论文标题
路边摄像头图像和天气数据的整合，以监视冬季路面条件
Integration of Roadside Camera Images and Weather Data for Monitoring Winter Road Surface Conditions
论文作者
论文摘要
在冬季，对道路表面条件的实时监控对于驾驶员和道路维护操作的安全至关重要。先前的研究通过处理安装在RWIS（Road Weather Information System）站的路边摄像机中的图像来评估图像分类方法检测道路积雪的潜力。但是，加拿大安大略省的RWIS车站数量有限。因此，网络减少了空间覆盖范围。在这项研究中，我们建议通过将RWIS车站收集的图像和天气数据与其他MTO（安大略省运输部）路边摄像机和来自加拿大环境环境部的天气数据的图像和天气数据的图像和天气数据进行集成来提高此任务的性能。我们使用空间统计数据来量化整合安大略省南部的三个数据集的好处，显示出可用的路边摄像头数量增加了六倍的证据，从而改善了安大略省人口最多的生态区的空间覆盖率。此外，我们评估了三种空间插值方法，用于在没有天气测量工具的情况下推断位置中的天气变量，并确定一种在准确性和易于实施之间提供最佳折衷的方法。
During the winter season, real-time monitoring of road surface conditions is critical for the safety of drivers and road maintenance operations. Previous research has evaluated the potential of image classification methods for detecting road snow coverage by processing images from roadside cameras installed in RWIS (Road Weather Information System) stations. However, there are a limited number of RWIS stations across Ontario, Canada; therefore, the network has reduced spatial coverage. In this study, we suggest improving performance on this task through the integration of images and weather data collected from the RWIS stations with images from other MTO (Ministry of Transportation of Ontario) roadside cameras and weather data from Environment Canada stations. We use spatial statistics to quantify the benefits of integrating the three datasets across Southern Ontario, showing evidence of a six-fold increase in the number of available roadside cameras and therefore improving the spatial coverage in the most populous ecoregions in Ontario. Additionally, we evaluate three spatial interpolation methods for inferring weather variables in locations without weather measurement instruments and identify the one that offers the best tradeoff between accuracy and ease of implementation.
