Resurfacing Research Articles

Research on the Mechanical, Thermal and Induction Healing Properties of Asphalt Wearing Course with Steel Fibers.

Induction healing technology can effectively repair microcracks in asphalt mixtures and is a promising maintenance technology for asphalt pavements. However, it requires the addition of steel wool fibers to asphalt mixtures and cannot be directly used to repair existing pavements. In order to improve the practicality of the induction healing technology, this article designs a wearing course asphalt mixture with induction healing function that is going to be paved above the existing road surface. The AC-10 asphalt wearing course for induction heating was prepared by adding steel fiber (SF). Analysis of the overall temperature of the surface revealed the unevenness of the temperature distribution, and the healing properties were investigated through protective heating that controlled the maximum temperature of the upper surface. The results show that the addition of SF can improve the high-temperature stability, low-temperature and intermediate-temperature crack resistance, and moisture stability of asphalt wearing courses; however, it has adverse effects on volumetric performance and skid resistance. The heating temperature increases with the increase in SF content, but higher maximum temperature heating rate causes worse heating uniformity and lower healing effect. The maximum heating rate of the sample with 10% SF reaches 3.92 °C/s, while its heating rate at minimum temperature is similar to that of the sample with 6% SF, which is only 0.7 °C/s, indicating the worst heating uniformity. The best healing effect occurs when the maximum temperature of the upper surface reaches 160 °C. The recommended optimal SF content is 6% of the asphalt volume. The asphalt mixture with 6% SF has an appropriate volume performance, moisture stability, and skid resistance; additionally, it has the best high-temperature stability, as well as low-temperature and intermediate-temperature crack resistance. Meanwhile, it also has uniform temperature distribution and efficient healing efficiency.

Ensemble Learning Approach for Developing Performance Models of Flexible Pavement

This research utilizes the Long-Term Pavement Performance database, focusing on devel-oping a predictive model for flexible pavement performance in the Southern United States. Analyzing 367 pavement sections, this study investigates crucial factors influencing asphaltic concrete (AC) pavement deterioration, such as structural and material components, air voids, compaction density, temperature at laydown, traffic load, precipitation, and freeze–thaw cycles. The objective of this study is to develop a predictive machine learning model for AC pavement wheel path cracking (WpCrAr) and the age at which cracking initiates (WpCrAr) as performance indicators. This study thoroughly investigated three ensemble machine learning models, including random forest, extremely randomized trees (ETR), and extreme gradient boosting (XGBoost). It was observed that XGBoost, optimized using Bayesian methods, emerged as the most effective among the evaluated models, demonstrating good predictive accuracy, with an R2 of 0.79 for WpCrAr and 0.92 for AgeCrack and mean absolute errors of 1.07 and 0.74, respectively. The most important features influencing crack initiation and progression were identified, including equivalent single axle load (ESAL), pavement age, number of layers, precipitation, and freeze–thaw cycles. This paper also showed the impact of pavement material combinations for base and subgrade layers on the delay of crack initiation.

Characterization of road surface topography changes during polishing process with characteristic roughness parameter

In this paper, three-dimensional structure feature points of the asphalt mixture were obtained based on the industrial CT. The surface morphology of the asphalt mixture was then extracted through image morphology processing and evolutionary matching algorithms. A series of accelerated polishing tests were utilized to simulate the long-term polishing effect of the tire load on the asphalt pavement. Combining with digital image analysis, the full-life morphology evolution trend of the asphalt pavement was analyzed, and the full-life surface morphology datasets of the asphalt pavement were constructed. By comparing the power spectrum density of the asphalt pavement morphology at different wear stages, a segmented evaluation method based on the spectral fractal method was proposed. Combining the spectral fractal parameters and statistical geometric parameters, a multi-scale asphalt pavement surface morphology feature evaluation index was proposed. This provides a more intricate and detailed analysis of the surface structure of asphalt mixtures.

Tire–road contact modelling for multibody simulations with regularised road and enhanced UA tire models

AbstractMultibody simulations play an important role in the study of the dynamic behaviour of road vehicles, being one of the key aspects the modelling of the tire-road interaction. The tire-road contact modelling is required to detect the contact between the tire and road and to obtain the tire contact forces from the tire-road relative motion. The first part of the problem is solved by a contact detection algorithm, whereas the second part is handled by a tire model. In both cases, the trade-off between accuracy and computational cost needs to be addressed in the road dynamics scenario. For the contact detection problem, this work proposes a methodology in which the road is described by a mesh of triangles while the tire is described by a toroid. The triangular mesh road input can be directly extracted from any CAD model. To avoid the geometric discontinuities due to the transition between triangular patches, a set of auxiliary points is used to obtain a continuous variation of the road surface normal, based on the method proposed by Rill (Multibody Syst. Dyn. 45:131–153, 2019. In the process of contact detection, all kinematic quantities required by the tire-road contact model are evaluated. The tire–road forces are evaluated using the enhanced UA tire model, which is a numerically stable and efficient development of the original tire model proposed by Gim and Nikravesh (Int. J. Veh. Des. 12, 1991; Int. J. Veh. Des. 12:19–39, 1991; Int. J. Veh. Des. 12:217–228, 1991). Both the contact and tire models have smooth transitions between their internal transitions, thereby reducing the need for the variable-step ODE integrators to unnecessarily decrease time steps. The advances on contact detection and the tire–road contact models, proposed here, are demonstrated with a set of road vehicle simulation scenarios in which the influence of various modelling parameters on the stability and efficiency of the simulations is addressed. The novelties of the work consist not only in the demonstration of the enhanced UA tire model in practical situations but also in the description of the new tire–road contact methodology that employs a simple description of the road geometry, to achieve smooth tire–road contact forces.

Slip modes modeling of electric vehicle asynchronous electric drive

Purpose. Creation a computer model of electric drive of an electric vehicle with the possibility of modeling in slippage modes, as well as the subsequent determination of slippage detection method as early as possible to further reducing or preventing slippage. Methodology. Mathematical analysis and modeling. Findings. Mathematical models in the structural form of the control system, power converter and motor are added in a simplified way, as the blocks. For compiling a computer model, the blocks of the control system and the power converter will be assembled using models of elements of power electric circuits. The model allows the ED simulation with a huge number of parameters varying – modeling slipping processes with one or two wheels, a sharp change in engine parameters, failure of one of the components of the power circuit, etc. Using the Simulink and SimPower packages of the Matlab software package, as well as the block diagram of the electric drive, a computer model of the ED was synthesized. Computer simulation of dynamic processes in the electric drive during a collision, as well as pulling away on the road surface with a reduced coefficient of friction, was carried out. The obtained diagrams fully correspond to the real physical processes occurring in the electric vehicle and give reason to believe that the constructed mathematical and computer models are adequate. Proceeding from this, electromechanical transients in the electric drive during acceleration with slipping and one wheel runover a road surface with reduced traction were obtained and analyzed. As a result of this analysis, the most optimal and reliable way to determine the skidding mode for its further elimination was determined. Originality. Computer model of the asynchronous electric drive of an electric vehicle with a detailed mechanical part, taking into account the mechanical differential gear, was built. The model allows the ED simulation with a huge number of parameters varying – modeling slipping processes with one or two wheels, a sharp change in engine parameters, failure of one of the components of the power circuit, etc. Practical value. Computer simulation was carried out for two modes: start of electric vehicle movement , when one of the wheels has been of on the road surface with a reduced coefficient of adhesion (0,1) since launch and acceleration with a collision with a surface with a reduced coefficient of adhesion (0,1) with one wheel at the moment of time of 6.3 s.

Examining the effectiveness of municipal street sweeping in removing road-deposited particles and metal(loid)s of respiratory health concern

Road dust is a demonstrated source of urban air pollution. Given this, the implementation of street sweeping strategies that effectively limit road dust accumulation and resuspension should be a public health priority. Research examining the effectiveness of street sweeping for road dust removal in support of good air quality has been limited to date. To address this, the study aimed to assess the use of a regenerative-air street sweeper to efficiently remove road dust particles and metal(loid)s in size fractions relevant for respiratory exposure in Toronto, Canada. As part of this, the mass amounts, particle size distribution and elemental concentrations of bulk road dust before and after sweeping at five arterial sites were characterized.Sweeping reduced the total mass amount of thoracic-sized (<10 µm) road dust particles by 76 % on average. A shift in the size distribution of remaining particles toward finer fractions was observed in post-sweeping samples, together with an enrichment in many metal(loid)s such as Co, Ti and S. Overall, the mass amounts of metal(loid)s of respiratory health concern like Cu and Zn were greatly reduced with sweeping. Traffic volume and road surface quality were predictors of dust loadings and elemental concentrations. Road surface quality was also found to impact street sweeping efficiencies, with larger mass amounts per unit area collected post-sweeping where street surfaces were distressed.This study demonstrates that street sweeping using advanced technology can be highly effective for road dust removal, highlighting its potential to support air quality improvement efforts. The importance of tailoring sweeping service levels and technologies locally as per the quality of road surface and traffic patterns is emphasized. Continued efforts to mitigate non-exhaust emissions that pose a respiratory health risk at their source is essential.

Vehicle–Bridge Coupling Vibration of Long-Span Concrete-Filled Steel Tubular Arch Bridge

An increase in the nonlinearity of the bridge will lead to a more obvious vehicle–bridge interaction effect and affect the vehicle response and driving comfort as the span increases. This paper establishes a refined finite element model of vehicle–bridge coupling for the 575 m long-span concrete-filled steel tubular arch bridge, Pingnan Third Bridge. Coupling vibration responses under different vehicle speeds, vehicle weights and road conditions of this bridge is analyzed. Then its comfort is assessed according to the comfort criterion. The results show that the vehicle–bridge coupling model has good agreement with the field test. The dynamic response of the bridge within the speed limit has no significant linear relationship with the magnitude of the vehicle speed without considering the pavement class. When the vehicle exceeds the speed limit, the dynamic response increases sharply with the increase in speed. The increase in vehicle weight leads to an increase in the maximum dynamic deflection of the bridge and a decrease in the impact coefficient, but the actual total response of the bridge does not decrease. The worse the road surface condition, the more dramatic the dynamic response of the bridge structure, taking into account the road surface level.

A Review of Electrically Conductive Cement Concrete Pavement for Sustainable Snow-Removal and Deicing: Road Safety in Cold Regions

Within cold U.S. regions, winter storms can cause interruptions in transportation networks, affecting transportation entities’ revenue streams. Conventional snow-removal methods on roadways efficiently remove snow and ice, yet their adverse environmental impacts further make winter maintenance more challenging. In response to these concerns, electrically conductive cement concrete (ECCC) pavement has become an effective alternative for deicing and snow melting on road surfaces. ECCC utilizes the Joule heating principles to effectively melt snow and ice by incorporating conductive elements into conventional concrete. This paper comprehensively reviews the current literature on ECCC pavement. Previous studies have diligently explored various aspects of ECCC pavement, including concrete conductivity enhancement, heat transfer processes, and meticulous performance assessments, ranging from controlled laboratory scale experiments to small-scale field evaluations. The conclusions drawn from these investigations highlight the potential of ECCC pavement to considerably enhance winter road maintenance, consequently improving road safety and minimizing traffic interruptions during winter storms. The present review emphasizes ECCC pavement as a promising paradigm for effectively addressing the complexities associated with winter road maintenance in colder regions. Moreover, its environmentally friendly deicing capabilities present a sustainable departure from conventional methodologies. However, certain limitations currently impede widespread adoption of ECCC pavement, mainly concerning optimizing cost-effective construction techniques, ensuring long-lasting durability, and enhancing energy efficiency. Addressing these limitations could accelerate the broader adoption of ECCC pavement, promoting safer and more sustainable winter transportation practices.

Intelligent Tire Prototype in Longitudinal Slip Operating Conditions.

With the recent advances in autonomous vehicles, there is an increasing need for sensors that can help monitor tire-road conditions and the forces that are applied to the tire. The footprint area of a tire that makes direct contact with the road surface, known as the contact patch, is a key parameter for determining a vehicle's effectiveness in accelerating, braking, and steering at various velocities. Road unevenness from features such as potholes and cracks results in large fluctuations in the contact patch surface area. Such conditions can eventually require the driver to perform driving maneuvers unorthodox to normal traffic patterns, such as excessive pedal depressions or large steering inputs, which can escalate to hazards such as the loss of control or impact. The integration of sensors into the inner liner of a tire has proven to be a promising method for extracting real-time tire-to-road contact patch interface data. In this research, a tire model is developed using Abaqus/CAE and analyzed using Abaqus/Explicit to study the nonlinear behavior of a rolling tire. Strain variations are investigated at the contact patch in three major longitudinal slip driving scenarios, including acceleration, braking, and free-rolling. Multiple vertical loading conditions on the tire are applied and studied. An intelligent tire prototype called KU-iTire is developed and tested to validate the strain results obtained from the simulations. Similar operating and loading conditions are applied to the physical prototype and the simulation model such that valid comparisons can be made. The experimental investigation focuses on the effectiveness of providing usable and reliable tire-to-road contact patch strain variation data under several longitudinal slip operating conditions. In this research, a correlation between FEA and experimental testing was observed between strain shape for free-rolling, acceleration, and braking conditions. A relationship between peak longitudinal strain and vertical load in free-rolling driving conditions was also observed and a correlation was observed between FEA and physical testing.

A model-free adaptive predictive path-tracking controller with PID terms for tractors

An efficient tractor path-tracking method can increase the operational accuracy, improve land utilisation, and more efficiently provide services for autonomous tractors and precision agriculture. In this study, a model-free adaptive predictive control-proportional-integral-derivative (MFAPC-PID) method was devised. Based on preview theory, a tracking system based on course deviation angle was designed. After the tracking system was linearised, an MFAPC tracking system was developed, and its inherent defects were analysed. Referring to the control structure of an incremental PID algorithm, the MFAPC was considered as an adaptive integral item, and the MFAPC-PID controller was obtained by adding adaptive proportional and differential terms. To verify the proposed controller, collaborative simulation and hardware-in-the-loop tests were performed. The controller was simulated and tested under different paths, road surfaces, and tractor models. Compared with other methods, the MFAPC-PID path-tracking method exhibits superior comprehensive performance, adaptability, universality, and robustness. Moreover, the MFAPC-PID method is insensitive to external interference and model changes of controlled objects.

Roadway Inspection System

Traditional road inspections are manual processes, prone to human error and inefficiencies. This paper presents a novel approach for automated roadway inspection using a Convolutional Neural Network (CNN) model. Our system leverages computer vision techniques to detect potholes and speed breakers on road surfaces from images. We developed a CNN model trained on a comprehensive dataset of road images containing various pothole and speed breaker types, lighting conditions, and road backgrounds. The model achieved an accuracy of 93% in detecting these road defects, demonstrating the effectiveness of deep learning for automated roadway inspections. This system has the potential to significantly improve the efficiency and objectivity of road inspections, leading to faster repairs and improved road safety

A physical tire model for real-time simulations

We present a novel tire brush model with carcass flexibility, primarily designed for real-time applications while preserving the physical significance of its parameters. The tire’s geometry is discretized and represented using a series of ribs, each intersecting the local road surface independently, on the other hand carcass in-plane fore-aft displacement, in-plane deflection, and out-of-plane sidewall torsion are approximated through a second-order polynomial. Forces and torques originating from the tire–road contact area are described using brush mechanics, where bristles’ kinematics is directly linked to carcass deformation. Extensive effort is dedicated to understanding the possible sources of instability, as well as describing and implementing a robust numerical scheme to effectively solve the nonlinear system of equations arising from the modeling. Timing performance results are provided to demonstrate the suitability of the presented model for demanding hard real-time simulations. Finally, validation of the presented tire model is carried out by fitting experimental data and comparing it with the state-of-the-art Magic Formula model, proving its reliability and accuracy in reproducing tire behavior.

Driving adaptability of highway steel-concrete composite beam bridge with multiple damages: theory, technology and practice

ABSTRACT To enhance the post-disaster highway driving speed evaluation system for steel-concrete composite beam bridge (SCCBB), a human-vehicle-bridge coupled driving adaptability analysis method is proposed. This method integrates considerations of both driving safety and comfort. Numerical simulation is employed to utilize a damaged SCCBB as the engineering context for studying vehicle rollover safety, vibration acceleration limits, and driving comfort. The analysis considers various vehicle types, including sedan cars, mini vans, motor buses, and trucks, resulting in recommended speed limits for each category. The study reveals that trucks demonstrate lower susceptibility to rollover accidents compared to sedan cars, mini vans, and motor buses under constant damage grade and vehicle speed conditions. However, increased damage grade and road surface roughness elevate rollover risks and diminish driver comfort. To ensure smooth post-disaster traffic flow on SCCBB and maintain acceptable driving comfort and speed levels, it is recommended to enforce speed limits: below 60 km/h for sedan cars, below 40 km/h for mini vans and motor buses, and below 80 km/h for trucks. The primary aim of this study is to provide technical guidance for the safe operation and maintenance of post-disaster SCCBB.

Contact-point response reconstruction for indirect bridge monitoring via Bayesian expectation-maximization based augmented Kalman filter

The drive-by bridge dynamic measurement using an instrumented vehicle during its passage over the bridge can be an indirect bridge structural health monitoring system. The identification of the contact-point (CP) responses between vehicle wheels and bridge structure from vehicle responses offers an efficient and economical way for the modal identification and condition assessment of short- to mid-span road bridges. This paper proposes a novel method for the contact-point displacement response reconstruction of the vehicle-bridge interaction (VBI) system in a joint input-state estimation manner based on Bayesian Expectation-Maximization (BEM). An analytical model of VBI including the road approach in front of a simply-supported bridge is presented firstly. Then the augmented state-space model of the vehicle with the contact-point responses included as variables along with vehicle states is established. A new method by integrating the augmented Kalman filter (AKF) with a BEM strategy is proposed to solve the state estimation problem without knowing the vehicle axle responses. The vehicle states and CP displacement responses are identified simultaneously. The effects of the measurement noise, road surface roughness and vehicle speed on the identified results are investigated using numerical simulations. The experimental tests are used to further verify the feasibility and accuracy of the proposed method. The results show that the proposed method has potential for drive-by bridge condition assessment using on-board vehicle response measurements for a large population of short- to mid-span bridges.

Analysis of the properties of chemical content and the use of local material Kinang Jingkion as coarse aggregate against the characteristics of WC last (AC) mixture in Yalimo Regency

This study aims to analyze the nature of chemical content, analyze abrasion value and optimum asphalt content using local Kinang Jingkion as road pavement material. In this study, XRF and XRD testing methods were used to determine the chemical content of Kinang Jingkion local material, then testing the characteristics of rough aggregate to determine the abrasion value, testing fine aggregate characteristics, and testing asphalt pen 60/70 and marshall test to determine the optimum asphalt content (KAO). The tests obtained the results of comparative data analysis between laboratory tests and literature standards that meet the eligibility requirements. The results showed that: First, the results of XRF and XRD analysis on Wilihingke samples, 0.15 mm granules, 2.36 mm granules, and Kinang Holuwon found that all chemical elements contained in the four samples were solid metal inorganic chemical elements. The results of this analysis prove that the four samples, which are fine aggregates and coarse aggregates, do not contain organic substances. Where the results of this analysis are by the requirements of aggregates which are one of the components in the concrete mixture, where the aggregates used must meet certain conditions, one of which must not contain excess organic matter because it can undergo decay by bacteria using dissolved oxygen; Second, from the test results obtained, the aggregate wear value (abrasion) was 32.69%; Third, based on the test results, the value of the Marshall parameter with Optimum Asphalt Content (KAO) obtained a value of 6.125%.

Data-driven Koopman model predictive control for hybrid energy storage system of electric vehicles under vehicle-following scenarios

In the current studies on energy management strategy (EMS) for vehicle-following scenarios, the accuracy of vehicle state predictions based on mechanistic models is influenced by the time-varying conditions, affecting the optimization control performance. To address this issue, a data-driven Koopman model predictive control for hybrid energy storage system (HESS) of electric vehicles (EVs) in vehicle-following scenarios is proposed, combining the safety speed planning and energy management strategy. Firstly, a data-driven Koopman vehicle state prediction model is constructed in the upper layer for estimating parameters, such as road surface smoothness and slope. This model is then integrated into Model Predictive Control (MPC) to optimize the speed of the following vehicle. Subsequently, in the lower layer, utilizing the output from the upper layer and predicting load power, the load power is further allocated within the HESS. Simulation results demonstrate that in scenarios considering factors like slope, the hierarchical MPC with the Koopman model reduces energy consumption by 5.55% compared to the hierarchical MPC with mechanistic model.

Influence of Vertical Load, Inflation Pressure, and Driving Speed on the Emission of Tire–Road Particulate Matter and Its Size Distribution

As fleet electrification progresses, vehicles are continuously becoming heavier, while the used electric motors, with their high torques, enable longitudinal dynamics to be maintained or even increased. This raises the question of what effect electric vehicles have on the emission of tire–road particulate matter (PM). To answer this question, investigations were carried out in this study on a tire internal drum test bench with real road surfaces. In addition to the vertical load, the tire inflation pressure and the driving speed were varied. PM emissions were recorded in real time, resulting in emission factors (emission per kilometer driven) for different load conditions. This allows statements to be made about both the effect on the total emission and on the particle size distribution. It was shown that the PM emission increases linearly with the vertical load at constant longitudinal dynamics. If the tire inflation pressure is increased, the emission also increases linearly, and the increases in emission are equally large for both influences. A clear influence of the driving speed on the emission factor could not be determined. With regard to the particle size distribution, the following correlations were found: higher vertical load and higher tire inflation pressure result in a larger mean particle diameter, while a higher driving speed reduces it. Thus, this study contributes to a better understanding of the expected changes in tire-road PM emissions as a result of electrification.

Weather event characterization: a catalyst for improved spatial mapping and benefit quantification in winter road maintenance

Monitoring winter road surface conditions (RSC) is essential for optimizing winter road maintenance operations and ensuring public traffic safety and mobility. However, many existing RSC estimation methods focus primarily on local or site-specific conditions, thus lacking effectiveness in comprehensive spatial mapping under varying winter weather events. Additionally, the potential financial benefits these methods could provide to maintenance authorities remain largely unexplored. This study addresses these gaps by introducing a refined methodology that harnesses the K-Means algorithm to characterize various weather events – a significant stride towards improving the generalizability and accuracy of road surface temperature (RST) estimations. The study also incorporates regression kriging (RK), a renowned geostatistical technique, as an integral component of a system for constructing an extensive spatial map of RSC across large highway networks. This strategy maximizes the use of data from existing road weather information systems (RWIS) to bridge their large spatial gaps. This study further quantifies the potential savings derived from optimally placing RWIS stations and reducing traffic collisions. The efficacy of these methods is validated through a real-world case study on two major interstate highways in Iowa, US, with RST estimation discrepancies as low as 0.619 °C. The findings also indicate potential cost savings by conserving up to 10 RWIS stations, which can be further translated into monetary savings of RWIS capital costs, traffic collisions prevention, enhanced traffic mobility and savings in maintenance material usage.

Cooperative truck platooning trial on Canadian public highway under commercial operation in winter driving conditions

Cooperative truck platooning, a convoy of trucks driving together while communicating and coordinating with each other, represents a technology-driven approach to improve energy conversion efficiency, lower greenhouse gas emissions, and enhance road safety. Despite numerous studies have explored these potentials, there is a scarcity of empirical investigations into on-road cooperative truck platooning during commercial operations, particularly in winter driving conditions. This paper presents the findings of an experimental study on the first commercially focused truck platooning implementation on a Canadian public highway in the winter season, using two SAE level 2 class 8 trucks. The on-road trials took place on the Queen Elizabeth II Highway, between Calgary and Edmonton, with ambient temperatures ranging from −27°C to 12°C, and truck weights spanning 16–39 tons. Nine well-trained and experienced drivers conducted 41 incident-free (platooning and baseline) test trips, covering a distance of 22,855 km. The experimental results confirmed the feasibility of operating commercial truck platooning with 3–5 s time gaps on public roads during the Canadian winter season including various road surface conditions. The results also show that the platooning engagement ratio reached up to 88.9%, with an average of 61.6% across 25 platooning trips. Furthermore, the follower truck achieved a 1.6% fuel savings on flat road sections during platooning, but its freight transportation specific fuel consumption was higher than that of the lead truck on hilly terrain. Test results indicate the lighter truck exhibited higher specific nitrogen oxides (NOx) emissions. Moreover, the frequent engagement and disengagement of the cooperative truck platooning system had adverse effects on the powertrain system of the truck, leading to increased fuel consumption and engine-out NOx emissions. This study provides real-world data to identify limitations and needed areas for improvement in adapting cooperative truck platooning technology to commercial operations on public roads.

A driver perspective-based investigation for lane line visibility of pavements on drivers’ lane utilization behaviors

There have been significant advances in vehicle safety and road construction in all over the world and driver misbehaviours are still shown as one of the most important reasons of traffic accidents. When the current traffic accidents are examined, the negative effect of lane line quality and level of visibility related on the accidents comes to the forefront in underdeveloped and developing countries. In this study, the effect of lane line visibility level on drivers' lane utilization was investigated in detail. For this aim, a total of 17 different points in Türkiye were selected and the lateral positions of the vehicles on the road surface at these points were determined with the help of the screen scaling technique. In order to determine the level of lane line quality for an expert group and driver perspective, a face-to-face survey was conducted. From the analysis results, the lane line quality was determined low in urban and intercity roads, and the average maximum evaluation score was determined as 4.7 and the lowest score was determined as 1.42 where the best line quality was 5. This result showed that the level of lane line quality had significant differences at different points. Although, it was observed that the lateral position distributions in the examined points fit the normal distribution, the distribution of both the right/left wheels and their gravity center skewed to the right or left and deviated from the ideal conditions on road surfaces. It showed that the vehicles do not center the lanes exactly, but usually move close to the right or left edge of the lane. This situation also clearly revealed that the lane utilization disciplines of drivers are quite weak with the effect of the low lane line quality on road surfaces.