Federal Highway Administration Research Articles

Development of a Machine-Learning-Based Tool to Predict Retroreflectivity of Pavement Markings Across the U.S.

Retroreflectivity plays a crucial role in pavement markings as it enhances nighttime visibility for drivers. Yet, owing to budget constraints, many U.S. state agencies rarely monitor the retroreflectivity of their markings, and instead, restripe markings based on visual inspection or fixed schedule. To address this issue, Federal Highway Administration (FHWA) announced a new final rule, effective September 6, 2022, that establishes a new minimum standard for pavement marking retroreflectivity. This rule also requires state agencies to implement a method within four years for maintaining pavement marking retroreflectivity at or above minimum levels. The ultimate objective of this research was to help state agencies comply with this new rule by developing a new decision-making tool for predicting retroreflectivity of pavement markings for up to three years using only measured initial retroreflectivity (at installation date) and other readily available project data (marking color, marking type, etc.). As such, data from National Transportation Product Evaluation Program (NTPEP) were retrieved and analyzed using six robust machine learning algorithms, and the algorithm yielding highest accuracy was selected to be incorporated into the proposed software. Findings indicated that random forest demonstrated best performance among the six algorithms. As expected, prediction accuracy decreased over time. Within the first year, random forest models successfully predicted retroreflectivity with a coefficient of determination (R2) of 0.97 and root mean square error (RMSE) of ± 31.12 mcd/m2/lux. Within the second and third years, R2 and RMSE were 0.92/±43.7 mcd/m2/lux, and 0.91/±36.0 mcd/m2/lux, respectively, which are more accurate as compared with previous studies.

Macroscopic State-Level Analysis of Pavement Roughness Using Time–Space Econometric Modeling Methods

This paper used pavement condition data collected by the Federal Highway Administration (FHWA) between 2001 and 2006 aggregated by U.S. states to identify macroscopic factors affecting pavement roughness in time and space. To account for prior pavement conditions and preservation expenditure over time, time autocorrelation parameters were introduced in a spatial modeling scheme that accounted for spatial autocorrelation and heterogeneity. The proposed framework accommodates data aggregation in network-level pavement deterioration models. Because pavement roughness across different roadway classes is anticipated to be affected by different explanatory parameters, separate time–space models are estimated for nine roadway classes (rural interstate roads, rural collectors, urban minor arterials, urban principal arterials, and other freeways). The best model specifications revealed that different time–space models were appropriate for pavement performance modeling across the different roadway classes. Factors that were found to affect state-level pavement roughness in time and space included preservation expenditure, predominant soil type, and predominant climatic conditions. The results have the potential to assist governmental agencies in planning effectively for pavement preservation programs at a macroscopic level.

Design System of Solar-Powered Safety Lamp Integrated Proximity Sensor as a Solution for Accident Prevention at Blind Spot Areas

The curved contour of the road creates blind spots which increase the risk of traffic accidents. Throughout 2019, there were 107,500 accident cases recorded in Indonesia. Therefore, efforts are needed to provide road infrastructure that supports driving safety, such as traffic signs to reduce the risk of accidents in blind spot areas. This research aims to design a smart safety light warning system that is integrated with solar-based street lighting. The design uses a design-based research approach referring to several established standards including Indonesia National Standard (SNI) 7931: 2008, SNI IEC 60529:2014, Regulation of the Minister of Transportation of the Republic of Indonesia Number PM 13 of 2014, Manual on Uniform Traffic Control Devices (MUTCD) issued by the Federal Highway Administration (FHWA) of the United States Department of Transportation (USDOT), and Article 115 of Minister of Transportation Republic of Indonesia Regulation PM 27 of 2018. This study used Design-Based Research (DBR) as a research methodology. The result of this study is a prototype design and development of smart safety light warning system which can be optimized. Validation of the design and circuit is conducted using prototyping modeling 1:20 from the original dimensions to measure the function and performance of the circuit. The prototype is capable of turning on warning lights and siren notifications when detecting objects with a maximum distance of 350 cm as measured by the HC – SR04 sensor. Charging the stored solar panels via the accumulator allows the technology to continue working for 45 hours without charging. For power efficiency purposes, the technology is equipped with an LDR (Light Dependent Resistor) sensor with a minimum limit of 341-lux which regulates the lighting. The development of these safety lights will increase driver awareness when passing through blind spot areas and have positive implications for reducing the number of accidents in Indonesia.

Evaluation of transition to 100% electric vehicles (EVs) by 2052 in the United States

With the rising need to transition from fossil fuel consumption to renewables, the transportation industry is foreseeing large-scale adoption of electric vehicles (EVs). According to various studies, the petroleum resources will last until or around 2052. Utilizing the US Federal Highway Administration (FHWA) published data on the number of registered vehicles by each state, the profile of vehicles by 2052 was forecasted using a time series. The vehicle profile comprised vehicles by each type classified as automobiles, buses, trucks, and motorcycles. This future profile became the basis of the quantification of total EVs by the end of 2052. Based on the available fuel economy data published by the US Department of Energy, the average energy consumption per mile (kWh/mile) of each vehicle type was factored into the energy demand calculation for 100% electrification by 2052. A roof-top solar photovoltaic system is easy to install on unoccupied roof space for US house owners. Obtaining the capacity of such a roof-top solar PV system acts as a good decision-making criterion for both house owners and developers. However, for city-dwellers living in multi-family residential buildings, the potential of utilization of roof-top solar PV becomes a subject of future work when effective sharing of resources for local power generation by renewables and development of community-shared EV charging infrastructure is concerned. How well EVs performed against internal combustion engine (ICE) vehicles in terms of fuel efficiency and reduced carbon emissions is a driving factor for this transition. For energy demand calculations, a base model was framed, and results were obtained by utilizing mathematical and statistical tools. After accounting for the effects of improved fuel efficiency or reduced energy consumption by EVs over time, the model was modified to obtain revised energy demand and, thus, effective energy generation required. Accordingly, the capacity of EV charging infrastructure required by each state determined the need for preparation by utility companies and local jurisdictions. With limitations on battery size and thus increased frequency for drivers to return for charging, additional fast chargers (level 3) at existing gasoline stations become an option that requires further assessment.

Distributional equity impacts of automated vehicles: A disaggregated approach

Better understanding of the equity impacts of automated vehicles (AV) is needed to design equitable deployment plans for AV technology. The goal of this study was to develop and demonstrate a modeling framework to support distributional equity assessments of AV systems prior to their wide adoption. This modeling framework takes a disaggregated approach that integrates agent-based simulation, diverse transportation outcomes, and equity analyses. The framework uses individual-level data to capture detailed disparities in transportation outcomes, applies a state-of-the-art multi-agent traffic simulation model (MATSim) to simulate privately-owned and shared AVs simultaneously, and considers distributional equity from multiple perspectives. To test and demonstrate the framework, a case study was conducted for the Tampa Bay region. Five scenarios were considered with different AV market shares and integration strategies based on scenario planning by the U.S. Federal Highway Administration. Results reveal that high AV penetration rates were required for substantial reductions in inequality. The introduction of AVs led to a more even distribution of accessibility, but slightly more uneven distribution of traditional mobility and affordability. Impacts of disparities in outcomes for disadvantaged groups were mixed. These results suggest that AVs will likely perpetuate existing inequity in the transportation system as long as its fundamental structure remains the same as today. Results highlight the importance of planning and design strategies that directly address the distributional impacts to ensure that AV technology is deployed equitably.

Development of a Practical Procedure for Data-Driven Weigh-in-Motion Equipment Calibration Scheduling to Assure Data Accuracy and Consistency over Time

Weigh-in-motion (WIM) technology is a traffic monitoring technology that highway agencies use to obtain information about the weight, axle loading, and configuration of heavy vehicles moving at operational speed. To ensure high-quality WIM data, the Federal Highway Administration (FHWA) recommends regular calibration of WIM equipment. This study addresses the need to optimize the allocation of the limited resources that agencies have for WIM equipment calibration by developing a procedure for data-driven calibration scheduling. This was accomplished through an analysis of WIM measurement errors from test truck data collected during field performance validation and calibration events and an analysis of monthly changes in truck weight and axle loading characteristics, based on WIM data collected between calibration events. The analysis results were used to draw conclusions on the functional performance of different WIM sites. The study also demonstrates how the newly developed National Cooperative Highway Research Program (NCHRP) WIM Data Quality Assurance Analysis Tool can be used to compute truck weight and axle loading parameters and visualize data analysis results using four case studies: two WIM sites with piezo quartz sensors in asphalt pavements and two WIM sites with bending plate sensors in concrete pavements. This paper provides a practical procedure and recommendations that highway agencies can use to develop data-driven WIM calibration schedules that will ensure consistent high-quality WIM data for sites managed by an agency with the aid of the NCHRP WIM Data Quality Assurance Analysis Tool.

Numerical investigations of GRS wall performance with tiered configurations

Geosynthetic Reinforced Soil (GRS) walls have become increasingly popular as a result of their numerous advantages. In some cases these structures are constructed in a multi-tiered configuration, which makes their behavior more complicated. Nevertheless, the response of the multi-tiered walls is insufficiently explained by the existing design manuals and literature studies. This paper investigated the performance of GRS walls with multi-tiered configurations using two-dimensional (2D) finite element numerical models. This study compares the performance of two-tiered GRS walls with simple GRS walls (single-tiered). It also examines the impact of offset distance, backfill strength properties, and reinforcement parameters (vertical spacing and reinforcement length) on horizontal deformations and reinforcement tensile loads in two-tiered GRS walls. Adopting a multi-tiered configuration for GRS walls can significantly reduce both the horizontal wall deformation and the maximum reinforcement tensile loads. Additionally, the critical offset distance found in this study is significantly smaller than that recommended by the Federal Highway Administration (FHWA) guidelines. The finite element results also demonstrate that using high-quality backfill soil can minimize interaction between lower and upper tiers. Using a uniform reinforcement length of 0.6H for both tiers significantly reduces horizontal deformation compared to the FHWA recommendation of 0.6H and 0.35H for the lower and the upper tier respectively.

Retrospective Cost Saving Comparison Between TNM 2.5 and TNM 3.2 Barrier Analysis Results

In December of 2021, the Federal Highway Administration (FHWA) Traffic Noise Model (TNM) version 3.1 was released to the public for use on traffic noise projects and subsequently updated to version 3.2 in December 2023. However, most or all state Departments of Transportation (DOT) continue to accept and prefer the use of TNM 2.5 (released in 2004). A presentation at NoiseCon 2023 (TNM 2.5 vs TNM 3.1 Noise Level Prediction Comparison by Mukul Pal and Michael Parsons) came to the conclusion that, generally, TNM 2.5 tends to overpredict traffic noise levels in comparison to the TNM 3.1, resulting in additional impacts and subsequent additional mitigation that may not be necessary when analyzed with the updated algorithms. In this paper, the authors determine potential cost savings by migrating from TNM 2.5 to TNM 3.2 by comparing results from previously approved traffic noise projects. By determining the potential cost savings of a migration, DOTs may be better informed about whether or not they should continue to utilize TNM 2.5 or make the transition to TNM 3.2.

Applying reflections in the FHWA Traffic Noise Model

The latest version of the Federal Highway Administration (FHWA) Traffic Noise Model (TNM), version 3.2, was released early 2024 and is encouraged for use to conduct highway noise impact and noise abatement analyses. TNM 3.2 has the advantage of allowing single order reflections in addition to a parallel barrier/wall analysis. This paper presents a process to apply single order reflections considering both their sound contribution and their efficiency in modeling and calculations. The process was applied to a real highway project to evaluate sound level increases due to single reflections and to compare predicted sound levels with and without reflections to measured sound levels. Reflecting surfaces evaluated include noise walls on one side of a highway, building facades facing the highway, and sides of buildings.

Advancing safety in short-term utility work zones: Assessing the role of work zone intrusion alert technologies (WZIATs)

Work zone safety is significantly impacted by vehicle intrusions, which are challenging incidents to control. Although the Federal Highway Administration and US state transportation agencies have implemented safety procedures to enhance worker and motorist safety, the rate of intrusions in work zones remains high, necessitating the development of additional solutions. Work zone intrusion alert technologies (WZIATs) have shown promise in highway work zones; however, their application in short-term utility settings remains insufficiently studied. This research aims to characterize the features of intrusion alert systems that can enhance the safety of workers in utility work zones. To achieve this aim, the study adopted a mixed-method design approach, relying on literature review, archival data analysis, experimental assessments, and a case study. The study reveals that motorist/driver behavior is a primary safety risk factor in utility work zone intrusions. Insights from deploying two technologies, AWARE and Alpha SafeNet, in experimental and live utility work zones indicate that an effective WZIAT must be able to alert both drivers and workers, be deployable in less than three minutes, integrate audio, visual, and haptic alarms, have an adjustable warning light, incorporate a pneumatic tube triggering mechanism, and be waterproof. This study contributes to both knowledge and practice by characterizing the safety risks associated with utility work zones and identifying key performance and operational features for deploying WZIATs in short-term work zones.

Numerical analysis on the differential settlement of geosynthetic reinforced soil integrated bridge system

Abstract Bump scenarios at the bridge approach frequently occur due to the differential settlement at the transition of the approach pavements and the bridge decks. The differential settlement between the approach roadway and the bridge deck is due to the greater consolidation settlement of the existing subsoil supporting the approach road relative to the foundation supporting the bridge deck. To overcome this problem, an economical method known as the geosynthetic reinforced integrated bridge system (GRS-IBS) is utilized. In this paper, a detailed description of the modelling approach to the GRS-IBS is presented. A validation analysis of the numerical model in terms of settlement, horizontal wall displacement, vertical pressure below the foundation, and lateral earth pressure shows good agreement with field observations. An analysis of the differential settlement of the GRS-IBS proves that it could eliminate the bump problems at the bridge approach. Then, further study was carried out to investigate the behaviour of GRS-IBS founded on soft soil. Finding shows that the bump formation for this system is out of tolerance. This finding aligns with the Federal Highway Administration (FHWA) guides, in which the GRS-IBS are not suitable where highly compressible soil is encountered. Several recommendations for future studies are given at the end of this paper.

Evaluating of 14H-Piles’ Load Capacity in Cohesionless Soils for Edward’s Project Using ?-Method and Finite Element Method

Abstract: This paper presents a comprehensive study on evaluating the load capacity of 14H-piles in cohesionless soils for Edward's infrastructure project[1]. The project, overseen by the Federal Highway Administration (FHWA) and the Colorado Department of Transportation (CDOT), focuses on bridge replacements in Edwards, Colorado, with an emphasis on foundation improvements using driven pile foundations[2]. The research utilizes Finite Element Analysis (FEA) and the β-method to assess the total bearing capacity of 14H-piles in sandy soil conditions[3]. Field exploration, including Standard Penetration Tests (SPT), is conducted to determine soil parameters, followed by calibration of soil strength parameters. The study involves modelling the piles using the Soil-Structure Interaction 3-D program (SSI3D) and comparing the total bearing capacity obtained from FEA with nominal capacities calculated by the β-method. Additionally, the paper discusses load resistance and factor design, as well as the importance of static load testing for validation[4]. The results highlight the efficacy of FEA in predicting pile behaviour and provide valuable insights for foundation design in similar geological settings.

Answers to Your Resistivity Questions and Helpful Findings to Develop a Robust Resistivity Specification

Many states are currently in the process of evaluating the use of surface resistivity (SR) or bulk resistivity (BR) tests to assess permeability of concrete in lieu of the standard test method for electrical indication of concrete’s ability to resist chloride ion penetration (AASHTO T 277 and ASTM C1202) which is commonly referred to as the rapid chloride permeability test (RCPT). A few states, such as Louisiana and Maine, implemented resistivity testing in their specifications many years ago. As with any test method implementation, it is important to learn from data generated and experience gained to further refine the specifications in respect of both testing protocols and the specification limits. In this paper, the Federal Highway Administration’s Mobile Concrete Technology Center SR and BR data from 30 field projects in 25 states is analyzed to provide information that could be helpful to agencies as they specify or improve their specifications with respect to resistivity testing. This paper attempts to answer questions related to 1) change in resistivity testing results with age, 2) range and variability of resistivity data from mainline paving mixtures, 3) comparison of test results between SR and BR data, 4) comparison of resistivity data gathered with equipment from various vendors, and 5) use of 28 versus 56 day data.

Development of Macrotexture Test Method for Dense-Graded Asphalt Mixtures

The design and construction of pavements necessitate careful consideration of longevity, cost-effectiveness, sustainability, and safety, with a focus on enhancing the durability and lifespan of dense-graded asphalt (DGA) surface mixtures. This paper discusses a research project the Federal Highway Administration (FHWA) initiated to assess the macrotexture characteristics of DGA mixture pavement—particularly concerning safety at higher speeds. The project aims to develop an efficient testing procedure for measuring macrotexture during mixture design and field-testing processes, striking a balance between longevity, cost-effectiveness, sustainability, and safety in pavement design and construction. The study began with a comprehensive review of existing practices, leading to the selection and testing of a unique laser texture scanner capable of noncontact macrotexture measurement. The subsequent phase involved validating the suggested macrotexture measurement system through data collected from State-sponsored projects under the FHWA Mobile Asphalt Technology Center program. Macrotexture measurements were collected from field cores and laboratory-compacted specimens from various State departments of transportation projects and analyzed. Preliminary analysis of the limited dataset to date revealed a positive correlation between average macrotexture measurements from gyratory specimens’ top surface and field core macrotexture measurements. The FHWA developed a draft test method for measuring macrotexture on laboratory specimens and field cores, thereby aiming to reduce procedural variability. Interlaboratory macrotexture test results demonstrated good reproducibility, with an average coefficient of variation of approximately 20%. Overall, this research project seeks to advance the understanding and measurement of macrotexture in DGA mixture pavements and thereby contribute to the development of safe pavements.

Advancing the understanding of successful technology implementation factors within state DOTs: a maturity model perspective

In an effort to improve the security, dependability, and quality of infrastructure systems, the implementation of complex transportation construction and maintenance projects is essential. Building upon a previous study that identified six crucial factors for effectively implementing new and emerging technologies in the State Department of Transportations (DOTs), this research aims to comprehensively explore six critical factors using a maturity model perspective. DOTs have shown increasing interest in adopting emerging and wireless technologies, as evident in various Federal Highway Administration Every Day Counts initiative. This study employs a mixed-methods approach, using survey responses from DOT personnel to evaluate the relative significance of technology implementation factors and subfactors. The six factors for successful technology implementation explored in this study are Organization Structure, Information Technology Infrastructure, Data Security, Information Workflow, Personnel Training, and Stakeholder Engagement. The study also evaluates the relative importance of People, Process, and Technology for each of these six factors. The paper focuses on providing detailed insights into the different criteria of each technology implementation factor and subfactor to guide agencies in successful technology deployment. Understanding these factors is critical to deploying emerging technologies successfully, which, in turn, leads to much-needed efficiency and productivity in highway construction and asset management. The findings of this study can help DOTs prioritize their technology investments and ultimately contribute to the development of a more advanced and sustainable transportation infrastructure system.

Federal Highway Administration (FHWA) Update

Federal Highway Administration (FHWA) Update

Progression of e-ticketing implementation for state transportation agencies for asphalt materials

E-ticketing, which has been promoted by the Federal Highway Administration (FHWA) “every day counts” (EDC) initiative, utilizes software applications to digitally track and store information regarding highway construction materials paid by state transportation agencies (STAs) by weight in unit bid contract structures. STAs often face implementation barriers such as institutional inertia, or the resistance by stakeholders to adopt changes from the status quo, including new technologies. The purpose of this paper is to determine the progression of STA e-ticketing policy adoption, specifically with a focus on asphalt paving operations, due to the COVID-19 pandemic. To accomplish this research effort, previous FHWA data, National Cooperative Highway Research Program (NCHRP) data, and other literatures are reviewed to determine an implementation baseline. Additional data is collected from the American Association of State Highway and Transportation Officials Committee on Construction to gain current feedback from STAs and their highway contractor partners after the COVID-19 pandemic. Additionally, a case study featuring the Kentucky Transportation Cabinet (KYTC), the Kentucky Association of Highway Contractors (KAHC), and the Plantmix Asphalt Industry of Kentucky (PAIKY) is performed to provide more in-depth analysis. The major finding includes a statistically significant result indicating increased implementation of e-ticketing for asphalt operations within the last two years, along with noting benefits including employee safety, task loading, and project documentation along with concerns regarding cellular connectivity and procurement responsibilities. These findings indicate the importance of STAs investing in partnership with contractors to improve stakeholder buy-in before proceeding towards e-ticketing adoption.

Proposed automated contrast-enhanced microscopical method for evaluating air void structure in compacted asphalt concrete mixtures

There is no direct, less expensive, and practical method to measure the interconnectivity and parameters of air voids in asphalt mixtures other than the total air void volume calculated traditionally using bulk and maximum theoretical specific gravities. Unlike the conventional method, this paper proposes a method that is suitable for advanced applications and studies, such as for the evaluation of air void distribution and air void gradient to model and simulate long-term aging, predict top-down fatigue cracking, evaluate the effectiveness of compaction of thick lifts, and satisfy the representative volume element (RVE) requirements (a homogenous and representative specimen) for performance tests. Developing the proposed compact asphalt evaluation method included modifying the sample preparation methods and analysis from the American Society of Testing & Materials (ASTM), C457 Method C, and recently applied in the analyses of two independent studies at the Federal Highway Administration (FHWA). The first study was focused on checking the air void distribution within the small specimen for dynamic modulus and uniaxial cyclic fatigue tests using AASHTO TP 132 and TP 133, respectively. Results of air void analyses conducted on the interior and exterior sides of several slices sawn from different compacted asphalt mixtures consistently showed that the air void contents on the external sides are higher than on the interior. The second study was to evaluate whether the thicker-than-typical lift thickness to nominal maximum aggregate (NMAS) ratio was causing any top-to-bottom air void gradient in the pavement. Results showed that air void sizes and contents are consistently higher along the top ends of all the cores, and the total air void contents measured using the new method on several specimens from the field were comparable with the indirectly calculated air void percent with up to ± 0.4 percent random error. Overall, the new method is practical to apply to larger-size samples and is a tool that both agencies and contractors can use for confirming in situ void uniformity. Its application can support innovations and improve performance in asphalt pavement construction.

Modeling retroreflectivity degradation of pavement markings across the US with advanced machine learning algorithms

Retroreflectivity is the primary metric that controls the visibility of pavement markings during nighttime and in adverse weather conditions. Maintaining the minimum level of retroreflectivity as specified by Federal Highway Administration (FHWA) is crucial to ensure safety for motorists. The key objective of this study was to develop robust retroreflectivity prediction models that can be used by transportation agencies to reliably predict the retroreflectivity of their pavement markings utilizing the initially measured retroreflectivity and other key project conditions. A total of 49,632 transverse skip retroreflectivity measurements of seven types of marking materials were retrieved from the eight most recent test decks covered under the National Transportation Product Evaluation Program (NTPEP). Decision Tree (DT) and Artificial Neural Network (ANN) algorithms were considered for developing performance prediction models to estimate retroreflectivity at different prediction horizons for up to three years. The models were trained with randomly selected 80% data points and tested with the remaining 20% data points. Sequential ANN models exhibited better performance with the testing data than the sequential DT models. The training and testing R2 ranges of the sequential ANN models were from 0.76 to 0.96 and 0.55 to 0.94, respectively, which were significantly higher than the R2 range (0.14 to 0.75) from the regression models proposed in past studies. Initial or predicted retroreflectivity, snowfall, and traffic were found to be the most important inputs to model predictions.

Development of a Mechanistic-Empirical-Based Highway Cost Allocation Model for Flexible Pavements

Construction, operation, and maintenance of a pavement network requires funding, partially sourced from road user taxes. Recent studies showed that lightweight vehicles are typically taxed higher compared with heavy trucks that damage the roads the most. To facilitate the equity and fairness of the allocated costs to different vehicles in the United States (U.S.), Highway Cost Allocation Studies (HCAS) were performed using various pavement performance prediction models. Reviewed literature showed the lack of mechanistic-empirical (ME)-based HCAS models for the flexible pavement network. In this study, a national-level ME-based HCAS model was developed, and the damage shares of different vehicle classes have been estimated for 67,583 pavement sections in the U.S. Highway Performance Monitoring System (HPMS) database. The proposed HCAS model was compared with the existing Federal Highway Administration (FHWA) HCAS model (i.e., National Pavement Cost Model [NAPCOM]). The analysis of the traffic data showed that two-axle single-unit trucks (SU2) and tractor-semitrailers with two tandem and one single axle (CS5T) were the most frequent users of the pavement network. The results showed that the damage share of SU2 is dominant in minor roadways, while the damage share of the heavier vehicles in the CS5T class is dominant in major arterials and interstates. In addition, it was found that, although the geographical location and environmental condition of the pavement section affects the magnitude of the pavement distresses, the distribution of the damage shares remains almost the same. This can be attributed to the similarities in the traffic data, for example, vehicle class distribution and axle load spectra.