Search Results (40)

Wind and sand pose a significant threat to operational safety along the route of the Golmud-Korla Railway. To combat the adverse effects of these hazards, numerous sand retaining dikes and sand intercepting ditches have been constructed along the railway corridor. However, the deposition and erosion mechanisms of sand particles in close proximity to these structures have yet to be fully investigated. Therefore, it uses numerical simulations to study the structure of the wind-sand flow field around the sand retaining dike and the sand intercepting ditch, under varying spacing conditions, with an analysis of sand deposition and erosion laws. The results indicate that vortices form on the leeward side and within the sand intercepting ditch. Among these, the vortex flow occurring on the downstream side of the sand retaining dike exhibits a flow reattachment phenomenon at specific locations (i.e., attachment points). As the spacing increases, clockwise vortices Rd1 and Rd2, develop on the leeward side and inside the ditch, respectively. The leeward side of the spacing range of 0–8H is characterized by reverse erosion and deposition processes. When the spacing is 10–15H, a forward erosion zone emerges and expands progressively with the increase in spacing. When the spacing exceeds 10H, i.e., as the sand intercepting ditch is positioned downstream of the vortex reattachment point of the sand retaining dike, its sand interception efficiency is markedly enhanced. It not only elucidates the wind-sand flow and deposition patterns surrounding sand retaining dike and sand intercepting ditch under various spacing configurations but also offers valuable insights for the future design and implementation of protective structures for railways in wind-sand affected regions. Full article

The rapid advancement of artificial technology is giving rise to new forms of cyber threats like memorization-based APT attacks, which not only pose significant risks to critical infrastructure but also present serious challenges to conventional security architectures. As a crucial service information system in railway passenger stations, the Railway Passenger Service System (RPSS) is particularly vulnerable due to its widespread terminal distribution and large attack surface. This paper focuses on two key challenges within the RPSS Cloud Center’s Double-Layer Dynamic Heterogeneous Redundancy (DDHR) architecture under such attacks: (i) the inability to accurately estimate redundant executor scheduling time, and (ii) the absence of an intelligent defense scheduling method capable of countering memorization-based attacks within a unified and quantifiable environment. To address these issues, we first establish the problem formulation of optimizing defender’s payoff under incomplete information, which applies information entropy of DDHR redundant executors to reflect attacking and defending behaviors. Then a method of estimating attacking time is proposed in order to overcome the difficulty in determining scheduling time due to incomplete information. Finally, we introduce the PPO_HE approach—a Proximal Policy Optimization (PPO) algorithm enhanced with quantifiable information Entropy and Heterogeneity of DDHR redundant executors. Extensive experiments were conducted for evaluation in terms of the two entropy-related metrics: information entropy decay amount and information entropy decay rate. Results demonstrate that the PPO_EH approach achieves the highest efficiency per scheduling operation in countering attacks and provides the longest resistance time against memorization-based attacks under identical initial information entropy conditions. Full article

Shield tunneling beneath existing railways remains a critical challenge in urban infrastructure development, as it risks destabilizing overlying soil structures and compromising railway safety. This study presents an integrated methodology combining physical model tests and three-dimensional numerical simulation, validated by their mutual agreement, to capture the settlement and deformation induced by twin shield tunneling beneath an operational railway under the complex geological conditions of the Qingdao Metro. A parametric study was subsequently conducted to systematically evaluate the influence of critical construction parameters, including grouting pressure, grout stiffness, and chamber pressure, on railhead settlement. Additionally, a comparative analysis assessed the effectiveness of settlement control measures, including D-type beam reinforcement, deep-hole grouting reinforcement, and their combined application. Results show that railhead deformation primarily manifests as settlement, with cumulative effects from sequential tunneling of the left and right lines. Proximity to fault zones intensifies crown subsidence, while tunneling induces significant soil stress relaxation, particularly in geologically weaker strata. Within optimal ranges, increased grouting pressure, chamber pressure, and grout stiffness effectively reduce railhead settlement; however, their efficacy diminishes beyond specific thresholds. The combined D-type beam and deep-hole grouting reinforcement scheme proved most effective in controlling settlement, ensuring railway operational safety and construction stability. These findings provide essential theoretical and practical guidance for optimizing shield tunneling strategies in complex urban environments, enhancing the safety and reliability of critical railway infrastructure. Full article

Severe snowstorms pose multiple threats to high-speed rail systems, including sudden drops in track friction coefficients, icing of overhead contact lines, and reduced visibility. These conditions can trigger dynamic risks such as train speed restrictions, cascading delays, and operational disruptions. Addressing the limitations of traditional scheduling methods in spatio-temporal modeling during blizzards, real-time multi-objective trade-offs, and high-dimensional constraint solving efficiency, this paper proposes a collaborative optimization approach integrating temporal forecasting with deep reinforcement learning. A dual-module LSTM-PPO model is constructed using LSTM (Long Short-Term Memory) and PPO (Proximal Policy Optimization) algorithms, coupled with a composite reward function. This design collaboratively optimizes punctuality and scheduling stability, enabling efficient schedule adjustments. To validate the proposed method’s effectiveness, a simulation environment based on the Lanzhou-Xinjiang High-Speed Railway line was constructed. Experiments employing a three-stage blizzard evolution mechanism demonstrated that this approach effectively achieves a dynamic equilibrium among safety, punctuality, and scheduling stability during severe snowstorms. This provides crucial decision support for intelligent scheduling of high-speed rail systems under extreme weather conditions. Full article

The metro railway system is pivotal not just as a crucial transportation network for daily commuters but also as a significant enhancer of urban vibrancy, especially through its role in attracting a substantial volume of non-commuters. This study focuses on non-commuting travel behaviors around metro stations, exploring how the built environment affects non-commuters’ destination choices. A Random Forest model is developed based on data from Chengdu, China. The model is interpreted with SHapley Additive exPlanations (SHAP) analysis. Route length, building coverage, greenery, and proximity are key factors and indicate a nonlinear impact on non-commuters’ destination choices. The impact of these factors was found to vary significantly depending on the scale and context, indicating a need for nuanced urban planning approaches. The findings highlight the need for sophisticated urban planning that balances functionality and needs in transit-oriented development, aiming to cater to non-commuters and promote sustainable, vibrant urban spaces. Full article

Numerous rural residential buildings exhibit inadequate seismic performance when subjected to blast-induced vibrations, which poses potential threats to their overall stability and structural integrity when in proximity to blasting project sites. The investigation conducted in conjunction with the Qianshi Mountain blasting operations along the Wenzhou segment of the Hangzhou–Wenzhou High-Speed Railway integrates household field surveys and empirical measurements to perform modal analysis of rural residential buildings through finite element simulation. Adhering to the principle of stratified arrangement and composite measurement point configuration, an effective and reasonable experimental observation framework was established. In this investigation, the seven-story rural residential building in adjacent villages was selected as the research object. Strong-motion seismographs were strategically positioned adjacent to frame columns on critical stories (ground, fourth, seventh, and top floors) within the observational system to acquire test data. Methodical signal processing techniques, including effective signal extraction, baseline correction, and schedule conversion, were employed to derive temporal dynamic characteristics for each story. Combined with the Fourier transform, the frequency–domain distribution patterns of different floors are subsequently obtained. Leveraging the structural dynamic theory, time–domain records were mathematically converted to establish the structure’s maximum response spectra under blast-induced loading conditions. Through the analysis of characteristic curves, including floor acceleration response spectra, dynamic amplification coefficients, and spectral ratios, the dynamic response patterns of rural residential buildings subjected to blast-induced vibrations have been elucidated. Following the normalization of peak acceleration and velocity parameters, the mechanisms underlying differential floor-specific dynamic responses were examined, and the layout principles of measurement points were subsequently formulated and summarized. These findings offer valuable insights for enhancing the seismic resilience and structural safety of rural residential buildings exposed to blast-induced vibrations, with implications for both theoretical advancements and practical engineering applications. Full article

Non-active current in the time domain is considered for application to the diagnostics and classification of loads in power grids based on waveform-distortion characteristics, taking as a working example several recordings of the pantograph current in an AC railway system. Data are processed with a deep autoencoder for feature extraction and then clustered via k-means to allow identification of patterns in the latent space. Clustering enables the evaluation of the relationship between the physical meaning and operation of the system and the distortion phenomena emerging in the waveforms during operation. Euclidean distance (ED) is used to measure the diversity and pertinence of observations within pattern groups and to identify anomalies (abnormal distortion, transients, …). This approach allows the classification of new data by assigning data to clusters based on proximity to centroids. This unsupervised method exploiting non-active current is novel and has proven useful for providing data with labels for later supervised learning performed with the 1D-CNN, which achieved a balanced accuracy of 96.46% under normal conditions. ED and 1D-CNN methods were tested on an additional unlabeled dataset and achieved 89.56% agreement in identifying normal states. Additionally, Grad-CAM, when applied to the 1D-CNN, quantitatively identifies the waveform parts that influence the model predictions, significantly enhancing the interpretability of the classification results. This is particularly useful for obtaining a better understanding of load operation, including anomalies that affect grid stability and energy efficiency. Finally, the method has been also successfully further validated for general applicability with data from a different scenario (charging of electric vehicles). The method can be applied to load identification and classification for non-intrusive load monitoring, with the aim of implementing automatic and unsupervised assessment of load behavior, including transient detection, power-quality issues and improvement in energy efficiency. Full article

This study investigates the spread patterns of tunnel fires and examines issues related to emergency response. It focuses on the temperature characteristics, spread patterns, conditions leading to multi-source fires, and the efficacy of water mist suppression methods in heavy-haul railway tunnel fires. The research employs theoretical derivations and numerical simulations to achieve its objectives. It was discovered that, during a fire in a heavy-haul railway tunnel, the temperature inside the tunnel can exceed 500 °C. Furthermore, depending on the nature of the goods transported by the train and under specific wind speed conditions, the fire source has the potential to spread to other carriages, resulting in a multi-source fire. Using the numerical simulation software Pyrosim 2022, various wind speed conditions were simulated. The results revealed that at lower wind speeds, the smoke demonstrates a reverse flow phenomenon. Concurrently, when the adjacent carriage on the leeward side of the fire is ignited, the high-temperature reverse flow smoke, along with the thermal radiation from the flames, ignites combustible materials in the adjacent carriage on the windward side of the burning carriage. Through theoretical derivation and numerical simulation, the critical wind speed for the working conditions was determined to be 2.14 m/s. It was found that while a higher wind speed can lead to a decrease in temperature, it also increases the flame deflection angle. When the wind speed exceeds 2.4 m/s, although the temperature significantly drops in a short period, the proximity of combustible materials on the leeward side of the carriage becomes a concern. At this wind speed, the flame deflection angle causes heat radiation on the leeward side, specifically between 0.5 m and 3 m, to ignite the combustible materials on the carriage surface, resulting in fire spread and multiple fire incidents. The relationship between wind speed and the angle of deflection from the fire source was determined using relevant physics principles. Additionally, the relationship between wind speed and the trajectory of water mist spraying was established. It was proposed to optimize the position of the water mist based on its deviation, and the results indicated that under critical wind speed conditions, when the water mist spraying is offset approximately 5 m towards the upwind side of the fire source, it can act more directly on the surface of the fire source. Numerical simulation results show a significant reduction in the maximum temperature and effective control of fire spread. Under critical wind speed conditions, the localized average temperature of the fire decreased by approximately 140 °C when spraying was applied, compared to the conditions without spraying, and the peak temperature decreased by about 190 °C. This modification scheme can effectively suppress the threat of fire to personnel evacuation under simulated working conditions, reflecting effective control over fires. Additionally, it provides theoretical support for the study of fire patterns in tunnels and emergency response measures. Full article

The ecosystems in the mountainous region of Southwest China are exceptionally fragile and constitute one of the global hotspots for wildfire occurrences. Understanding the complex interactions between wildfires and their environmental and anthropogenic factors is crucial for effective wildfire modeling and management. Despite significant advancements in wildfire modeling using machine learning (ML) methods, their limited explainability remains a barrier to utilizing them for in-depth wildfire analysis. This paper employs Logistic Regression (LR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) models along with the MODIS global fire atlas dataset (2004–2020) to study the influence of meteorological, topographic, vegetation, and human factors on wildfire occurrences in the mountainous region of Southwest China. It also utilizes Shapley Additive exPlanations (SHAP) values, a method within explainable artificial intelligence (XAI), to demonstrate the influence of key controlling factors on the frequency of fire occurrences. The results indicate that wildfires in this region are primarily influenced by meteorological conditions, particularly sunshine duration, relative humidity (seasonal and daily), seasonal precipitation, and daily land surface temperature. Among local variables, altitude, proximity to roads, railways, residential areas, and population density are significant factors. All models demonstrate strong predictive capabilities with AUC values over 0.8 and prediction accuracies ranging from 76.0% to 95.0%. XGBoost outperforms LR and RF in predictive accuracy across all factor groups (climatic, local, and combinations thereof). The inclusion of topographic factors and human activities enhances model optimization to some extent. SHAP results reveal critical features that significantly influence wildfire occurrences, and the thresholds of positive or negative changes, highlighting that relative humidity, rain-free days, and land use land cover changes (LULC) are primary contributors to frequent wildfires in this region. Based on regional differences in wildfire drivers, a wildfire-risk zoning map for the mountainous region of Southwest China is created. Areas identified as high risk are predominantly located in the Northwestern and Southern parts of the study area, particularly in Yanyuan and Miyi, while areas assessed as low risk are mainly distributed in the Northeastern region. Full article

The objective of this paper is to contribute to the understanding of forest cover loss patterns and the protection role of Indigenous peoples in the forests of Araucanía, Chile. Previous research indicated lower rates of forest cover loss in land managed by Indigenous peoples; however, this was primarily focused on tropical forests. This paper focuses on the temperate forests in the region of Araucanía and hypothesizes that there will be a similar trend, with lower rates of deforestation in areas owned by Indigenous peoples. A logistic regression model was used which included multiple underlying drivers that have shown to impact deforestation rates. The results of this study corroborated the hypothesis that lands owned by Indigenous peoples have lower rates of deforestation, and that protection status, agricultural function, and railway proximity have a strong influence on forest clearing, while slope, elevation, and proximity to urban areas demonstrated a minimal impact. Full article

Forests are important ecosystems that face threats from climate change and global environmental shifts, with invasive alien plant species being a significant concern. Some of these invasive species have already become established, while others are in the process of naturalisation. Although forests are a relatively stable ecosystem, extreme weather events increase their vulnerability to change, and clearings left after natural disturbances are particularly susceptible to invasion by alien plant species (IAPS). We created risk maps of two species that have spread rapidly in the last decade: American pokeweed (Phytolacca americana) and the tree of heaven (Ailanthus altissima). We prepared a generalised linear model based on the occurrence data collected within the LIFE ARTEMIS project. Eleven environmental variables were used to determine habitat characteristics. We constructed two models for each species: one covering the entirety of Slovenia and the other specifically for the forested areas in Slovenia, with the latter incorporating forest-specific variables (such as forest sanitation felling and monocultures). We observed the presence of both species at lower altitudes and in close proximity to water sources. American pokeweed tends to occur nearer to railways, while the presence of the tree of heaven is associated with areas lacking carbonate parent material and influenced by land use patterns. In forested areas, the occurrence of American pokeweed is influenced by forest habitat characteristics, such as disturbances caused by extreme weather events or the prevalence of Norway spruce monocultures. In contrast, the occurrence of the tree of heaven is influenced by more general environmental variables, such as altitude and proximity to railways. Consequently, we have generated risk maps for the entirety of Slovenia and separately for forested areas, both of which indicate similar levels of risk, particularly for the tree of heaven. The risk map for American pokeweed highlights numerous vulnerable areas, especially forest edges, which are highly susceptible to invasion. Furthermore, there is a higher likelihood of this species occurring in areas that have undergone sanitation felling. This study suggests that the production of risk maps of IAPS could be improved by focussing on habitat types and taking into account habitat-specific variables. This approach could enhance the early detection and management of these invasive species. Full article

In this study, we tested the use of lichen biomonitoring techniques for the assessment of air quality disparities at the urban scale. We based our evaluation on the results of a previous lichen biomonitoring study carried out in Milan (Northern Italy), which estimates the contamination by potentially toxic elements (PTEs) and its distribution over the area, also providing an evaluation of the main emission sources. Therefore, we used the traditional methodologies for environmental justice assessment: the proximity-based and the distribution-based approaches. The workflow we propose is a data-driven selection of emission sources that contributes to overcoming the dichotomy between the two approaches and is now widely debated in the scientific community. A socio-economic deprivation index was elaborated for each census unit of Milan city and then related to the proximity of the emission sources previously selected. The results suggested that in the surrounding of industries and railways, the deprivation is higher, while the proximity of main roads is inhabited by wealthier populations. The distribution-based approach was run through a quantile regression analysis, and the outcome indicated that among the wealthier groups, an increase in contamination is followed by an increase in socio-economic deprivation, whilst among the deprived groups, people with greater economic opportunities tend, however, to live in worse air quality conditions due to the proximity of communication routes. This study poses the potential to review the classical methods of EJ assessment, providing a reliable workflow applicable in urban areas—the most vulnerable in terms of air quality disparities in the present and in the future. Full article

In recent decades, Bangkok has experienced substantial investments in its urban railway network, resulting in a profound transformation of the city’s landscape. This study examines the relationship between railway development and property value uplift, particularly focusing on network centrality, which is closely linked to urban structure. Our findings are based on two primary analyses: network centrality and spatial hedonic models. The network centrality analysis reveals that closeness centrality underscores the city’s prevailing monocentric structure, while the betweenness centrality measure envisions the potential emergence of urban subcenters. In our hedonic analysis of condominiums near railway stations, we formulated various regression models with different specifications, incorporating spatial effects and network centrality. With Bangkok’s predominant monocentric structure in mind, we found that the spatial regression model, including a spatial error specification and closeness centrality, outperforms the others. This suggests that the impact of railways on property values extends beyond station proximity and encompasses network centrality, intricately linked with the city’s urban structure. We applied our developed model to estimate the expected increase in property values at major interchange stations with high network centralities. These numerical values indicate a considerable potential for their evolution into urban subcenters. These insights offer valuable policy recommendations for effectively harnessing transit-related premiums and shaping the future development of both the railway system and the city. Full article

In this research, our primary objective is to dissect the influence of specific locational elements—proximity to international borders, substantial ports, and significant railway junctions—on the economic vitality of Hungary’s counties from 2001 to 2020. The aim is to reveal how these factors individually contribute to economic disparities and to demonstrate their compounded effect on regional prosperity. This analysis is particularly timely and pertinent as regional inequalities are becoming more pronounced globally, making understanding such disparities crucial for effective policy formulation and regional planning. Utilizing GDP per capita as a fundamental indicator of economic health, we meticulously categorized counties, revealing a clear correlation between these locational advantages and economic performance. We innovatively employed Python to script a unique code, creating a matrix that enriches the presentation of our results, thereby facilitating a more nuanced understanding of these correlations. Our findings are significant in the current socio-economic climate, highlighting the need for tailored strategies considering unique regional attributes. This study is instrumental for policymakers and stakeholders in formulating informed, targeted strategies to harness these locational advantages, fostering balanced development, and narrowing the economic divide within the nation. The actuality of our research lies in its immediate relevance, offering insights critical to current discussions and decisions in regional development planning. Full article

In 2016, Carlos Moreno proposed the concept of “15-minute cities” based on the principles of proximity, diversity, density, and ubiquity. In fact, he re-formulated (“re-invented”) some of the already existing planning principles, making them recognized and desired by non-professionals. Moreno, however, paid little attention to the external connectivity of neighborhoods, assuming that most needs would be satisfied locally. This paper aims to discuss the concept of “15-minute cities” from the transport planning point of view and investigate how the concept can contribute to such planning. The research review conducted in this paper suggests that similar actions in the past caused a modal shift from the use of cars to public transport, rather than a radical limitation of total transport needs. To simplify, if a neighborhood is well designed, people are more likely to walk, ride a bike, and use public transport, but the majority will still commute outside of the neighborhood for work. In the metropolis of the ideal “15-minute city”, Barcelona, the majority of the inhabitants travel to work either by car or public transport, which proves that they need to move outside the neighborhood. This leads us to the conclusions that (1) “15-minute cities” should incorporate the transit-oriented development concept and include public transport nodes, such as railways or underground stations, as the central point of walkable, multifunctional neighborhoods, and (2) railway/underground station planners should pay more attention to the creation of a proper mix of services at and around the stations according to “15-minute cities” principles. In the future, there should also be more emphasis on re-allocating workplaces to neighborhoods, as well as on researching the actual impact of the (improved and current) “15-minute cities” design on transport volumes. Full article