Surface Transportation Systems Research Articles

Characterizing the Self‐Healing Asphalt Materials: A Neutron Imaging Study

Given the significant role of asphaltic pavement in surface transportation systems, even minor enhancements in asphalt materials hold the potential for cost savings and reduced environmental impacts. The healing properties of asphalt have drawn interest for sustainability, yet much remains unknown about the causes and influencing factors. To leverage self‐healing for extending pavement life, understanding the mechanisms and conditions that stimulate microcrack healing is essential. This study aims to investigate the dynamic progress of microcrack closure in asphalt mastics. The time‐series evolution of crack closure led by self‐healing is tracked by neutron computed tomography, with image processing facilitating volumetric analysis over a 7 h period. The study examines the healing process in mastic samples with three different sand filler contents (10, 20, and 30%). Results show that for all the investigated samples, the healing rate declines exponentially over time, with 100% recovery not achieved after 7 h at room temperature. Filler content‐influenced healing speed and 20% sand demonstrate the best performance. Meanwhile, the healing performance varies within the 10% and 30% groups, depending on initial crack size. Additionally, the study finds that initial crack size influences healing behavior in the samples.

Particulate matter and black carbon exposure in Seoul subway: Implications for human health risk

Air pollution and the associated health risks to humans caused by exposure to particle matter in enclosed subway systems are currently concerns. This research first time investigated the substantial health hazards (cancerous, non-cancerous, relative risk, and excess mortality rates associated with all-cause, cardiovascular, and respiratory mortality) posed by metals and black carbon (BC) bound to particulate matter (PM10, PM2.5) within subway systems. The Seoul subway system has been found to show notably higher levels of PM (2–1.5 times) and BC (2.3–1.3 times) compared to outdoor environments. Inhalation of PM-containing trace metals such as Cr, Ni, Cd, Pb, and BC significantly contributes to cancer risk, irrespective of age or PM type. The risk of developing inhalation-related cancer due to PM10 and PM2.5-bound metals is notably higher (1.2–2 times) within the subway environment compared to outdoors. Inhalation cancer risk levels (ICRBC) associated with PM-bound BC are 1.8 times greater at subway stations compared to outdoor stations, with a strong association (1.93–1.98 times) with PM2.5-bound BC compared to PM10. Notably, PM levels in subway stations have a considerable impact on relative mortality risk, with approximately 1.98 times the observed increase in mortality attributed to PM2.5-bound BC. The trace metals and BC bound to PM that contribute to human health risks might originate from vehicle emissions. This includes both surface and underground transportation systems. These findings underscore the significance of addressing air quality within subway systems to mitigate health risks.

An explicit approach for modeling the performance of transportation networks immediately after an earthquake

Surface transportation systems play a vital role in supporting a region’s functionalities. They are expected to remain operational before and even after a hazardous event (e.g. an earthquake). The importance is evident to estimate the post-disaster performance of traffic systems under a probability-based framework, considering the uncertainties arising from both the hazards and the transportation infrastructure fragilities. This paper proposes an explicit approach for evaluating the performance of transportation systems immediately after an earthquake event. The method estimates the spatial distribution of vehicles in the traffic network in a closed form and thus is relatively efficient compared with traditional methods (e.g. an agent-based method). The applicability of the proposed approach is demonstrated through an application to the post-earthquake performance assessment of the traffic network in Tangshan City, China, a city that suffered catastrophically from the 1976 Tangshan Earthquake. Analytical results show that the proposed method can well reflect both the temporal and the spatial variations of the traffic flow, and thus offers rational support for predicting the post-earthquake traffic scenarios and for optimizing strategies to improve the transportation capability under emergent conditions.

Bimetallic oxide Cu–Fe3O4 nanoclusters with multiple enzymatic activities for wound infection treatment and wound healing

Infections and oxidative stress complicate wound healing. In recent years, nanomaterials with natural enzymatic activities have enabled the development of new antibacterial pathways. In this study, Cu–Fe3O4 nanoclusters with multienzyme properties were synthesized. Interestingly, they exhibited activity similar to that of horseradish peroxidase (POD) in acidic environments but their functions resembled superoxide dismutase and catalase in neutral or weakly alkaline environments. In vitro studies have demonstrated the good free-radical scavenging activity of Cu–Fe3O4 nanoclusters in a neutral environment. Under acidic conditions, Cu–Fe3O4 nanoclusters combined with H2O2 demonstrated good antibacterial activity against methicillin-resistant Staphylococcus aureus (MRSA) and Escherichia coli (E. coli). The combination of Cu–Fe3O4 and H2O2 was found to be effective in preventing MRSA infections and promoting wound healing in animal models. RNA sequencing (RNA-seq) technology revealed that chemodynamic therapy (CDT) using nanoparticles can interfere with metabolic processes such as galactose metabolism in MRSA bacteria, destroy the transport system on the surface of MRSA, and affect quorum sensing to hinder the formation of biofilms, thus achieving effective antibacterial efficacy. The use of Cu–Fe3O4 nanoclusters as a novel class of multi-catalytically active nanozymes in the anti-infection of disease-causing pathogens and wound healing has significant potential. Statement of significanceCu–Fe3O4 nanoclusters with multienzyme properties were successfully prepared by a solvothermal method. Cu–Fe3O4 nanoclusters exhibited horseradish peroxidase (POD)-like activity in acidic environments and also showed synergistic effects similar to superoxide dismutase peroxidase in neutral or weakly basic environments. More importantly, these Cu–Fe3O4 nanoclusters showed high biosafety with no apparent in vivo toxicity. Chemodynamic therapy (CDT) using Cu–Fe3O4 nanoclusters was revealed by RNA sequencing (RNA-Seq) technology to interfere with the metabolic processes of MRSA bacteria, such as galactose metabolism, disrupt the MRSA surface transport system, and impede biofilm formation, resulting in effective antibacterial efficacy. The use of Cu–Fe3O4 nanoclusters for anti-infection and wound healing of pathogenic pathogens has significant potential as a novel class of multi-catalytic active nanoclusters.

A Theoretical Framework for Developing a National Transportation Strategy

Developing the surface transportation systems the United States needs will require asking and answering a series of questions, beginning with the most basic:What is the main purpose of surface transportation?What resources are available and what is the best way to deploy those resources?How do we measure success?Determining what future transportation systems should look like will require analyzing a range of economic, demographic, technological and socio-political variables.As management guru Peter Druker famously said, “The primary goal of every enterprise should be to create customers”. Transportation leaders must undertake the critical task of determining what transportation customers want and are willing to pay for. In doing so, they must understand that there are many categories of customers—such as commuters, businesses, and logistics companies—each with different priorities. Measurements of success should also be customer-focused.Finally, state departments of transportation—not their federal counterparts—are often best positioned to ask these questions and develop the right answers for their own states.

Development of FWD based hybrid back-analysis technique for railway track condition assessment

Track substructure is a key component of the railway transportation system. Similar to the built environment of other surface transportation systems, track substructures are subjected to aging and deterioration. This, frequently leads to the failure and collapse of the transportation systems, resulting in the imposition of costly repairs and maintenance. At the same time, the emergence of high-speed trains and heavier axle loads, together with a need for sustainable designs, has put additional pressure on asset owners. It has been shown that frequent condition assessments of railway substructures can considerably reduce the overall annual maintenance costs. Furthermore, limited knowledge of the substructure condition leads to the employment of inefficient, time-consuming, and expensive maintenance actions. Therefore, development of time- and cost-efficient techniques to frequently monitor existing railway track substructures is vital. The falling weight deflectometer (FWD) is recognised as an effective non-destructive test used to survey ballasted railway substructures through a back-analysis process. This paper presents a novel hybrid back-analysis technique that includes an artificial neural network (ANN) and genetic algorithm (GA) to estimate the substructure layer moduli of railway tracks using FWD testing data. To this aim, firstly a dynamic finite element (FE) model was developed and validated against experimental data from the literature. This FE model then were employed to generate a reliable dataset to train the ANN. In the next step, GA was employed as an optimisation tool within the back-analysis technique/framework to optimise the layer moduli (the ANN’s input). A comparison study was performed to evaluate the performance of the developed technique. The results of this comparison revealed excellent performance and robustness of the developed technique.

Assessment of Surface Transportation Security Training Needs and Delivery Preferences

America’s surface transportation system features long and inter-connected routes and open access to stations and vehicles, making the system vulnerable to terrorist attack. While a variety of security-related training products exist for the transportation sector, government and other reports have called for improvements in the transportation security training curriculum in terms of quality and consistency. At the same time, tight economic times have left few extra financial resources available for systems to deliver adequate training to employees. This study assessed the needs for additional security training through focus groups and interviews with representatives of major surface transportation stakeholders and agencies. Training content needs and the approaches that will most effectively implement training in the field are identified. The results are intended to inform those responsible for supporting, developing, or implementing security training about where gaps may exist and about which delivery mechanisms are preferred and most workable.

Machine Learning Applications in Surface Transportation Systems: A Literature Review

Surface transportation has evolved through technology advancements using parallel knowledge areas such as machine learning (ML). However, the transportation industry has not yet taken full advantage of ML. To evaluate this gap, we utilized a literature review approach to locate, categorize, and synthesize the principal concepts of research papers regarding surface transportation systems using ML algorithms, and we then decomposed them into their fundamental elements. We explored more than 100 articles, literature review papers, and books. The results show that 74% of the papers concentrate on forecasting, while multilayer perceptions, long short-term memory, random forest, supporting vector machine, XGBoost, and deep convolutional neural networks are the most preferred ML algorithms. However, sophisticated ML algorithms have been minimally used. The root-cause analysis revealed a lack of effective collaboration between the ML and transportation experts, resulting in the most accessible transportation applications being used as a case study to test or enhance a given ML algorithm and not necessarily to enhance a mobility or safety issue. Additionally, the transportation community does not define transportation issues clearly and does not provide publicly available transportation datasets. The transportation sector must offer an open-source platform to showcase the sector’s concerns and build spatiotemporal datasets for ML experts to accelerate technology advancements.

Adopting a Whole Systems Approach to Transport Decarbonisation, Air Quality and Health: An Online Participatory Systems Mapping Case Study in the UK

In a drive to achieve net zero emissions, U.K. transport decarbonisation policies are predominantly focussed on measures to promote the uptake and use of electric vehicles (EVs). This is reflected in the COP26 Transport Declaration signed by 38 national governments, alongside city region governments, vehicle manufacturers and investors. However, emerging evidence suggests that EVs present multiple challenges for air quality, mobility and health, including risks from non-exhaust emissions (NEEs) and increasing reliance on vehicles for short trips. Understanding the interconnected links between electric mobility, human health and the environment, including synergies and trade-offs, requires a whole systems approach to transport policymaking. In the present paper, we describe the use of Participatory Systems Mapping (PSM) in which a diverse group of stakeholders collaboratively constructed a causal model of the U.K. surface transport system through a series of interactive online workshops. We present the map and its analysis, with our findings illustrating how unintended consequences of EV-focussed transport policies may have an impact on air quality, human health and important social functions of the transport system. We conclude by considering how online participatory causal modelling techniques could be effectively integrated with empirical metrics to facilitate effective policy design and appraisal in the transport sector.

Optimal pricing and investment in a multi-modal city — Introducing a macroscopic network design problem based on the MFD

Improving the performance of an existing transportation system is a challenge for engineers and policy makers as many dimensions and system design variables are interacting. In this paper, we propose the three-dimensional macroscopic fundamental diagram network design problem (3D-MFD-NDP). It is a strategic macroscopic tool to identify the directions of decision making in a multimodal transportation system, where the provision of roads and public transport services are interacting with costs for cars and public transport services in the performance of the entire road surface transportation system. The 3D-MFD-NDP models their effects aggregated at the network level and does not locate all measures to the road network. The objective function of the introduced 3D-MFD-NDP is minimizing the total travel time, while the design variables of the problem are the user costs for cars and bus tickets, the bus headway, the share of dedicated bus lanes and the length of the road network. The advantage of the 3D-MFD-NDP compared to existing approaches is that it is formulated as a mathematical program with equilibrium constraints (MPEC) that allows a fast closed-form solution instead of being simulation-based, usually computing many details not required in strategic decision making. We apply the 3D-MFD-NDP to the greater area of Zurich to study two different problems. First, we investigate how the current network performance can be increased by pricing and investment measures. Despite difficulties in identifying reliable cost information for the provision of roads, we find that substantial travel time savings are possible, especially when limiting car use by restricting its space and increasing its costs. Second, we investigate the response to a 20% population growth in urban and suburban regions with car and public transport prices as well as bus frequency as the free design variables. We find that the system can accommodate the population growth, but as the system costs are shared among more users, the costs per trip are lessened, which attenuates the steering effect of prices.

Mycobacterium abscessus Genetic Determinants Associated with the Intrinsic Resistance to Antibiotics.

Mycobacterium abscessus subsp. abscessus (MAB) is a fast-growing nontuberculous mycobacterium causing pulmonary infections in immunocompromised and immunocompetent individuals. The treatment of MAB infections in clinics is extremely challenging, as this organism is naturally resistant to most available antibiotics. There is limited knowledge on the mechanisms of MAB intrinsic resistance and on the genes that are involved in the tolerance to antimicrobials. To identify the MAB genetic factors, including the components of the cell surface transport systems related to the efflux pumps, major known elements contributing to antibiotic resistance, we screened the MAB transposon library of 2000 gene knockout mutants. The library was exposed at either minimal inhibitory (MIC) or bactericidal concentrations (BC) of amikacin, clarithromycin, or cefoxitin, and MAB susceptibility was determined through the optical density. The 98 susceptible and 36 resistant mutants that exhibited sensitivity below the MIC and resistance to BC, respectively, to all three drugs were sequenced, and 16 mutants were found to belong to surface transport systems, such as the efflux pumps, porins, and carrier membrane enzymes associated with different types of molecule transport. To establish the relevance of the identified transport systems to antibiotic tolerance, the gene expression levels of the export related genes were evaluated in nine MAB clinical isolates in the presence or absence of antibiotics. The selected mutants were also evaluated for their ability to form biofilms and for their intracellular survival in human macrophages. In this study, we identified numerous MAB genes that play an important role in the intrinsic mechanisms to antimicrobials and further demonstrated that, by targeting components of the drug efflux system, we can significantly increase the efficacy of the current antibiotics.

Surface Transportation System Funding Alternatives Grants (DOT)

Surface Transportation System Funding Alternatives Grants (DOT)

Freight flow optimization to evaluate the criticality of intermodal surface transportation system infrastructures

The concept of intermodality has been gaining importance in both freight and passenger transportation across the world. It is essential to identify critical intermodal infrastructures that will help stakeholders prioritize protection initiatives or add necessary redundancy to maximize intermodal network resiliency. In this paper, we identify the critical infrastructures of an integrated intermodal network. We present an optimization model that facilitates all OD-specific traffic flow with optimum cost, utilizing an integrated intermodal network consisting of three surface transportation modes: highway, railway, and waterway. The proposed model considers the congestion effects of each mode and intermodal transfer terminals, which may happen under heavy traffic congestion. We implement a real-world case study to show the critical infrastructures for a given intermodal network of the USA. An integrated way to utilize all surface transportation modes is expected to improve the overall freight flow significantly. Making quick and correct traffic management decisions is a big challenge in a damaged or highly congested transportation network. In addition to criticality analysis, the model will help traffic management decision-making in a disaster by rerouting the traffic in the residual network. The proposed model provides the optimal routing and rerouting paths for the overall freight flow during and after a disaster to enhance surface transportation resilience and security.

Extracellular haem utilization by the opportunistic pathogen Pseudomonas aeruginosa and its role in virulence and pathogenesis.

Iron is an essential micronutrient for all bacteria but presents a significant challenge given its limited bioavailability. Furthermore, iron's toxicity combined with the need to maintain iron levels within a narrow physiological range requires integrated systems to sense, regulate and transport a variety of iron complexes. Most bacteria encode systems to chelate and transport ferric iron (Fe3+) via siderophore receptor mediated uptake or via cytoplasmic energy dependent transport systems. Pathogenic bacteria have further lowered the barrier to iron acquisition by employing systems to utilize haem as a source of iron. Haem, a lipophilic and toxic molecule, presents a significant challenge for transport into the cell. As such pathogenic bacteria have evolved sophisticated cell surface signaling (CSS) and transport systems to sense and obtain haem from the host. Once internalized haem is cleaved by both oxidative and non-oxidative mechanisms to release iron. Herein we summarize our current understanding of the mechanism of haem sensing, uptake and utilization in Pseudomonas aeruginosa, its role in pathogenesis and virulence, and the potential of these systems as antimicrobial targets.

Commercial Cloud Computing for Connected Vehicle Applications in Transportation Cyberphysical Systems: A Case Study

This article focuses on the feasibility of commercial cloud services for connected vehicle (CV) applications in a transportation cyberphysical systems (TCPS) environment. TCPS implies that CVs, in addition to being connected with each other, communicate with the transportation and computing infrastructure to fulfill application requirements. The motivation of this study is to accelerate commercial cloud-based CV application development by presenting the lessons learned from implementing a CV mobility application using Amazon Web Services (AWS). The feasibility of our cloud-based CV application is assessed at three levels: 1) the development of a cloud-based TCPS architecture, 2) the deployment of a cloud-based CV application using AWS, and 3) the evaluation of the cloud-based CV application. We implemented this CV mobility application using a serverless cloud architecture and found that such a cloud-based TCPS environment could meet the permissible delay limits of CV mobility applications. As the CV penetration levels on the surface transportation systems increase significantly over the next several years, this article illustrates how the commercial cloud could automatically scale the backend infrastructure to meet the rapidly increasing demand of real-world CV applications. Through real-world experiments, we demonstrated how commercial cloud services, along with a serverless cloud architecture, can advance the transportation digital infrastructure for supporting CV mobility applications in a TCPS environment. In addition, to enable other researchers to recreate the serverless cloud computing environment in AWS efficiently and to further contribute to commercial cloud utilization for developing CV applications, an AWS CloudFormation template related to our CV mobility application is published in an open source data management platform and is referred to in this article.

Homologous Recombination in Clostridioides difficile Mediates Diversification of Cell Surface Features and Transport Systems.

Illness caused by the pathogen Clostridioides difficile is widespread and can range in severity from mild diarrhea to sepsis and death. Strains of C. difficile isolated from human infections exhibit great genetic diversity, leading to the hypothesis that the genetic background of the infecting strain at least partially determines a patient's clinical course. However, although certain strains of C. difficile have been suggested to be associated with increased severity, strain typing alone has proved insufficient to explain infection severity. The limited explanatory power of strain typing has been hypothesized to be due to genetic variation within strain types, as well as genetic elements shared between strain types. Homologous recombination is an evolutionary mechanism that can result in large genetic differences between two otherwise clonal isolates, and also lead to convergent genotypes in distantly related strains. More than 400 C. difficile genomes were analyzed here to assess the effect of homologous recombination within and between C. difficile clades. Almost three-quarters of single nucleotide variants in the C. difficile phylogeny are predicted to be due to homologous recombination events. Furthermore, recombination events were enriched in genes previously reported to be important to virulence and host-pathogen interactions, such as flagella, cell wall proteins, and sugar transport and metabolism. Thus, by exploring the landscape of homologous recombination in C. difficile, we identified genetic loci whose elevated rates of recombination mediated diversification, making them strong candidates for being mediators of host-pathogen interaction in diverse strains of C. difficileIMPORTANCE Infections with C. difficile result in up to half a million illnesses and tens of thousands of deaths annually in the United States. The severity of C. difficile illness is dependent on both host and bacterial factors. Studying the evolutionary history of C. difficile pathogens is important for understanding the variation in pathogenicity of these bacteria. This study examines the extent and targets of homologous recombination, a mechanism by which distant strains of bacteria can share genetic material, in hundreds of C. difficile strains and identifies hot spots of realized recombination events. The results of this analysis reveal the importance of homologous recombination in the diversification of genetic loci in C. difficile that are significant in its pathogenicity and host interactions, such as flagellar construction, cell wall proteins, and sugar transport and metabolism.

Post-earthquake modelling of transportation networks using an agent-based model

Surface transportation systems are an essential part of urban transportation infrastructure and are susceptible to damage from earthquakes. This damage, along with the lack of prior warning of earthquake events, may lead to severe and unexpected disruption of normal traffic patterns, which may seriously impair post-disaster response. Accordingly, it is important to understand the performance of urban transportation systems immediately following an earthquake, to evaluate their capability to support emergency response, e.g., the movement of firefighters, search and rescue teams and medical personnel, and the transportation of injured people to emergency treatment facilities. For this purpose, a scenario-based methodology is developed to model the performance of a transportation network immediately following an earthquake using an agent-based model. The model accounts for the abrupt changes in destination, irrational behavior of drivers in the chaotic aftermath of a severe earthquake, unavailability of traffic information and impairment in traffic capacity due to bridge damage and building debris. An illustration using the road network of Tangshan City, China shows that the method can capture the traffic flow characteristics immediately after an earthquake and can determine the capability of the transportation network to transfer injured people to hospitals. Thus, it can provide rational support for evaluating the performance of the surface transportation system under immediate post-disaster emergency conditions.

Informed decision-making by integrating historical on-road driving performance data in high-resolution maps for connected and automated vehicles

Connected and automated vehicles (CAVs) are already part of the surface transportation system. In order for a CAV to operate safely, it needs information such as static data (high-resolution navigation maps) and real-time dynamics from various sensors, some of which exchange information with other vehicles or roadside units. High resolution navigation maps can integrate historical on-road driving performance data to help CAVs and drivers operating vehicles with low level automation make informed proactive decisions. This study proposes that navigation maps on CAVs come pre-installed with historical driving data and that they work together with real-time sensors to help CAVs plan maneuvers. Historical driving data offers insights about decisions made by drivers at locations along a route, e.g., where drivers often make sharp turns or where they accelerate and decelerate hard. A pre-installed record of historical driving decisions will support informed decision-making and proactively “warn” CAVs and drivers about potential hazards. This study explores location-based driving volatility as a key to improving safety through CAVs. Location-based volatility is a measure of historical driving performance, defined as the percentage of extreme maneuvers performed on a location in road network. For demonstration, we modeled and visualized real-world high-resolution geo-referenced data. The data comes from a connected vehicle safety pilot program in Ann Arbor, Michigan. We found measured location-based volatility is significantly related to safety outcomes. Therefore, location-based driving volatility can serve as a valuable piece of information to be added to navigation maps in CAVs in order to help them navigate volatile hot-spots.

An adaptive big data weather system for surface transportation

Operating modern multi-modal surface transportation systems are becoming increasingly automated and driven by decision support systems. One aspect necessary for successful, safe, reliable, and efficient operation of any transportation network is real-time and forecasted weather and pavement condition information. Providing such information requires an adaptive system capable of blending large amounts of observational and model data that arrives quickly, in disparate formats and times, and blends and optimizes their use via expert systems and machine-learning algorithms. Quality control of the data is also essential, and historical data is required to both develop expert-based empirical algorithms and train machine learning models. This paper reports on the open-source Pikalert® system that brings together weather information and real-time data from connected vehicles to provide crucial information to enhance the safety and efficiency of surface transportation systems. This robust framework can be applied to a diverse array of user community specifications and is designed to rapidly ingest more, unique data sets as they become available. Ultimately, the developmental framework of this system will provide critical environmental information necessary to promote the development, growth, refinement, and expanded adoption of automated and connected multi-modal vehicular systems globally.

Surface Transportation System Funding Alternatives (DOT)

Federal Grants & ContractsVolume 43, Issue 18 p. 2-3 Grant Alerts Surface Transportation System Funding Alternatives (DOT) First published: 20 August 2019 https://doi.org/10.1002/fgc.30593Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume43, Issue1822 August 2019Pages 2-3 RelatedInformation