Pavement Marking Retroreflectivity Research Articles

Field Data Analysis of Pavement Marking Retroreflectivity and Its Relationship with Paint and Glass Bead Characteristics

Pavement marking retroreflectivity, a critical factor for safe driving, depends on the characteristics of both the paint and the embedded glass beads. However, traditional methods for predicting pavement marking service life often overlook these materials properties. This study investigates the influence of paint and glass bead characteristics on pavement marking retroreflectivity performance and addresses the characterization of glass bead size distribution by the coefficient of uniformity and curvature. Three field test sites on a Brazilian highway with various paint and glass bead combinations were evaluated. A statistical model, GAMLSS (Generalized Additive Model for Location, Scale, and Shape), was adjusted to evaluate the performance of the markings’ retroreflectivity as a function of paint and glass bead characteristics. The model revealed that well-graded glass beads increased retroreflectivity by around 10%, while paints with a higher volume of solids improved service life around 65%. Therefore, the results show that acrylic water-based paints with higher volumes of solids and well-graded glass beads with better shape characteristics should be preferred to improve pavement markings’ retroreflectivity and service life. The statistical model identified the key characteristics with the greatest impact on pavement marking retroreflectivity, offering valuable insights for real-world applications, which will assist pavement marking practitioners and road authorities in selecting appropriate materials to achieve enhanced durability.

Feature-Centric Approach for Learning-Based Prediction of Pavement Marking Retroreflectivity from Mobile LiDAR Data

Given the crucial importance of pavement marking retroreflectivity in ensuring visibility for road safety, this research investigates the correlation between pavement marking reflectivity and LiDAR data. Empirical data were collected from eight road sections using both a handheld retroreflectometer and a mobile LiDAR. The approach proposed focuses on extracting important features from pavement marking regions of the LiDAR point cloud. A comprehensive feature extraction and feature selection process was employed. In addition, a well-rounded selection of learning algorithms was evaluated. A rigorous hold-out evaluation was incorporated, ensuring that the reported performance metrics were robustly generalizable. The best performing model was able to achieve an R2 of 0.824 on unseen data. The findings of this study illuminate the potential for leveraging relatively inexpensive mobile LiDAR sensors in combination with machine learning techniques in conducting efficient pavement marking assessments, not only to detect completely degraded markings, but to accurately estimate retroreflective properties.

Characterization of glass beads by image methods for pavement marking retroreflectivity

Road safety is a worldwide concern. Traffic injuries are the main cause of death for children and young people. One alternative to improve traffic safety is the enhancement of pavement markings visibility. Pavement marking efficiency depends on day and night visibility. Nighttime visibility (retroreflectivity) occurs due to the presence of glass beads. This paper compares the performance of two glass beads: G1 – Brazilian glass bead produced with recycled glass; and G2 – imported glass bead produced with new material. The research evaluated the retroreflectivity decrease of a test deck using two water-based paints from different manufacturers. The glass beads characterization process used the Computerized Optical Equipment (COE), CamSizer, and the Aggregate Image Measurement System (AIMS) to evaluate the shape, size, and gradation. Results indicated different retroreflectivity decrease trends for pavement markings with distinct glass beads, which could be associated with their shape characteristics and grain size distribution. The glass beads G1 presented the worst shape properties which lead to the poorest retroreflectivity at the test site. Thus, image characterization is helpful in estimating markings retroreflectivity prior to its application. Therefore, G1 cannot be used to substitute G2, regarding the retroreflectivity requirements in Brazil.

Pavement Marking Practices, Standards, Applications, and Retroreflectivity

Pavement markings are important elements of roadway networks and help guide traffic flow in an orderly manner. In recent years, transportation agencies are facing challenges to manage pavement markings because of the advent of new vehicle technologies. Existing pavement marking standards were developed for serving human road users but they may not be effective for both human drivers and machine vision systems. Therefore, the National Committee on Uniform Traffic Control Devices suggested amendments to the current pavement marking standards. Therefore, it is necessary to explore pavement marking practices for a variety of states. With this aim, the Wyoming Technology Transfer Center conducted an online survey as part of a comprehensive research project on developing pavement marking management plans for the Wyoming Department of Transportation. The main objective of the survey is to document the various state Departments of Transportation (DOTs’) pavement marking management plans, how the plans are developed, strategies for pavement marking data collection, and pavement marking retroreflectivity. The survey has 31 questions dealing with pavement marking striping, pavement marking data collection, pavement marking retroreflectivity, and a few miscellaneous questions. There are 29 DOTs who responded to the survey. This paper summarizes the responses from the survey on evaluating pavement marking management practices at a national level.

Raised Pavement Marker Assessments Using Mobile Retroreflectivity Unit Technology

Currently, the Florida Department of Transportation (FDOT) State Materials Office collects 25,000 lane miles of line-stripe pavement marking (PM) retroreflectivity data each year using RoadVista Laserlux G7 (LLG7) mobile retroreflectivity units (MRUs). The focus of this study was to configure an LLG7 MRU to accurately and repeatably detect and measure the retroreflectivity of in-service Class B raised pavement markers (RPMs) while simultaneously collecting PM retroreflectivity data. At this time, FDOT assesses the quality of in-service RPMs as part of a visual inspection. Currently, there are no standards or devices for mobile RPM retroreflectivity measurements. As these LLG7 MRUs were designed to measure PMs in units of RL (mcd/m2/lux), an additional objective was to derive regression equations that correlate the industry-standard units for RPMs (RI [mcd/lux]) and the RL values collected by the MRU. Through research-based experiments and with coordination from RoadVista, modifications were made to the LLG7 MRU to detect and measure RPM retroreflectivity in relation to RL. Regression equations were derived pertaining to retroreflectivity measurements between a handheld RPM retroreflectometer (RI) and an LLG7 MRU (RL) on in-service RPMs. The results indicate that the modified MRU can be used to effectively assess RPM retroreflectivity in tenth-mile intervals in a highly repeatable manner with an average error in estimated RI of ±13.5% without affecting PM data. Overall, utilizing the modified MRU for statewide RPM assessments is a significant improvement from the current process of visual assessments and allows the FDOT to quantify RPM quality on a network level.

Probabilistic Service Life Model of Pavement Marking by Degradation Data

Pavement markings are important features for improving road safety. As these markings must possess adequate retroreflectivity, road agencies are concerned about retroreflectivity maintenance. Consequently there is an increased focus on modeling the durability of pavement markings. Such models use time-to-failure data from life test experiments. The current study proposes a new method that consists of three steps: (i) designing an accelerated experiment for data collection; (ii) fitting a pavement marking retroreflectivity model using a generalized linear mixed model and the degradation data from the planned experiment, confirming the goodness of fit of the fitted model through the residual analysis; and (iii) conducting Monte Carlo simulation (with at least 10,000 runs of simulated vectors of the fitted model’s coefficients) to obtain the pavement marking service life distribution for a failure retroreflectivity threshold level. The simulation of service life of pavement marking provides relevant information, such as service life expectation and empirical quantities of interest (as median). The proposed method is applied to data on retroreflectivity degradation obtained from an experimental test site, a Brazilian highway with a high traffic volume, with two fixed factors (waterborne paints and glass bead application rates, both with three levels). The model indicates that retroreflectivity decreases at an average rate of 2% per day, and how materials differ from each other. The proposed model is easily implementable and can help management teams to adequately plan the maintenance time for pavement markings.

Mobile LiDAR Deployment Optimization: Towards Application for Pavement Marking Stained and Worn Detection

Clear and distinctive pavement markings play a critical role in providing traffic information and avoiding traffic crashes at night and in adverse weather conditions. Periodically inspecting pavement marking is essential but requires vast human and material resources currently. This paper attempts to use the low-channel LiDAR sensor to detect and evaluate the stain, wear, and cracks of pavement marking based on the research of Pike and Che that the retroreflectivity of pavement markings is highly correlated with the laser intensity of LiDAR. Thus, the deployment location of mobile LiDAR based on its built-in characteristics and mechanical structure is optimized. An optimization model is formed and solved with the elitist preservation genetic algorithm (EGA). The results show that the best installation height is 0.5 m. The rotation angle of LiDAR is 128.4°, 148.0° in the urban and highway scenarios, respectively. The field test results show that the pavement marking can obtain best the coverage, density, and effective use of LiDAR point cloud with the above setting. The field test’s mean relative error (MRE) is less than 5% and 10% in vertical installation and vertical installation with an inclination, respectively.

Leveraging LiDAR Intensity to Evaluate Roadway Pavement Markings

The United States has over 8.8 million lane miles nationwide, which require regular maintenance and evaluations of sign retroreflectivity, pavement markings, and other pavement information. Pavement markings convey crucial information to drivers as well as connected and autonomous vehicles for lane delineations. Current means of evaluation are by human inspection or semi-automated dedicated vehicles, which often capture one to two pavement lines at a time. Mobile LiDAR is also frequently used by agencies to map signs and infrastructure as well as assess pavement conditions and drainage profiles. This paper presents a case study where over 70 miles of US-52 and US-41 in Indiana were assessed, utilizing both a mobile retroreflectometer and a LiDAR mobile mapping system. Comparing the intensity data from LiDAR data and the retroreflective readings, there was a linear correlation for right edge pavement markings with an R2 of 0.87 and for the center skip line a linear correlation with an R2 of 0.63. The p-values were 0.000 and 0.000, respectively. Although there are no published standards for using LiDAR to evaluate pavement marking retroreflectivity, these results suggest that mobile LiDAR is a viable tool for network level monitoring of retroreflectivity.

Human Factors Assessment of Pavement Marking Retroreflectivity in Simulated Rain and Dry Conditions

Pavement marking retroreflectivity standards are typically developed with dry conditions in mind, however, driving at night during rainfall is seemingly one of the most challenging and stressful situations for a driver. Furthermore, existing research indicates continuous wet retroreflectivity is relatively weakly correlated with dry retroreflectivity and deteriorates differently over time, leading to the obvious conclusion that dry retroreflectivity standards alone are not enough to ensure that pavement markings meet the needs of drivers across the breadth of roadway conditions that may occur. Consequently, developing standards for minimum continuous wet retroreflectivity for new installations and for maintenance purposes represents an important area for research. This study aims to develop new installation and maintenance values for continuous wet retroreflectivity based on a multifaceted, closed-course study of detectability of pavement markings in simulated rain and dry conditions. A series of 20 pavement marking samples was evaluated in relation to detection distance and subjective rating. The results of the study indicated that pavement markings need to be maintained at a continuous wet retroreflectivity value of 50 mcd/m2/lux based on a participant pool that skewed older in age, but that likely represents something close to the 85th-percentile driver. Additional salient findings included observed wet retroreflectivity loss in the existing literature of approximately 7% per month, as well as the maximum preview time in simulated rain conditions being substantially lower than in dry conditions.

Degradation of retro-reflectivity of thermoplastic pavement markings on trunk roads in Ghana

Retro-reflective thermoplastic pavement markings enhance roadway visibility at night and help avoid lane-departure crashes. Markings decay in retro-reflectivity, due to material degradation over time and must be remarked to restore marking visibility. However, road agencies lack data and information on how the material degrades to permit effective management of re-marking interventions. Thus, thermoplastic pavement markings were installed on trunk roads in 3 eco-climatic zones and their retro-reflectivity monitored over time to establish rate of degradation. Retro-reflectivity at any time since marking installation was characterized by a multiple linear function involving initial retro-reflectivity and time. The rate of retro-reflectivity loss was the same for the Coastal Savanna and Forest zones (0.15mcd/m2/lux/day) but significantly different from that for the Northern Savanna zone (0.267mcd/m2/lux/day) at 5% significance level (p<0.05). Additionally, data suggests there is an initial appreciation in retro-reflectivity within the first few months of marking installation before the onset of decay.

Pavement markings: identification of relevant covariates and controllable factors of retroreflectivity performance as a road safety measure

Abstract In this paper we present relevant contributions and important features related to the study of the retroreflectivity performance of pavement markings. The contribution of this paper is threefold. First, we propose an artificial scheme to allow some randomization of the treatments owing to several restrictions imposed on the choice of the experimental units. It is an experiment involving one fixed factor (three types of materials) in a randomized block design executed on a high-traffic-volume highway. Under this condition, the traffic volume works as a stress factor and the degradation of the retroreflectivity of pavement markings is faster than the degradation on rural roads or streets. This is related to the second contribution: the possibility of a reduction of experimental time. The current experiment spent 20 weeks to collect the data. And finally a mixed linear model considering three random effects and several fixed effects is fitted and the most relevant effects pointed out. This study can help highway managers to improve road safety by scheduling the maintenance of pavement marks at the appropriate time, choosing adequate material for the pavement markings and applying the proposed artificial scheme in future studies.

Degradation of the retroreflectivity and diffuse illumination of thermoplastic pavement markings: a case study in Thailand

Pavement marking is an essential task of road construction and maintenance. Pavement markings are beneficial because they provide information regarding the road alignment and road conditions ahead. To fully utilize their benefits, the retroreflectivity (RL30) and diffuse illumination (QD30) of pavement markings need to be above a minimum threshold. The degradation of RL30 and QD30 depends on several factors, such as the traffic exposure, age of the pavement marking, and weather. This research investigated the reflective efficiency of thermoplastics on an asphaltic concrete pavement marking with a glass bead rate of 359 to 553 grams per square meter. The reflective data were collected at the beginning of a period of eight months at two-week intervals. The factors that have a statistically significant effect on the pavement marking RL30 and QD30 include the traffic exposure and pavement marking position. The dirt coating on top of the marking is the most crucial factor that temporarily deteriorates the retroreflectivity, which can recover to some degree by rainfall.

Effect of Glass Bead Refractive Index on Pavement Marking Retroreflectivity Considering Passenger Vehicle and Airplane Geometries

Pavement marking retroreflectivity depends on several factors including but not limited to the type of retroreflective optics, binder, installation quality, marking condition, and measurement type/geometry. Texas A&amp;M Transportation Institute (TTI) evaluated the retroreflectivity of 19 pavement marking panels, prepared with different types of glass bead at standard 30-m and non-standard measurement geometries. The non-standard geometries represented typical commercial airplane configurations. The primary purpose of the research was to evaluate the effects of the glass bead refractive index (RI) (1.5 versus 1.9) and the measurement geometry on pavement marking retroreflectivity. Results showed a decrease in retroreflectivity with an increase in observation and entrance angles for both 1.5 and 1.9 RI. The study showed higher retroreflectivity levels for high RI beads at all the geometries evaluated. The result showed that 1.9 RI beads exhibit an average of 171% (862 mcd/m2/lx) higher retroreflectivity at standard 30-m car geometry as well as 102% (193 mcd/m2/lx) higher retroreflectivity at airplane geometry compared with 1.5 RI beads. The study found that an increase in bead RI had a larger effect on retroreflectivity than did an increase in bead size. The effect of the measurement geometry on the retroreflectivity level of the markings highlights a limitation of using the 30-m car geometry to represent the visibility of markings from a pilot’s perspective. Additional work should be conducted to better assess the visibility of markings from various vehicle platforms and how to best represent the visibility through retroreflectivity measurement.

Evaluation of bio-based fog seal for low-volume road preservation

While asphalt pavement is common in the United States, it is susceptible to oxidation as being exposed to environmental effects, resulting in the surface deterioration. To maintain the performance of a road surface and extend its service life, traditional fog sealers such as asphalt emulsion are used to mitigate micro-cracking, prevent oxidation and reduce water infiltration. Due to the relatively high cost and environmental concerns of petroleum-based sealants, the use of bio-based products as fog sealers has attracted more and more attention. Some new bio-based sealants derived from agricultural oil have been used as fog sealers in many states. To evaluate the effectiveness of a bio-sealant as an alternative to preserve asphalt pavements, a 5.3 km test section was selected for application of a soy-based fog sealant with three different application rates to conduct a two-year investigation of pavement marking retroreflectivity, surface friction, growth rate of cracking, laboratory water absorption, and air permeability. A control section without bio-sealant was also set up for comparison purposes. The field results revealed that, after application, a short-term decrease in retroreflectivity and skid resistance was restored to the original condition after two weeks and several months, respectively. The treated sections also exhibited a better control of growth rate of cracking than that of control section. The laboratory results indicated that the bio-sealant treated specimens applied at the highest application rate exhibited the lowest water absorption and air permeability. Such findings indicate that bio-sealant can be a sustainable preservation alternative for asphalt pavement.

How Might Wet Retroreflective Pavement Markings Enable More Robust Machine Vision?

This study is a preliminary investigation of the effects of levels of wet retroreflectivity of pavement markings on factors that determine robust feature detection in machine vision and light detection and ranging (LiDAR) systems in continuously wet road conditions. Luminance and Weber contrast of a range of pavement markings were characterized as functions of wet retroreflectivity and distance based on calibrated charge-coupled device (CCD) camera measurements. Both were found to trend with wet retroflectivity over the range of distances considered in this study. Artifacts arising from glare sources in wet conditions and their intensities relative to pavement markings of different wet retroreflectivity levels were demonstrated. Image data suggests that markings with high wet retroreflectivity may help to mitigate identification of these artifacts as false positives in lane awareness/lane detection algorithms. As LiDAR presents a viable sensor fusion approach to identifying and avoiding these false positives and artifacts in both nighttime wet and daytime wet road conditions, LiDAR return was characterized on pavement markings comprising both optics designed only for dry retroreflectivity and optics designed to be retroreflective in both dry and wet conditions. Preliminary results suggest that for common pavement marking constructions based on exposed beaded optics that might be completely immersed by a rainstorm or puddling, incorporation of high index (n~2.4) wet retroreflective beaded optics is likely to be advantageous to both visible machine vision systems and LiDAR for detection of those retroreflective markings in both night and day.

PAVEMENT MARKING REFLECTIVITY EVALUATION THROUGH RADIOMETRIC CALIBRATION OF THE LEICA P40 TERRESTRIAL LASER SCANNER

Abstract. Pavement markings serve as important traffic control devices, delineating traffic lanes and conveying regulations, guidance or warnings to roadway users. To ensure that pavement markings are clearly visible, especially at night, transportation agencies periodically assess the retroreflectivity of various categories of markings through manual approaches at discrete location. Because the radiometric information such as intensity in the lidar data cannot inherently be considered as a retroreflectivity measurement without additional processing, this study rigorously assesses the ability to determine pavement marking retroreflectivity from the Leica ScanStation P40 through radiometric calibration. For the evaluation, data were collected at a study site in Philomath, Oregon using the Leica P40, Leica Pegasus: Two mobile lidar system, and a handheld retroreflectometer as a reference. The results show that, with appropriate calibration, the lidar data can adequately assess the retroreflectivity of pavement markings. Additionally, while corrections have been proposed for range and angle of incidence, these corrections are not straightforward to apply for retroreflective materials, as will be discussed herein. While mobile lidar technology is ideal for a system-wide asset management framework, terrestrial laser scanning can be utilized for detailed investigations at sites such as intersections with highly variable wear where both can enable significant cost savings and applied for a variety of purposes simultaneously including asset management and project development.

Efficient and robust lane marking extraction from mobile lidar point clouds

Surveys of roadways with Mobile Laser Scanning (MLS) are now being conducted on a regular basis by many transportation agencies to provide detailed geometric information to support a wide range of applications, including asset management. Most MLS systems provide intensity (return signal strength) data as a point attribute in georeferenced point clouds, which may be used to estimate retro-reflectivity of pavement markings for effective maintenance. Nevertheless, the extraction of pavement markings from mobile lidar data remains an open challenge, due to variable noise, degree of wear on the markings, and road conditions. This paper addresses these challenges, presenting a novel approach for efficient, reliable extraction of lane markings, including those that have been significantly worn. First, using the MLS trajectory information, the lidar data is discretized into smaller sections, and then transformed to the local coordinate system, such that the road surface is near-horizontal for reliable extraction on roads with significant grade. Subsequently, the road surface is extracted using the constrained Random Sampling and Consensus (RANSAC) algorithm and then rasterized into a 2D intensity image to apply image processing techniques, namely: image segmentation to separate the lane markings from the road pavement, and a morphological opening operation to remove small objects. However, the extracted lane markings are prone to over-segmentation, due to occlusions or worn portions caused by moving vehicles. To rectify this, topologically-similar lane markings are associated with each other by computing line parameters (i.e., orientation and distance from the origin), which enables the gaps to be filled among the associated lanes. Finally, the remaining incorrect lane markings are detected and removed through a noise filtering phase using Dip test statistics. Examples of the effectiveness and application of the methodology are shown for a variety of sites with stripes of variable condition to highlight the robustness of the approach. Using optimized parameter values, the algorithm achieved F1 scores of 89–97% when tested on a variety of datasets encompassing a wide range of road scene types.

Development and Implementation of a Pavement Marking Management System in Florida

The visibility of pavement markings is an important aspect of a safe transportation system as the markings convey vital roadway warnings and guidance information to the traveling public. Therefore, it is beneficial to maintain acceptable visibility levels of markings on pavements under all weather and lighting conditions. To ensure the intended in-service visibility level is adequately maintained, the reflectivity must be monitored and quantified accordingly. Historically, visibility or retroreflectivity of in-service pavement markings has been measured with handheld devices and visual inspections. However, visual surveys are considered subjective and the handheld measurements are tedious and potentially hazardous. Consequently, the Florida Department of Transportation (FDOT) has focused on the use of a non-contact technology capable of assessing pavement markings continuously at highway speeds with improved safety and efficiency. The use of mobile technology for measuring reflectivity has allowed FDOT to develop and, subsequently, implement a Pavement Marking Management System (PMMS) to improve the safety and nighttime visibility of its roadways. Implementation of such a system provides an efficient and less subjective methodology to identify conditions that are detrimental to roadway safety, and strategize mitigating solutions including the selection of appropriate materials and application techniques. The system will ultimately result in an effective use of state funds while ensuring the safety of the traveling public. This paper presents a description of the Florida Pavement Markings Management System and its subsequent implementation including FDOT’s effort to ensure the quality, consistency, repeatability, and accessibility of statewide pavement marking retroreflectivity data.

Pavement Marking Retroreflectivity and Crash Frequency: Segmentation, Line Type, and Imputation Effects

This paper investigates the effects of segment length, line types, and imputed retroreflectivity values on pavement-marking retroreflectivity and crash frequency analyses. Road data, crash data, pavement condition data, and pavement-marking retroreflectivity data from Iowa Department of Transportation (DOT) databases were acquired and spatially integrated. Data sets for 1-, 3-, and 5-mi segments were prepared to investigate the effect of segmentation. Additional data sets with imputed and measured retroreflectivity data were prepared for comparison. A series of negative binomial regression analyses were run to estimate the expected number of crashes on varying segment lengths and data subsets based on the two retroreflectivity collection methods. The findings show that using smaller segments and data sets with measured retroreflectivity rather than imputed retroreflectivity leads to a more significant relationship between the retroreflectivity of longitudinal pavement markings and crash frequency. The findings also suggest that keeping longitudinal pavement markings in good condition has significant positive effects on safety. The results further suggested that the expected number of annual crashes significantly decreased with the increasing retroreflectivity of white-edge lines (WELs) and yellow-edge lines (YELs) for four-lane road segments. In addition, a significant relationship between pavement condition, measured with the International Roughness Index (IRI) and the expected number of crashes was found for all data sets.

Piecewise Multiple Linear Models for Pavement Marking Retroreflectivity Prediction Under Effect of Winter Weather Events

Most existing models for prediction of pavement marking retroreflectivity have been developed from data collected in locations with similar weather conditions; therefore, the effect of different weather conditions, such as winter weather events, has not been extensively studied. This study develops degradation models that can predict retroreflectivity of durable pavement marking materials under various winter weather conditions. Piecewise multiple linear models are proposed to explicitly account for the effect of winter weather events in this study. In comparisons of the proposed models with conventional multiple linear models (MLMs) developed from the same set of data, the proposed models outperformed the MLMs, and the overall root-mean-square error improved from 204.6 mcd/m2/lux for MLMs to 106.5 mcd/m2/lux for piecewise MLMs. The proposed models also show robust and consistent results in predicting retroreflectivity of different materials in different states. The results indicate that the proposed method can be adopted by various states and regions for comprehensive retroreflectivity prediction under various weather conditions.