Pavement Surface Condition Research Articles

Predicting pavement surface conditions through artificial neural networks

In this study, six distinct types of pavement distress were evaluated using the pavement condition index (PCI) rating: ravelling, rutting, cracking (including alligator, transverse and longitudinal cracks), shoving, patching and potholes. The severity levels of these distress types are utilised for classification, and these levels are subsequently converted into PCI values. The proposed methodology incorporates a thorough analysis, considering various variables such as average daily traffic, land-use classification, number of lanes, road width and length, pavement age, road alignment, vehicle type and weather conditions in Berzhite, Tirana, Albania. To predict PCI values, an artificial neural network model was employed due to its versatility in handling diverse inputs. Key performance metrics, including R2, mean squared error and mean absolute error, are used for model assessment. Notably, model 2, featuring two hidden layers, exhibits superior performance over model 1. However, due to size constraints in testing and validation datasets in this study, the accuracy of the model is somewhat limited. Future directions for research involve expanding the dataset to enhance model accuracy and incorporating advanced features to capture a more complex comprehension of pavement conditions.

Explainable highway performance degradation prediction model based on LSTM

With the dense and huge highway network in China, the highway maintenance management system has become the most concerned issue for Chinese highway managers in recent years. Therefore, based on the highway network in Guizhou Province of China, this paper established the semi-rigid asphalt pavement performance multi-output Long Short-Term Memory (LSTM) prediction model with regional applicability, including Pavement Surface Condition Index (PCI), Pavement Rutting Depth Index (RDI), Pavement Riding Quality Index (RQI) and Pavement Skidding Resistance Index (SRI) and the hyperparameters of the model were optimized by Bayesian optimization algorithm and the casual analysis of the model was conducted based on SHapley Additive exPlanations (SHAP), providing a reference for China's highway network-level preventive maintenance decision-making. The results prove that the established model can effectively predict the performance of the following year through the data of the previous two years. The established model has a better comprehensive prediction effect in comparison to similar multi-output models. The average coefficient of determination (R2) of the four prediction indexes can reach 0.823. Besides, the prediction effect among the indexes is stable and multiple pavement performance indexes can be effectively and reliably predicted at the same time. In addition, the contribution of input feature variables to output is quantified based on SHAP. According to quantified the contribution, the association relationship of the neural network model is set-valued mapped to the causality relationship in the post hoc. The input feature variables with the main contribution are extracted for causal analysis. The causal analysis shows that the degradation of the four pavement performances all have obvious time memory characteristics and have significant differences, but their pavement performance degradation is the result of nonlinear changes caused by the coupling effects of traffic load, road age, climate, and pavement structure.

Theoretical Considerations from the Modelling of the Interaction between Road Design and Fuel Consumption on Urban and Suburban Roadways

A roadway path is most commonly perceived as a 3-D element structure placed within its surrounding environment either within or outside urban areas. Design guidelines are usually strictly followed to ensure safe and comfort transportation of people and goods, but in full alignment with the terrain configuration and the available space, especially in urban and suburban areas. In the meantime, vehicles travelling along a roadway consume fuel and emit pollutants in a way that depends on both the driving attitude as well as the peculiar characteristics of road design and/or pavement surface condition. This study focuses on the environmental behavior of roadways in terms of fuel consumption, especially of heavy vehicles that mainly serve the purpose of freight transportation within urban areas. The impact of horizontal and vertical profiles of a roadway structure is theoretically considered through the parameters of speed and longitudinal slope, respectively. Based on theoretical calculations with an already developed model, it was found that the slope plays the most critical role, controlling the rate of fuel consumption increase, as an increase ratio of 2.5 was observed for a slope increase from 2% to 7%. The variation was less intense for a speed ranging from 25 to 45 km/h. The investigation additionally revealed useful discussion points for the need to consider the environmental impact of roadways during the operation phase for a more sustainable management of freight transportation procedures, thereby stimulating an ad hoc development of fuel consumption models based on actual measurements so that local conditions can be properly accounted for and used by road engineers and/or urban planners.

Characteristics of Skid Resistance for Pavements with Black Ice

Korea experiences a four-season climate, leading to significant fluctuations in seasonal road surface conditions and contributing to numerous traffic accidents. Over the period of five years since 2016, approximately 4,800 traffic accidents have been attributed to icy and frosty road conditions, with a fatality rate 1.5 times higher than that of general traffic accidents. This study aims to analyze the skid resistance properties of domestic asphalt pavements as an initial step toward establishing indicators to mitigate and manage traffic accidents caused by black ice. In this study, the BPN was measured using BPT equipment specified in the ASTM and KS standards. The road pavement surface conditions were assessed under three categories: dry, wet, and frozen (black ice). The recorded BPN values decreased in the following order: dry, wet, and frozen conditions. Under wet conditions, a comparative analysis was performed between dense asphalt pavements and drainable pavements. Furthermore, the evaluation under frozen conditions revealed no significant disparity between the types of road pavement. Notably, the exposure of the road pavement aggregate owing to freezing and the resultant surface roughness had a minimal impact on the skid resistance.

Design and Construction Aspects of Concrete Block Paved Vertical Traffic-Calming Devices Located in Home Zone Areas

In recent years, speed reduction measures have been increasingly used, especially in heavily urbanised areas. If local conditions allow, traffic-calming schemes are implemented, which include a variety of traffic-calming measures (TCM). Some of the most common traffic-calming measures are concrete block paved vertical traffic-calming devices (VTCDs), including speed tables, speed humps, speed bumps and raised pedestrian crossings. Different design and construction recommendations apply to the respective VTCD types. The aim of this article is to examine the effectiveness of VTCDs in speed reduction and the impact of horizontal forces on pavement conditions, both within and beyond VTCDs, after over a dozen years of exposure to traffic. For this purpose, speed surveys were carried out on selected two-way streets running through home zones in Poland. The pavements on selected VTCDs were identified and visually assessed for damage, and subsidence areas were estimated using the terrestrial laser scanning (TSL) technique. The analysis resulted in the development of the Deviation Model 3D (DM3D), showing local deviations from the theoretical surface, obtained by superimposing the two models, the Real Surface Digital Terrain Model (RS DTM) and the Theoretical Surface Digital Terrain Model (TS DTM). A comparative analysis of the pavement surface condition and the magnitudes of horizontal forces allowed us to identify the locations of critical spots in VTCDs. The results were used as the basis for developing recommended pavement structures and deriving engineering recommendations for concrete block paving in VTCDs.

Estimating impact of pavement surface condition and geometrics design on two-wheeler run-off road crashes on horizontal curves

Introduction-Motorized Two-Wheeler (MTW) is a convenient and affordable mode of transportation despite being highly vulnerable to rollover crashes. The possible fact may be that MTW is a single-track vehicle and thus lack longitudinal stability, particularly on horizontal curves (HC). This stability depends on external factors such as curve geometrics and contact surface properties. Therefore, this research aims to identify the impact of pavement surface conditions and roadway geometrics on MTW run-off road crashes on HC. Method-To consider the temporal effect of pavement surface conditions, this study analyzed different combinations of light and surface moisture conditions for pre- and post-pavement surfacing conditions. Thus, Generalized Estimating Equation (GEE) linked with the log function was used to account for repeated observations. Sample data were collected bi-annually from 76 HC for two years of pre- and post-resurfacing. Pavement surface condition parameter includes skid resistance, rut depth, and international roughness index (IRI). However, to restrain the impact of pavement surface conditions, a separate analysis was carried out to investigate the impact of geometrical design attributes. Results-The analysis concluded that surface moisture condition, light condition, rut depth, and their interactions with AADT were found significant for pre-resurfacing. For post-resurfacing, surface moisture condition, and light condition, IRI and its interaction with AADT were found significant. Similarly, roadway geometrics such as curve radius and their interaction with the presence of sight distance, public activity centre and cross-slope were found significant. Conclusion-Overall, the study insights and confirms how and to what extent pavement surface properties and geometric attributes impact the subject crashes. The results will enhance the understanding of roadway architecture and may help highway designers to implement, either at the intervention or treatment level, to improve safety and comfort.

Asphalt Pavement Damage Detection through Deep Learning Technique and Cost-Effective Equipment: A Case Study in Urban Roads Crossed by Tramway Lines

Asphalt pavements are subject to regular inspection and maintenance activities over time. Many techniques have been suggested to evaluate pavement surface conditions, but most of these are either labour-intensive tasks or require costly instruments. This article describes a robust intelligent pavement distress inspection system that uses cost-effective equipment and the ‘you only look once’ detection algorithm (YOLOv3). A dataset for flexible pavement distress detection with around 13,135 images and 30,989 bounding boxes of damage was used during the neural network training, calibration, and validation phases. During the testing phase, the model achieved a mean average precision of up to 80%, depending on the type of pavement distress. The performance metrics (loss, precision, recall, and RMSE) that were applied to estimate the object detection accuracy demonstrate that the technique can distinguish between different types of asphalt pavement damage with remarkable accuracy and precision. Moreover, the confusion matrix obtained in the validation process shows a distress classification sensitivity of up to 98.7%. The suggested technique was successfully implemented in an inspection car. Measurements conducted on urban roads crossed by tramway lines in the city of Palermo proved the real-time ability and great efficacy of the detection system, with potentially remarkable advances in asphalt pavement examination efficacy due to the high rates of correct distress detection.

Identification and Ranking of Road Safety Hazardous Locations: Case Study of Kotre–Aabhukhaireni Section of Prithvi Highway

In Nepal, due to rapid urbanization, there are many emerging towns and settlements near highways, which have raised issues of increased traffic density resulting in frequent road crashes like in Kotre- Aabhukhaireni section of Prithvi Highway. Higher crash rates depict lower safety conditions. To improve safety conditions and reduce the loss of life and property, hazardous conditions along the highways need to be identified and ranked for treatment purpose. This study is based on a methodological framework for identifying hazardous locations based on a Field Survey (condition rating) and Analytical Hierarchy Process (AHP) for ranking road safety hazardous locations of Kotre- Aabhukhaireni road Section of Prithvi Highway by weighing the safety parameters of the road section and calculating the Safety Hazardous Index (SHI). Field condition rating with a focus on pavement surface conditions, structural conditions, shoulder and drainage conditions, pedestrian infrastructure and vehicle restraint system, street lighting, traffic signs, island and road marking conditions was conducted, and risk factors were identified by comparing these variables against norms.
 The study identified 15 different locations as safety hazardous locations. The results showed Yampa, Baradi, and Chirrkeni as the most hazardous ones with an SHI index of 0.852, 0.773 and 0.712 respectively, among the study section considered. In contrast, Dhaaptar was considered as the least hazardous one with SHI value of 0.210.

A review into studies related to the effect of the pavement surface condition on traffic safety: A scientometric analysis

Traffic crashes are one of the main reasons for the death of many people and the loss of property Therefore, it is important to conduct research and studies to reduce the risk of accidents and identify the causes that lead to their occurrence. The condition of the pavement is one of the main factors that lead to accidents, as several studies have been presented that show the impact of pavement defects such as rutting, roughness, and skid resistance on traffic safety. This research was conducted to find out and compile the most important research papers using the web of science (WOS) and then analyzing the data using the VOSviewer program, and to know the most countries and journals published on the related pavement condition of road crashes, as well as to know the authors and their cooperation. In addition to knowing the keywords that help researchers research this topic. In order to benefit from these studies in knowing the causes of accidents, analyzing and treating them, and improving the performance of roads using modern analysis and maintenance methods that ensure traffic safety.

Nondestructive Detection and Early Warning of Pavement Surface Icing Based on Meteorological Information.

Monitoring and warning of ice on pavement surfaces are effective means to improve traffic safety in winter. In this study, a high-precision piezoelectric sensor was developed to monitor pavement surface conditions. The effects of the pavement surface temperature, water depth, and wind speed on pavement icing time were investigated. Then, on the basis of these effects, an early warning model of pavement icing was proposed using an artificial neural network. The results showed that the sensor could detect ice or water on the pavement surface. The measurement accuracy and reliability of the sensor were verified under long-term vehicle load, temperature load, and harsh natural environment using test data. Moreover, pavement temperature, water depth, and wind speed had a significant nonlinear effect on the pavement icing time. The effect of the pavement surface temperature on icing conditions was maximal, followed by the effect of the water depth. The effect of the wind speed was moderate. The model with a learning rate of 0.7 and five hidden units had the best prediction effect on pavement icing. The prediction accuracy of the early warning model exceeded 90%, permitting nondestructive and rapid detection of pavement icing based on meteorological information.

Research on distresses detection, evaluation and maintenance decision-making for highway pavement in reconstruction and expansion project

Artificial walking survey, ground penetrating radar (GPR) and falling weight deflectometer (FWD) were used to test the actual condition of the highway pavement in reconstruction and expansion project for better maintenance decision-making. Therefore, a decision-making system with the application of four evaluation indexes including deflection value (DV), pavement surface condition within 20 m(PCI20), maintenance effect (ME) and internal crack rate (ICR) was constructed. Through the pavement empirical-mechanical calculation and the Delphi method analysis, the corresponding threshold for the DV, PCI20 and ICR were 27 (0.01 mm), 80, and 20 m/100 m2 respectively. Then, the decision-making system was applied for the 5.5 km section with the detection and evaluation results, and the advised maintenance plans of different lanes were determined. Lastly, the effect of the decision-making system was analyzed. Results show that the decision-making system can scientifically decide the treatment plans and save the maintenance cost by about 30.1 %. The deflection near the grouting holes significantly decreases resulting from the high-density cement slurry distribution. The milling and repaving have a high structural reinforcement ability of reducing deflection up to more than 66 %, which is positively related to the milling depth.

Evaluation of the Pavement Distress and its Impact on the Sustainability of the Traffic Operation for Selected Roads in Al-Diwaniyah City

Roads and transportation infrastructure are critical assets for supporting political stability as well as economic and sustainable expansion in developing countries. Yet, pavement maintenance backlogs and the high capital expenditures of road rehabilitation need the adoption of pavement evaluation methods to ensure the best value for the investment. road maintenance is considered one of the main factors for preserving the road structurally and functionally, and before carrying out maintenance, pavement defects must be identified by evaluating the condition of the pavement to perform the appropriate maintenance. In this study, the Pavement Condition Index (PCI) method was relied upon to evaluate roads in Al-Diwaniyah City. Where several data were collected for the study, including the number of crashes for each region, the number of lanes, street width, the presence of a median, and the pavement condition evaluated for the regions with the highest crash rate, which are Al-Askari District with 707 crashes, Al-Urouba District with 379 crashes, Al-Wahda District with 271 crashes, Al-Jamhouri District with 235 crashes, and Al-Jazaer District with 212 crashes. This study is considered the first study to evaluate roads in Al-Diwaniyah city. The results of the study showed that the condition of the roads ranged from poor to satisfactory and there is no road reach to serious and failed. The results showed that the worst road is Al-Askari District, which lacks a lot of maintenance, at a rate of PCI 52.1. It is followed by Al-Wahda District with a PCI rate of 57.6, Al-Jamhouri District with a PCI rate of 67.2, Al-Urouba District with a PCI rate of 70.7, and Al-Jazaer District with a PCI rate of 73.7. The deterioration of the pavement surface condition affects the rash rate, and it is more dangerous when it is correlated to other road factors. Additionally, it was noted that the most prominent defects in the pavement surface of the five evaluated roads are: potholes, rutting, longitudinal and transverse cracks, corrugation, patching, raveling, depressions, and alligator cracks. The study recommended conducting more research on evaluating more roads in Al-Diwaniyah City on an annual basis and conducting periodic maintenance in order to improve the status of this city, which suffers from neglect in all respects, measures must be taken to enhance the environment and economy, sustain the streets, reduce maintenance costs, ensure traffic safety, and protect the lives of citizens.

CARACTERIZAÇÃO E DIAGNÓSTICO DAS PATOLOGIAS EM UM TRECHO DA RODOVIA BR-010 BELÉM-BRASÍLIA: subtrecho entre os municípios de Imperatriz (MA) e Governador Edson Lobão (MA)

This article assessed the main problems encountered on the BR-010 highway, between Imperatriz and Gov. Edson Lobão cities, Maranhão state. The section was subdivided into ten parts with similar characteristics for flexible pavement conservation evaluation, according road agency technical criteria. The criteria set out in DNIT 005/2003 - TER were applied for pavement investigation and defects evaluation; and, a continuous visual survey for problems types quantification was applied, provided by DNIT 008/2003 - PRO, which resulted in three qualitative indices for the pavement running condition, surface condition and degradation degree. These methods provided a road section overview, which can be extrapolated to other sections given similar maintenance conditions, indicating recovery and conservation planning problems for great importance road in national transport, ensuring economy and comfort to users of the highway.

Proposed severity ranking for ASTM E3303-21 protocol to quantify asphalt pavement cracking from automated 3D surveys

Cracking is one of the essential indicators to evaluate pavement surface conditions; however, it is challenging to rate pavement cracking automatically from 3D digital images. Recently, a two-level automated crack rating system was proposed for pavement management in Singapore where Level 1 provides detailed crack information including cracking extent, types, and severity. Level 2 is a macro-indicator ranging from 0 to 5 based on crack extent over a 10-m length pavement section, with 0 being excellent condition and 5 being very bad condition. On the other hand, the new ASTM E3303-21 standard has introduced the Pavement Surface Cracking Metric which is a dimensionless measure equivalent to crack density and the Pavement Surface Cracking Index which provides ratings of pavement cracking ranging from 0 to 100, with 0 being the worst possible condition and 100 being the best possible condition. This study was conducted to compare and potentially bridge the gaps between the two mentioned cracking rating methods. Cracking data were collected from the Singapore road network using the Laser Crack Measurement System-2 (LCMS-2). Based on the study findings, three severity ranks (low, medium, and high) were proposed to facilitate the inclusion of the ASTM E3303-21 cracking protocol into Pavement Condition Index calculations.

Deep learning framework for intelligent pavement condition rating: A direct classification approach for regional and local roads

Transport authorities rely on pavement characteristics to determine a pavement condition rating index. However, manually computing ratings can be a tedious, subjective, time-consuming, and training-intensive process. This paper presents a deep-learning framework for automatically rating the condition of rural road pavements using digital images captured from a dashboard-mounted camera. The framework includes pavement segmentation, data cleaning, image cropping and resizing, and pavement condition rating classification. A dataset of images, captured from diverse roads in Ireland and rated by two expert raters using the pavement surface condition index (PSCI) scale, was created. Deep-learning models were developed to perform pavement segmentation and condition rating classification. The automated PSCI rating achieved an average Cohen Kappa score and F1-score of 0.9 and 0.85, respectively, across 1–10 rating classes on an independent test set. The incorporation of unique image augmentation during training enabled the models to exhibit increased robustness against variations in background and clutter.

Using supervised machine learning algorithms in pavement degradation monitoring

• Dynamic pavement monitoring system using vibration-based method. • Noise cancelling and vibration data smoothing. • Degradation detection and classification using machine learning. Pavement monitoring plays a vital role in maintaining sustainable road network conditions and provides road users with satisfactory comfort riding. Regular and routine monitoring provide clear information on road conditions and the level of damage to pavement surfaces. In this study, a vibration-based method was used as a monitoring technique to evaluate the pavement surface conditions on local roads. Pre-processing techniques were used to cancel noise and prepare the data for feature extraction and prediction. Two multi-classification Machine Learning (ML) models were used, including Random Forest (RF) and Decision Tree (DT), for the automated classification and detection of different types of pavement distresses. In addition, a Support Vector Machine (SVM) technique was used to develop a binary ML model for the same classification and detection purposes. The results showed that the developed ML models provide high accuracy in predicting the road degradation classification with about 93% accuracy using the RF and 90% accuracy using the DT. Using the SVM model, the overall average accuracy of detection and classification of pavement defects was about 96%.

Development of Pavement Maintenance Management System for Baghdad Urban Roadway Network

The road transportation system is considered as major component of the infrastructure in any country, it affects the developments in economy and social activities. The Asphalt Concrete which is considered as the major pavement material for the road transportation system in Baghdad is subjected to continuous deterioration with time due to traffic loading and environmental conditions, it was felt that implementing a comprehensive pavement maintenance management system (PMMS), which should be capable for preserving the functional and structural conditions of pavement layers, is essential. This work presents the development of PMMS with Visual inspection technique for evaluating the Asphalt Concrete pavement surface condition; common types of Asphalt Concrete distress including (bleeding of Asphalt, patching, block cracking, edge cracking, longitudinal and transverse cracking, rutting, pot holes, longitudinal and transverse deformation) with their various severity and intensity conditions have been included in the system as data base. The surface of the pavement was divided into sections, and the pavement condition is visually evaluated by the raters using specially designed forms, each type of defect was measured, classified, and rated according to type, severity, and extent. Data will be fed to the system using the computer, various types of intensity and severity of distress were analyzed by the system, the present condition rating (PCR) of the pavement section is determined, and the system suggests the required maintenance action. The developed system which is assigned (PMMS-09) was verified in evaluating the pavement surface condition at AL-Jaderiah campus roadway network. The results indicated that the system is sound in evaluation of the pavement condition and in suggestion of the proper maintenance to reserve the pavement condition.

Effect of Pavement Roughness on Arterial Noise Using Different Vehicle Types

For a healthy and sustainable environment, it is necessary to reduce the negative impact of noise on neighborhoods adjacent to arterials. The development of the automotive industry, the introduction of hybrid and electric vehicles, and improvements to vehicle bodies have made tire–pavement interaction the primary source of noise at regular traffic speeds. The objective of this study was to investigate the effect of pavement surface condition on noise levels for different vehicles, including gasoline, hybrid, electric vehicles, and a single-unit truck. Both interior and exterior noises were measured along rural and urban arterials. The exterior noise was measured using the controlled pass-by method. The results of the analysis indicated that pavement roughness and traffic speed had significantly influenced the interior noise level. It was found that the relationship between interior noise and vehicle speed, and pavement roughness is linear. Compared to gasoline car, hybrid and electric cars may reduce interior noise by 7 and 11 dB(A), respectively. For rural arterials and speeds of 80 km/h, exterior noise was strongly affected by pavement roughness. Analysis of exterior noise on urban arterials at 50 and 60 km/h speeds showed that pavement roughness has a significant impact on the noise level. In contrast, the noise analysis under two-speed levels, which are too close to each other, did not help in capturing the impact of speed on exterior noise. Therefore, further studies are recommended to highlight this issue. For a sustainable and healthy environment, it is vital to keep the pavement in good condition and increase the proportion of electric and hybrid cars in the vehicle fleet.

Data-Driven Web-Based Patching Management Tool Using Multi-Sensor Pavement Structure Measurements

Automating pavement maintenance suggestions systems is challenging, especially for actionable recommendations such as patching location, depth, and priority. It is a common practice among state agencies to manually inspect road segments of interest and decide maintenance requirements based on the pavement condition index (PCI). However, standalone PCI only evaluates the pavement surface condition which, coupled with the variability in human perception of pavement distress, limits the accuracy and quality of current pavement maintenance practices. In this case, there is a need for multi-sensor data integrated with standardized pavement distress condition ratings. This study explores estimating the appropriate pavement patching strategy (i.e., patching location, depth, and quantity) by integrating pavement structural and surface condition assessment with pavement ratings of distress. In particular, it combines pavement structural condition parameters and falling weight deflectometer deflections with surface condition parameters, international roughness index, and cracking density, for a better representation of overall pavement distress conditions. Then, a pavement-specific, threshold-based patching suggestion algorithm is designed to suggest pavement maintenance operations. The thresholds were determined based on a reliability concept and were verified with the structural number ratio. The threshold values were then used in the patching suggestion algorithm to create patching suggestion tables. A web-based Patching Management Tool (PMT) was designed as an interactive tool to visualize these patching suggestion maps and analyze the pavement distress data using geographical maps and graphs. The PMT was validated with road surface and right-of-way images obtained from three-dimensional laser sensors, and it could successfully capture localized distresses in existing pavements.

ЕФЕКТИВНІСТЬ ЗАСТОСУВАННЯ СИСТЕМ WIM НА АВТОМОБІЛЬНИХ ДОРОГАХ

Introduction. The introduction to the paper highlights the general role of the weight control of motor vehicles on roads by WiM – methods (Wegh-in-Motion) in the assurance of the traffic safety and in the transportation infrastructure preservation. Problem statement. The issues of the paper concern the problems of implementation of contemporary technologies of the High-Speed WiM (HS-WiM) where the conditions of the measurements of the loads from the vehicle generate high measurement error. Purpose. The main goal of the paper is to perform an analytical review of methods of the weight control of vehicles and of competitive aspects of existing WiM technologies and also the analysis of the practice of WiM application. Results. The analytical reviewing led to the following results: it was revealed that the basic principle of WiM systems operation consists in determining the dynamic wheel/axle loading of a vehicle transmitted to the pavement; data thus obtained shall be used to reveal the overloading. Modern WiM systems give possibilities to obtain data concerning axle number, axle spaces, vehicle length and speed, axle (or wheel) dynamic loading, number of equivalents of standard axle loading, and also data regarding the traffic flow characteristics; it was concluded that WiM systems can be divided into two main types: the systems operating at low speed of a vehicle (up to 15 km/h, LS WiM) and those operating at steady speed of the traffic flow (up to 130 km/h, or high speed, HS WiM). LS WiM ensure the accuracy of the measurement in a range from 3 % to 5 % and belong to the methods of weight control of motor vehicles approved by the legal metrology as well as the static weighing does. HS WiM control is more sensitive to the external factors due to the effect of a vehicle dynamic and thus cannot assure the above stated accuracy of measurements, moreover, cannot assure the appropriate accuracy for the satisfactory number of trucks. HS WiM systems are not approved by the legal metrology; factors affecting the accuracy of measurements in HS WiM systems were analyzed. It was revealed that the effect of a vehicle dynamic on the measurement error can be mitigated assuring the compliance of the road pavement surface condition with the standard requirements mainly with those concerning pavement surface roughness.