Search Results (145)

Conventional commuting mode split models are characterized by inherent limitations in dynamic adaptability, primarily due to persistent dependence on periodic survey data with significant temporal gaps. A dominant transportation distance-based modeling framework for commuting mode choice is proposed, formalizing a generalized cost function. Through the application of random utility theory, probability density curves are generated to quantify mode-specific dominant distance ranges across three demographic groups: car-owning households, non-car households, and collective households. Empirical validation was conducted using Dongguan as a case study, with model parameters calibrated against 2015 resident travel survey data. Parameter updates are dynamically executed through the integration of big data sources (e.g., mobile signaling and LBS). Successful implementation has been achieved in maintaining Dongguan’s transportation models during the 2021 and 2023 iterations. Full article

In this study, a 3D model is developed to simulate multi-hole leakage scenarios in buried pipelines transporting hydrogen-blended natural gas (HBNG). By introducing three parameters—the First Dangerous Time (FDT), Ground Dangerous Range (GDR), and Farthest Dangerous Distance (FDD)—to characterize the diffusion hazard of the gas mixture, this study further analyzes the effects of the number of leakage holes, hole spacing, hydrogen blending ratio (HBR), and soil porosity on the diffusion hazard of the gas mixture during leakage. Results indicate that gas leakage exhibits three distinct phases: initial independent diffusion, followed by an intersecting accelerated diffusion stage, and culminating in a unified-source diffusion. Hydrogen exhibits the first two phases, whereas methane undergoes all three and dominates the GDR. Concentration gradients for multi-hole leakage demonstrate similarities to single-hole scenarios, but multi-hole leakage presents significantly higher hazards. When the inter-hole spacing is small, diffusion characteristics converge with those of single-hole leakage. Increasing HBR only affects the gas concentration distribution near the leakage hole, with minimal impact on the overall ground danger evolution. Conversely, variations in soil porosity substantially impact leakage-induced hazards. The outcomes of this study will support leakage monitoring and emergency management of HBNG pipelines. Full article

Multi-rotor ducted propellers, which integrate the high-efficiency characteristics of ducted propellers with the layout flexibility and safety advantages of distributed propulsion, are extensively utilized in the propulsion systems of low-altitude transport systems and large-scale unmanned aerial vehicles. This study numerically investigates the effects of spanwise distance, streamwise distance, rotational consistency, and rotational phase gap on the unsteady aerodynamic characteristics of multi-rotor ducted propellers under hovering conditions. A parameterized numerical computation model and an Aligned Rank Transform Analysis of Variance (ART-ANOVA) method suitable for small datasets exhibiting regular patterns were developed. Initially, numerical simulations investigated the aerodynamic performance of multi-rotor ducted propeller models with varying layout parameters. The aerodynamic coefficients of the propellers monotonically decrease as the layout spacing increases; however, the change trends differ. Aerodynamic interference reduces the airflow velocity and influences the distribution of high-pressure zones, consequently impacting thrust and efficiency. Subsequently, this paper examined the coupled effects of two rotational characteristics. The relationship between propeller aerodynamic performance and rotational phase gap exhibits distinct trigonometric function characteristics. The presence of the duct mitigates the mutual interference between blades, thereby altering the amplitude and phase of these characteristics. Finally, an ART-ANOVA method was employed to quantify the main and interaction effects, revealing that rotational consistency has a dominant influence on all aspects of aerodynamic performance. Insights into aerodynamic performance are crucial for advancing low-altitude transport systems that utilize ducted propeller propulsion systems. Full article

This study proposes an integrated, machine learning-based multi-objective optimization framework to evaluate and optimize the utilization of steel slag in road base layers, simultaneously addressing economic costs and environmental impacts. A comprehensive dataset of 482 scenarios was engineered based on literature-informed parameters, encompassing transport distance, processing energy intensity, initial moisture content, gradation adjustments, and regional electricity emission factors. Four advanced tree-based ensemble regression algorithms—Random Forest Regressor (RFR), Extremely Randomized Trees (ERTs), Gradient Boosted Regressor (GBR), and Extreme Gradient Boosting Regressor (XGBR)—were rigorously evaluated. Among these, GBR demonstrated superior predictive performance (R² > 0.95, RMSE < 7.5), effectively capturing complex nonlinear interactions inherent in slag processing and logistics operations. Feature importance analysis via SHapley Additive exPlanations (SHAP) provided interpretative insights, highlighting transport distance and energy intensity as dominant factors affecting unit cost, while moisture content and grid emission factor predominantly influenced CO₂ emissions. Subsequently, the Gradient Boosted Regressor model was integrated into a Non-Dominated Sorting Genetic Algorithm II (NSGA-II) framework to explore optimal trade-offs between cost and emissions. The resulting Pareto front revealed a diverse solution space, with significant nonlinear trade-offs between economic efficiency and environmental performance, clearly identifying strategic inflection points. To facilitate actionable decision-making, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method was applied, identifying an optimal balanced solution characterized by a transport distance of 47 km, energy intensity of 1.21 kWh/ton, moisture content of 6.2%, moderate gradation adjustment, and a grid CO₂ factor of 0.47 kg CO₂/kWh. This scenario offered a substantial reduction (45%) in CO₂ emissions relative to cost-minimized solutions, with a moderate increase (33%) in total cost, presenting a realistic and balanced pathway for sustainable infrastructure practices. Overall, this study introduces a robust, scalable, and interpretable optimization framework, providing valuable methodological advancements for sustainable decision making in infrastructure planning and circular economy initiatives. Full article

Generation Z (Gen Z; born 1997–2012) is reshaping global tourism through digital fluency, ethical awareness, and a desire for authentic, sustainable travel experiences. This study surveys 413 Gen Z travelers across France, Portugal, Romania, and the USA to map their booking behaviors, information sources, transport modes, accommodations, dining practices, and leisure activities. The findings reveal a strong preference for independent online bookings and social-media-influenced destination choices (Instagram, TikTok), with air and car travel being used for long-distance journeys and walking/public transit being used for local journeys. Accommodation spans commercial hotels and private rentals, while informal, local dining and nature- or culture-centered leisure prevail. Chi-square tests were performed to identify differences between countries. To reveal distinct traveler segments and their country’s modulations towards sustainability, a hierarchical cluster analysis was performed. The results uncover four segments: “Tech-Active, Nature-Oriented Minimalists” (32.3% in France); “Moderate Digital Planners” (most frequent across all countries, particularly dominant among Romanian respondents); “Disengaged and Indecisive Travelers” (overrepresented in the USA); and “Culturally Inclined, Selective Sustainability Seekers” (>30% in France/Portugal). Although sustainability is widely valued, only some segments of the studied population consistently act on these values. The results suggest that engaging Gen Z requires targeted, value-driven digital strategies that align platform design with the cohort’s diverse sustainability commitments. Full article

Objectives: The aim of this systematic review is to analyze the research journals, sample characteristics and research methodology used in the studies about school neighborhood built environment (SNBE) and the physical health of children and adolescents. Methods: Using 124 key terms across four databases (Web of Science, PubMed, Sportdiscus and Transportation Research Board), 8837 studies were identified, and 55 were selected. The research question and evidence search were guided by the “Population, Intervention, Comparison, Outcomes” (PICO) framework. Results: Most studies were published in health-related research journals (67.3%) and conducted in 16 countries, primarily urban contexts (44.4%). Cross-sectional designs dominated (89.1%), with participation ranging from a minimum of 7 schools and 94 students to a maximum of 6362 schools and 979,119 students. Street network distances are often defined by 1000 or 800 m. The SNBE variables (135 total) were often measured via GIS (67.2%). In contrast, 70.6% of the 45 physical health measures relied on self-reports. Conclusions: This systematic review highlights the diverse approaches, gaps, and common patterns in studying the association between the SNBE and the physical health of children and adolescents. Therefore, this manuscript may serve as a valuable resource to examine the current landscape of knowledge and to guide future research on this topic. Full article

Scientifically and accurately assessing the interaction between changes in human activity intensity and the surrounding ecological environment along the Qinghai–Tibet Railway is of great significance for the optimized construction of the railway and the restoration of the regional ecological environment. Based on different spatial distribution scales and construction phases of the Qinghai–Tibet Railway, this study integrates multi-source remote sensing data to construct a long-term spatiotemporal dataset of human activity intensity in the region. Drawing on analytical methods from production theory, a coupling theoretical framework based on remote sensing ecological models is proposed to quantitatively reveal the coupling relationships between the ecological environment and human activities across varying spatiotemporal scales along the Qinghai–Tibet Railway. The study finds that (1) the spatiotemporal distribution of human activity intensity along the Qinghai–Tibet Railway demonstrates clear patterns, with expansion primarily radiating from transportation corridors and their intersections, and marked spatial heterogeneity across different segments. Overall, human activity intensity increased slowly between 1990 and 2002, followed by a significant rise during the construction and opening of the Golmud–Lhasa section (2001–2007). From 2013 to 2020, the growth rate began to slow. Within a 0–30 km buffer zone centered on railway station locations (with a 15 km radius), the growth rate of human activity intensity generally decreased with increasing distance from the railway. In the 30–60 km buffer zone, this trend tended to stabilize. (2) The coupling process between ecological quality and human activity intensity across different spatiotemporal scales along the railway exhibits considerable spatial and temporal heterogeneity and complexity. The decoupling relationship is dominated by strong and weak decoupling patterns, with strong decoupling being the most prevalent. Weak decoupling is mainly distributed along the sides of the railway. Overall, in most areas along the railway, ecological quality has shown a certain degree of improvement alongside increasing human activity intensity; however, the rate of ecological improvement is generally lower than the rate of increase in human activity intensity. In some areas adjacent to the railway, intensified human activities have led to a decline in ecological quality, though the resulting ecological pressure remains relatively low. Full article

In the field of intelligent decision-making, time-series data collected by sensors serves as the core carrier for interaction between the physical and digital worlds. Accurate analysis is the cornerstone of decision-making in critical scenarios, such as industrial monitoring and intelligent transportation. However, the inherent spatio-temporal coupling characteristics and cross-period long-range dependency of sensor data cause traditional time-series prediction methods to face performance bottlenecks in feature decoupling and multi-scale modeling. This study innovatively proposes a Spatio-Temporal Attention-Enhanced Network (TSEBG). Breaking through traditional structural designs, the model employs a Squeeze-and-Excitation Network (SENet) to reconstruct the convolutional layers of the Temporal Convolutional Network (TCN), strengthening the feature expression of key time steps through dynamic channel weight allocation to address the redundancy issue of traditional causal convolutions in local pattern capture. A Bidirectional Gated Recurrent Unit (BiGRU) variant based on a global attention mechanism is designed, leveraging the collaboration between gating units and attention weights to mine cross-period long-distance dependencies and effectively alleviate the gradient disappearance problem of Recurrent Neural Network (RNN-like) models in multi-scale time-series analysis. A hierarchical feature fusion architecture is constructed to achieve multi-dimensional alignment of local spatial and global temporal features. Through residual connections and the dynamic adjustment of attention weights, hierarchical semantic representations are output. Experiments show that TSEBG outperforms current dominant models in time-series single-step prediction tasks in terms of accuracy and performance, with a cross-dataset R² standard deviation of only 3.7%, demonstrating excellent generalization stability. It provides a novel theoretical framework for feature decoupling and multi-scale modeling of complex time-series data. Full article

As critical hubs for long-distance transportation, railway passenger stations (RPSs) significantly influence energy conservation and CO₂ mitigation. This study investigates the spatiotemporal patterns and driving factors of CO₂ emissions across 247 Chinese RPSs (2014–2023), proposing region-specific decarbonization strategies. The key findings include: (1) Emissions increased universally during 2014–2023, with severe cold zones and developed cities hosting the most high-emission RPSs; (2) purchased thermal energy dominated the emissions in severe cold/cold zones, while purchased electricity prevailed in other zones; (3) the heating area (HA) was a primary emission driver, whereas the percentage of lighting energy consumption (PLEC) served as a key constraint, as shown by correlation and PCA analyses; (4) CO₂ emissions in severe cold zones exhibited strong correlations with heating-related factors, whereas emissions in other zones were predominantly linked to energy structure-related factors. These findings provide region-specific, actionable strategies to support CO₂ emission reduction planning for RPSs. Full article

Wastewater treatment plants (WWTPs) have been confirmed as reservoirs of antibiotic resistance genes (ARGs). This study systematically investigated the distribution patterns of ARGs across different treatment units in municipal WWTPs, along with the environmental drivers, dissemination characteristics, and exposure risks of aerosol-borne ARGs in aerated tank environments. The results revealed a high compositional similarity in aerosol-borne ARGs across the sampling sites, with multidrug ARGs predominating at an average relative abundance of 52%, followed sequentially by tetracycline (11%), MLS (10%), and glycopeptide resistance genes (7%). The diffusion of aerosol-borne ARGs is significantly influenced by environmental factors including temperature, relative humidity, wind speed, and total suspended particulate (TSP) concentration, with temperature being the most dominant factor affecting the dispersion of ARGs. The atmospheric dispersion model demonstrates that aerosol-borne ARGs decay with increasing downwind distance, showing potential for transport from aeration tanks to locations exceeding 1500 m along the prevailing wind direction. Both within wastewater treatment units and downwind areas, adult males had higher respiratory exposure doses but lower skin contact doses compared to females, with respiratory doses exceeding skin contact by 3–4 orders of magnitude. This study highlights the potential health risks posed by aerosol-borne ARG transmission from WWTP operations. Full article

In recent years, the use of unmanned aerial vehicles (UAVs) has expanded in and across various fields, including agriculture, observation, and transportation. Generally, the landing distance of fixed-wing UAVs increases with speed. In particular, the landing distance in the flare phase is proportional to the flight speed. To expand the range of applications for missions by the UAV, it is necessary to develop a short-distance landing control technique. This study focuses on reducing the landing distance during the flare phase before touchdown. The flare path is dominated by the flare time constant. The smaller the flare time constant, the greater the curvature of the flight path and the shorter the horizontal distance. Therefore, we propose a method to determine the flare time constant by applying a nonlinear optimization in which the horizontal distance during the flare phase is used as the evaluation function. The method uses a motion model that incorporates both translational and rotational motion in the longitudinal direction, which is more comprehensive than a point mass model. After solving the nonlinear optimization problem to obtain the flare time constant, we first conduct longitudinal flight simulation to confirm both the accuracy of the optimal solution and the validity of the motion model used in the nonlinear optimization problem and, then, confirm the feasibility of the landing control technique with the optimized flare time constant using a six-degrees-of-freedom simulation. Full article

It is particularly important to improve the emergency rescue response ability of heavy-haul railways to ensure the safety of personnel and the efficiency of material transportation. The current research has achieved some results for multi-objective material dispatching, but it does not consider the impact of accident response level and material type on material dispatching scheme. In this study, a heavy-haul railway in China was selected as the research object. By designing a dual-objective material scheduling model, an optimal material scheduling scheme was obtained, and the optimal solution was solved by a non-dominated sorting genetic algorithm (NSGA-II). Under the condition of keeping the station unchanged and ensuring that the total amount of materials remained unchanged, an optimization scheme of relief material reserves that match the risk characteristics of the line is proposed. The results show that, based on the utility theory, the minimum distance of the improved dual-objective material dispatching is reduced by 34.8% (single accident point) and 62.99% (multiple accident points), and the total distance of material dispatching is reduced by 37.92% and 70.57%, respectively, indicating that the optimized reserve scheme can effectively shorten the response time and improve the rescue efficiency. The material reserve optimization scheme for emergency rescue stations proposed in this study has important reference value for improving the emergency rescue efficiency of heavy-haul railways. Full article

With the global shift to sustainability, the energy sector faces pressure to adopt low-carbon solutions. Blue ammonia (BA), derived from natural gas (NG) with carbon capture, presents significant opportunities but requires a holistic sustainability assessment. This study conducts a novel life cycle sustainability assessment (LCSA) of BA, evaluating environmental, economic, and social impact performance from feedstock processing to maritime transport for a 1.2 MMTPA production capacity. Process simulations in Aspen HYSYS V12 and the ammonia maritime transport operations’ sustainability assessment model provide critical insights. The ammonia converter unit contributes the highest emissions (17.9 million tons CO₂-eq), energy use (963.2 TJ), and operational costs (USD 189.2 million). CO₂ removal has the most considerable land use (141.7 km²), and purification records the highest water withdrawal (14.8 million m³). Carbon capture eliminates 6.5 million tons of CO₂ annually. Economically, ammonia shipping dominates gross surplus (USD 653.9 million, 72%) and tax revenue (USD 65.3 million) despite employing just 43 workers. Socially, the ammonia converter unit has the highest human health impact (16,621 DALY, 54%). Sensitivity analysis reveals transport distance (46.5% CO₂ emissions) and LNG fuel prices (63.8% costs) as key uncertainties. Findings underscore the need for optimized logistics and alternative fuels to enhance BA sustainability. Full article

Urumqi is located at the northern foot of the Tianshan Mountains. Its topographical features have a significant impact on the transport and dispersion of air pollutants. Moreover, its winter is extremely long, lasting up to six months. A combination of an irrational energy consumption structure, unique meteorological conditions, and complex geographical terrains has led to a substantial increase in NO₂ emissions, severely damaging the local ecological environment. In this study, we integrate Geographic Information System (GIS) and Computational Fluid Dynamics (CFD). By leveraging GIS’s powerful spatial analysis capabilities and CFD’s high-precision fluid simulation technology, we significantly enhance the simulation accuracy of complex phenomena like airflow and pollutant diffusion. Additionally, the inverse distance weighted interpolation method is comprehensively employed to analyze the Air Quality Indices (AQIs) of typical pollutants in different districts of Urumqi during winter. The results reveal that high altitude causes instability of the dominant near-surface winds within the atmospheric boundary layer. The increasing frequency of surface calm winds reduces the advective transport of atmospheric pollutants. Topography and winter meteorological conditions are identified as the primary factors contributing to pollutant accumulation. This research not only unveils the fundamental mechanisms of pollutant dispersion in mountainous terrains but also validates the practicality of coupling GIS and CFD, providing a theoretical basis for pollution dispersion studies in this region. This study reveals the general laws of pollutant dispersion in mountainous terrain, resolves the issue of establishing complex geographical models, and demonstrates the feasibility of coupling the GIS and CFD. Meanwhile, it provides a theoretical basis for pollution dispersion in this region. Full article

The Pearl River Mouth Basin (PRMB) had two potential provenances (intrabasinal and extrabasinal) during the Paleogene Enping Formation period. However, the understanding of their differences in source supply and evolution over time and space is limited due to the regional restriction in borehole coverage. This study aims to address the knowledge gap by utilizing detrital zircon U-Pb dating data, seismic data, and borehole data. Specifically, this study focuses on examining the characteristics of provenance evolution and sedimentary infilling within the Enping Formation in various sags of the northern PRMB. The results indicate temporal and spatial variability in provenance from the lower Ep⁴ and Ep³ to the upper Ep² and Ep¹ Members. The influence of extrabasinal provenance from the South China Block (SCB) was prominent in the northern region of the Zhu I Depression during the deposition of Ep⁴ and Ep³ Members, while intrabasinal provenance from local uplifts remained a significant source for most sags. During this period, sediment transportation occurred over short distances, leading to the widespread development of smaller fan deltas and braided river deltas. In contrast, extrabasinal provenance became dominant during the deposition of Ep² and Ep¹ Members throughout the entire Zhu I Depression. This shift promoted the development of large-scale, shallow, braided river deltas with sediment transported over long distances. The analysis reveals a close correspondence between the shifting provenance and the evolution of sedimentary infilling patterns in the PRMB. As a result, the sags transitioned from being under-filled or balanced-filled to being balanced-filled or over-filled. This study holds immense significance for oil and gas exploration as well as the prediction of favorable sedimentary sand bodies in offshore petroliferous basins. Full article