Search Results (3,366)

As cities densify, deep underground infrastructure construction such as mass rapid transit (MRT) systems increasingly demand smarter, digitalized, and more sustainable approaches. RC diaphragm walls, essential to these systems, present challenges due to complex rebar configurations, spatial constraints, and high material usage and waste, factors that contribute significantly to carbon emissions. This study presents an AI-assisted rebar optimization framework to improve constructability and reduce waste in MRT-related diaphragm wall construction. The framework integrates the BIM concept with a custom greedy hybrid Python-based metaheuristic algorithm based on the WOA, enabling optimization through special-length rebar allocation and strategic coupler placement. Unlike conventional approaches reliant on stock-length rebars and lap splicing, this approach incorporates constructability constraints and reinforcement continuity into the optimization process. Applied to a high-density MRT project in Singapore, it demonstrated reductions of 19.76% in rebar usage, 84.57% in cutting waste, 17.4% in carbon emissions, and 14.57% in construction cost. By aligning digital intelligence with practical construction requirements, the proposed framework supports smart city goals through resource-efficient practices, construction innovation, and urban infrastructure decarbonization. Full article

Transit systems play a vital role in urban mobility, yet predicting individual travel behavior within these systems remains a complex challenge. Traditional machine learning approaches struggle with transit trip data because each trip may consist of a variable number of transit legs, leading to missing data and inconsistencies when using fixed-length tabular representations. To address this issue, we propose a transformer-based dynamic-sequence approach that models transit trips as variable-length sequences, allowing for flexible representation while leveraging the power of attention mechanisms. Our methodology constructs trip sequences by encoding each transit leg as a token, incorporating travel time, mode of transport, and a 2D positional encoding based on grid-based spatial coordinates. By dynamically skipping missing legs instead of imputing artificial values, our approach maintains data integrity and prevents bias. The transformer model then processes these sequences using self-attention, effectively capturing relationships across different trip segments and spatial patterns. To evaluate the effectiveness of our approach, we train the model on a dataset of urban transit trips and predict first-mile and last-mile travel times. We assess performance using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Experimental results demonstrate that our dynamic-sequence method yields up to a 30.96% improvement in accuracy compared to non-dynamic methods while preserving the underlying structure of transit trips. This study contributes to intelligent transportation systems by presenting a robust, adaptable framework for modeling real-world transit data. Our findings highlight the advantages of self-attention-based architectures for handling irregular trip structures, offering a novel perspective on a data-driven understanding of individual travel behavior. Full article

Path flow always plays a critical role in extracting vehicle travel patterns and reflecting network-scale traffic features. However, the comprehensive topological structure of urban road networks induces massive route choices, so frequent travel routes have been gradually regarded as an ideal countermeasure to represent traffic states. Widely used license plate recognition (LPR) devices can collect the abundant traffic features of all vehicles, but their sparse spatial distributions restrict the conventional models in frequent travel identification. Therefore, this study develops a network reconstruction method to construct a topological network from the LPR dataset, avoiding the adverse effects caused by the sparse distribution of detectors on the road network and further uses the Snake algorithm to fully utilize the road network structure and traffic attributes for clustering to obtain various travel patterns, with frequent routes under different travel patterns finally identified based on Steiner trees and frequent item recognition. To address the sparse spatial distribution of LPR devices, we utilize the word2vec model to extract spatial correlations among intersections. A threshold-based method is then applied to transform the correlation matrix into a reconstructed network, connecting intersections with strong vehicle transition relationships. This community structure can be interpreted as representing different travel patterns. Consequently, the Snake algorithm is employed to cluster intersections into distinct categories, reflecting these varied travel patterns. By leveraging the word2vec model, the detector installation rate requirement for Snake is significantly reduced, ensuring that the clustering results accurately represent the intrinsic relevance of traffic roads. Subsequently, frequent routes are identified from both macro- and micro-perspectives using the Steiner tree and Frequent Pattern Growth (FP Growth) algorithm, respectively. Validated on the LPR dataset in Changsha, China, the experiment results demonstrate that the proposed method can effectively identify travel patterns and extract frequent routes in the sparsely installed LPR devices. Full article

For the last few decades, the use of High-Speed Trains (HSTs) has been growing rapidly in various parts of the world. Despite rapid global expansion, many HST projects fail due to demand overestimation and cost overruns. This study analyzes factors influencing HST demand in Indonesia, aiming to identify impactful determinants from user perspectives. Employing a quantitative cross-sectional approach, this research utilized questionnaires distributed to users of different modes of transportation in the Jakarta–Bandung area, including trains, buses, travel services, and private cars. Structural Equation Modeling (SEM) via Lisrel software was used to analyze the data. The results indicate that Transit-Oriented Developments (TOD) and new urban areas significantly increase HST demand by facilitating urban growth and development. Additionally, supporting infrastructure and external factors such as road accessibility, parking availability, shuttle services, and environmental integration are pivotal in shaping commuter preferences. Although factors such as safety, comfort, and reliability are important, they alone may not be adequate to persuade consumers to use high-speed trains for their travel. Full article

The transition to smart, zero-carbon cities relies on advanced, sustainable energy solutions, with artificial intelligence (AI) playing a crucial role in optimizing renewable energy management. This study evaluates state-of-the-art AI models for solar power forecasting, emphasizing accuracy, reliability, and environmental sustainability. Using operational data from Benban Solar Park in Egypt and Sakaka Solar Power Plant in Saudi Arabia, two of the world’s largest solar installations, the research highlights the effectiveness of hybrid AI techniques. The hybrid Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model outperformed other models, achieving a Mean Absolute Percentage Error (MAPE) of 2.04%, Root Mean Square Error (RMSE) of 184, Mean Absolute Error (MAE) of 252, and R² of 0.99 for Benban, and an MAPE of 2.00%, RMSE of 190, MAE of 255, and R² of 0.98 for Sakaka. This model excels at capturing complex spatiotemporal patterns in solar data while maintaining low computational CO₂ emissions, supporting sustainable AI practices. The findings demonstrate the potential of hybrid AI models to enhance the accuracy and sustainability of solar power forecasting, thereby contributing to efficient, resilient, and zero-carbon urban environments. This research provides valuable insights for policymakers and stakeholders aiming to advance smart energy infrastructure. Full article

Urban public transportation systems face increasing pressure from shifting travel patterns, rising peak-hour demand, and the need for equitable and resilient service delivery. While complex network theory has been widely applied to analyze transit systems, limited attention has been paid to behavioral segmentation within such networks. This study introduces a frequency-based framework that differentiates high-frequency (HF) and low-frequency (LF) passengers to examine how distinct user groups shape network structure, congestion vulnerability, and robustness. Using over 20 million smart-card records from Beijing’s multimodal transit system, we construct and analyze directed weighted networks for HF and LF users, integrating topological metrics, temporal comparisons, and community detection. Results reveal that HF networks are densely connected but structurally fragile, exhibiting lower modularity and significantly greater efficiency loss during peak periods. In contrast, LF networks are more spatially dispersed yet resilient, maintaining stronger intracommunity stability. Peak-hour simulation shows a 70% drop in efficiency and a 99% decrease in clustering, with HF networks experiencing higher vulnerability. Based on these findings, we propose differentiated policy strategies for each user group and outline a future optimization framework constrained by budget and equity considerations. This study contributes a scalable, data-driven approach to integrating passenger behavior with network science, offering actionable insights for resilient and inclusive transit planning. Full article

The social acceptance of Renewable Energy Sources (RESs) is a decisive factor in the successful implementation of clean energy projects. This study explores the attitudes, demographic profiles, and common misconceptions of citizens in the Region of Epirus, Greece, toward photovoltaic and wind energy installations. Special attention is given to the role of education, age, and access to information—as well as spatial factors such as visual exposure—in shaping public perceptions and influencing acceptance of RES deployment. A structured questionnaire was administered to 320 participants across urban and rural areas, with subdivision between regions with and without visual exposure to RES infrastructure. Findings indicate that urban residents exhibit greater acceptance of RES, while rural inhabitants—especially those in proximity to installations—express skepticism, often grounded in esthetic concerns or perceived procedural injustice. Misinformation and lack of knowledge dominate in areas without visual contact. Statistical analysis confirms that younger and more educated participants are more supportive and environmentally aware. The study highlights the importance of targeted educational interventions, transparent consultation, and spatially sensitive communication strategies in fostering constructive engagement with renewable energy projects. The case of Epirus underscores the need for inclusive, place-based policies to bridge the social acceptance gap and support the national energy transition. Full article

Wildfire regimes are closely linked to changes in landscape structure, yet the influence of accelerated land use transitions on fire activity remains poorly understood, particularly in rapidly transforming regions like central Chile. Although land use change has been extensively documented in the country, the specific role of the speed, extent, and spatial configuration of these transitions in shaping fire dynamics requires further investigation. To address this gap, we examined how landscape transitions influence fire frequency in central Chile, a region experiencing rapid land use change and heightened fire activity. Using multi-temporal remote sensing data, we quantified land use transitions, calculated landscape metrics to describe their spatial characteristics, and applied intensity analysis to assess their relationship with fire frequency changes. Our results show that accelerated landscape transitions significantly increased fire frequency, particularly in areas affected by forest plantation rotations, new forest establishment, and urban expansion, with changes exceeding uniform intensity expectations. Regional variations were evident: In the more densely populated northern areas, increased fire frequency was primarily linked to urban development and deforestation, while in the more rural southern regions, forest plantation cycles played a dominant role. Areas with a high number of large forest patches were especially prone to fire frequency increases. These findings demonstrate that both the speed and spatial configuration of landscape transitions are critical drivers of wildfire activity. By identifying the specific land use changes and landscape characteristics that amplify fire risks, this study provides valuable knowledge to inform fire risk reduction, landscape management, and urban planning in Chile and other fire-prone regions undergoing rapid transformation. Full article

Research on commuting patterns has mainly focused on urban and metropolitan areas, and such studies are not typically applied to rural and small-town regions, where workers often face longer commutes due to limited job opportunities and inadequate public transportation. By using the Census Transportation Planning Package (CTPP) data, this research fills that gap by analyzing commuting behavior by ethnic group and industry in south-central Minnesota, which is a predominantly rural area of 13 counties in the United States. The results show that both white and minority groups in District 7 experienced an increase in excess commuting from 2006 to 2016, with the minority group in Nobles County showing a significantly higher rise. Analysis by industry reveals that excess commuting in the leisure and hospitality sector (including arts, entertainment, and food services) in Nobles County increased five-fold during this time, indicating a severe spatial mismatch between jobs and affordable housing. In contrast, manufacturing experienced a decline of 50%, possibly indicating better commuting efficiency or a loss of manufacturing jobs. These findings can help city and transportation planners conduct an in-depth analysis of rural-to-urban commuting patterns and develop potential solutions to improve rural transportation infrastructure and accessibility, such as promoting telecommuting and hybrid work options, expanding shuttle routes, and adding more on-demand transit services in rural areas. Full article

Due to small population size, Population Viability Analysis (PVA) of endangered species often pools all individuals into a single matrix to decrease variation in estimation of transition rates. These pooled populations may mask significant environmental variation among populations, affecting estimates. Using 10 years of population data (2000–2010) on the endangered plant Jacquemontia reclinata in Southeastern Florida, USA, I parameterized a stage-structured matrix model and calculated annual growth rates (lambdas)and elasticity for each year using stochastic matrix models. The metapopulation model incorporating actual dynamics of the two largest populations showed a lower occupancy rate and higher risk of extinction at an earlier time compared to a model that used the average of all natural populations. Analyses were consistent that incorporating population variation versus average dynamics in modeling J. reclinata demography results in more variation and greater extinction risk. Local variation may be due to both weather (including minimum winter temperature and total annual precipitation) and local disturbance dynamics in these urban preserves. Full article

This article aims to analyze the role of migration in the process of the third demographic transition (TDT) in the context of key mediating determinants, such as migrants’ religiosity and economic conditions in the countries of origin and settlement. TDT refers to population changes resulting from migration as a demographic compensatory mechanism in countries with a low total fertility rate (TFR). The study is based on a network analysis of keywords in the scientific literature using the Scopus database and VOSviewer. The results point to three main research approaches to TDT—investigating quantitative population changes, the sociodemographic consequences of migration, and its effect on urbanization—and to the fact that economic and axionormative determinants are under-researched. This article contributes to TDT theory, pointing to the need for that theory to include cultural, economic, and axiological factors as key determinants influencing the permanence of TDT. Full article

Urban rail transit networks’ rapid expansions have led to increasing intersections between existing and new lines, particularly in dense urban areas where new stations must underpass existing infrastructure at zero distance. Deformation controls during construction are critical for maintaining the operational safety of existing stations, especially in soft soil conditions where construction-induced settlement poses significant risks to structural integrity. This study systematically investigates the influence mechanisms of different construction schemes on base plate deformation when an open-top UBIT (underground bundle composite pipe integrated by transverse pre-stressing) underpasses existing stations. Through precise numerical simulation using PLAXIS 3D, the research comparatively analyzed the effects of 12 pipe jacking sequences, 3 pre-stress levels (1116 MPa, 1395 MPa, 1674 MPa), and 3 soil chamber excavation schemes, revealing the mechanisms between the deformation evolution and soil unloading effects. The continuous jacking strategy of adjacent pipes forms an efficient support structure, limiting maximum settlement to 5.2 mm. Medium pre-stress level (1395 MPa) produces a balanced deformation pattern that optimizes structural performance, while excavating side chambers before the central chamber effectively utilizes soil unloading effects, achieving controlled settlement distribution with maximum values of −7.2 mm. The optimal construction combination demonstrates effective deformation control, ensuring the operational safety of existing station structures. These findings enable safer and more efficient urban underpassing construction. Full article

Metro carriages, as enclosed transport microenvironments, have been understudied regarding pollution characteristics and health risks from ACs, especially during high-temperature summers that amplify exposure. This study applied NTS techniques for the first time across three major Chengdu metro lines, systematically identifying sixteen ACs, including hazardous species such as acetophenone, benzonitrile, and benzoic acid that are often overlooked in conventional BTEX-focused monitoring. The TAC concentration reached 41.40 ± 5.20 µg/m³, with half of the compounds exhibiting significant increases during peak commuting periods. Source apportionment using diagnostic ratios and PMF identified five major contributors: carriage material emissions (36.62%), human sources (22.50%), traffic exhaust infiltration (16.67%), organic solvents (16.55%), and industrial emissions (7.66%). Although both non-cancer (HI) and cancer (TCR) risks for all population groups were below international thresholds, summer tourists experienced higher exposure than daily commuters. Notably, child tourists showed the greatest vulnerability, with a TCR of 5.83 × 10⁻⁷, far exceeding that of commuting children (1.88 × 10⁻⁷). Benzene was the dominant contributor, accounting for over 50% of HI and 70% of TCR. This study presents the first integrated NTS and quantitative risk assessment to characterise ACs in summer metro environments, revealing a broader range of hazardous compounds beyond BTEX. It quantifies population-specific risks, highlights children’s heightened vulnerability. The findings fill critical gaps in ACs exposure and provide a scientific basis for improved air quality management and pollution mitigation strategies in urban rail transit systems. Full article

Amid China’s national strategic prioritization of the Chengdu–Chongqing Economic Circle and accelerated territorial spatial planning, this study deciphered the synergistic evolution of Land Use Intensity (LUI) and Balance Degree of Land Use Structure (BDLUS) during rapid urbanization. Leveraging 1 km grid units and integrating emerging spatiotemporal hotspot analysis, BFAST, and geographic detectors, we systematically analyzed spatiotemporal patterns and drivers of LUI, BDLUS, and their Coupling Coordination Degree (CCD) from 2000 to 2022. Key findings: (1) LUI strongly correlated with economic growth, with core areas reaching high-intensity development (average > 2.96) versus ecologically constrained marginal zones (<2.42), marked by abrupt changes during 2011–2014; (2) BDLUS improvements covered 82.22% of the study area, driven by the Yangtze River Economic Belt strategy (21.96% hotspot concentration), yet structural imbalance persisted in transitional zones (18.81% cold spots); (3) CCD exhibited center-edge dichotomy, contrasting high-value cores (CCD > 0.68) with ecologically sensitive edges (9.80% cold spots), peaking in regulatory shifts around 2010; (4) terrain constraints and intensified human activities (the interaction effect between nighttime lighting and population density increased by 219.49% after 2020) jointly governed coupling mechanisms, with urbanization and industrial transition becoming dominant drivers. This research advances an “intensity–structure–coordination” framework and elucidates “dual-core resonance” dynamics, offering theoretical foundations for spatial optimization and ecological civilization. Full article

To sustainably power future urban communities, cities require advanced solar energy planning tools that overcome the limitations of traditional approaches, such as data fragmentation and siloed decision-making. SDTs present a transformative opportunity by enabling precision urban modelling, integrated simulations, and iterative decision support. However, their application in solar energy planning remains underexplored. This study introduces SDT4Solar, a novel SDT-based framework designed to integrate city-scale rooftop solar planning through 3D building semantisation, solar modelling, and a unified geospatial database. By leveraging advanced spatial modelling and Internet of Things (IoT) technologies, SDT4Solar facilitates high-resolution 3D solar potential simulations, improving the accuracy and equity of solar infrastructure deployment. We demonstrate the framework through a proof-of-concept implementation in Ballarat East, Victoria, Australia, structured in four key stages: (a) spatial representation of the urban built environment, (b) integration of multi-source datasets into a unified geospatial database, (c) rooftop solar potential modelling using 3D simulation tools, and (d) dynamic visualization and analysis in a testbed environment. Results highlight SDT4Solar’s effectiveness in enabling data-driven, spatially explicit decision-making for rooftop PV deployment. This work advances the role of SDTs in urban energy transitions, demonstrating their potential to optimise efficiency in solar infrastructure planning. Full article