Pavement Markings Research Articles

Study on Initial Retroreflectivity of Pavement Marking by the Refractive Index of Glass Bead

Study on Initial Retroreflectivity of Pavement Marking by the Refractive Index of Glass Bead

Measuring the Macrotexture of Pavement Surface Using an Image Processing Technique

The objective of this study was to propose an image processing-based index for measuring pavement macrotexture at the network level. This index enables macrotexture to be measured easily and inexpensively using images collected at traffic speed. The study involved collecting pavement surface images at a constant traffic speed on a test section specifically designed and constructed for this purpose, with three surface course mixes that are commonly used in India, namely, bituminous concrete, stone matrix asphalt, and gap-graded rubberized bituminous mix. Additionally, macrotexture data with regard to mean texture depth (MTD) from the sand patch test and mean profile depth (MPD) from laser sensor-based measurements were obtained at the locations where the images were captured. The surface macrotexture index (SMI), which was derived from wavelet transform-based image texture analysis, was compared with the MTD and MPD data. The results showed that the SMI is an accurate indicator of pavement surface macrotexture. In addition, the study showcased the application of an unsupervised machine learning algorithm to identify and replace outliers in the SMI data that resulted from isolated spots with dirt, pavement markings, and wet surfaces. The research also established relationships between the proposed SMI and MTD/MPD. These relationships are reliable and can be used to predict the commonly used pavement surface construction quality measure MTD and the network-level skid resistance indicator MPD.

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.

The role of traffic conflicts in roundabout safety evaluation: A review

The roundabout is one type of at-grade intersection commonly seen in many countries. The evaluation of roundabout safety is usually counted on conflict analysis of the roundabout traffic due to random and limited records of real accidents. This paper surveyed published papers and reports that investigate the role of traffic conflicts in roundabout safety evaluation. It summarized the definitions and observation methods of roundabout conflicts and classified the attributing factors of roundabout conflicts and the countermeasures to control the conflicts. This study found that although unique traffic flow movements at roundabouts create special patterns of roundabout conflicts, the methods of roundabout conflict analysis used in most existing studies were inherited from the studies of highway or cross-intersection conflicts, including conflict definitions, conflict measurements, and thresholds of conflict severity. Special or improper designs of roundabout configurations or basic geometry elements could arouse roundabout conflicts. The most common vehicle-to-vehicle conflicts were entering-circulating conflicts, sideswipe conflicts, and exiting-circulating conflicts. The conflicts among vehicles and vulnerable road users (VRUs) easily evolved into serious collisions, but these conflicts did not get deserved attention in previous studies. Drivers’ familiarity with roundabouts also affected road users’ safety. Traffic signs and pavement markings were commonly used to control roundabout conflicts, while traffic signals were more effective methods for the roundabouts with uneven distribution of approaching traffic or high traffic volume. Based on the analysis of existing studies, this paper pointed out seven future directions of further research in term of conflict measurement, data collection, infrastructure and access management, geometry, drivers and VRUs, signal control, and vehicle control.

Safety impact of highway geometrics and pavement parameters on crashes along mountainous roads

Having the capability of estimating both the number of crashes and their severity levels, crash prediction models are a precious tool in highway safety. However, there hasn't been any research on predicting traffic crashes on Indian mountainous rural highways. The primary objective of this research is to develop safety performance functions (SPFs) for traffic crashes occurring on rural roads located in the mountainous region of Uttarakhand, India.For analysis, the study utilized five years of crash data collected from different types of rural roads. The road network was divided into constant segments of 500m each, and separate models were developed for single (TSVC) and multi-vehicle (TMVC) crashes using the negative binomial regression approach. These SPFs highlight important significant variables in terms of positive and negative association and a potential change in subject crash frequencies. The results concluded that different types of risk factors impact both types of crashes, with horizontal (HC) and vertical curves (VC) in common. For instance, spot speed increases TSVC crashes by 3.87 %, whereas HC and VC tend to increase subject crashes by 8.32 % and 29.95 %, respectively.Similarly, TMVC is influenced by carriageway (CW) and shoulder width (SW). The result proposed that an increase in CW and SW can decrease frequencies by 0.668 times and 0.819, respectively. Additionally, the model highlighted the importance of rut-depth and the presence of pavement markings in the road safety analysis. At last, further research scope is suggested based on the limitations of this study.

Design and performance evaluation of the epoxy-based self-luminous pavement marking

This study aims to prepare the self-luminous pavement marking (SLPM) to enhance driving safety and reduce the risk of traffic accident. Firstly, the composition of the SLPM was determined by the mechanical properties and luminous performance tests. The optimal mass proportions of epoxy resin, toughening agent, curing agent, curing accelerator, luminous powder, reflective powder, glass bead and glass powder was 1.0:0.1:0.3:0.006:0.4:0.3:0.25:0.55. In addition, test results showed that the SLPM has satisfactory mechanical properties, durability and luminous performance, which met the engineering requirements for practical application. The afterglow time was more than 11 h at night in laboratory test. Moreover, it is feasible to prepare different colors of the SLPM according to the RGP principle. The afterglow color and luminous intensity of SLPM strongly depend on the luminous material and its proportions. Overall, this paper provides a reference for designing and applying SLPM materials in road engineering.

Evaluation of the Selected Sustainable Indicators for Urban Roads Network: Al-Kufa City as a Case Study

One of the major concerns of the modern world is sustainable development. Sustainable transportation development is crucial in light of the increasing congestion and pollution in urban areas. Al-Kufa City is one of the most significant cities in Iraq. This study focuses on specific criteria such as public transportation, pedestrian facilities, pavement markings, noise level, and population density on the number of existing roads. The collected data showed that the average percentage of public transportation at selected locations is 4% for buses and 24.6% for minibuses with unplanned routes. Furthermore, pavement markings cover only about 0.48% of the whole network. Regarding the noise level, it was high and exceeded the standard limitations, reaches to 93.48 dBA. At the same time, the indicator of pedestrian facilities showed a significant lack of footbridges. Finally, the density of roads per population is 60.32km/100000 which is less than the expected value. Therefore, actions and improvements are necessary for the short and long term to develop a sustainable city living environment.

Evaluation of Driver Comprehension and Compliance of Red-Colored Pavement Markings for Transit Lanes in Portland, Oregon

Many U.S. agencies have experimented with red-colored pavement markings in transit lanes to enhance the message that they are restricted to transit vehicles. This study evaluated nontransit drivers’ comprehension of and compliance with red-colored lane markings in transit priority lanes intended to communicate lane restrictions and appropriate turning and merging locations. Two complementary research methods were used: 1) an online survey of drivers’ comprehension of red-colored pavement markings, and 2) evaluation of video collected at locations pre and post installation of red-colored pavement markings. In the survey, most drivers recognized the red pavement color as a restriction on use, but most did not associate it specifically with transit, or understand the intended meaning of the broken red bar patterns designed to designate a merge area. However, we observed relatively high compliance rates with the transit lane restrictions, and the red pavement coloring was generally associated with improved compliance. Of the 22,803 vehicles observed at these locations, 206 were observed driving in the transit lanes, accounting for a violation rate of 0.9%. For the locations with pre and post data, driving-in-transit-lane violations were either reduced or there was no significant change. For locations with shared turn and transit lanes, between 62% and 76% of drivers merged as intended, and we observed an increase in appropriate merging behavior with the addition of red pavement color.

Assessing the effects of geometric layout and signing on drivers’ behavior through work zones

Work zones are commonly deployed within or near the traveled way during roadway maintenance, repair, and construction works. Work zones could translate into hazardous spots requiring higher driver attention and possibly leading to increased crash rates and crash severity. This research addresses the effects of various design elements on drivers' behavior through work zones. Chekka diversion, currently implemented along the main Coastal Highway M-51 in Lebanon, is analyzed as it currently presents numerous non-standard design elements. A proposed standard diversion design is compared to the current non-standard diversion layout. A driving simulator has been used to model the current and the proposed diversions. Four driving scenarios involving the combination of the two diversion designs under different visibility conditions are used to evaluate drivers' behavior and responses through the diversion zones. The simulator study results showed that the proposed work zone geometry significantly improved the drivers' behavior compared to the exiting diversion. The visibility conditions have proven to have a more profound effect on driver behavior through the current non-standard diversion design. The pavement markings used along the proposed diversion reduce the vehicles' lateral fluctuations by 50% compared to the current design. The recorded driving patterns prove useful for work zone design to minimize the Lebanese drivers' risky behavior and enhance road safety.

How Much Do We Know about Low-Speed Vehicle and Golf Cart Communities and Crashes: A Case Study in Nocatee, Florida

Low Speed Vehicle (LSV)/Golf Cart (GC) communities are those modern residential areas that fully or partially allow LSVs/GCs to travel within the community. Whereas LSV/GC communities are increasing rapidly, the safety impacts of LSV/GC on the community have rarely been investigated. Also, no national uniform manuals are available for guiding the proper design of signs, pavement markings, and signals in the community. In this study, five years (2016–2020) of fatal crash data that involved LSV/GC were extracted from the National Highway Traffic Safety Administration (NHTSA) Fatality Analysis Reporting System (FARS) database to: (1) provide an overview of LSV/GC fatal crashes in the United States, (2) discuss characteristics of LSV/GC fatal crashes, and (3) delineate significant contributing factors. A case study in a large LSV/GC community in Florida was conducted to identify current issues and recommend improvements on: (1) driving behavior of LSV/GC drivers, (2) LSV/GC-related traffic control devices, and (3) LSV/GC community planning. The findings can help national and state agencies make proactive decisions to enhance traffic safety and livability for LSV/GC communities.

Detecting School Zones on Florida’s Public Roadways Using Aerial Images and Artificial Intelligence (AI2)

Collecting up-to-date roadway geometry data is essential for transportation agencies so they can undertake planning, maintenance, design, and rehabilitation of roadways. Collection methods can be categorized into two distinct groups: land-based methods (e.g., field inventories, mobile mapping, and image logging); and aerial-based methods (e.g., satellite imagery, drones, and laser scanning). Because using land-based methods for thousands of miles of highways is tedious, costly, and risky for crew members, there is a need to develop better methodologies to collect these data faster, and more safely and cheaply. The increasing availability of high-resolution images and recent advances in computer vision and object detection techniques have enabled the automated extraction of roadway geometry features. This novel study proposes a computer vision-based methodology to detect school zone pavement markings from high-resolution aerial images and determine school zones on Florida’s public roadways. This is critical information for transportation agencies, and they use it for a variety of different purposes: identifying those markings that are old and invisible; comparing the school zone locations with other geometric features such as crosswalks; and analyzing crashes that occur around the zones. Compared with the ground truth data obtained for Leon County, Florida, 94% accuracy was observed at the 90% confidence level. The model was then used to detect school zones in Orange County, Florida, and approximately 500 school zone markings were identified automatically. The road geometry data extracted can be integrated with crash and traffic data to advise policymakers and roadway users.

Point-Based Visual Status Evaluation of Worn Pavement Markings Based on a Feature-Binary-PointNet Network and Shape Descriptors Using LiDAR Point Clouds: A Case Study of an Expressway

Inspecting the visual wear status of pavement markings is important to guide the maintenance plans of roads and ensure traffic safety. This study proposes a point-based visual status evaluation system for pavement markings using light detection and ranging (LiDAR) technology. The system includes a feature-binary-PointNet (FBP) network to accurately extract pavement markings from raw point clouds. Furthermore, multi-shape descriptors are constructed to efficiently identify different types of pavement markings, and a four-level wear-grading system is established based on two indices (point-based visual wear area and wear area proportion) of each pavement marking. The proposed method was tested using the mobile laser scanning technique considering a case study of an expressway. The proposed FBP network performed better than the existing state-of-the-art networks in extracting the pavement markings, with accuracy, recall, precision, and mean intersection over union scores of 97.04, 84.42, 91.11, and 79.36%, respectively. The main types of expressway markings were rapidly identified, and maintenance advice was provided based on the two computed indices and the wear grade of each marking. The study findings can guide managing agencies in allocating the maintenance budget for fixing highly defective road sections.

Enhancing Pavement Marking Practices in Wyoming: A Roadmap for Comprehensive Management and Statewide Implementation

Enhancing Pavement Marking Practices in Wyoming: A Roadmap for Comprehensive Management and Statewide Implementation

Intelligent pixel-level pavement marking detection using 2D laser pavement images

This paper proposes a robust semantic segmentation algorithm named as Marking-DNet to implement pixel-level recognition of pavement markings. The proposed Marking-DNet presents an improved encoder-decoder architecture based on DeepLabV3+. Different from DeepLabv3+, feature maps of four different scales are imported from the encoder into the decoder of Marking-DNet, resulting in more levels of information exchange. Additionally, the Object-Contextual Representation and the Convolutional Block Attention Module are both employed in Marking-DNet to conduct contextual learning more efficiently and implement spatial and channel attention explicitly. The F-measure and Intersection-Over-Union attained by the Marking-DNet on 1500 testing images are 93.59% and 87.96% respectively. Experimental results demonstrate that Marking-DNet outperforms six state-of-the-art semantic segmentation models in detecting pavement markings using both private and public image datasets. According to the performances on 5,506 pavement images, it is found that Marking-DNet can detect various complex pavement markings accurately, except for some heavily-worn markings.

Longitudinal Degradation of Pavement Marking Detectability for Mobile LiDAR Sensing Technology in Real-World Use

Recent advancements in vehicle automation and driver-assistance systems that detect pavement markings has increased the importance of the detectability of pavement markings through various sensor modalities across weather and road conditions. Among the sensing techniques, light detection and ranging (LiDAR) sensors have become popular for vehicle-automation applications. This study used low-cost mobile multi-beam LiDAR to assess the performance of several types of pavement marking materials installed on a limited-access highway in various conditions, and quantified the degradation in detection performance over three years. Four marking materials, HPS-8, polyurea, cold plastic, and sprayable thermoplastic, were analyzed in the current study. LiDAR reflectivity data extracted from a total of 210 passes through the test sections were analyzed. A new detectability score based on LiDAR intensity data was proposed to quantify the marking detectability. The results showed that the pavement marking detectability varied across the material types over the years. The results provide guidance for selecting materials and developing maintenance schedules when marking detectability by LiDAR is a concern.

Study on the Reflective Principle and Long-Term Skid Resistance of a Sustainable Hydrophobic Hot-Melt Marking Paint

Road marking is very important for driving safety and reducing the accident rate as a basic component of highway construction. However, traditional road marking paints are prone to be worn after short-term application and have poor durability and reflective performance. To address these problems, the marking paint was modified using the organic polymer material polytetrafluoroethylene to create a durable hydrophobic hot-melt marking paint. The factors affecting the reflective performance of marking lines are analyzed, and artificial accelerated abrasion tests were carried out to analyze the skid resistance and marking retroreflection coefficient of hydrophobic coatings. Results show that the texture of the glass beads and the quality of the coating plays a major role in the reflective performance of the marking line. The friction coefficient value of the modified marking paint is 4.62% higher than that of the traditional hot-melt marking paint. The retroreflection performance of the marking paint with 4% hydrophobic material is 8.45% higher than the initial value of the retroreflection coefficient of the traditional hot-melt marking paints. This sustainable hydrophobic hot-melt marking paint is safer and more durable than traditional pavement marking paints, which may save follow-up maintenance resources and cost from the sustainable aspect.

Preparation and Performance Characterization of an Active Luminous Coating for Asphalt Pavement Marking

For improving the night recognition of road markings and enhancing the driving safety of asphalt pavements, single-factor optimization is used to investigate the effects of the component materials, including luminescent power, pigment, filler, and anti-sedimentation agent, on the luminous performance of a coating. Additionally, their composition ratios are optimized using response surface methodology. A phosphorescent marking coating is prepared to investigate the micromorphology, excitation, and emission properties using scanning electron microscopy (SEM) and molecular fluorescence spectroscopy (MFS). The optimum thickness of the coating on an asphalt pavement is investigated, and the durability of the coating on asphalt pavement using a wheel rutting test is evaluated. The results show that the 300 mesh yellow-green luminous powder has the optimal overall performance, with an initial luminescence that exceeds that of orange and sky blue by three times. Initial brightness is mainly influenced by aluminate luminescent powder (ALP), which increases with the dosage. ALP and fumed silica powder (FSP) have a positive effect on brightness after centrifugation, and the effect of FSP dosage is more significant. ALP, rutile titanium dioxide powder (RTDP), and FSP influence the wear value of the coating, and the magnitude of the effect is RTDP > FSP > ALP. The optimal dosages of the main component are 27% ALP, 5% RTDP, and 0.8% FSP. The results of SEM show that the components in the coating are evenly dispersed, and the surface of the coating is rough. The peak excitation wavelength of 420 nm means that the coating has the best excitation effect in UV light, and its emission spectrum in the 440–760 nm wavelength range is well within the sensitive recognition zone of the human eye. The initial brightness gradually reached 4.38 cd/m2 when the coating thickness was increased from 482 μm to 546 μm, and the optimal application thickness of the luminous coating was determined to be 500 μm. At high and normal temperatures, the rutting stripping rates of the luminous marking coating are 16.8% and 8.2%, indicating its satisfactory durability. This study provides an experimental basis for the ratio optimization design of a luminous coating for asphalt pavements.

The influence of LED road stud color on driver behavior and perception along horizontal curves at nighttime

Scotopic lighting conditions (reduced level of natural light or presence of artificial lighting) may impair driving performance and, therefore, impact on road safety. Thanks to technological developments, low-cost light emitting diode (LED) studs are now being considered as an alternative and affordable pavement marking solution to assist drivers in these conditions. By helping them to maintain their vehicle within the marked lane, the studs should prevent any deterioration in driver performance when negotiating curves at nighttime. However, the few studies that investigated the impact of LED studs on driving performance produced inconsistent results, and the question of whether they actively improve driver performance remains open. Furthermore, while international road regulations allow the use of LED studs, they do not provide consistent prescriptions for their lighting color.Here, we assessed the influence of different LED lighting colors (red, white, and unlit) on longitudinal and transversal driver behavior when negotiating road curves with different radii and sense of direction. In the study, thirty-six drivers drove a dynamic virtual scenario featuring twenty-four curves. After the driving simulation, participants completed a static perception test in which they assessed each curve in terms of the perceived levels of risk, pleasantness, and arousal they experienced while driving on it.In comparison with the unlit and red lit curves, those marked with white lighting LED studs were perceived as less risky, less arousing, and more pleasant independently of the radii and curve direction. Furthermore, when entering these curves, participants tended to shift their driving trajectories towards the center of the road. This effect was most evident on the central part of the curve. Further studies are expected to corroborate these results by focusing on different road geometries and LED stud layouts, as well as testing driving behavior in controlled road field studies.

Analysis of Problems with National Pavement Marking Standards and Proposals for Improvement

Analysis of Problems with National Pavement Marking Standards and Proposals for Improvement

Traffic Data on-the-Fly: Developing a Statewide Crosswalk Inventory Using Artificial Intelligence and Aerial Images (AI2) for Pedestrian Safety Policy Improvements in Florida

Pedestrian fatalities have been rising sharply in the U.S. over the last decade. To reverse this trend, several pedestrian safety countermeasures are developed by the authorities, particularly for the uncontrolled crosswalks where the majority of those fatalities occurred. Although geocoded and categorized crosswalk inventories are essential to determine where to implement the most appropriate countermeasure, building such an inventory database may take years with manual observations. Thus, this study proposes a framework using Aerial Imagery and Artificial Intelligence (AI2) to map all crosswalks on Florida public roadways according to their control strategy. A transfer learning approach with anchor box modifications is applied to the You-Only-Look-Once (YOLOv2) object detection algorithm. Crosswalks with various pavement markings and width/height ratios are identified by running YOLOv2 crosswalk detector on statewide high-resolution aerial images. Additionally, Geographic Information System-GIS techniques are used to reduce the input data, establish a geocoded connection for each detected crosswalk, and conduct a sensitivity analysis to identify signal, midblock, and driveway crosswalks. According to the performance evaluations conducted with a manually generated ground truth dataset, the proposed approach can inventory the crosswalks with 85.9% recall and 88.7% precision, while accuracy is significantly higher for signal crosswalks (94%) and major road crossings (96.6%) regardless of zebra or non-zebra pavement markings. The results revealed that there are 861 midblock, 30,784 signal, and 29,307 driveway crosswalks on Florida State Roads. The proposed framework and findings of this study can contribute to the wider use of machine learning in transport policy.