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Updates to Research on Recommended Minimum Levels for Pavement Marking Retroreflectivity to Meet Driver Night Visibility Needs

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3. RESEARCH METHODOLOGY

The scope of this research effort did not include the collection of additional human factors data from the field. The focus of this research was to examine the body of pavement marking visibility research in order to understand how different factors affect pavement marking visibility. Using the understanding gained from existing research, a visibility model (TARVIP) was employed to evaluate the effect that various pavement marking visibility parameters have upon the minimum retroreflectivity required by nighttime drivers. Some factors were analyzed in the TARVIP model while others were held constant to represent either reasonably worst-case visibility conditions (such as the absence of roadway lighting or the driver being of advanced age) or the conditions most likely faced by drivers on the roadway (such as conventional markings consisting of paint with beads). The results of the TARVIP analysis were then used, in conjunction with previous research findings reported herein, to provide recommended minimum pavement marking retroreflectivity levels.

3.1 Description of Tarvip

TARVIP is a deterministic model for evaluating the nighttime visibility of retroreflective objects from a driver’s perspective (the latest version of TARVIP may be obtained from the University of Iowa’s Operator Performance Laboratory website at http://opl.ecn.uiowa.edu/tarvip). TARVIP has a physical subsystem and a human factors subsystem. The physical subsystem determines the actual luminance contrasts of retroreflective objects on the roadway and is influenced by the following factors:

• Three-dimensional spatial locations of the vehicle headlamps, driver eyes, and pavement markings.
• Two-dimensional matrices of headlamp luminous intensity with respect to vertical and horizontal beam angle.
• Two-dimensional matrices of pavement surface and pavement marking retroreflectivity coefficients (R_L) with respect to entrance and observation angle.
• Environmental data such as windshield transmission, atmospheric transmissivity, and ambient luminance.

3.2 Using the TARVIP Model

The process used in creating TARVIP models for the purpose of recommending minimum pavement marking retroreflectivity is detailed in the following paragraphs. In creating a visibility scenario, the first geometric component to create is the road itself. The user may select the width of roadway available on either side of the road center line and the geometry of the road in terms of plan and profile.

The user is then asked if the visibility of pavement markings, signs, or a diffuse (non-retroreflective) target is to be analyzed. Once pavement markings are selected, the user can choose which pavement marking configuration is to be analyzed. The three choices on the left are for center line configurations without edge lines. The “Combined” choice is for a single solid plus a single dashed center line (such as for a one-way no-passing zone). The final two options are for a single dash or double solid center line with solid white edge lines.

If the “Full + Dash” option is selected, the user is then prompted to define the widths of each pavement marking line and its lateral location on the roadway. The user is also asked for the longitudinal cycle length and the longitudinal gap length for the center line skip. If the “Full + Double Solid” option had been selected, the latter two would have been replaced with a prompt for the lateral separation between the two lines. The user is also asked to supply ASCII files containing tab-delimited text of the retroreflectivity matrices for the pavement surface and the pavement marking material. The efficiency of the material can be adjusted to represent old or worn marking materials.

The user may then define the length of the road and the location of a point of interest for use in tracking changes in photometric relationships as the vehicle moves along the roadway. Once this has been done, the vehicle and headlamp details can be selected. The user can select the speed of the vehicle, the ability of the windshield to allow light to pass, the location of the vehicle on the roadway, and the increments at which calculations should be performed. The user is also asked to supply ASCII files containing tab-delimited text of the luminous intensity matrices of the vehicle’s headlamps.

Once the file is supplied, the user can adjust the dimensions of the headlamp locations relative to a point halfway between the two headlamps on the pavement surface (termed the car origin), as well as adjusting the headlamp efficiency to represent deficient headlamps. TARVIP uses the headlamp luminous intensity matrices to project the headlamp beams onto the road surface for the visibility analysis (figure 4).

In the “Driver” window, the user may set the driver age, exposure time, minimum preview time, and the driver’s eye location relative to the car origin. If visibility under glare conditions is to be modeled, the user may enter the horizontal and vertical angle components of the driver’s glance away from the center of the traveled way.

Figure 4. Plot of iso-lux curves on road surface (TARVIP screen shot).

The “Environment” window allows the user to adjust ambient luminance (cd/m²) and atmospheric transmissivity (km -1). The user can also enable fog luminance calculations and specify a fog droplet diameter if such analysis is to be undertaken.

3.3 Validation of the TARVIP Model

The ability of TARVIP to generate reasonable measures of pavement marking visibility under various scenarios was ascertained by comparison of its outputs to data from various pavement marking visibility studies. The validation was performed using the study data from two Texas Transportation Institute (TTI) studies, reported in TTI Technical Report 5008-1 ⁽²⁾ and TTI Technical Report 4269-1. ⁽¹⁵⁾ In those two studies, dry, nighttime pavement marking detection distances were collected. The data were used to compare to TARVIP predictions. The comparison showed that the TARVIP curve fell within the 95 percent confidence range of the TTI study data under most conditions.

3.3.1 TTI Report 5008-1 Study Comparison

TTI Report 5008-1 was for a study evaluating the performance of several varieties of pavement markings in wet weather. However, as part of this research, dry detection distance data for each marking were also collected. Subjects drove a 2004 Ford Taurus with a researcher in the passenger seat. The research was conducted on an isolated test track. The subjects drove with the cruise control set at 48.3 km/h (30 mi/h) and told the researcher when they could see a pavement marking (white lines, yellow lines, or RRPMs). Distracter markings were located outside the travel lane to minimize the possibility of the subjects becoming accustomed to the pavement marking locations and guessing their location before actually seeing them. The markings of interest to the researchers were isolated skips located in the center of the travel lane. When the subject alerted the researcher when he/she could identify a pavement marking and its type, the researcher recorded the location values from a distance-measuring instrument.⁽²⁾

The research conditions used by Carlson et al. were duplicated as closely as possible in the TARVIP software; however, some assumptions were required. The pavement surfaces that were available in TARVIP when this analysis was conducted did not include an old or weathered asphalt pavement (after this phase of the research, the research team was able to add pavement- marking retroreflectivity files for old asphalt, which were ultimately used to generate the final recommendations in this report). However, the TTI study was conducted on old asphalt. Therefore, for the purposes of this validation effort, old concrete was used. Old concrete would most likely provide the nearest approximation of old asphalt in terms of contrast with pavement markings of any of the available pavement surfaces. Because old concrete has a lower retroreflectivity than old asphalt in the TARVIP pavement surface files, it will provide more luminance contrast with the pavement marking than old asphalt. Therefore, the resulting detection distances from the TARVIP analysis will be higher with old concrete than they would be with old asphalt, so the reported detection distances are more liberal than they would have been had an old asphalt pavement surface file been used in the analysis.

When comparing a tape product’s performance in TARVIP to its performance as reported by Carlson et al., ⁽²⁾ it was assumed that the product used in the TTI study had similar retroreflective characteristics to the pavement-marking file in TARVIP. To adjust for any differences, a material efficiency is used. This material efficiency is a ratio between the R_L reading of the actual marking used and the 30-m (98-ft) geometry R_L contained in the TARVIP pavement marking file. Such an adjustment assumes that this ratio is valid across the entire viewing matrix based on one R_L value.

For the TARVIP thermoplastic marking files (as well as any of the other nonmanufactured markings), no information is available regarding the thickness, application method, binder, or bead gradation of these markings. Therefore, it was necessary to assume that the TARVIP thermoplastic marking file was similar to the material used in the TTI study.

It should be noted that the researchers used a 2003 Ford Taurus headlamp for the modeling, and the TTI study used a 2004 Ford Taurus. It was confirmed with a local Ford dealership that the headlamps of these two model years are compatible. The locations of the headlamp relative to the car origin and the driver’s eye relative to the headlamps were measured using the original vehicle from the study.

The TARVIP default values for windshield transmission, ambient luminance, and atmospheric transmissivity were retained since no data for these values are available from the TTI study.

The results of the comparison showed that the TARVIP curve fell within the 95 percent confidence range of the TTI study data (see figure 5 and figure 6).

Figure 5. Detection distance versus driver age--Structured tape.

Figure 6. Detection distance versus driver age--Thermoplastic.

3.3.2 TTI Report 4269-1 Study Comparison

TTI Report 4269-1 was for a study evaluating the visibility of signs and pavement markings from the perspective of passenger car drivers and commercial vehicle drivers (only the results from the passenger car drivers are reported herein). Two different types of pavement marking tape products were investigated, one of which was used both in new condition and with a clear mask applied. Thus, three different markings were tested, representing low, medium, and high retroreflectivity coefficients. Pavement marking detection distance data were collected in a 1998 Chevrolet Lumina traveling at 48.3 km/h (30 mi/h). Data were recorded similarly to that recorded by Carlson et al., except that participants were following a solid white right edge line and asked to state when they could clearly see the end of the pavement marking. The course also had signs that they were asked to identify during each run.⁽¹⁵⁾

The TTI study was performed on old concrete, so that pavement marking file was used. Assumptions regarding material efficiencies and TARVIP default values were the same as those made for the TTI Report 5008-1 study comparisons.

The most problematic assumption involved the vehicle headlamps, as TARVIP contains no Chevrolet Lumina headlamp file. TARVIP does have a 2001 UMTRI 50 percent low-beam headlamp file—the only headlamp that it has in common with the ERGO software used for sign visibility. Using ERGO, the luminance values recorded in the sign luminance portion of the study by Finley et al. and the output of scenarios modeled in ERGO were compared. From these two values, a headlamp efficiency value for each point was obtained. These efficiencies were then averaged to obtain one efficiency value. The two obvious limitations are

• It is assumed that the average obtained is an effective method for comparing the Chevrolet Lumina headlamp to the UMTRI 50 percent low-beam headlamp.
• The points of comparison are all located above the horizontal while the points of interest are below the horizontal.

The results of the comparison showed that the TARVIP curve fell within the 95 percent confidence interval for older drivers but slightly below the interval for younger driver s (approximately 50 years old and younger). See figure 7.

Figure 7. Detection distance versus driver age—Standard tape.

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