Analysis of In-Service Traffic Sign Visual Condition: Tree-Based Model for Mobile LiDAR and Digital Photolog Data-Reference-Cited by-同舟云学术

Analysis of In-Service Traffic Sign Visual Condition: Tree-Based Model for Mobile LiDAR and Digital Photolog Data

Published:2018-06 Issue:6 Volume:144 Page:04018017
ISSN:2473-2907
Container-title:Journal of Transportation Engineering, Part A: Systems
language:en
Short-container-title:J. Transp. Eng., Part A: Systems

Author:

Khalilikhah M.¹,Fu G.²,Heaslip K.³,Carlson P.⁴

Affiliation:

1. Presently, Senior Planning Specialist, Long Range Planning Division of Tennessee Dept. of Transportation, 505 Deaderick St., Suite 900, Nashville, TN 37243; formerly, Research Associate, Dept. of Civil and Environmental Engineering, Virginia Tech, 900 North Glebe Rd., Arlington, VA 22203 (corresponding author).

2. Assistant Professor, Dept. of Mathematics and Statistics, Utah State Univ., 3900 Old Main Hill, Logan, UT 84322.

3. Associate Professor, Dept. of Civil and Environmental Engineering, Virginia Tech, 900 North Glebe Rd., Arlington, VA 22203.

4. Operations and Roadway Safety Division Head, Texas A&M Transportation Institute, 3135 TAMU, College Station, TX 77843.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Transportation,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/JTEPBS.0000132

Reference52 articles.

1. Analyzing angle crashes at unsignalized intersections using machine learning techniques

2. Critical Assessment of an Enhanced Traffic Sign Detection Method Using Mobile LiDAR and INS Technologies

3. Role of drivers’ personal characteristics in understanding traffic sign symbols

4. ArcGIS [Computer software]. ESRI Redlands CA.

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