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首页> 外文期刊>Journal of transportation safety & security >Evaluation of Remote Sensing Technologies for Collecting Roadside Feature Data to Support Highway Safety Manual Implementation
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Evaluation of Remote Sensing Technologies for Collecting Roadside Feature Data to Support Highway Safety Manual Implementation

机译：评估用于收集路边特征数据以支持公路安全手册实施的遥感技术

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摘要

Roadside feature data are critical inputs to highway safety models as described in the Highway Safety Manual (HSM). Collecting safety-related roadside feature data is an important step for HSM implementation. Many states' department of transportations (DOTs) routinely collect data on roadside objects using a variety of sensing methods, which often incur in significant costs. At present, it is unknown which of these data collection methods or any combination of them is capable of efficiently collecting safety-related roadside feature data while minimizing costs and safety concerns. This research is designed to identify required roadside feature data for various types of facilities in the HSM and to characterize the capabilities of existing remote sensing methods (e.g.. Mobile LiDAR) to collect those required data. To accomplish this objective, tasks such as literature reviews, a nation-wide survey, and large-scale field trial are performed in this research. The findings of this research suggest that either the mobile UDAR or the combination of the video/photo log method with the aerial imagery method is capable of collecting required HSM-related roadside information. However, due to the high data reduction effort, the current mobile LiDAR method needs significant improvement in the data processing and in the feature extraction stage.
机译：如《公路安全手册》（HSM）中所述，路边特征数据是高速公路安全模型的关键输入。收集与安全相关的路边特征数据是实施HSM的重要步骤。许多州的交通运输部（DOT）经常使用各种传感方法来收集路边物体的数据，这通常会产生高昂的成本。目前，尚不知道这些数据收集方法中的哪一种或它们的任何组合能够有效地收集与安全相关的路边特征数据，同时将成本和安全问题降至最低。这项研究旨在识别HSM中各种类型设施所需的路边特征数据，并表征现有遥感方法（例如Mobile LiDAR）收集这些所需数据的能力。为了实现这一目标，在这项研究中执行了诸如文献综述，全国范围的调查和大规模的现场试验等任务。这项研究的发现表明，移动式UDAR或视频/照片记录方法与航拍图像方法的组合都能够收集所需的HSM相关路边信息。但是，由于需要大量的数据缩减工作，因此当前的移动LiDAR方法需要在数据处理和特征提取阶段进行重大改进。

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