Research – Pavement Markings

Chapter 7. Pavement Marking Selection Tools

Research has produced various pavement marking material selection tools.(See references 50 through 55) These studies were generally conducted at the State level and produced recommended marking materials using a matrix of factors. Criteria for marking material selection in the various reports included traffic volume, road surface, environmental conditions, remaining expected road surface life, placement of new markings vs. restriping existing markings, costs, and location of marking.

An FHWA study nearing completion has the goal of developing a pavement marking selection tool to aid in selecting the most appropriate material for a given situation.(50) The report and tool are expected to provide a more effective and uniform guide to select pavement marking material. The tool was designed to take user inputs, perform service life and cost analysis, and then provide recommendations for selecting pavement marking materials. The tool has retroreflectivity degradation factors based on pavement marking test deck data from the National Transportation Product Evaluation Program and other sources in the literature.

The computational procedure of the tool consists of the following steps:

• A user enters site characteristics, cost data, and material choices for the analysis.
• A computational engine selects initial retroreflectivity models that fit the site characteristics to estimate initial retroreflectivity values for all the material options. The computed values use the default initial values unless a user provides specific initial values instead.
• A computational engine selects bi-exponential decay models that fit the site characteristics to estimate the expected service life for each material choice. The expected service life is defined as the time that it takes for the pavement marking retroreflectivity to reach the specified minimum retroreflectivity values. A Solver add-in in Microsoft® Excel® is used to perform this analysis.
• A computational engine performs cost analysis for each material to estimate the expected unit cost over the remaining road surface life.
• A computational engine ranks the material by cost within the given constraints. The tool provides the top two cost-effective materials as recommended options.

Figure 3 shows the results screen, which provides the top two materials based on the life cycle cost analysis, given the project characteristics and constraints. Each recommendation includes the expected unit cost of material, expected project cost, annual life-cycle cost, and expected service life of the recommended material.

Figure 3. Screenshot. Results screen. (50)

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