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Integrating advanced air mobility (AAM) into the logistics of high-value electronic commodities can enhance efficiency and promote sustainability. The objective of this study is to optimize the logistics network for high-value electronics by integrating AAM solutions, specifically using heavy-lift cargo drones for middle-mile transport and using the mostly rural and small urban U.S. state of North Dakota as a case study. The analysis utilized geographic information system (GIS) and spatial optimization models to strategically assign underutilized airports as multimodal freight hubs to facilitate the shift from long-haul trucks to middle-mile air transport. Key findings demonstrate that electronics, because of their high value-to-weight ratio, are ideally suited for air transport. Comparative analysis shows that transport by drones can reduce the average cost per ton by up to 60% compared to traditional trucking. Optimization results indicate that a small number of strategically placed logistical hubs can reduce average travel distances by more than 13% for last-mile deliveries. Cost analyses demonstrate the viability of drones for middle-mile transport, especially on lower-volume rural routes, highlighting their efficiency and flexibility. The study emphasizes the importance of utilizing existing infrastructure to optimize the logistics network. By replacing truck traffic with drones, AAM can mitigate road congestion, reduce emissions, and extend infrastructure lifespan. These insights have critical implications for supply chain managers, shippers, urban planners, and policymakers, providing a decision support system and a roadmap for integrating AAM into logistics strategies. [ABSTRACT FROM AUTHOR]

This study examines the balance between urban transportation security and supply chain efficiency amid global disruptions like the COVID-19 pandemic, attacks on maritime lanes, and geopolitical conflicts. The authors combine bibliometric network analysis and natural language processing (NLP) to review scholarly literature from 2017 to 2023. This approach identifies and visualizes evolving thematic trends. Findings reveal a shift towards integrating advanced technologies, such as blockchain, AI, and IoT, to enhance security without compromising efficiency. This study highlights the significance of integrating urban planning principles and sustainable practices to enhance transportation security and supply chain efficiency within urban environments. Actionable insights for policymakers and supply chain managers emphasize the adoption of integrated, technology-driven security solutions to foster resilient and sustainable supply chains. This research contributes to academic discourse by refining the understanding of evolving priorities in transportation security and supply chain management, offering both theoretical and practical implications for secure and efficient global supply chains. [ABSTRACT FROM AUTHOR]

Electrifying aircraft, a crucial advancement in the aviation industry, aims to cut pollutive emissions and boost energy efficiency. Traditional aircraft depend on fossil fuels, which contribute significantly to greenhouse gas emissions and environmental pollution. Despite progress in electric propulsion and energy storage technologies, challenges such as low energy density and integration issues persist. This paper provides a comprehensive thematic and bibliometric analysis to map the research landscape in aircraft electrification, identifying key research themes, influential contributors, and emerging trends. This study applies natural language processing to unstructured bibliographic data and cross-sectional statistical methods to analyze publications, citations, and keyword distributions across various categories related to aircraft electrification. The findings reveal significant growth in research output, particularly in energy management and multidisciplinary design analysis. Collaborative networks highlight key international partnerships, with the United States and China being key research hubs, while citation metrics highlight the impact of leading researchers and institutions in these countries. This study provides valuable insights for researchers, policymakers, and industry stakeholders, guiding future research directions and collaborations. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive exploration of vertical take-off and landing (VTOL) aircraft within advanced air mobility (AAM), examining the crucial challenges of integrating these innovative technologies into transportation systems. AAM promises transformational social change by enhancing transportation energy efficiency, safety, and operational effectiveness. This research utilizes a methodical approach that juxtaposes a systematic review of patents with an extensive analysis of the academic literature to map the innovation landscape of VTOL technology. This dual analysis reveals a dynamic progression in VTOL advancements, highlighting significant strides in aerodynamic optimization, propulsion technology, and control systems. The novelty of this study lies in its dual-method approach, combining patent analysis with the academic literature to provide a holistic view of VTOL technological evolution. The patent analysis reveals that companies have been most productive on innovations relating to VTOL aircraft transition efficiency, control enhancement, and energy management. The literature review identifies key trends such as the rise in electric propulsion technologies and the integration of AI-driven control mechanisms. These results provide new engineering knowledge that can guide future VTOL development and policy formulation. The original contributions include a detailed mapping of VTOL innovation trends, identification of key technological advancements, and a predictive lens into future directions. These findings offer a valuable resource for aerospace engineers, policymakers, and urban planners. This study contributes a detailed assessment of both theoretical foundations and practical applications, fostering a holistic view of the challenges and innovations shaping the future of AAM. By connecting research and practical development, this study serves as a critical tool for strategic decision making and policy formulation towards advancing the integration of VTOL aircraft into sustainable urban transportation networks. [ABSTRACT FROM AUTHOR]

The transportation of patients in emergency medical situations, particularly in rural areas, often faces significant challenges due to long travel distances and limited access to healthcare facilities. These challenges can result in critical delays in medical care, adversely affecting patient outcomes. Addressing this issue is essential for improving survival rates and health outcomes in underserved regions. This study explored the potential of advanced air mobility to enhance emergency medical services by reducing patient transport times through the strategic placement of vertiports. Using North Dakota as a case study, the research developed a GIS-based optimization workflow to identify optimal vertiport locations that maximize time savings. The study highlighted the benefits of strategic vertiport placement at existing airports and hospital heliports to minimize community disruption and leverage underutilized infrastructure. A key finding was that the optimized mixed-mode routes could reduce patient transport times by up to 21.8 min compared with drive-only routes, significantly impacting emergency response efficiency. Additionally, the study revealed that more than 45% of the populated areas experienced reduced ground travel times due to the integration of vertiports, highlighting the strategic importance of vertiport placement in optimizing emergency medical services. The research also demonstrated the replicability of the GIS-based optimization model for other regions, offering valuable insights for policymakers and stakeholders in enhancing EMS through advanced air mobility solutions. [ABSTRACT FROM AUTHOR]

Ships connect the global economy through maritime transport. However, their susceptibility to increasing geopolitical conflicts has heightened concerns about the risks to crew safety and navigation security. This systematic literature review (SLR), utilizing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, rigorously examines the safety and security of autonomous ships in maritime transport. The methodology employs a comprehensive search across major databases including Scopus, Google Scholar, and Science Direct, based on explicit inclusion criteria focusing on recent advancements from 2014 to 2023. By methodically analyzing 58 relevant publications screened from an initial pool of 1407, this paper highlights critical trends and gaps in the application of advanced sensor technologies, cybersecurity measures, and autonomous navigation systems. The findings provide insights into the operational challenges and technological developments shaping the future of maritime safety and security, offering valuable guidance for policymakers and industry stakeholders. This research contributes to scholarly discourse in this industry by mapping the trajectory of technological integration and its implications for maritime operations in a global context. [ABSTRACT FROM AUTHOR]

Micromobility is an evolving form of transportation modality that uses small human- or electric-powered vehicles to move people short distances. Planners expected that bike sharing, the first form of micromobility, would reduce traffic congestion, cut travel cost, reduce pollution, enable connectivity with other modes of transport, and promote public health. However, micromobility options also brought new challenges such as the difficulty of placement decisions to encourage adoption and to minimize conflict with other transport modes. Sound deployment decisions depend on the unique environmental characteristics and demographics of a location. Most studies analyzed deployments in high-density urban areas. This research determines the best locations for 5 new bike-sharing stations in Fargo, North Dakota, a small urban area in the rural United States. The workflow combines a geographic information system (GIS), level of traffic stress (LTS) ratings, and location-allocation optimization models. The spatial analysis considered 18 candidate station locations and eliminated those that fell within the 700-meter isochrone walking distance of the 11 existing stations. This case study demonstrates a scalable workflow that planners can repeat to achieve sustainable micromobility deployments by considering the land use, population density, activity points, and characteristics of the available pathways in their unique setting., Author(s): Taraneh Askarzadeh [1]; Raj Bridgelall (corresponding author) [1] 1. Introduction Transportation planners worldwide view new micromobility options such as bike sharing as another important means towards achieving sustainable transportation [...]

The emergence of autonomous vehicles (AVs) presents a transformative shift in transportation, promising enhanced safety and economic efficiency. However, a fragmented legislative landscape across the United States hampers AV deployment. This fragmentation creates significant challenges for AV manufacturers and stakeholders. This research contributes by employing advanced machine learning (ML) techniques to analyze state data, aiming to identify factors associated with the likelihood of passing AV-friendly legislation, particularly regarding the requirement for human backup drivers. The findings reveal a nuanced interplay of socio-economic, political, demographic, and safety-related factors influencing the nature of AV legislation. Key variables such as democratic electoral college votes per capita, port tons per capita, population density, road fatalities per capita, and transit agency needs significantly impact legislative outcomes. These insights suggest that a combination of political, economic, and safety considerations shape AV legislation, transcending traditional partisan divides. These findings offer a strategic perspective for developing a harmonized regulatory approach, potentially at the federal level, to foster a conducive environment for AV development and deployment. [ABSTRACT FROM AUTHOR]

It is expensive to scale existing methods of road condition monitoring for more frequent and network-wide coverage. Consequently, defects that increase ride roughness or can cause accidents will go undetected. This paper presents a method to enable network-wide, continuous monitoring by using low-cost GPS receivers and accelerometers onboard regular vehicles. The technique leverages the large volume of sensor signals from multiple traversals of a road segment to enhance the signal quality by ensemble averaging. However, ensemble averaging requires position-repeatable signals which are not possible because of the low resolution and low accuracy of GPS receivers and the non-uniform sampling of accelerometers. This research overcame those challenges by integrating methods of interpolation, signal resampling and correlation alignment. The experiments showed that the approach doubled the peak of the composite signal by decreasing signal misalignment by a factor of 67. The signal-to-noise ratio increased by 10 dB after combining the signals from only six traversals. A probabilistic model developed to estimate a dynamic signal-detection threshold demonstrated that both the false-positive and false-negative rates approached zero after combining the signals from 15 traversals. The method will augment the efficiency of follow-up inspections by focusing resources to locations that consistently produce rough rides. [ABSTRACT FROM AUTHOR]

The inefficiency of transporting goods contributes to reduced economic growth and environmental sustainability in a country. Autonomous trucks (ATs) are emerging as a solution, but the imbalance in the weight moved and ton-miles produced by long-haul and short-haul trucking creates a challenge in targeting initial deployments. This study offers a unique solution by presenting a robust method that combines data mining and geographic information systems (GISs) to identify the optimal routes for ATs based on a top-down approach to maximize business benefits. Demonstrated in a U.S. case study, this method revealed that despite accounting for only 16% of the weight moved, long-haul trucking produced 56% of the ton-miles, implying a high potential for ATs in this segment. The method identified eight key freight zones in five U.S. states that accounted for 27% of the long-haul weight and suggested optimal routes for initial AT deployment. Interstate 45 emerged as a pivotal route in the shortest paths among these freight zones. This suggests that stakeholders should seek to prioritize funding for infrastructure upgrades and maintenance along that route and the other routes identified. The findings will potentially benefit a broad range of stakeholders. Companies can strategically focus resources to achieve maximum market share, regulators can streamline policymaking to facilitate AT adoption while ensuring public safety, and transportation agencies can better plan infrastructure upgrades and maintenance. Users globally can apply the methodological framework as a reliable tool for decision-making about where to initially deploy ATs. [ABSTRACT FROM AUTHOR]

This study tackles the urgent need for efficient condition monitoring of road and rail infrastructure, which is integral to a nation's economic vitality. Traditional methods proved both costly and inadequate, resulting in network gaps and accelerated infrastructure decay. Employing connected vehicles with integrated sensors and cloud computing capabilities can provide a cost-effective, sustainable solution for comprehensive infrastructure monitoring. In advocating for international standardization, this study furnishes compelling evidence—encompassing trends in transportation, economics, and patent landscapes—that underscores the necessity and advantages of such standards. The analysis confirmed that trucks and rail will remain dominant in freight transport as infrastructure limitations intensify. A noteworthy finding is the absence of patented solutions in this domain, which simplifies the path toward global standardization. By integrating data from diverse sources, agencies can optimize maintenance triggers and allocate funds more strategically, thus preserving vital transportation networks. These insights not only offer an effective alternative to current practices but also have the potential to influence policymaking and industry standards for infrastructure monitoring. [ABSTRACT FROM AUTHOR]

The gradual adoption of electric vehicles (EVs) globally serves as a crucial move toward addressing global decarbonization goals for sustainable development. However, the lack of cost-effective, power-efficient, and safe chargers for EV batteries hampers adoption. Understanding the research needs and identifying the gaps in EV charger innovation informs investments and research to address development challenges. This study developed a unique text mining workflow to classify themes in EV charger technology and product development by analyzing U.S. patent award summaries. The text mining workflow combined the techniques of data extraction, data cleaning, natural language processing (NLP), statistical analysis, and unsupervised machine learning (ML) to extract unique themes and to visualize their relationships. There was a 47.7% increase in the number of EV charger patents issued in 2022 relative to that in 2018. The top four themes were charging station management, power transfer efficiency, on-board charger design, and temperature management. More than half (53.8%) of the EV charger patents issued over the five-year period from 2018 to 2022 addressed problems within those four themes. Patents that addressed wireless charging, fast charging, and fleet charging accounted for less than 10% each of the EV charger patents issued. This suggests that the industry is still at the frontier of addressing those problems. This study further presents examples of the specific EV charger problems addressed within each theme. The findings can inform investment decisions and policymaking to focus on R&D resources that will advance the state of the art and spur EV adoption. [ABSTRACT FROM AUTHOR]

Traditional hot spot and cluster analysis techniques based on the Euclidean distance may not be adequate for assessing high-risk locations related to crashes. This is because crashes occur on transportation networks where the spatial distance is network-based. Therefore, this research aims to conduct spatial analysis to identify clusters of high- and low-risk crash locations. Using vulnerable road users' crash data of San Francisco, the first step in the workflow involves using Ripley's K-and G-functions to detect the presence of clustering patterns and to identify their threshold distance. Next, the threshold distance is incorporated into the Getis-Ord Gi* method to identify local hot and cold spots. The analysis demonstrates that the network-constrained G-function can effectively define the appropriate threshold distances for spatial correlation analysis. This workflow can serve as an analytical template to aid planners in improving their threshold distance selection for hot spot analysis as it employs actual road-network distances to produce more accurate results, which is especially relevant when assessing discrete-data phenomena such as crashes. [ABSTRACT FROM AUTHOR]

The successful deployment of Advanced Air Mobility (AAM) requires the safe integration of electric vertical takeoff and landing (eVTOL) aircraft in non-segregated airspace. However, a lack of practical deployment demonstrations can slow the regulatory progress. This study presents a practical deployment framework for the integration of eVTOL aircraft into public airspace, in accordance with the Federal Aviation Administration (FAA) special airworthiness criteria. This study focused on a corporate use case to provide a comprehensive scenario for enhancing intra-company mobility. The scenario supports early implementation and demonstration goals by removing reliance on air traffic management systems or air traffic controller separation services. The practical deployment framework included facility selection, route planning, terrain mapping, collision avoidance, aircraft selection, infrastructure requirements, vertiport placement, electric chargers, ground handling, operational staff, and cost estimates. Planners can use the framework as a template to inform the implementation of real-world AAM services using eVTOL aircraft. [ABSTRACT FROM AUTHOR]

The adoption of connected and autonomous vehicles (CAVs) is in its infancy. Therefore, very little is known about their potential impacts on traffic. Meanwhile, researchers and market analysts predict a wide range of possibilities about their potential benefits and the timing of their deployments. Planners traditionally use various types of travel demand models to forecast future traffic conditions. However, such models do not yet integrate any expected impacts from CAV deployments. Consequently, many longrange transportation plans do not yet account for their eventual deployment. To address some of these uncertainties, this work modified an existing model for Madison, Wisconsin. To compare outcomes, the authors used identical parameter changes and simulation scenarios for a model of Gainesville, Florida. Both models show that with increasing levels of CAV deployment, both the vehicle miles traveled and the average congestion speed will increase. However, there are some important exceptions due to differences in the road network layout, geospatial features, sociodemographic factors, land-use, and access to transit., 1. Introduction Transportation demand modeling has advanced in many ways in recent years, including activity-based modeling and simulation modeling [1]. However, the fundamental approach of the traditional four-step model has [...]