Roadway Infrastructure Research Articles

Development of high-efficient asphalt pavement modeling software for digital twin of road infrastructure

To develop digital twin (DT) of road infrastructure, one critical element is computation of pavement responses (strains, stresses, and deflections) under traffic and environmental loading. This study aims to develop high-efficient asphalt pavement modeling software based on semi-analytical finite element method (SAFEM) for DT application. The algorithms address important aspects in vehicle-tire-pavement interaction modeling, such as dynamic vehicular loading, three-dimensional (3-D) non-uniform tire contact stress, viscoelastic behavior of asphalt material, and interface bonding condition. The simulation accuracy is verified by comparison with full-scale test and field measurements, and the relative differences are around 5 % to 20 %. Techniques including optimized discrete Fourier transform, parallel computing, graphics processing unit (GPU) acceleration, and sparse matrices are implemented for computation efficiency. As compared to the traditional 3-D FEM, SAFEM shows significant savings in computation time and storage usage. The high efficiency and accuracy make the software full of potential to be applied for DT of roadway infrastructure.

Public Support for a Safe System Approach to Reducing Motor Vehicle Crashes Deaths in the United States

Conventional road safety strategies are yielding diminishing returns. The Safe System approach is based on the premise that human errors will occur on the roadway and that the system should be designed to accommodate these errors, so they do not result in death or serious injury. Elements of the system include the roadway infrastructure, vehicle engineering, and speed management.Political support for U.S. Safe System implementation gained momentum with $5 billion in appropriated funds in the 2021 Bipartisan Infrastructure Law. The purpose of this study was to measure public support for Safe System implementation. We fielded a nationally representative survey of US adults from May 4 to June 10, 2022 (N=2,274). We measured whether respondents considered individual- or systems-oriented approaches as the most effective way to prevent traffic deaths. We also assessed support for various roadway interventions and vehicle safety technologies. Most respondents agreed that system-level changes were the most effective way to prevent traffic deaths. Endorsement for system-level changes was significantly higher among younger age groups. Support for roadway improvements and vehicle safety technologies varied according to the type of intervention that was proposed. Public support for a Safe System approach is high, particularly among younger adults, which suggests that the momentum for the movement can be sustained over the long term.

Older adults’ perceptions and behaviors on driving: A mixed method study

Understanding older adults' driving behavior based on their personal assessment, along with challenges they encounter when driving will support community leaders and policymakers to develop effective supportive strategies for their safety on the road. This study uses a mixed methodology approach, including a survey and an interview to examine older drivers' perceptions and concerns regarding their driving habits. Collecting data from cities in Texas and Nevada, to achieve diverse perspectives on older adults who live in environments with different land use patterns and road geometry.Using the survey to understand the older adults' self-evaluated driving skills, where most consider themselves very good drivers even though they have some challenges, based on the roads, their reflexes changing over the years, and the impact of their physical and cognitive health. Supplementing the survey results with the interview allows the study to identify older drivers' major challenges including adverse weather, construction sites, and in-vehicle distractions. Supporting that visual and cognitive challenges are limiting older adults’ driving patterns (e.g., not driving at night).To avoid any conflicts with aggressive drivers, the older adults are passively driving, and if accessible, utilizing in-vehicle technologies to increase their safety. However, older drivers suggested that all drivers, particularly younger drivers, should have access to more educational materials. They recommended that older adults' licensing requirements should be evaluated based on their health condition rather than age. Participants’ concern regarding roadway infrastructure elements and driver behavior can be resolved through improvements in road design and driver education.

A Review of Vision-Based Pothole Detection Methods Using Computer Vision and Machine Learning.

Potholes and other road surface damages pose significant risks to vehicles and traffic safety. The current methods of in situ visual inspection for potholes or cracks are inefficient, costly, and hazardous. Therefore, there is a pressing need to develop automated systems for assessing road surface conditions, aiming to efficiently and accurately reconstruct, recognize, and locate potholes. In recent years, various methods utilizing (a) computer vision, (b) three-dimensional (3D) point clouds, or (c) smartphone data have been employed to map road surface quality conditions. Machine learning and deep learning techniques have increasingly enhanced the performance of these methods. This review aims to provide a comprehensive overview of cutting-edge computer vision and machine learning algorithms for pothole detection. It covers topics such as sensing systems for acquiring two-dimensional (2D) and 3D road data, classical algorithms based on 2D image processing, segmentation-based algorithms using 3D point cloud modeling, machine learning, deep learning algorithms, and hybrid approaches. The review highlights that hybrid methods combining traditional image processing and advanced machine learning techniques offer the highest accuracy in pothole detection. Machine learning approaches, particularly deep learning, demonstrate superior adaptability and detection rates, while traditional 2D and 3D methods provide valuable baseline techniques. By reviewing and evaluating existing vision-based methods, this paper clarifies the current landscape of pothole detection technologies and identifies opportunities for future research and development. Additionally, insights provided by this review can inform the design and implementation of more robust and effective systems for automated road surface condition assessment, thereby contributing to enhanced roadway safety and infrastructure management.

A Review on Seismic Resilience of Roadway Infrastructure

A Review on Seismic Resilience of Roadway Infrastructure

On CO2 efficiency and trade-offs between sustainability, safety, economy and resilience in infrastructure developments

The escalating crisis of climate change underscores the pressing need to reduce global CO2e emissions substantially. At the same time, a major increase is expected in global demand for infrastructure systems. In meeting this challenge, we here provide and illustrate a novel modeling framework that affords decision support in pursuit of sustainable developments of infrastructure systems. Based on this framework, for the case of roadway traffic infrastructure systems, it is shown that different strategies for the development of roadway traffic infrastructure systems, yielding approximately the same economic benefits to society, can differ in their embedded carbon footprint by up to a factor of three. This knowledge has the potential to contribute significantly to sustainable developments in general and mitigation of climate change in particular. Our models and results may readily assist decision-makers in the construction industry, at national and supra-national scales, in identifying optimal strategies for infrastructure developments. Furthermore, our work may straightforwardly support regulatory bodies and code writers in defining requirements for material and structural performances for the next generation of standards and codes.

Application of Greenroads Rating System and Life Cycle Assessment in Informing the State of the Practice in Sustainable Roadway Construction

This paper highlights the application of the Greenroads Rating System in collecting, measuring, and communicating sustainable best practice data in the context of roadway infrastructure construction. Once measured, meaningful comparisons between common practice and what goes above and beyond routine can be recognized, educated, advertised, spread, and finally, implemented. This study collected, organized, and analyzed data from 33 Greenroads-certified roadway construction projects completed between 2011 and 2018 with a total value of over 2 billion USD. We believe such a database is scarce within the literature and is worth exploring. Greenroads scorecards, which indicate the level of achievement in sustainable construction practices, were analyzed to show the frequency with which best practices are pursued. Findings show that projects typically perform activities that can be considered common practice and avoid activities that fall outside of their prior experience realm. Realizing that roadway surfaces including pavement structures often contribute to a high proportion of a roadway project’s sustainability efforts, the last piece of this study is dedicated to the life cycle assessment (LCA) of paving materials as conducted and reported by project teams. Integrating LCA results with financial information from pay item lists, correlation analyses between carbon and energy footprints and the price of major paving materials such as asphalt mixtures and portland cement concrete were carried out. Results show that the quantified environmental footprints of projects and the materials used to build them strongly correlate with the total price of projects and the price of constituent materials, respectively.

Nanotechnology-enhanced roadway infrastructure in the U.S: An interdisciplinary review of resilience, sustainability, and policy implications

Nanotechnology-enhanced roadway infrastructure in the U.S: An interdisciplinary review of resilience, sustainability, and policy implications

Roadway construction as a natural experiment to examine air pollution impacts on infant health

Traffic-related air pollution (TRAP) poses a significant public health risk that is associated with adverse birth outcomes. Large roadway infrastructure projects present a natural experiment to examine how resulting congestion change is associated with adverse birth outcomes for nearby populations. This study is designed to examine the influence of living close to a roadway before, during, and after a construction project using a difference-in-differences design. We integrated data on all large roadway construction projects (defined as widening of existing roads, building new roads, improving bridges, installing intelligent transportation systems, improving intersections, and installing or upgrading traffic signals) in Texas from 2007 to 2016 with Vital Statistic data for all births with residential addresses within 1 km of construction projects. Our outcomes included term low birth weight, term birth weight, preterm birth, and very preterm birth. Using a difference-in-differences design, we included births within 3 years of construction start and 2 years of construction end. In our main model, the exposed group is limited to pregnant individuals residing within 300 m of a construction project, and the control group includes those living within 300–1000 m from a project. We used regression models to estimate the influence of construction on infant health. We included 1,360 large roadway construction projects linked to 408,979 births. During construction, we found that the odds of term low birth weight increased by 19% (95% CI: 1.05, 1.36). However, we saw little evidence of an association for other birth outcomes. Contrary to our hypothesis of decreased TRAP after construction ends, we did not observe consistent improvements post-construction for pregnant individuals living within 300 m. Continued consideration of the influence of traffic congestion programs on birth outcomes is necessary to inform future policy decisions.

Metal-rich biochar as an asphalt modifier to improve sustainability and reduce VOC emissions

Asphalt-surfaced areas have been recently reported as non-combustion sources emitting organic chemical compounds. These emissions from asphalt not only accelerate its aging but also negatively affect air quality and the health of people who inhale the emitted compounds. Trying to mitigate the latter emission and inspired by the phytoremediation capacity of the acacia plant, we have developed a metal-rich carbonaceous adsorbent (biochar) from the acacia plant. Here, we evaluate the efficacy of our acacia-derived adsorbent to reduce the hazardous volatile organic compounds (VOCs) emitted from asphalt-surfaced areas. We further compare the efficacy of our acacia-derived adsorbent with an adsorbent made from a low-metal plant (silver grass). The higher metal content of the adsorbent made from acacia correlated with its higher efficacy compared to that of silver grass in reducing the release of VOCs from the corresponding asphalt, displaying emissions of 16.9% in asphalt binder containing acacia biochar compared to emissions of 21.2% using silver-grass biochar. Using density functional theory (DFT)-based molecular modeling, we specifically investigated the adsorption behavior of three prominent metals present in acacia biochar: Ca with ≈8 wt%, Al with 6.9 wt%, and Fe with 4.4 wt%. Using DFT, we demonstrated the individual contribution of each metal to adsorbing each of six chemical air pollutants. The DFT results revealed the superiority of Fe at interacting with VOCs across all N-containing zones of the biochar surface: pyridine, pyrrole, amine, and amide. Specifically, in the pyridine and pyrrole zones of the biochar surface, the trend of interaction energy for gas pollutants is as follows: Fe > Ca > Al > no metal. Considering the high concentration of Ca in acacia biochar (≈8 wt%), this trend of energy (Fe > Ca > Al > no metal) highlights the crucial role of Ca in enhancing biochar adsorption. Our findings highlight the potential of using inherently metal-rich biomass feedstocks to reduce volatile emissions from asphalt-surfaced areas, improving air quality and extending the service life of roadway infrastructure.

From biowaste to BioPave: Biological pathways for sequestration of anthropogenic CO2 and enhancing durability of roadway infrastructures

Biomass-derived bio-oils are emerging as sustainable, low-carbon alternatives for construction materials, particularly in innovative pavement applications, 'BioPave'. This study evaluates bio-oils from various sources, including algae and wastewater biomass, revealing variations in carbon, nitrogen, and sulfur contents, along with viscosity. We focused on their application in outdoor construction, assessing their resistance to thermal and solar radiation, and moisture. The study also examines their interaction with siliceous surfaces and their influence on asphalt adhesion in BioPave systems under moisture conditions. Laboratory experiments and computational modeling demonstrated molecular composition significantly influenced bio-oils' responses to thermal and UV exposure. Bio-oils rich in polar groups showed hardening upon thermal treatment, while those with higher concentrations of saturated aliphatics remained more stable. Furthermore, our findings highlight the significance of dosage control in maximizing beneficial effects of bio-oils in intermolecular interactions at bitumen-aggregate interface in BioPave applications. These findings offer insights into the potential of bio-oils in sustainable construction and the need for dosage control to optimize pavement performance.

Sustainability of Dynamic Wireless Power Transfer Roadway for In-Motion Electric Vehicle Charging

Numerous articles have been written regarding the potential affects that electric vehicle (EV) charging stations can have on power distribution systems. With the advent to adopt a dynamic wireless power transfer (DWPT) roadway charging infrastructure, there lies a potential for hazardous effects to occur. Therefore, this paper presents a novel methodology to analyze the sustainability and the potential effects that a DWPT roadway can have on electric power distribution grid. Historical traffic flow (HTF) data is converted using a Monte Carlo method to develop an EV traffic flow (EV-TF). Then, a full DWPT simulation of a 1-mile and 10-mile roadway is performed to determine the power fluctuations that can occur as different densities of EVs traverse the roadway. This method creates simulations using an IEEE 33-Bus power distribution system to evaluate if a DWPT roadway for in-motion EV charging causes voltage magnitudes to fluctuate beyond operational standards.

Improving the EAR Index for Flexible Pavement and a Preliminary Definition of an Environmental Index (ECR) for Rigid Pavement

Nowadays, roadway infrastructures are designed in order to satisfy technical and economical requirements, as well as to guarantee advanced environmental performance. Focusing on that, this paper deals with an innovative procedure for the characterization of pavement materials, both asphalt and cement-bound mixtures. The methodology takes its cue from a previous study in which the so-called Environmental Asphalt Rating (EAR) was firstly introduced as a reference parameter for asphalt pavements to evaluate technical offers and for the assignment of scores, in terms of environmental impacts, during the tender phase. In this work, the EAR methodology is revised with a focus on the main variations and improvements related to the new version of the ISO standard. By applying the same approach to rigid or concrete pavements, a preliminary version of the Environmental Concrete Rating (ECR) is presented. For ECR, a correction is provided regarding functionality through a fatigue-related parameter and the surface characteristics related to the IRI value. Despite its strong applicability to the pavement sector, the strength of the proposed method is its ability to be fine-tuned to different fields by varying the associated performance coefficients.

Estimating added roadway GHG emissions from climate change effects and related adaptations

Civil infrastructure is vulnerable to climate-based event impacts that are increasing. A comparative with-and-without simulation-based GHG emissions change estimation methodology is proposed for estimating the changes in roadway transportation-related GHG emissions, a key contributor to climate change, from repairs required to mitigate damage and the execution of adaptations taken to prevent future damage. Riverine flooding from precipitation, coastal flooding from surge and high tide, and pavement deterioration from high temperatures and dry conditions are considered. A GHG emissions-based link criticality index is developed for identifying links for proactive adaptations. The tool provides insights for climate impact reduction policy creation and evaluation, and can aid in assessing the effectiveness of roadway infrastructure investment plans for combatting climate change effects. The methodology is illustrated on a case study in Northern Virginia. The results uncover how climate change events and actions taken can exacerbate conditions that accelerate climate change in the long term.

Decoding the impacts of contributory factors and addressing social disparities in crash frequency analysis

Understanding the relationship between social disparities and traffic crash frequency is essential for long-term transportation planning and policymaking. Few studies have systemically examined the influence of socioeconomic and infrastructure-related disparities in macro-level traffic crash frequency. This study provides a framework to spatially examine the relationships between crash rates and demographic and socioeconomic characteristics, as well as roadway infrastructure and traffic characteristics at the Census Block Groups (CBGs) level. Spatial autocorrelation analysis was first performed on the residual of the Ordinary Least Squares (OLS) model to identify whether non-stationarity exists. Then, the Geographically Weighted Regression (GWR) model and the Multiscale Geographically Weighted Regression (MGWR) model were applied to assess the impacts of these factors on crash rates spatially and statistically. Our findings indicate that MGWR outperforms both OLS and GWR in uncovering the spatial relationships between contributing factors and both fatal and injury (FI) crashes as well as property damage only (PDO) crashes. A thorough examination of local coefficient maps highlighted six pivotal variables that significantly influenced a majority of CBGs. Improving infrastructure, including pedestrian pathways and public transit facilities, in low-income areas can offer significant benefits. These findings and recommendations can inform the development of effective strategies for reducing crashes and guide the appropriate selection of modeling techniques for macro-level crash analysis.

The relationship between cognitive functioning and street-crossing behaviours in adults: A systematic review and meta-analysis

BackgroundPedestrians are vulnerable road users, with 26 % of global road fatalities being pedestrians (and other vulnerable road users). It is argued that pedestrians are involved in crashes due to errors in decision-making due to deficits in cognitive skills. To date there has limited research into pedestrian decision-making. There currently stands no theoretical model to help understand how cognitive function impacts on pedestrian street crossing decisions. ObjectivesThe aim of this review was to synthesize the literature on the relationship between cognitive functioning and street-crossing behaviours, in two population groups: all adults, and older adults with or without a cognitive impairment. Data sourcesPublished literature from three databases (PsycINFO, PubMed and Medline) was searched in February 2022. Studies were required to have investigated the relationship between cognitive functioning and street-crossing behaviours. ResultsNine studies were identified for the systematic review, with four cognitive domains and nine street-crossing behaviours examined. Findings from the systematic review suggested that poorer processing speed and visual attention predicted increased unsafe street-crossing behaviours across the two population groups. Additionally, most studies demonstrated a non-significant relationship between executive function and street-crossing behaviours. Results from the meta-analysis on all adults, demonstrated a small effect size for the strength of the relationship between overall cognitive functioning and street-crossing behaviours, with stronger effects noted in individual domains of selective attention and inhibition. LimitationsThe small number of studies in this space combined with considerable variability in cognitive domains measured, assessment tools utilized, and street-crossing behaviours examined across studies limit conclusions about patterns of the relationship between cognitive functioning and pedestrian safety. ConclusionsFindings highlight the important role of visual attention in enabling the engagement of safe street-crossing behaviours, which may assist in the development of targeted interventions to reduce risk of harm to pedestrians. Given unexpected findings regarding the influence of executive functioning, as well as limited findings on other cognitive domains such as mental status and memory, future research should aim to elucidate their role in pedestrian safety. Further research into cognitive function and pedestrian street crossing behaviours is critical if we are to develop a theoretical framework for how pedestrians make road-crossing decisions. If we can better understand the factors that influence street crossing decisions, roadway infrastructure and training programs can be developed to improve outcomes for this vulnerable population.

Multi-Parametric Delineation Approach for Homogeneous Sectioning of Asphalt Pavements

The demand for preserving existing roadway infrastructure has been increasing to regulate expensive reconstruction activities. The maintenance of homogeneous road sections is one of the approaches to economize the overall management of pavement systems. The existing homogeneous delineation methods consider one or two parameters for segmenting the pavements based on similar characteristics, which are found to be a repetitive process. Also, there is a need to consider multiple parameters that represent the functional, structural, and traffic characteristics in segmentation process. Therefore, the objective of this study was to develop a multi-parameter-based delineation approach (MPDA) to segment the pavements into subsections with similar features considering functional, structural, and traffic characteristics. Deflection bowl parameters, unified pavement health index (functional performance metric), surface layer modulus, and traffic reported in terms of AADT were employed for developing a multi-parametric delineation index (MPDI). A total of 1781 datapoints covering 26 road sections in the State of Andhra Pradesh, India, were used. The C-charts method-based segmentation for MPDI was applied to obtain the homogeneous sections. The devised approach was found to be efficient in segmenting the pavements as well as robust in selecting suitable maintenance strategies for each group of the homogeneous sections. Further, the segmentation processes were automated for easier implementation by the agencies.

Model-assisted clustering for automated operational modal analysis of partially continuous multi-span bridges

Long multi-span bridges represent a broad section of the civil roadway infrastructure. Despite being so common, their condition-based maintenance through vibration-based Structural Health Monitoring (SHM) has been scarcely investigated in the literature. The dynamic identification of such structures through Operational Modal Analysis (OMA) is especially challenging due to of their quasi-periodic nature and the common existence of weak inter-span coupling. Even when designed following an isostatic scheme, there always exists a certain degree of coupling between spans due to the continuity of the deck, the pavement and imperfect expansion joints. Hence, the modal poles of the spans typically appear as dense clusters with closely spaced frequencies and mode shapes with similar wavelengths, which significantly hinders the identification of physical poles through stabilization diagrams. In this light, this paper proposes a model-based machine learning approach to conduct and interpret the OMA results of partially continuous multi-span bridges. The proposed method is a hierarchical clustering approach that leverages on the analytical solution of the vertical free vibration response of multi-span girders with weak inter-span rotational coupling, allowing the estimation of the modal features of any bridge configuration ranging from simply supported to perfectly continuous conditions. Detailed parametric analyses and discussions are presented to appraise the correlation between the inter-span rotational coupling and the clustering of the modal poles of multi-span bridges, as well as the influence of damage conditions of varying severity and extension. On this basis, a model-based cut-off distance threshold for hierarchical clustering of stable poles is proposed to assist the automation of the global OMA of multi-span bridges. The developed formulation is tested in a real-world in-operation seven-spans reinforced concrete girder bridge, the Trigno V Bridge in Italy.

Changes in traffic congestion and air pollution due to major roadway infrastructure improvements in Texas

Traffic-related air pollution (TRAP) is an established health hazard, and roadway construction has the potential to affect TRAP by relieving congestion. The relationship between roadway construction and congestion is of policy importance, but few studies examine it using large samples of construction projects and detailed traffic and air pollution data. We create a dataset of construction projects in Texas and link them to data on air pollution and three variables operationalizing congestion: average annual daily traffic (AADT), AADT per lane, and delay in hours. We use difference-in-difference methods to estimate the effect of widening and intersection improvements on congestion and air pollution. On average over the period during construction, we find that widening increases delay by 42% (95% CI: 30, 56%), but intersection projects do not affect delay. On average and over the first three years post-construction, we find that widening reduces delay by 33% (95% CI: −41, −24%) and reduces NO2 levels within 500 m by 13% (95% CI: −22, −2%), and intersection projects reduce delay by 52% (95% CI: −65, −35%) and reduce NO2 levels within 500 m by 12% (95% CI: −18, −5%). These short-term impacts are relevant for understanding the impact of roadway construction on human health.

Bi-Usage Energy Efficient Techniques (BUEET) for Energizing the Vehicle and Broadcasting the Information in Highway Scenario

The proposed technique BUEET is introduced mainly for two major reasons such as (i) RSU broadcast the emergency messages and the energy status to the vehicles without any interruption. It helps to shake hands with the neighboring node for energy sharing and (ii) RSU boost up the energy efficiency level with the help of energy sharing by the adjacent vehicles. Most of the self-organizing protocols in wireless sensor networks considers only initial energy consumption phase and neglects the maintenance phase of topology. The vehicles are cooperatively interacted to form a reliable network structure. RSU’s are placed in the roadway infrastructure and On-Board Units (OBU’s) are placed in the vehicles, then the communication takes place with the help of these devices. Therefore, this provides the clear roadmap structure by receiving the information from various sources of vehicles. If the information received from the RSU is not relevant to the vehicles then it automatically transmits the information to the adjacent vehicles travelling in the highway. The adjacent vehicle checks the information and transmits to another vehicle if it is relevant. Therefore, the proposed method considers two parameters such as packet transmission and energy efficiency on NS3 simulation. Packet transmission among the vehicles plays a vital role in the proposed technique and the nodes without any route breaks and the loss of connection leads to a strong network. Energy efficiency is analyzed and compared with other existing schemes; in result it is proved that BUEET’s energy efficient is higher in all the nodes. It helps to communicate among the nodes without energy loss. When the energy among the nodes in the network is high, then the performance of the nodes also good. The maximum number of Hop’s helps to transmit the information without any delay.