Search Results (179)

As low-altitude transportation becomes essential to global integrated transport systems, developing extensive and well-structured networks in urban agglomerations is crucial for fostering regional synergy and enhancing three-dimensional transport. Focusing on the Beijing–Tianjin–Hebei urban agglomeration, this study integrates multi-source data within a three-stage research framework: (1) node suitability assessment, (2) route optimization, and (3) network structure evaluation. It systematically evaluates the suitability of county-level general aviation airports and township-level vertiports. Building on the suitability analysis, a hierarchical route network is constructed using a modified gravity model augmented by spatial correction mechanisms. Finally, spatial syntax analysis, supplemented with equity and robustness assessments, is applied to evaluate network accessibility, topological efficiency, and resilience. The key findings are as follows: (1) The suitability classification identifies 43 Class A, 86 Class B, and 71 Class C general aviation airports, revealing a spatial pattern characterized by higher density in the east, lower density in the west, and a multi-nodal clustering structure. Township-level vertiports markedly increase terminal-node coverage. (2) The optimized hierarchical network includes 114 primary, 180 secondary, and 366 tertiary routes, bridging previous regional connectivity gaps. (3) High values of network integration, choice, spatial intelligibility, and equity-adjusted accessibility indicate robust performance, fairness in service distribution, and resilience under potential disruptions. This study offers a methodological paradigm for the systematic development of low-altitude transport networks and provides valuable references for evidence-based planning of urban agglomeration air mobility systems and the strategic development of regional low-altitude economies. Full article

This study examines how air transport infrastructure and trade flows influence economic growth across ASEAN countries, with a focus on identifying the threshold levels at which these factors begin to enhance growth. Despite increasing investment in regional logistics and connectivity, policymakers often lack evidence-based thresholds to guide infrastructure and trade policy for long-term development. Addressing this gap, this study applies a Dynamic Panel Threshold Model to uncover the tipping points at which improvements in air cargo volume (lnCargo) and air transport infrastructure quality (lnQAir) translate into stronger economic growth. By employing System-GMM and First-Difference GMM estimations, the analysis captures the threshold effects of air cargo volume (lnCargo) and air transport infrastructure quality (lnQAir) on economic growth over varying regimes. The results reveal significant single-threshold effects for both lnCargo and lnQAir, indicating that their contributions to economic growth become substantial after surpassing specific critical levels. When air cargo volume exceeds approximately 267,067 tons per year (lnCargo > 5.5875), its positive effect on economic growth strengthens, particularly when accompanied by high-quality infrastructure. Similarly, air transport infrastructure quality exhibits a significantly stronger impact on economic growth once it exceeds the critical threshold of lnQAir = 1.5476 (≈4.7001 index points). These findings emphasize the complementarity between trade flows and infrastructure, aligning with endogenous growth theory, which suggests that infrastructure investments yield increasing returns when integrated with trade expansion. Policy implications suggest that ASEAN economies should adopt demand-driven infrastructure development aligned with trade dynamics, prioritizing regional connectivity, logistics efficiency, and investment attraction to sustain long-term economic growth. Full article

The siphon effect between airports has amplified the polarization in passenger throughput, undermining the balanced development and sustainability of airport clusters. The airport siphon effect occurs when one airport attracts a disproportionate share of passengers, concentrating traffic at the expense of others, which affects the overall resilience of the entire airport cluster. To address this issue, this study proposes a siphon index, expands the range of ground transportation options for passengers, and establishes a zero-siphon model to assess the impact of siphoning on the resiliency of airport clusters. Using this framework, four major airport clusters in China were selected as research subjects, with regional aviation accessibility serving as a measure of resilience. The results showed that among the four airport clusters, the siphon effect is most pronounced in the Guangzhou region. To explore the implications of this effect further, three airport disruption scenarios were simulated to assess the resilience of the Pearl River Delta airport cluster. The results indicated that the intensity and timing of disruptive events significantly affect airport cluster resilience, with hub airports being particularly sensitive. This study analyzes the risks associated with excessive route concentration, providing policymakers with critical insights to enhance the sustainability, equity, and resilience of airport clusters. The proposed strategies facilitate coordinated infrastructure development, optimized air–ground intermodal connectivity, and risk mitigation. These measures contribute to building more sustainable and adaptive aviation networks in rapidly urbanizing regions. Full article

Urban Air Mobility (UAM) is envisioned to revolutionize urban transportation by improving traffic efficiency and mitigating surface-level congestion. One of the fundamental challenges in implementing UAM systems lies in the optimal siting of vertiports, which requires a delicate balance among infrastructure construction costs, passenger access costs to their assigned vertiports, and the operational connectivity of the resulting vertiport network. This study develops an integrated mathematical model for vertiport location decision, aiming to minimize total system cost while ensuring UAM network connectivity among the selected vertiport locations. To efficiently solve the problem and improve solution quality, a hybrid genetic algorithm is developed by incorporating a Minimum Spanning Tree (MST)-based connectivity enforcement mechanism, a fundamental concept in graph theory that connects all nodes in a given network with minimal total link cost, enhanced by a greedy initialization strategy. The effectiveness of the proposed algorithm is demonstrated through numerical experiments conducted on both synthetic datasets and the real-world transportation network of New York City. The results show that the proposed hybrid methodology not only yields high-quality solutions but also significantly reduces computational time, enabling faster convergence. Overall, this study provides practical insights for UAM infrastructure planning by emphasizing demand-oriented vertiport siting and inter-vertiport connectivity, thereby contributing to both theoretical development and large-scale implementation in complex urban environments. Full article

The extension of high-speed rail (HSR) lines around the world is increasing. The largest network today is in China, followed by Spain, Japan, France, and Italy; currently, new lines are being built in Morocco and Saudi Arabia. The goal of the new lines built is to drastically reduce the time distances between the extreme railway terminals by intervening on the two main components of time: space and speed. The two components have been investigated in various fields of engineering for design conditions (ex ante/a priori). In the literature, there is no analysis of what happened in the realization of the projects (ex post/retrospective). The research problem that arises is to analyze the high-speed lines built in order to verify, given a pair of extreme terminals, how much the length is reduced by passing from a conventional line to a high-speed line, and to verify how this length is getting closer and closer to the distance as the crow flies. The reduction of spatial distance produces direct connections between two territories, making the railway system (HSR) more competitive compared to other transport alternatives (e.g., air travel). To address the problem posed, information and data are collected on European HSR lines, which constitute a sufficiently homogeneous set in terms of railway and structural standards. The planimetric characteristics of specially built lines such as HSR are examined. A test method is proposed, consisting of a model that is useful to compare the length along the HSR line, with direct lengths, and existing conventional lines. The results obtained from the elaborations offer a first answer to the problem posed, demonstrating that in the HSR lines realized the spatial distances approach the distance as the crow flies between the cities located at the extremes, and are always shorter than the lengths of conventional lines. The final indications that can be drawn concern the possibility of using the results obtained as a reference for decision-makers and planners involved in the transport planning process at national and international level. Future research directions should study the values of the indicators in other large HSR networks, such as those built in Asia, and more generally study all the elements of the lines specially built to allow better sustainable planning, reducing the negative elements found and increasing the positive ones. Full article

The exposure to elevated levels of ozone contributes to respiratory diseases and ecosystem degradation. Mediterranean countries are among those most affected by high ozone concentrations, which are generally overestimated by chemistry transport models underscoring the importance of improving the accuracy of air quality modelling. This study introduces an enhanced Mediterranean dry deposition description within the LOTOS-EUROS model framework, focusing on refining key vegetation parameters for the Mediterranean climate zone, with the goal to better estimate deposition and connected concentration values. Adjustments were made to the vegetation type dependent Jarvis functions for temperature and vapour pressure deficit, as well as to the maximum stomatal conductance across four land use types: arable land, crops, deciduous broadleaf forest, and coniferous evergreen forest. The model’s baseline run showed a widespread overestimation of ozone. Adjustments to the dry deposition routines reduced this overestimation, but the model simulation incorporating all changes still showed elevated ozone levels. Both runs displayed moderate spatial correlation with observations from 117 rural background monitoring stations, and most stations exhibited a temporal correlation between 0.5 and 0.8. An improved RMSE and bias were noted at the majority of the stations (114 out of 117) for the model simulation incorporating all changes. The monthly analysis indicated consistent overestimation at two Portuguese sites beginning in March. The model effectively tracked temporal changes overall. However, the diurnal analysis revealed site-specific differences: an overestimation at the station closest to highly populated areas at night, while rural stations aligned better with observed values. These results highlight the benefits of region-specific model adaptations and lay the groundwork for further advancements, such as incorporating detailed vegetation classifications and seasonal variations. Full article

Generation Z, known for its digital fluency and distinct consumer behaviors, is an increasingly influential demographic in the tourism industry. As a sustainability-focused generation, their preferences and behaviors are shaping the future of travel. This study explores the tourism experiences of Romanian Generation Z members, focusing on their travel patterns, motivations, information sources, and service preferences. A bibliometric analysis of the existing literature was conducted to identify research trends and gaps in understanding Generation Z’s tourism behaviors. Using a mixed-method approach, the study integrates survey data from 399 respondents with AI-generated insights from ChatGPT 4o mini to compare traditional research methods with AI-driven analysis. It examines how AI interprets and predicts travel behaviors, highlighting the reliability and biases inherent in AI models. Key discrepancies between the two methods were found: The survey indicated a preference for car travel and commercial accommodation, while AI predictions favored air travel and private accommodation. Additionally, AI emphasized a growing interest in eco-friendly transportation and connections to natural and cultural environments, offering a broader scope than the survey alone. Both methods revealed a trend toward digital platforms for travel planning, moving away from traditional agencies. The findings suggest that AI can complement traditional research by providing actionable insights, though its limitations emphasize the need for a balanced integration of both methods. This study offers new perspectives on Generation Z’s tourism experiences. Full article

This paper presents a comprehensive review of tethered unmanned aerial vehicles (UAVs), focusing on their challenges and potential applications across various domains. We analyze the dynamic characteristics of tethered UAV systems and address the unique challenges they present, including complex tether dynamics, impulsive forces, and entanglement risks. Additionally, we explore application-specific challenges in areas such as payload transportation and ground-connected systems. The review also examines existing tethered UAV testbed designs, highlighting their strengths and limitations in both simulation and experimental settings. We discuss advancements in multi-UAV cooperation, ground–air collaboration through tethers, and the integration of retractable tether systems. Moreover, we identify critical future challenges in developing tethered UAV systems, emphasizing the need for robust control strategies and innovative solutions for dynamic and cluttered environments. Finally, the paper provides insights into the future potential of variable-length tethered UAV systems, exploring how these systems can enhance versatility, improve operational safety, and expand the range of feasible applications in industries such as logistics, emergency response, and environmental monitoring. Full article

Recent advancements in aviation and automation technologies have catalyzed the emergence of Urban Air Mobility (UAM), an innovative transportation paradigm involving the use of automated vertical take-off and landing aircraft for intra-city passenger travel. Despite growing global interest, the development and application of integrated geospatial frameworks for UAM infrastructure planning—particularly vertiport siting—remain limited. Thus, this study proposes a three-stage geospatial analysis framework, which consists of (1) Suitability analysis, employing multi-criteria decision-making techniques; (2) Regulation analysis, which screens out parcels restricted by aviation safety standards, land-use policies, and other statutory constraints; and (3) Location-allocation analysis, which spatially optimizes vertiport distribution in accordance with urban master plans and strategic transport priorities. Then, this framework is empirically applied to two South Korean UAM pilot sites—Busan and Jeju. The findings reveal that high-suitability areas are predominantly concentrated in dense urban cores with strong multimodal connectivity and mixed land-use configurations. However, a significant proportion of these zones are rendered infeasible due to regulatory exclusions, such as military flight paths and restricted airspace. Additionally, areas with lower suitability—often home to marginalized populations—raise critical equity concerns. This study contributes to the advancement of urban geospatial analytics by presenting a replicable methodological framework for vertiport site selection, while offering strategic insights to inform early-stage UAM deployment initiatives. Full article

Technological innovation toward electrification and digitalization is revolutionizing aviation, paving the way for new aeronautical paradigms and novel modes to transport goods and people in urban and regional environments. Advanced Air Mobility (AAM) leverages vertical and digital mobility, driven by safe, quiet, sustainable, and cost-effective electric vertical takeoff and landing (VTOL) aircraft. A key enabler of this transformation is the development of vertiports—dedicated infrastructure designed for VTOL operations. Vertiports are pivotal in integrating AAM into multimodal transport networks, ensuring seamless connectivity with existing urban and regional transportation systems. Their design, placement, and operational framework are central to the success of AAM, influencing urban accessibility, safety, and public acceptance. These facilities should accommodate passenger and cargo operations, incorporating charging stations, takeoff and landing areas, and optimized traffic management systems. Public and private sectors are investing in vertiports, shaping the regulatory and technological landscape for widespread adoption. As cities prepare for the future of aerial mobility, vertiports will be the cornerstone of sustainable, efficient, and scalable air transportation. Full article

Many important practical problems connected to energy efficiency in buildings, ecology, metallurgy, the development of wireless communication systems, the optimization of radar technology, quantum computing, pharmacology, and seismology are described by large-scale mathematical models that are typically represented by systems of partial differential equations. Such systems often involve numerous input parameters. It is crucial to understand how susceptible the solutions are to uncontrolled variations or uncertainties within these input parameters. This knowledge helps in identifying critical factors that significantly influence the model’s outcomes and can guide efforts to improve the accuracy and reliability of predictions. Sensitivity analysis (SA) is a method used efficiently to assess the sensitivity of the output results from large-scale mathematical models to uncertainties in their input data. By performing SA, we can better manage risks associated with uncertain inputs and make more informed decisions based on the model’s outputs. In recent years, researchers have developed advanced algorithms based on the analysis of variance (ANOVA) technique for computing numerical sensitivity indicators. These methods have also incorporated computationally efficient Monte Carlo integration techniques. This paper presents a comprehensive theoretical and experimental investigation of Monte Carlo algorithms based on “symmetrized shaking” of Sobol’s quasi-random sequences. The theoretical proof demonstrates that these algorithms exhibit an optimal rate of convergence for functions with continuous and bounded first derivatives and for functions with continuous and bounded second derivatives, respectively, both in terms of probability and mean square error. For the purposes of numerical study, these approaches were successfully applied to a particular problem. A specialized software tool for the global sensitivity analysis of an air pollution mathematical model was developed. Sensitivity analyses were conducted regarding some important air pollutant levels, calculated using a large-scale mathematical model describing the long-distance transport of air pollutants—the Unified Danish Eulerian Model (UNI-DEM). The sensitivity of the model was explored focusing on two distinct categories of key input parameters: chemical reaction rates and input emissions. To validate the theoretical findings and study the applicability of the algorithms across diverse problem classes, extensive numerical experiments were conducted to calculate the main sensitivity indicators—Sobol’ global sensitivity indices. Various numerical integration algorithms were employed to meet this goal—Monte Carlo, quasi-Monte Carlo (QMC), scrambled quasi-Monte Carlo methods based on Sobol’s sequences, and a sensitivity analysis approach implemented in the SIMLAB software for sensitivity analysis. During the study, an essential task arose that is small in value sensitivity measures. It required numerical integration approaches with higher accuracy to ensure reliable predictions based on a specific mathematical model, defining a vital role for small sensitivity measures. Both the analysis and numerical results highlight the advantages of one of the proposed approaches in terms of accuracy and efficiency, particularly for relatively small sensitivity indices. Full article

Bike-sharing initiatives play a crucial role in sustainable urban transportation, addressing vehicular congestion, air quality issues, and sedentary lifestyles. However, the connection between bike-sharing facilities and the advantages perceived by users remains insufficiently explored particular in post-industrial regions, such as Silesia, Poland. This study develops a multidimensional framework linking infrastructure elements—such as station density, bicycle accessibility, maintenance standards, and technological integration—to perceived benefits. Using a mixed-methods approach, a survey conducted in key Silesian cities combines quantitative analysis (descriptive statistics, factor analysis, and regression modelling) with qualitative insights from user feedback. The results indicate that the most valuable benefits are health improvements (e.g., improved physical fitness and mobility) and environmental sustainability. However, infrastructural deficiencies—disjointed bike path systems, uneven station placements, and irregular maintenance—substantially hinder system efficiency and accessibility. Inadequate bike maintenance adversely affects efficiency, safety, and sustainability, highlighting the necessity for predictive upkeep and optimised services. This research underscores innovation as a crucial factor for enhancing systems, promoting seamless integration across multiple modes, diversification of fleets (including e-bikes and cargo bikes), and the use of sophisticated digital solutions like real-time tracking, contactless payment systems, and IoT-based monitoring. Furthermore, the transformation of post-industrial areas into cycling-supportive environments presents strategic opportunities for sustainable regional revitalisation. These findings extend beyond the context of Silesia, offering actionable insights for policymakers, urban mobility planners, and Smart City stakeholders worldwide, aiming to foster inclusive, efficient, and technology-enabled bike-sharing systems. Full article

Tourism is a critical driver of economic growth, particularly in BRICS+ nations, where it contributes significantly to GDP and fosters multilateral cooperation. However, challenges such as limited direct air connectivity, regulatory barriers, and financial constraints hinder seamless travel experiences and impact tourism and trade. This study investigates the potential of direct air transport links among BRICS+ nations to alleviate these challenges, focusing on economic benefits, policy recommendations, and sustainability implications. Using a mixed-methods approach, including policy analysis and economic impact modeling, the findings reveal that enhanced air connectivity could reduce logistical inefficiencies, promote cultural exchange, and drive sustainable economic growth. This study concludes with strategies for implementing direct air routes and collaborative frameworks to advance the sustainable development goals of BRICS+ countries. Full article

We propose a vertiport location-allocation methodology for urban air mobility (UAM) from the perspective of transportation network topology. The location allocation of vertiports within a transportation network is a crucial factor in determining the unique characteristics of UAM compared to existing transportation modes. However, as UAM is still in the pre-commercialization phase, with significant uncertainties, there are limitations in applying location-allocation models that optimize objective functions such as maximizing service coverage or minimizing travel distance. Instead, vertiport location allocation should be approached from a strategic perspective, taking into account public capital investments aimed at improving the transportation network by leveraging UAM’s distinct characteristics compared to existing urban transportation modes. Therefore, we present a methodology for evaluating the impact of vertiport location-allocation strategies on changes in transportation network topology. To analyze network topology, we use the Seoul Metropolitan railway network as the base network and construct scenarios where vertiports are allocated based on highly connected nodes and those prioritizing structurally vulnerable nodes. We then compare and analyze global network efficiency, algebraic connectivity, average shortest path length, local clustering coefficient, transitivity, degree assortativity and modularity. We confirm that while allocating vertiports based on network centrality improves connectivity compared to vulnerability-based allocation, the latter approach is superior in terms of network efficiency. Additionally, as the proportion of vertiports increases, the small-world property of the network rapidly increases, indicating that the vertiport network can fundamentally alter the structure of multimodal transportation systems. Regardless of whether centrality or vulnerability is prioritized, we observe that connectivity increase exponentially, while network efficiency changes linearly with the increase in vertiport proportion. Our findings highlight the necessity of a network-based approach to vertiport location allocation in the early stages of UAM commercialization, and we expect our results to inform future research directions on vertiport allocation in multimodal transportation networks. Full article

This study presents an improved deep-learning model, termed Enhanced Time Series Mixer (E-TSMixer), for the prediction of particulate matter. By analyzing the temporal evolution of PM_2.5 concentrations from multivariate monitoring data, the model demonstrates significant prediction capabilities while maintaining consistency with observed pollutant transport characteristics in the urban boundary layer. In E-TSMixer, a fully connected output layer is proposed to enhance the predictive capability for complex spatiotemporal dependencies. The relevant data on air quality and traffic flow are fused to achieve high-precision predictions of PM_2.5 concentrations through a multivariate time-series forecasting model. An asymmetric penalty mechanism is added to dynamically optimize the loss function. Experimental results indicate that the proposed E-TSMixer model achieves higher accuracy for the prediction of PM_2.5, which significantly outperforms the traditional models. Additionally, an intelligent dual regulation of fixed and dynamic threshold model is introduced and combined with E-TSMixer for the decision-making model of the real-time adjustments of the frequency, routes, and timing of water truck operation in practice. Full article