Federal Highway Administration Research Articles

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.

Innovative Imaging and Analysis Techniques for Quantifying Spalling Repair Materials in Concrete Pavements

Traditional spalling repair on concrete pavement roads is labor-intensive. It involves traffic blockages and the manual calculation of repair areas, leading to time-consuming processes with potential discrepancies. This study used a line scan camera to photograph road surface conditions to analyze spalling without causing traffic blockage in an indoor setting. By using deep learning algorithms, specifically a region-based convolutional neural network (R-CNN) in the form of the Mask R-CNN algorithm, the system detects spalling and calculates its area. The program processes data based on the Federal Highway Administration (FHWA) spalling repair standards. Accuracy was assessed using root mean square error (RMSE) and Pearson correlation coefficient (PCC) via comparisons with actual field calculations. The RMSE values were 0.0137 and 0.0167 for the minimum and maximum repair areas, respectively, showing high accuracy. The PCC values were 0.987 and 0.992, indicating a strong correlation between the actual and calculated repair areas, confirming the high calculation accuracy of the method.

Improving road safety at horizontal curves using V2I speed warning messages

Introduction: According to the Federal Highway Administration, a quarter of fatal collisions has occurred at horizontal curves. The average collision rate at horizontal curves was found to be three times higher than other types of highway segments. The lack of compliance with the speed limit and driver-related factors are among the main contributing factors to those collisions. Vehicle to Infrastructure (V2I) communications can address these limitations by providing drivers with valuable in-vehicle warning messages based on operational and safety data. There is limited effort investigating the impact of different types of V2I warning messages at horizontal curves and among different profiles of drivers. This study aims to thoroughly examine drivers’ behavior and compliance with different V2I warning messages when approaching horizontal curves. Methods: A driving simulator experiment and self-reported survey were conducted. Three main hypotheses were analyzed in this study. First, whether supplying drivers with in-vehicle V2I warning messages will positively affect drivers’ behavior at horizontal curves compared to the standard road signs. Second, whether there will be a significant difference in drivers’ behavior when receiving text and audio V2I warning messages. Third, whether seniors and female drivers will comply more with speed limit advisory provided through V2I message than younger and male drivers. Results: The Analysis of Covariance confirmed the first two hypotheses. Two main measures of drivers’ behavior found to be lower in the V2I communication scenarios compared to the base one. The audio warning message was found to be more promising in increasing drivers’ compliance with speed limit advisory when approaching the curves. Analyzing the third hypothesis revealed that younger and male drivers had higher curve initiation speed compared to females and seniors. Practical Applications: The findings of this study can be used by transportation researchers, authorities, and car manufacturers to improve the effectiveness of in-vehicle V2I warning messages among different profiles of drivers.

A surrogate safety analysis at sharp gore areas of diverging freeway ramps using micro simulation under congested traffic conditions

Safety at the gore areas near diverging ramps is very crucial during planning and implementation of highway safety improvement programs. Limited research has been conducted on safety at the gore areas on arterial roads. This study aims at investigating the impact of improving a sharp gore area in Lincoln, Nebraska by performing a micro simulation before-and-after study with respect to its underlying state of safety and congestion. Data on travel times and traffic volumes for peak hours are incorporated after successful calibration to find out how a geometric intervention can decrease mobility issues as well as the likelihood of crash involvement. This study has utilized VISSIM software package to run the simulation however, to perform safety analysis, Surrogate Safety Assessment Model (SSAM) is used which is developed by the Federal Highway Administration (FHWA). During the model calibration, custom driving behaviors are created to represent driving tendencies of familiar drivers. The simulation results indicated that by adding an auxiliary lane near the gore area, the mobility issues such as bottle necks, lane changing dilemmas and queue lengths are substantially decreased. However, geometric interventions such as provision of a separate lane, increasing ramp spacing, nose spacing, deceleration area and queue storage area considerably reduced the likelihood of rear-end and lane changing crashes. Surrogate safety assessment in diverging ramps, particularly for sharp gores, has not previously been studied, and this study can serve as a primary footmark for future research on ramp-gores safety.

Hurricane Sandy’s impact on the Borough of Mantoloking, NJ, October 2012

Hurricane Sandy hit Mantoloking, New Jersey, 29 October 2012, heavily damaging both residential and municipal infrastructure located on a barrier spit extending south from Manasquan Inlet in Ocean County, NJ. Due to the storm, residential property values dropped by $528.3 million and more than $5 million in recovery expenses were incurred by the borough. The Herbert Avenue inlet breach severed all infrastructure and critical services to the rest of the peninsula with the Federal Highway Administration, the New Jersey Department of Environmental Protection (NJDEP) and the New Jersey Department of Transportation (NJDOT) bearing $265 million to restore State Highway 35, including $23.8 million to install a vertical steel bulkhead along 3.5 miles of oceanfront shoreline. In addition, individual oceanfront residential owners living north of Lyman Street spent between $48,000 and $72,000 each on a rock revetment linking the steel wall with a pre-existing rock revetment which ended in Bay Head, the neighboring community to the north. The oceanfront owners formed an alliance allowing them to contract as a group for this project. Finally, the U.S. Army Corps of Engineers, Philadelphia District, entered into an agreement with the State of New Jersey to construct a dune 14 miles long and 22 feet high (NAVD 88) with between a 75- and 220-foot-wide berm seaward of that dune between Seaside Park in the south and Point Pleasant Beach just north of Bay Head. This project was completed in Mantoloking in 2019 with an initial construction cost of $122.05 million. This project’s commitment is for 50 years after the Corps and New Jersey signed the state aid agreement extending into 2065.

Employing the maximum likelihood fitting method in the principle of probability and statistics to continually apply the original FHWA road traffic noise prediction model

The original federal highway administration (FHWA) model for road traffic noise (RTN) prediction in the United States (US) has been widely applied in many countries. However, the condition was that the intensities of vehicle noise sources at the reference distance must be determined based on the empirical formula related to vehicle speed and road surface obtained by applying the measured results of a large number of different vehicles in the US. Because the dominant vehicle types and road pavement structures in other countries are not the same, the classification of vehicles and the calculation formula for the source intensity will be different. Even in the US, because of the older modeling data, the current vehicles and their changed noise source, and the new traffic noise model (TNM), the original FHWA model may produce errors if applied in other countries. Therefore, by employing the maximum likelihood method in probability and statistics, this study proposes a method that can continue to directly apply the original FHWA model according to the local vehicle traffic flow, together with a simple method of measuring the attenuation of RTN with distance, to eliminate the uncertainty of the source intensity.

Mechanical Properties and Microscopic Mechanism of Basic Oxygen Furnace (BOF) Slag-Treated Clay Subgrades

Civil engineering faces a substantial challenge when dealing with soft and compressible clayey soils. Conventional soil stabilization techniques involving ordinary Portland cement (OPC) result in notable CO2 emissions. This study explores the utilization of basic oxygen furnace (BOF) slag, a by-product of steel production, for strengthening kaolin clay. This research investigates the influence of BOF slag particle size, BOF slag content, and the use of activators such as lime and ground granulated blast-furnace slag (GGBFS) on the stabilization of kaolin clay. The strength development is assessed through unconfined compressive strength (UCS) test, bender element (BE) test, and scanning electron microscopy (SEM). The findings reveal that higher BOF content and extended curing periods enhance soil strength, and lime and GGBFS effectively augment the stabilizing properties of BOF slag. Stabilizing kaolin clay with a 30% BOF/GGBFS mixture in a 50/50 ratio with 1% lime and curing for 7 days yielded a compressive strength of 753 kPa, meeting the Federal Highway Administration’s requirement for lime-treated soil. These combined measures contribute to developing a more robust and stable material with enhanced geotechnical properties.

Transferring Research Innovations in Bridge Inspection Planning to Bridge Inspection Practice: A Qualitative Study

Over the last two decades, many researchers have focused on providing new ideas and frameworks to help improve conventional bridge inspection planning approaches, however, little guidance is provided for implementing these new ideas in practice, resulting in limited change. Accordingly, this qualitative study aims to identify the factors that can help improve research products and accelerate research transfer to bridge inspection departments with the goal of enhancing bridge inspection practice. This study used semi-structured interviews, written interviews, and questionnaires for data collection to provide rich results. Responses from twenty-six bridge personnel from state Departments of Transportation (DOTs) across the United States (U.S.) were included in this study. The study found that most participants support a fixed inspection interval over a variable interval since fixed intervals are easier in scheduling and budget planning. Also, participants indicated that the barriers hindering the use of nondestructive techniques are the training required by inspectors, traffic control, and the required access equipment. The study presents the factors change leaders should focus on to facilitate organizational change in DOTs such as enhancing the capacity of DOT staff members and gaining support from the Federal Highway Administration (FHWA)

Numerical investigation of gravity-grouted soil-nail pullout capacity in sand

Abstract Soil nailing reinforces soil slopes and retaining walls by installing reinforcing elements into the soil, which rely on bonding strength with surrounding soil to transfer axial forces. This study focuses on the pullout capacity evaluation of grouted soil-nailing, which is conventionally determined through costly and time-intensive site-specific field pullout tests or simplified design methods, Federal Highway Administration (FHWA), and Effective Stress Method (ESM). However, these methods neglect important soil nail configuration parameters governing the pullout capacity. To address this limitation, we present a parametric study on a validated Finite-Element model using PLAXIS 3D to comprehensively investigate the pullout characteristics of grouted soil nailing. The study considers various factors such as sand relative densities, lengths, diameters, and inclination angles of the soil nails. The results reveal that the pullout capacity is significantly influenced by the sand relative density, diameter, and the length of the nailing, while the angle of inclination has only a marginal effect. The comparison with FHWA and ESM methods shows significant scatter, attributed to their lack of consideration for these governing parameters in determining pullout capacity. These findings provide valuable insights into the design and optimization of grouted soil nailing pullout capacity for geotechnical applications.

A comparison of licensed driver counts from the Federal Highway Administration and driver licensing agencies in 11 states

Objective: Those who study motor vehicle crashes may rely on counts of licensed drivers to estimate crash, injury, or fatality rates. These counts may be obtained from the U.S. Department of Transportation Federal Highway Administration’s (FHWA) annual Highway Statistics Series or directly from state driver licensing agencies. However, previous studies have questioned the accuracy of these counts provided by the FHWA.Methods: To investigate this issue, we compared counts of licensed drivers from the FHWA and state licensing agencies in 11 states, categorized by sex and age group, from 2013 through 2017. We then assessed the impact of any potential differences by fitting two sets of Poisson regression models to estimate age- and sex-based driver fatality rate ratios. One set of models used counts from the FHWA as the offset and the other used counts from state licensing agencies.Results: Our analysis found that the differences between FHWA and state counts varied markedly. Seven states had substantial differences for at least one age group that spanned the entire study period. In several cases, these differences in license counts were large enough to produce directly contradictory driver fatality rate ratio estimates when comparing age groups.Conclusions: These findings highlight the continued concern regarding the accuracy of licensed driver counts from the FHWA and extend previous studies by illustrating the impact of using FHWA counts on statistical inference. We recommend against using these data for traffic safety research or policy evaluation. Nevertheless, we acknowledge the need for a centralized, easily accessible database for licensed driver data.

Use of axle load spectra (ALS) for estimating calibration drift in weigh-in-motion (WIM) systems

The road agencies collect and submit weigh-in-motion (WIM) data to the Federal Highway Administration as part of their traffic monitoring program. Therefore, the WIM data should be precise and accurate. One way to evaluate WIM measurement errors is by using the test truck data collected immediately before and after equipment calibration. The limitation of this approach is that the data represent a snapshot in time and may not represent a long-term WIM site performance. This paper presents an approach for estimating WIM system accuracy based on axle load spectra attributes (normalized axle load spectra (NALS) shape factors). This alternative approach allows for characterizing temporal changes in WIM data consistency. The WIM error data collected before and after calibration were related to Class 9 NALS shape factors in the proposed methodology. This paper aims to determine WIM system errors based on axle loading without physically performing WIM equipment performance validation using test trucks. The presented methodology can be used to estimate systematic errors (drift) in the WIM system at any point in time after the equipment calibration. This approach can help highway agencies select optimum timings for routine maintenance and calibration of WIM equipment without compromising its accuracy. The results show that the WIM accuracy for the single axle (SA) and tandem axle (TA) can be estimated with SA and TA NALS shape factors with an acceptable degree of error for bending plate to quartz piezo sensors. Examples are included to demonstrate the application and significance of the developed models.

Corrosion studies of various salt solutions on metals and alloys

Corrosion studies of various salt solutions on metals and alloys

Using Connected Vehicle Data to Evaluate National Trip Trends

The National Household Travel Survey (NHTS), conducted by the Federal Highway Administration, has historically been used for documenting personal mobility trends. Current techniques using surveys to collect this data are labor-intensive and difficult to scale. Emerging connected vehicle (CV) data can provide an alternative data source to potentially provide a more scalable method to measure the temporal and spatial usage of passenger vehicles in near real-time. With an impending shift in the automobile industry towards alternative fuel vehicles (AFV), agile monitoring of trip trends is important to help guide state and national investments in AFV infrastructure. This study presents methodologies and visualizations summarizing observed trip characteristics using a sample of more than 500 billion CV records and nearly 1 billion CV trips for December 2022 in the United States. The analysis found very close agreement between trip lengths for internal combustion engine vehicles (ICEV) for CVs and those reported by the 2017 NHTS. Mean trip lengths and trip durations from CVs and NHTS for ICEVs are within 7.8% and 6.6% of each other. The 85th percentile comparison was similarly close, within 0.7% and 8.3%. A comparison of trip trends among states for ICEVs and AFVs as well as US census places and temporal trends for a selection of states, including Indiana, Texas, Wyoming, and California, is provided. The paper concludes that CV data is an important source to monitor trip characteristics across ICEVs and AFVs in near real-time, which will be particularly important to track during the anticipated change to AFVs.

A framework for proactive safety evaluation of intersection using surrogate safety measures and non-compliance behavior

In recent years, identifying road users' behavior and conflicts at intersections have become an essential data source for evaluating traffic safety. According to the Federal Highway Administration (FHWA), in 2020, more than 50% of fatal and injury crashes occurred at or near intersections, necessitating further investigation. This study developed an innovative artificial intelligence (AI)-based video analytic tool to assess intersection safety using surrogate safety measures and non-compliance behavior. To extract the trajectory data, a real-time AI detection model - YOLO-v5 with a tracking framework based on the DeepSORT algorithm was deployed. 54 h of high-resolution video data were collected at six signalized intersections (including three 3-leg and three 4-leg intersections) in Glassboro, New Jersey. Non-compliance behaviors, such as redlight running and pedestrian crossing outside the crosswalk, are captured to better understand the risky behaviors at these locations. The proposed approach achieved an accuracy of 92% to 98% for detecting and tracking road users' trajectories. Additionally, the developed tool also provided directional traffic volumes, pedestrian volumes, vehicles running a red light, pedestrian crossing outside the crosswalk events, and PET and TTC for crossing conflicts between vehicles. Furthermore, an extreme value theory (EVT) was used to estimate the number of crashes at each intersection utilizing the frequency of PETs and TTCs. Finally, the intersections were ranked based on the calculated score considering the severity of crashes. Overall, the developed tool and the crash estimation, as well as the model and ranking method, can provide valuable information for engineers and policymakers to assess the safety of intersections and implement effective countermeasures to mitigate intersection-involved crashes.