A remote sensing and GIS-enabled asset management system (RS-GAMS).

Details

• Creators:
  Tsai, Yichang ; Wang, Zhaohua
• Corporate Creators:
  Georgia Institute of Technology
• Subject/TRT Terms:
  • Asset management
  • Remote sensing
  • Geographic information systems
  • Validation
  • Pavements
  • Traffic signs
  • Laser radar
• Resource Type:
  Tech Report
• Geographical Coverage:
  Georgia
• Corporate Publisher:
  United States. Dept. of Transportation. Research and Innovative Technology Administration
• Abstract:
  Under U.S. Department of Transportation (DOT) Commercial Remote Sensing and ; Spatial Information (CRS&SI) Technology Initiative 2 of the Transportation ; Infrastructure Construction and Condition Assessment, an intelligent Remote Sensing and ; GIS-based Asset Management System (RS-GAMS) was developed and validated in this ; research project by integrating CRS&SI technology that can be operated non-destructively at highway speed to improve roadway asset management including ; pavements and traffic signs. ; For pavement asset, the validation focused on the automatic detection and measurement ; of asphalt pavement cracking and rutting using the emerging 3D line laser imaging ; technology (abbreviated as “3D line laser” thereafter), which operates at highway speed ; and captures the full-lane-width range (depth) change of pavement surface. As far as ; automatic pavement crack detection is concerned, this new technology has the inherent ; advantage in comparison with the traditional line scan cameras that suffer from ambient ; lighting conditions and pavement surface stains. In addition, the high-resolution and ; high-accuracy range data can be conveniently utilized to measure network-level asphalt ; pavement rutting and detect isolated ruts. The successful validation would provide ; transportation agencies an “all-in-one” technology for pavement condition assessment ; with higher accuracy and extended capabilities. ; Traffic signs are critical utilities for roadway safety and traffic regulation. The latest ; Manual on Uniform Traffic Control Devices (MUTCD) required each transportation ; agency to maintain the signs with an acceptable level of retroreflectivity. Thus, for traffic ; asset, the validation focused on the efficient sign inventory data collection and sign ; retroreflectivity condition assessment. Due to the fact that a state transportation agency ; needs to maintain millions of signs on roadways, it is very time-consuming and costly for ; sign inventory data collection by means of the paper-pencil method, handheld-based ; method, or even the method of reviewing millions of roadway video log images. This ; research project validated an enhanced sign inventory procedure by integrating various ; sensing technologies such as video log images, mobile Light Detection and Ranging ; (LiDAR) data, and image processing algorithms. In addition, mobile LiDAR was also ; evaluated for detecting sign retroreflectivity conditions because the traditional methods ; are either labor intensive or very inaccurate.
  More +
• Format:
  PDF
• Funding:
  DTOS59-10-H-0003
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/28724/dot_28724_DS1.pdf
• File Type:
  [PDF - 7.19 MB]
• Collection(s):
  University Transportation Centers ; US Transportation Collection
• Main Document Checksum:
  urn:sha-512:ab86b6aad712919acace7ee03f42c223bfe10d46eb2586028db907b37a6009911522555b03987653d9df2e8628c0a51fc193c6003c864869a0142aa12c0e87dc