LiDAR scanning systems collect huge amounts of geospatial data in a very short time; plan for major data reduction efforts that require intensive computing, software development, and technical expertise.
A review of highway inventory data collection methods vital to the implementation of the Highway Safety Manual (HSM) at the state level.
Made Public Date
08/13/2018
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Identifier
2018-L00830

A Comprehensive Assessment of Highway Inventory Data Collection Methods

Background

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
Total
Station
GPS Enabled
Photo/Video
Log
Satellite/ Aerial Imagery Mobile LiDAR Weighting
Factor
Field Data
Collection
Equipment Cost 3 2 4 5 1 0.25
Labor Cost 2 1 4 5 3 0.25  
Data Collection
Time
2 1 4 5 3 0.25  
Safety 2 1 4 5 3 1.00  
Data Completeness 3 4 2 1 5 2.00  
Data Quality 3 4 2 1 5 2.00  
Disruption to Traffic 2 1 4 5 3 1.00  
Field Data
Reduction
Software Cost 5 4 3 2 1 0.25
Labor Cost 5 3 4 2 1 0.25  
Data Reduction
Time
5 3 4 2 1 0.50  
Data Storage
Size
5 4 2 3 1 0.25  
Total Weighted Score   24 23 23 21 29  

Lessons Learned

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.

Goal Areas