Presenters: Chengbo Ai, UMass Amherst, & Neil Boudreau, MassDOT
Pavement markings are a vital transportation asset and traffic control device that facilitates safe and predictable driver behaviors. Pavement markings’ effectiveness depends on their condition, particularly during nighttime and adverse weather. The Federal Highway Administration (FHWA) has recently released Revision 3 of 2009 Manual for Uniform Traffic Control Devices (MUTCD), which includes new provisions for maintaining minimum levels of retroreflectivity for pavement marking. Regulatory compliance poses a challenge, as conventional visual inspection methods are labor-intensive, and the results can be subjective. There is a pressing need for the Massachusetts Department of Transportation (MassDOT) to develop and implement an effective, efficient inventory and reliable retroreflectivity condition assessment method for pavement marking. MassDOT has been actively pursuing new and more durable marking materials with reliable nighttime visibility. It has recently completed a research study achieving two goals: 1) to utilize emerging mobile light detection and ranging (LiDAR) data and develop an automated method for the localization, classification, and retroreflectivity condition assessment for pavement markings and 2) to investigate the feasibility of identifying deterioration trend of retroreflectivity conditions. The outcomes of this study have demonstrated the developed automated, LiDAR-based methods were able to accurately and efficiently inventory pavement marking and accurately and repeatably assess the corresponding retroreflectivity condition, and surface material completeness. This webinar will present the technical details of the research study and discuss future research and implementation plans for innovative pavement marking management.