Effective warning devices for parked/disabled vehicles : executive summary /

Mode of access: Internet.

Crash tests of single post sign installations using subcompact automobiles /

Mode of access: Internet.

Subregional route choice models with link travel times reflecting intersection flows : final report to Illinois Department of Transportation /

"Report no.: FHWA/IL/RC-008."--Documentation page.

Signing treatments for interchange lane drops /

Final report.

National signal timing optimization project : summary evaluation report /

"May 1982."

Crash tests of small highway sign supports /

Mode of access: Internet.

Radioluminescent lane delineators for wet-night visibility /

Mode of access: Internet.

Signals, traffic software, and lighting: courses and basic references.

Mode of access: Internet.

Road and Traffic Signs Recognition using Vector Machines

Intelligent Transportation System (ITS) is a system that builds a safe, effective and integrated transportation environment based on advanced technologies. Road signs detection and recognition is an important part of ITS, which offer ways to collect the real time traffic data for processing at a central facility.This project is to implement a road sign recognition model based on AI and image analysis technologies, which applies a machine learning method, Support Vector Machines, to recognize road signs. We focus on recognizing seven categories of road sign shapes and five categories of speed limit signs. Two kinds of features, binary image and Zernike moments, are used for representing the data to the SVM for training and test. We compared and analyzed the performances of SVM recognition model using different features and different kernels. Moreover, the performances using different recognition models, SVM and Fuzzy ARTMAP, are observed.

Color detection and segmentation for road and traffic signs

This paper aims to present three new methods for color detection and segmentation of road signs. The images are taken by a digital camera mounted in a car. The RGB images are converted into IHLS color space, and new methods are applied to extract the colors of the road signs under consideration. The methods are tested on hundreds of outdoor images in different light conditions, and they show high robustness. This project is part of the research taking place in Dalarna University / Sweden in the field of the ITS.

Detection and Tracking of Traffic Signs Using a Recursive Bayesian Decision Framework

In this paper we propose a new method for the automatic detection and tracking of road traffic signs using an on-board single camera. This method aims to increase the reliability of the detections such that it can boost the performance of any traffic sign recognition scheme. The proposed approach exploits a combination of different features, such as color, appearance, and tracking information. This information is introduced into a recursive Bayesian decision framework, in which prior probabilities are dynamically adapted to tracking results. This decision scheme obtains a number of candidate regions in the image, according to their HS (Hue-Saturation). Finally, a Kalman filter with an adaptive noise tuning provides the required time and spatial coherence to the estimates. Results have shown that the proposed method achieves high detection rates in challenging scenarios, including illumination changes, rapid motion and significant perspective distortion

Comparison of signs and markings for passing/no-passing zones. Executive summary.

Federal Highway Administration, Office of Research, Washington, D.C.

Evaluation of retroreflective sheetings for use on roadway traffic signs. Final report.

Oklahoma Department of Transportation, Oklahoma City

Relative visibility of increased legend size vs. brighter materials for traffic signs. Final report.

Federal Highway Administration, Office of Safety and Traffic Operations Research and Development, McLean, Va.

Variable message signing for traffic surveillance and control - a state of the art report. Final report.

Mode of access: Internet.