The purpose of this study was to identify cost-effective methods for collecting and processing highway infrastructure geo-referencing and geo-spatial data. The following data collection methods were compared using field trials on four types of representative roadway segments to assess the utility of each method with respect to enabling agencies to implement the Highway Safety Manual (HSM) and estimate potential crash frequency on local infrastructure.
GPS Data Logger - Integrated GPS/GIS field data logger is manually positioned to record and store inventory information.
Robotic Total Station - An electronic distance measuring system efficiently surveys the position and shape of infrastructure objects.
GPS Enabled Photo/Video Logging – Instrumented vehicles automatically record photo/video data that can be examined later to extract information.
Satellite/Aerial Imagery – High resolution images are taken from aircraft or satellite to identify and extract highway inventory information.
Mobile LiDAR - Instrumented vehicles collect 3-D precision point information using LiDAR systems traveling at highway speeds.
Eleven criteria were utilized to assess performance. Each criterion was assigned a score of 1 to 5 (5 being the best and 1 the worst) to indicate the relative performance of one method compared to the others. The results of the analysis excerpted from the source report are shown below.
|Criteria||GPS Data Logger||Robotic
|Satellite/ Aerial Imagery||Mobile LiDAR||Weighting
|Disruption to Traffic||2||1||4||5||3||1.00|
|Total Weighted Score||24||23||23||21||29|
The results demonstrated that mobile LiDAR has the highest overall score when data completeness and data quality are considered a top priority. This method is capable of collecting a large amount of geospatial data in a very short time, however, data reduction efforts will be a major undertaking. Processing data and accurately identifying the shape, position, and function of roadway features will require intensive computing, software development, and technical expertise.