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Electric vehicles (EVs) are widely recognized within the transportation industry as highly promising green technology that mitigates carbon dioxide emissions and high energy usage. The public, however, has demonstrated great hesitancy in embracing EVs, and it’s important to learn why this is so. This study employed an integrative approach to personality traits, beliefs, and intentions to understand consumers’ willingness or unwillingness to adopt EVs. The first step in this endeavor was to develop and distribute a survey to gain insight into what potential consumers see as EVs’ major advantages and disadvantages. Structural equation modeling (SEM) was performed on the 743 responses that were collected from the survey, and the results show that personal innovativeness, usefulness, and ease of use positively impact individual’s intentions to acquire an EV; risk was shown to be the most negative influence. A mediation analysis indicated that a person’s level of innovativeness influences their decision about whether or not to adopt an EV. These findings will add to the body of research on sustainable mobility and will equip policymakers and marketers with a deeper understanding of the public’s perceptions of EVs that will enable them to design effective marketing tools that will result in increased sales.

The mobility landscape is experiencing major changes due to two emerging transportation trends, autonomous vehicles (AVs) and on-demand transportation, and the convergence of these smart mobility innovations as shared autonomous vehicles (SAVs) can considerably alter travel behavior and consequently the ecological and societal aspects of the transportation sector. On-demand autonomous mobility is a promising transportation mode, but further research is necessary to evaluate its various aspects and implications prior to widespread adoption. Thus, this study investigates the effects of integrating automation and on-demand mobility by analyzing the effects on the environment, public transportation, land use, vehicle ownership, and public acceptance. A comprehensive literature review was performed, and through a detailed review of 210 articles, the impacts of each of these categories were determined and classified according to their causes, and the number of publications with which they were cited in the literature was determined. The review showed that SAVs can either positively or negatively impact categories and have the potential to minimize mobility obstacles and transportation inequity if legislators use technology to develop a better transportation system by initiating effective policies that govern the four impacted areas. A list of 22 policy recommendations designed to avoid the negative consequences of SAVs by maximizing the benefits of the technology while limiting the associated risks was also identified. The findings of this review will be beneficial to AV manufacturers, transportation professionals, and especially policymakers, who play an integral role in shaping how society benefits from SAV technology.

Past studies have identified the general public’s level of satisfaction with the service attributes of conventional fixed-route transit and ridesharing services, but few have limited their focus to students. This study employs latent class cluster analysis (LCCA) to identify clusters of university students, based on their satisfaction levels of the attributes of conventional fixed-route and ridesharing services, and uses a latent class behavioral model of a sample of university students in Arlington, Texas to explore the heterogeneity of their preferences toward ridesharing services. The results indicate that younger- and lower-income populations are more likely to be satisfied with on-demand ridesharing services than older- and higher-income populations, females are more likely to be satisfied with ridesharing services than males, and domestic students are more likely to be satisfied with ridesharing services than international students. The outcomes of the study will provide transportation planners with new insights about the significance of sociodemographic factors on the satisfaction level of those who use conventional transit and on-demand ridesharing services and will help them incorporate strategies that will make their services more attractive to their potential ridership.

Traffic-related fatalities, often caused by human error, present a global challenge. Autonomous vehicles (AVs), designed to reduce these fatalities by eliminating human error, still experience crashes. Prior research, limited by the scope of the data set and analytical depth, has not fully explored these factors, resulting in a lingering lack of clarity about the cause of the crashes. The study utilized a comprehensive dataset spanning from 2014 to May 2024 and implemented a stratified sampling alongside class weighting techniques to rectify imbalances within the crash data. A variety of machine learning algorithms such as logistic regression, decision trees, random forests, gradient boosting, bagging classifiers, and easy ensemble methods were utilized to perform the data analysis. The random forest model stood out for its balance in sensitivity and specificity, reducing false negatives. The study revealed that factors such as the extent of vehicle damage, the AV manufacturer, and type of collision were critical predictors of crash injuries. The research provides valuable insights for various stakeholders, including transportation professionals, AV manufacturers, and policymakers. The findings can guide the development of enhanced safety protocols, improved vehicle designs, and more informed policy frameworks aimed at reducing AV-related injuries.

Abstract Underground wastewater collection systems degrade with time, necessitating utility owners to engage in ongoing evaluations and enhancements of their asset management frameworks to preserve the performance of their assets. The inspection and condition assessment of sewer pipes are crucial for the effective operation and maintenance of sewer systems. The closed-circuit television (CCTV) is frequently employed to examine sewer pipes in the United States. This procedure is both costly and laborious because of the extensive number of pipes in a metropolis. Prioritisation of inspection for sanitary sewage pipe segments requiring repair or maintenance can be done in advance depending on their past performance. Hence, the aim of this study is to construct a predictive model for the state of sanitary sewer pipes, utilising data collected from a city located in the southcentral region of the United States. The main contribution is that this study used multiclass classification and predicted PACP scores of the pipes. Condition prediction models were developed using extensively utilised supervised machine learning algorithms including logistic regression (LR), k-nearest neighbors (k-NN), and random forest (RF). However, the bulk of the constructed models were assessed using a limited number of assessment measures, such as the receiver operator characteristic (ROC) curve and the area under the curve (AUC) value. This paper asserts that the assessment of the predictive capacity of these models cannot be determined only by relying on ROC and AUC values. Out of the three models evaluated in this study, the LR model had an AUC value of 0.76. However, this model had a higher number of misclassifications or inaccurate predictions compared to the other models. Consequently, these models were assessed using additional assessment measures, including precision, recall, and F-1 scores (which represent the harmonic mean of precision and recall). Curiously, the LR model achieved an F1-score of 0.28 on a scale ranging from 0 to 1. The RF model yielded an F1-score of 0.45 and an AUC value of 0.86. The existing model can be enhanced before it is employed by asset managers during the inspection phase to assess the state of their sanitary sewers and identify essential sewers that require immediate care.

The increasing utilization of autonomous vehicles (AVs) makes it critical to understand and mitigate their involvement in traffic accidents. This study, therefore, addresses a significant gap in the research on AV safety by focusing on predicting the possibility of injuries in AV-involved crashes. The California Department of Motor Vehicles’ comprehensive dataset of accidents that occurred from 2014 to May 2024 was utilized, and advanced machine learning techniques were applied to develop a model capable of predicting the outcomes of accidents involving AVs. The study found that the bagging classifier model outperforms other models in reliably predicting and identifying severe crashes and minimizing misclassification. Evaluations made through precision-recall, validation, and learning curves confirm the model's robustness, ability to generalize across data subsets, and effectiveness in increasing training data. Key predictors of crash severity include the extent of damage to the AV, vehicle type, manufacturer, and presence of a traffic signal. The study highlights the importance of tailored safety measures, robust safety mechanisms, and advanced traffic management systems to mitigate crash severity. The real-world application of this advanced model promises substantial benefits for vehicle manufacturers, urban planners, policymakers, and end-users, and will contribute to safer roadways.

Electric vehicles (EVs) have the potential to mitigate the severity of significant concerns including environmental pollution and reliance on fossil fuels; however, despite strong governmental promotional efforts, their market penetration is still at the nascent stage. This paper empirically investigates the factors that affect the consumers' intention to adopt EVs by conducting an exhaustive literature review. The initial search resulted in 1,690 publications, but after a thorough exclusion process, 537 articles were deemed relevant and were sorted by source, publication year, country of origin, data collection method, and research domain. The results revealed the influential factors over individuals’ desire to adopt an EV were categorized into four main types (contextual, situational, demographic, and psychological); situational factors, that can act as both barriers and motivators, had the most influencing components. The most cited barriers to adoption of EVs were found to be the lack of charging stations availability and their limited driving range. The most cited motivators to EV adoption were found to be reduction in air pollution and the availability of policy incentives. The findings of this study may guide policymakers in formulating effective transportation and energy policies, as well as provide guidance to those who are responsible for designing EVs that fit the needs and demands of potential consumers.

Traffic accidents are a nationwide public health concern, but autonomous vehicles (AVs) have the potential to significantly reduce accident severity and frequency by eliminating their most common cause, human error. By analyzing the data published by California’s Department of Motor Vehicles, researchers have identified the factors that influence AV crash severity, however, none do so through a literature review. This paper’s aims are multi-faceted: to understand AVs’ operation on public roadways by identifying and classifying the factors contributing to accident severity, to develop a list of strategies that address the public’s safety concerns, and to acknowledge the ethics of unavoidable collisions. To fulfill these objectives, a comprehensive literature review was conducted based on a keyword search. Following a multi-step screening and exclusion process, detailed review was performed of 107 relevant publications, and the factors contributing to increased crash severity were classified into 14 categories. The literature revealed that AVs are not at fault in most accidents, although they have a higher propensity than conventional cars to be involved in rear-end collisions, and they are specifically designed to minimize the number of accidents, but may face unavoidable ones. For the benefit of policymakers and manufacturers, 11 strategies that address the moral dilemma of these accidents and 7 strategies that address concerns about AV safety to improve public perception were identified.

Abstract The recent increase in the frequency and intensity of disasters has damaged and disrupted transportation infrastructures, thereby significantly increasing the economic losses and slowing the pace of recovery. Resilient infrastructures ensure functionality with minimal discontinuity, but there currently exists rarely a tool for assessing the resilience level of existing transportation infrastructures so that the information can be used to make future constructions more resilient. This study aims to identify the significant dimensions for measuring the resilience of transportation infrastructures and to utilize the dimensions to develop a decision-making tool that can be used to assess the level of resilience. A survey supported by a comprehensive literature review was conducted, and statistical tests were performed on the collected data. It was found that network characteristics (length of the link, number of lanes, number of optional routes, etc.), organizational characteristics (time to start reconstruction work, knowledge of the employee, resilience measurement experience, etc.), and information related to data (previous data availability and data accessibility, etc.) have major impacts on the resilience of transportation infrastructures. Based on the impact of statistically significant indicators, a resilience measurement tool was developed that provides a relative resilience score for projects and reveals how each statistically significant dimension affects the resilience. The outcome of this study will help decision-makers and practitioners prioritize their projects for resilience enhancement activities and provide funding accordingly.

In early project phases, cross-functional collaboration is required to lead a capital project from an investment option to a full definition before its design and construction commenced. Given that many capital projects are large, complex, and take years to develop, for stakeholders to realize effective project definition and development it is important that they identify—in the early phases—cross-functional collaboration barriers and their contributing factors. The current study conducted a total of 20 interviews with experts from both the business units and project teams in owner companies and Engineering, Procurement, and Construction (EPC) contractors. It identified 14 barriers to effective cross-functional collaboration and 43 associated contributing factors in the early phases of capital projects. To rank the barriers, researchers then applied the Delphi method with a panel of 12 subject matter experts (SMEs). The outcomes of this study can guide practitioners to improve the effectiveness of cross-functional collaboration in early project phases.

The global health crisis caused by the COVID-19 pandemic led to disruptions and challenges across many business sectors, including the construction industry. A serious ramification of the pandemic on the construction industry was an increase in the number of project schedule delays that affected a multitude of projects around the world. Although the current literature explores the factors that cause project schedule delays, little research is available on the impact of variables, such as the size of the industry, type of project, and category of the organization, on the delays. This study used a four-step approach to address this knowledge gap by performing a comprehensive literature search, developing a survey, distributing the survey online, and conducting a quantitative analysis of the responses. The study's findings revealed that the factors contributing to project schedule overruns during the pandemic were diverse and differed with the variables cited above. Distinct differences were observed in areas such as procurement of materials and changes in material specifications and prices. This research sheds light on the multifaceted nature of the variables that impacted construction project schedules during the COVID-19 pandemic and provides valuable insights for industry stakeholders as they seek to establish effective strategies that will minimize risks and enhance project delivery during future periods of crisis.

The ever-increasing concern over climate change is compelling global economies to employ alternative fuel technology to combat the vehicular emissions of greenhouse gases. Electric vehicles (EVs) offer a viable environmentally friendly alternative that has the potential to facilitate an effective shift towards a sustainable transportation system with low emissions while preserving environment; however, well-intentioned policies and other incentives have not been able to transcend the technological, infrastructure, and financial barriers that prevent their adoption. This study aims to develop a model that depicts the impact of the barriers on EV adoption. The model, developed and empirically validated with data collected from 733 respondents to a questionnaire survey, employed the structural equation modeling technique and revealed that of the four categories of barriers, only the environmental barriers are not significant. The outcomes of this study may aid policymakers in the development of effective transportation and energy policies and guide those responsible for designing EVs that fit the needs and demands of potential consumers.

On-demand ridesharing services are rapidly growing and are predicted to continue to do so, as they offer millions of trips to potential riders every day. Empirical research have extensively investigated the effects of ridesharing services on travel behaviour, traffic congestion, and environmental factors. However, the potential influence of these services on the occurrence of traffic crashes has been largely overlooked. Previous research examining the safety implications of ridesharing services has primarily focused on the existence of such services. Nevertheless, we contend that the mere presence of a ridesharing service does not inherently ensure either a favorable or unfavorable influence on traffic safety. It is the frequency of service consumption that is more likely to have an influence. The objective of this study is to examine the effects of ridesharing services on the incidence of traffic crashes and injuries. The analysis will focus on RideAustin, a locally based ridesharing service operating in Austin, TX, as a specific case study. The difference-in-difference methodology is employed in this study to examine the impact of RideAustin on traffic accidents at the U.S. census block group level. This analysis considers sociodemographic characteristics and built environment variables as control factors. The dataset utilised in this study comprises over 1.4 million trips from 2016 to 2017, sourced from RideAustin. Additionally, the traffic crash and injury data included in this study spans the period from 2012 to 2020 and encompasses approximately 373,000 traffic crashes, as obtained from the Texas Department of Transportation. The use of difference-in-difference models revealed that a statistically significant reduction in the occurrence of traffic crashes was observed solely when the number of trips within a certain block group above a specific threshold. The study determined that the mere presence of services and their infrequent utilization (less than one ride per block group) did not have a statistically significant effect on traffic safety. The results obtained from this study will contribute to a more comprehensive comprehension of the safety advantages associated with ridesharing services, consequently assisting transportation and policy planners in mitigating the occurrence of traffic accidents in metropolitan regions.

Hazard identification is a fundamental step in safety management that has the potential to reduce the number and severity of occupational injuries on construction sites. Researchers have identified and evaluated some of the hazards, but few have extensively discussed all of them and none have classified them by sector. The goal of this paper is to fill that research gap by considering hazard identification through an organized synthesis of the existing literature. After a comprehensive literature review, 236 publications were deemed eligible for further analysis. Eighteen safety hazards were identified and then categorized into four groups based on their physiological impacts, ranked based on frequency of citation, and classified by sector. The results revealed that falls from heights, material handling, and heavy machinery were the most frequently cited hazards and the most likely to impact all sectors. Mitigation strategies were also identified, and it was determined that most hazards can be mitigated through the use of personal protective equipment, and effective training and supervision.

Introduction: Universities and other educational institutions across the United States collaborate with service providers to afford their students, faculty, and staff transit services, such as dedicated buses, on-demand ridesharing, and public transportation. The university community relies on these services, especially in rural and low-density areas without fixed-route transit services. The literature on this topic has a wealth of detailed material about the travel and usage patterns of public transit systems, but it has little information about users’ satisfaction with the transit services provided by universities.Method: This study focuses on filling this gap by using descriptive statistics and ordered logistic regression to determine how satisfied users are with university transit services and what factors are associated with their level of satisfaction. Four public transit services available to the University of Texas at Arlington community served as our case studies.Results and Discussion: The results revealed that most users are satisfied with the services overall. Riders were highly satisfied with service attributes like ease of boarding, cleanliness, customer service, and vehicle speed of the four services. On the other hand, they were least satisfied with service availability and wait times of these services. The findings from this study may be used to provide the UTA community with better mobility and accessibility options and to evaluate the levels of satisfaction with similar services offered to other university communities.

Introduction: The benefits of incorporating technological advancements and digitalization into construction firms’ inspections and material supply procurement operations have been well documented. Despite this and the availability of the technology, however, there is a pervasive lack of understanding in the industry about the appropriate application(s) and benefits for highway construction. This study aims to 1) assess the merits of e-Ticketing technology based on the benefits it provides to the construction industry, and 2) determine the amount of time that it can save inspectors.Method: A structured survey questionnaire was designed and distributed to a variety of state DOTs to ascertain how they view the benefits of technology, and the advantages cited were ranked according to their importance.Results and Discussion: The time savings that could be realized by incorporating technology into their operations were estimated by comparing the number of personnel required for inspections before and after implementing e-Ticketing and revealed that 24% of the workforce could be eliminated or reassigned. This study contributes to the existing body of knowledge by providing information about how utilizing technology can help state departments of transportation improve the productivity of their workforce, increase safety and morale, minimize schedule delays, mitigate the prevailing shortage of inspection staff, and prepare for the next pandemic or similar event.

Shared autonomous vehicles (SAVs) have the potential to improve the efficiency, mobility, safety, and affordability of transportation systems; however, consumers will determine whether they can be successfully integrated into the current systems. This study aims to identify the perceptions, attitudes, preferences, and concerns of those who have and have not taken advantage of the AV technology by sharing the results of a self-driving technology demonstration in Arlington, Texas. A two-step approach was employed for this study: (a) quantitative analysis of a self-reported survey of SAV users and non-users who experienced SAVs on the road, and (b) a qualitative study of interviews conducted during the post-implementation phase of the project. The results from an ordinal logistic regression of a self-reported survey showed that the ease of using SAVs because of not having to worry about parking is positively associated with individuals' willingness to use them in the future. In contrast, concerns about possible confusion arising between human drivers and SAVs on the street decrease willingness to use SAVs. A qualitative analysis of interviews conducted on this subject indicated that waiting time, pick-up and drop-off locations, and the ability to make tight turns in intersections are the three major concerns. Potential riders anticipate that SAVs will be more cost-efficient, safer, and environmentally friendly than owner-operated vehicles. This study provides insights into the perceptions and attitudes of SAV users and non-users and identifies strategies for successfully integrating an SAV service with an existing on-demand ridesharing service, thereby enhancing future acceptance of the technology.

Sustainability principles should be incorporated into all decision-making stages for residential construction projects to ensure maximum revenue while maintaining essential residential building services. This study identifies and analyzes the critical success factors (CSFs) necessary for implementing agile project management (APM) in residential construction projects. Data were collected from 120 professionals in the Nigerian construction industry through questionnaire surveys to understand the implementation of APM. The CSFs were obtained from previous research and analyzed within the specific context of the Nigerian construction industry through questionnaire surveys. The CSFs were grouped into two main categories using exploratory factor analysis: dynamic project optimization and agile project foundations. The model for the CSFs was developed using partial least squares structural equation modeling. The study found that the dynamic project optimization element had the most significant impact on the model, highlighting its importance as a key CSF in APM implementation. These results support the adoption of APM within Nigeria's construction industry as stakeholders and professionals seek effective strategies to reduce costs and improve sustainability. [ABSTRACT FROM AUTHOR]

Highway and bridge construction projects are subject to cost and schedule overruns, as well as workforce shortages in remote locations, both of which can result in disputes over the quality of the end product. E-ticketing technology can improve the quality, however, while decreasing cost overruns and schedule delays. Despite the benefits of E-ticketing, many state departments and agencies are unwilling to transition into this technology. This study aims to identify the potential barriers to implementing E-ticketing, determine the adoption rate of state agencies/departments, and evaluate the benefits of employing an E-ticketing platform. The changes in technological trends in highway construction that are related to material tracking, inspection, and digitization are qualitatively analyzed, using meta-synthesis and interpretative analytical techniques. Key technologies that have the potential to be integrated into the E-ticketing platform to mitigate the limitations faced at the time of implementation are also discussed. The study’s findings suggest that the implementation process and regulations of an E-ticketing platform vary drastically from state to state, and a common set of guidelines is essential for obtaining long term success. The study advocates four key recommendations for widespread implementation of the E-ticketing platform and suggests directions for further research. The results of this study will assist DOT’s decision-makers and engineers in developing a common E-ticketing platform, adopting policies and guidelines, decreasing the costs of their projects, providing the initial investment, running pilot tests, enhancing the safety of their inspectors, and completing their projects in a timely and efficient manner.

The COVID-19 outbreak is the greatest global health crisis in many years. It has had a dramatic effect on workforces and workplaces all around the world, as it has spawned a massive change in the working atmosphere and raised the level of employees’ concerns about their mental health and physical wellbeing. The construction industry has been significantly affected by the COVID-19 pandemic and has been challenged to improve the safety and wellbeing of its workforce. The objectives of this study are to identify the health and safety issues that construction workers have encountered during the pandemic and to recommend management strategies to combat them. A thorough literature search on recently published literature, industry experiences, reports, and other related documents was performed to collect and categorize the required data. Seventeen COVID-19 challenges were identified and classified into five categories, and the results revealed that the lack of a safe environment in the workplace, heavy workloads, home situations, and concerns about job stability often contribute to anxiety, depression, and even suicide. Eleven strategies were identified to overcome these challenges, and the results demonstrated that redefining worksite safety by placing signs, ensuring a safe distance between workers, providing sanitizers and washing stations in the fields, and utilizing effective technologies would enhance project productivity while keeping workers safe. The findings of this study will help the project managers and authorities in the construction industry understand the challenges of the pandemic and adopt effective strategies that will improve the health and safety of their workforce.