Predicting Pavement Marking Retroreflectivity Using Artificial Neural Networks: Exploratory Analysis
Publication: Journal of Transportation Engineering
Volume 137, Issue 2
Abstract
Providing adequate nighttime visibility to roadway users is an important consideration for state and local transportation agencies. Driving at night is less dangerous when pavement markings are easily discernable. Retroreflectivity is a measure of nighttime visibility. Transportation agencies could use estimates of the expected service life of pavement markings to plan restriping operations at a time when markings are near a minimum threshold level of retroreflectivity. The present study proposes the use of an artificial neural network to predict pavement marking retroreflectivity as a function of initial retroreflectivity, the age of the markings, traffic flow, pavement marking type, and route location information using data from North Carolina. The results show that many of the input variables have a nonlinear association with pavement marking retroreflectivity. Surface plots of the degradation pattern are provided to illustrate the relationship between input and output variables. Estimates of service life are provided to show how the output can be used to manage pavement marking systems.
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Acknowledgments
The writers wish to acknowledge the financial support provided by the Federal Highway Administration Office of Safety R&D for the research presented in this paper. Dr. Kenneth Opiela is acknowledged for coordinating with the North Carolina Department of Transportation to obtain the data used in the analysis. The writers acknowledge the North Carolina Department of Transportation for providing these data.
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Information & Authors
Information
Published In
Journal of Transportation Engineering
Volume 137 • Issue 2 • February 2011
Pages: 91 - 103
Copyright
© 2011 ASCE.
History
Received: Aug 26, 2008
Accepted: Jun 23, 2010
Published online: Jan 14, 2011
Published in print: Feb 2011
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