Details:

Creators:
Tsai, Yichang ; Wang, Zhaohua
Tsai, Yichang ; Wang, Zhaohua Less -
Corporate Creators:
Georgia Institute of Technology
Subject/TRT Terms:
[+]
Asset Management Geographic Information Systems Laser Radar Pavements Remote Sensing Traffic Signs Validation
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.


Format:
PDF
Funding:
DTOS59-10-H-0003
Collection(s):
US Transportation Collection University Transportation Centers
Main Document Checksum:
[+]
urn:sha256:578f1b2fff164a6f95639239efe484383f3eba88b80b136d1c2654d3338e3654
Download URL:
https://rosap.ntl.bts.gov/view/dot/28724/dot_28724_DS1.pdf
File Type:

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