line feature kernel density estimation method, so that a continuously changing pavement condition map with the kernel density value as the index could be obtained. On the basis of the existing pavement condition evaluation criteria, a new evaluation criteria for pavement condition with the kernel density value as the index was established, thereby achieves the dynamic segmentation and evaluation for the asphalt pavement. Through the re-classification and combination method, the three indices of pavement damage, roughness and rut depth based on the kernel density value could be combined,and to further obtain a comprehensive segmentation scheme show multiple indices simultaneously. South Heping Road in Jinjiang was taken as an example, the application of this method in practice was demonstrated. At last, the proposed method was compared with the static road segmentation method and a dynamic road segmentation method based on cumulative difference approach. The results show that the proposed method can take the spatial autocorrelation of road condition data into consideration and obtain a fine segmentation result, which segments the road into sections, making the road conditions inside the same road section as similar as possible, while the ones between neighbor sections as different as possible. The pavement condition can be evaluated from different point of views, and from the view of the three indices individually or of the comprehensive one, which shows the overall distribution of the pavement condition and the urgency level of maintenance in different sections, and meanwhile retains the details of the basic detection data. The research also shows that this method can link up with the existing pavement management system well, which is conducive to the promotion and application of the dynamic segmentation method. 8 tabs, 6 figs, 29 refs.