This work presents an approach for collecting road surface data using connected vehicles. Road surface readings from multiple production vehicles were collected and aggregated to estimate road roughness measured by the International Roughness Index (IRI). The analysis compared multiple instances of connected vehicle data with high speed pavement profile vehicle (Class 1 profiler) data. A separate analysis compared multiple instances of connected vehicle data to an advanced walking profiler. Results demonstrate the feasibility of harvesting road surface data from the existing connected vehicles to support continuous road surface monitoring applications. Benefits include more timely acquisition of pavement data, broader coverage of the road network, and potential for aiding existing survey fleet in targeting early signs of pavement degradation. Collected roughness measurements were found to be closely aligned with reference devices that were employed as part of this study. A regional experiment in the Detroit Metropolitan area that covered 64 mi of roadways found that the connected vehicle data was highly correlated with Class 1 profiler data where 83% of traveled miles had a 0.8 or higher correlation. Moreover, 85% of the measurements had small absolute errors less than 50 in./mi and half of the measurements had absolute errors less than 20 in./mi. A test track experiment at Virginia Tech Transportation Institute Smart Road facility compared the connected vehicle data to the advanced walking profiler and showed that the correlations for repeatability and reproducibility are 0.90 and 0.91, respectively, which are very close to the standard requirement for certified profilers.

这项工作提出了一种使用联网车辆收集路面数据的方法。收集并汇总来自多辆生产车辆的路面读数，以估计由国际粗糙度指数 (IRI) 测量的道路粗糙度。该分析将联网车辆数据的多个实例与高速路面剖面车辆（1 类剖面仪）数据进行了比较。一项单独的分析将联网车辆数据的多个实例与高级步行分析器进行了比较。结果证明了从现有联网车辆收集路面数据以支持连续路面监测应用的可行性。好处包括更及时地获取路面数据、更广泛的道路网络覆盖以及帮助现有调查车队瞄准路面退化的早期迹象的潜力。发现收集的粗糙度测量值与作为本研究一部分的参考设备密切相关。在底特律都会区进行的覆盖 64 英里道路的区域试验发现，联网车辆数据与 1 类分析器数据高度相关，其中 83% 的行驶里程具有 0.8 或更高的相关性。此外，85% 的测量具有小于 50 英寸/英里的小绝对误差，一半的测量具有小于 20 英寸/英里的绝对误差。弗吉尼亚理工大学交通研究所智能道路设施的测试轨道实验将联网车辆数据与先进的步行轮廓仪进行了比较，结果显示可重复性和再现性的相关性分别为 0.90 和 0.91，非常接近认证轮廓仪的标准要求。