Evaluation of Remote Sensing Technologies for Collecting Roadside Feature Data to Support Highway Safety Manual Implementation

Abstract

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 LiDAR 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.

Comparative Analysis of Different Mobile LiDAR Mapping Systems for Ditch Line Characterization

“…The use of mobile LiDAR in transportation has been investigated in terms of data acquisition, quality of the acquired data, existing and emerging applications, and challenges [22][23][24][25][26]. In general, MLMS has a clear advantage for its efficient field survey, increased safety, and detailed information.…”

“…Ground systems, while they are more likely to miss the bottoms of ditches that cannot be seen from the road, have a better view of the sides of steep terrain and structures [25]. Reported limitations of MLMS in the literature include high cost for initial investment and need of significant research efforts in data processing and analysis [22][23][24].…”

Use of Mobile Lidar Data to Assess Hurricane Damage and Visualize Community Vulnerability

“…However, the mobility of static terrestrial laser scanning is too low to cover large disaster areas. Mobile lidar is a growing remote sensing technology that has been used to collect geospatial data along highway systems for asset management applications (23)(24)(25)(26)(27). Transportation agencies actively have been seeking a technology deployment standard for mobile lidar applications (28).…”

A Remote Sensing-based Approach for Assessing and Visualizing Post-Sandy Damage and Resiliency Rebuilding Needs

“…However, the mobility of static terrestrial laser scanning is too low to cover large disaster areas. Mobile LiDAR (Laser Detection and Ranging) is a growing remote sensing technology that has been used to collect geospatial data along highway systems for asset management applications (Gong et al 2012;Graham 2010;Vinent and Ecker 2010;Yen et al 2011;Jalayer et al 2013). Transportation agencies have been actively seeking a technology deployment standard for mobile LiDAR applications (Olsen et al 2013).…”