Zhang, Guanghua ; Joseph E. Hummer, Committee Co-Chair,William J. Rasdorf, Committee Co-Chair,John R. Stone, Committee Member,Daowen Zhang, Committee Member,Zhang, Guanghua ; Joseph E. Hummer ; Committee Co-Chair ; William J. Rasdorf ; Committee Co-Chair ; John R. Stone ; Committee Member ; Daowen Zhang ; Committee Member
This research evaluated longitudinal pavement marking performance and developed useful degradation models for paint pavement markings which can help transportation agencies meet the pending FHWA minimum retroreflectivity requirements.The impacts of several important factors (such as directionality, pavement type and roughness) on marking retroreflectivity were evaluated.With a large dataset in hand, we determined whether these factors had significant impacts on marking retroreflectivity.Paint pavement marking centerline retroreflectivity values measured in the direction of paint striping were found to be significantly higher than the values measured in the opposite direction.The mean values of the retroreflectivity measurements collected on the plant mixed pavements were found to be significantly larger than the values collected on the bituminous surface treatment (BST) pavements.Image processing techniques were used to analyze paint pavement marking surface glass bead density.Bead density values were found to have a positive correlation with marking retroreflectivity measurements.Higher glass bead density led to higher marking retroreflectivity.The research also compared existing marking retroreflectivity degradation models.A linear mixed effects model (LMEM) was selected as most appropriate for the paint marking retroreflectivity data.LMEMs were established for paint pavement markings based on the data collected in NC.The research outcomes can help transportation agencies have a better understanding of paint pavement marking performance, which can lead to cost savings by maximizing the marking service lifecycles.
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Performance Analysis and Strategic Management of Longitudinal Pavement Markings