Search Results (81)

The global low-carbon energy transition relies on the orderly integration of a high proportion renewable energy. As an important carrier of demand-side energy systems, parks are responsible for local balancing and the accommodation of distributed renewable energy. However, the energy systems of parks exhibit the integrated characteristics of heterogeneous energy sources, including electricity, heat, and gas. It also encompasses the entire source–network–load–storage process, which renders it huge and complex. For this reason, as a systematic review article, this paper aims to summarize the overall application of artificial intelligence technology in China’s park-level comprehensive energy system. First, the current status of technology applications in the corresponding scenarios is analyzed based on three dimensions: prediction, scheduling, and security. Subsequently, key challenges in applying AI technologies to these scenarios are identified, including multi-temporal and spatial synergy issues in source–load forecasting, multi-agent equilibrium problems in dispatch optimization, and cross-modal matching challenges in security operation and maintenance (O&M). Thereafter, the feasible directions to solve these bottlenecks will be discussed comprehensively in light of the latest research advancements. Finally, we propose a phased roadmap for technological development and to identify the key gaps in this research field, such as the lack of publicly available benchmark datasets, data exchange standards, and cross-campus validation frameworks. This article aims to provide a systematic theoretical reference and development framework for the in-depth empowerment of AI technology in the integrated energy system of industrial parks. Full article

Urban parking spaces are key city resources that directly affect how easily people get around and the quality of their daily travel. Accurately predicting future parking space availability can improve the efficiency of using parking spaces. For instance, it can enhance smart parking applications like shared parking and EV charging scheduling. However, because parking behavior is dynamic and constantly changing, it is challenging to predict parking space availability over the medium-to-long term. This paper proposes a Scale-Fusion Transformer model (SFFormer) to address dynamic changes in parking spaces availability caused by complex parking behaviors, as well as challenges in medium-to-long-term prediction modeling. The three key innovations are as follows: (1) a scale-fusion module integrating short-term and long-term parking trends, (2) an adaptive data compression mechanism for multi-scale prediction tasks, and (3) a Transformer-encoder-based time pattern capturing architecture, which is adaptable to diverse parking lots and long-term prediction scenarios. Experiments on real parking datasets demonstrate that the SFFormer model significantly outperforms state-of-the-art models such as iTransformer, PatchTST, DLinear, and Autoformer. Full article

This study develops a high-speed rail demand prediction model based on access probability, which quantifies the likelihood of passengers choosing a departure station among multiple alternatives. Traditional models assign demand to the nearest station or rely on manual calibration, often failing to reflect actual travel behavior and requiring excessive time and resources. To address these limitations, this study integrates survey data, real-world datasets, and machine learning techniques to model station choice behavior more accurately. Key influencing factors, including headway, access time, parking availability, and transit connections, were identified through passenger surveys and incorporated into the model. Machine learning algorithms improved prediction accuracy, with SHAP analysis providing interpretability. The proposed model achieved high accuracy, with an average error rate below 3% for major stations. Scenario analyses confirmed its applicability in network expansions, including GTX openings and the integration of mobility as a service. This model enhances data-driven decision-making for rail operators and offers insights for rail network planning and operations. Future research will focus on validating the model across diverse regions and refining it with updated datasets and external data sources. Full article

The efficient management of urban parking systems has emerged as a pivotal issue in today’s smart cities, where increasing vehicle populations strain limited parking infrastructure and challenge sustainable urban mobility. Aligned with the United Nations 2030 Agenda for Sustainable Development and the strategic goals of smart city planning, this study presents a sustainability-driven, multiagent simulation-based framework to model, analyze, and optimize smart parking dynamics in congested urban settings. The system architecture integrates ground-level IoT sensors installed in parking spaces, enabling real-time occupancy detection and communication with a centralized system using low-power wide-area communication protocols (LPWAN). This study introduces an intelligent parking guidance mechanism that dynamically directs drivers to the nearest available slots based on location, historical traffic flow, and predicted availability. To manage real-time data flow, the framework incorporates message queuing telemetry transport (MQTT) protocols and edge processing units for low-latency updates. A predictive algorithm, combining spatial data, usage patterns, and time-series forecasting, supports decision-making for future slot allocation and dynamic pricing policies. Field simulations, calibrated with sensor data in a representative high-density urban district, assess system performance under peak and off-peak conditions. A comparative evaluation against traditional first-come-first-served and static parking systems highlights significant gains: average parking search time is reduced by 42%, vehicular congestion near parking zones declines by 35%, and emissions from circling vehicles drop by 27%. The system also improves user satisfaction by enabling mobile app-based reservation and payment options. These findings contribute to broader sustainability goals by supporting efficient land use, reducing environmental impacts, and enhancing urban livability—key dimensions emphasized in sustainable smart city strategies. The proposed framework offers a scalable, interdisciplinary solution for urban planners and policymakers striving to design inclusive, resilient, and environmentally responsible urban mobility systems. Full article

Parking availability prediction is a critical component of intelligent transportation systems, aiming to reduce congestion and improve urban mobility. While traditional deep learning models such as Long Short-Term Memory (LSTM) networks have been widely applied, they lack mechanisms to quantify uncertainty, limiting their robustness in real-world deployments. This paper proposes a Bayesian Neural Network (BNN)-based framework for parking occupancy prediction that explicitly models both epistemic and aleatoric uncertainty. Although BNNs have shown promise in other domains, they remain underutilised in parking prediction—likely due to the computational complexity and the absence of real-time context integration in earlier approaches. Our approach leverages contextual features, including temporal and environmental factors, to enhance uncertainty-aware predictions. The framework is evaluated under varying data conditions, including data scarcity (90%, 50%, and 10% of training data) and synthetic noise injection to simulate aleatoric uncertainty. Results demonstrate that BNNs outperform other methods, achieving an average accuracy improvement of 27.4% in baseline conditions, with consistent gains under limited and noisy data. Applying uncertainty thresholds at 20% and 30% further improves reliability by enabling selective, confidence-based decision making. This research shows that modelling both types of uncertainty leads to significantly improved predictive performance in intelligent transportation systems and highlights the potential of uncertainty-aware approaches as a foundation for future work on integrating BNNs with hybrid neuro-symbolic reasoning to enhance decision making under uncertainty. Full article

Forest fires are one of the significant factors affecting forest ecosystems globally, with their impacts on soil microbial community structure and function drawing considerable attention. This study focuses on the short-term effects of different fire intensities on soil bacterial community structure and function in Abies (Pinus densata) forests within the Birishen Mountain National Forest Park in southeastern Tibet. High-throughput sequencing technology was employed to analyze soil bacterial community variations under unburned (C), low-intensity burn (L), moderate-intensity burn (M), and high-intensity burn (S) conditions. The results revealed that with increasing fire severity, the dominant phylum Actinobacteriota significantly increased, while Proteobacteria and Acidobacteriota markedly decreased. At the genus level, the relative abundance of Bradyrhizobium declined significantly with higher fire severity, whereas Arthrobacter exhibited a notable increase. Additionally, soil environmental factors such as available phosphorus (AP), dissolved organic carbon (DOC), C/N ratio, and C/P ratio displayed distinct trends: AP content increased with fire severity, while DOC, C/N ratio, and C/P ratio showed decreasing trends. Non-metric Multidimensional Scaling (NMDS) analysis indicated significant differences in soil bacterial community structures across fire intensities. Diversity analysis demonstrated that Shannon and Simpson indices exhibited regular fluctuations correlated with fire severity and were significantly associated with soil C/N ratios. Functional predictions revealed a significant increase in nitrate reduction-related bacterial functions with fire severity, while nitrogen-fixing bacteria declined markedly. These findings suggest that forest fire severity profoundly influences soil bacterial community structure and function, potentially exerting long-term effects on nutrient cycling and ecosystem recovery in forest ecosystems. Full article

Urbanization changes the environment through physical constructions, disturbances, and altered resource availability. These modifications influence both prey and predator assemblages. Several studies have indicated that hole-nesting birds outnumber ground nesters in cities. Differential nest predation can be one reason behind this observation. We conducted a multi-year artificial nest predation experiment along an urban gradient by using artificial ground nests and nestboxes in Rovaniemi, Finland. Because visually searching avian predators dominate in cities, we predicted that nest predation of ground nests will increase with urbanization, whereas nests in holes will be better protected than ground nests. Ground nest predation increased with urbanization, being lowest in forest and rural areas, intermediate in suburban area and highest in urban area. However, there was no year-effects on artificial ground nest predation, suggesting that even a single-year results of artificial nest predation experiment can be reliable. In the city, ground nest predation was greater than nestbox predation. In forests, nestbox predation was greater than ground nest predation. Among ground nests, predation was greater in the city than in forests. Among nestboxes, predation was greater in forest than in urban or suburban habitats. Only the ground nest predation was greater in managed than in un-managed parks. Ground nest predation decreased with tree cover and increased with the patch area. No variables were entered in the models of the nestboxes. The results indicated that ground nesters might avoid urban areas as nesting sites. We assume that visually searching avian predators benefit from the lack of covering vegetation in city parks. However, because most avian nest predators, like corvids, are not effective nest predators of hole-nesting birds, urban areas are safe nesting areas for hole-nesters. The results suggest that nest predation is one important factor that could explain, why hole-nesting bird species outnumbered ground-nesting species in cities. The result give support for the hypothesis that nest predation pressure can modify urban bird assemblage structure. Full article

Stormwater runoff containing road deicing salts has led to the increasing salinization of surface waters in northern climates, and urban municipalities are increasingly being mandated to manage stormwater runoff to improve water quality. We assessed chloride concentrations in runoff from late-winter snowmelt and rainfall events flowing into an urban Minnesota, USA, lake during two different years, predicting that specific stormwater drainages with greater concentrations of roadways and parking lots would produce higher chloride loads during runoff than other drainages with fewer impervious surfaces. Chloride levels were measured in runoff draining into Lake Winona via 11 stormwater outfalls, a single channelized creek inlet, and two in-lake locations during each snowmelt or rainfall event from mid-February through early April in 2021 and 2023. In total, 33% of outfall runoff samples entering the lake collected over two years had chloride concentrations exceeding the 230 ppm chronic standard for aquatic life in USA surface waters, but no sample exceeded the 860 ppm acute standard. Chloride concentrations in outfall runoff (mean ± SD; 190 ± 191 ppm, n = 143) were significantly higher than in-lake concentrations (43 ± 14 ppm, n = 25), but chloride levels did not differ significantly between snowmelt and rainfall runoff events. Runoff from highway locations had higher chloride concentrations than runoff from residential areas. Site-specific chloride levels were highly variable both within and between years, with only a single monitored outfall displaying high chloride levels in both years. There are several possible avenues available within the city to reduce deicer use, capture and treat salt-laden runoff, and prevent or reduce the delivery of chlorides to the lake. Full article

This paper aims to create an innovative approach to improving IoT-based smart parking systems by integrating machine learning (ML) and Artificial Intelligence (AI) with mathematical approaches in order to increase the accuracy of the parking availability predictions. Three regression-based ML models, random forest, gradient boosting, and LightGBM, were developed and their predictive capability was compared using data collected from three parking locations in Skopje, North Macedonia from 2019 to 2021. The main novelty of this study is based on the use of autoregressive modeling strategies with lagged features and Z-score normalization to improve the accuracy of regression-based time series forecasts. Bayesian optimization was chosen for its ability to efficiently explore the hyperparameter space while minimizing RMSE. The lagged features were able to capture the temporal dependencies more effectively than the other models, resulting in lower RMSE values. The LightGBM model with lagged data produced an R² of 0.9742 and an RMSE of 0.1580, making it the best model for time series prediction. Furthermore, an IoT-based system architecture was also developed and deployed which included real-time data collection from sensors placed at the entry and exit of the parking lots and from individual slots. The integration of ML, AI, and IoT technologies improves the efficiency of the parking management system, reduces traffic congestion and, most importantly, offers a scalable approach to the development of urban mobility solutions. Full article

This paper discusses SESAR’s Auto-Steer Taxi at Airport (ASTAIR) project, which seeks to advance airport ground operations including engine-off taxiing to move towards sustainable airports. The ASTAIR concept integrates human–AI teaming to optimize aircraft movement from gates to runways, with the primary objectives of improving predictability, efficiency, and environmental sustainability at large airports. Building on previous initiatives such as SESAR’s AEON, ASTAIR brings high-level automation to tasks like autonomous taxiing and vehicle routing. The system assists operators by calculating conflict-free routes for vehicles and dynamically adjusting operations based on real-time data. Based on workshops with several stakeholders, we describe the operational challenges involved in implementing ASTAIR, including managing parking stand availability and adapting to unforeseen events. A significant challenge highlighted is the human–automation partnership, where AI plays a supportive role but humans retain control over critical decisions, particularly in cases of system failure. The need for clear and consistent collaboration between AI and human operators is emphasized to ensure safety, efficiency, and improved compliance with take-off schedules, which in turn facilitates in-flight optimization. Full article

Rapid urbanization has led to increasingly serious problems, such as the heat island effect and environmental pollution, which seriously endanger people’s health. Greenspace exposure behavior, that is, the way and characteristics of people’s contact with greenspace (including frequency and duration of stay), is the key to exerting the health benefits of greenspace. There is little research on the factors influencing greenspace exposure behavior, which cannot reveal the mechanism of maintaining people’s physical and mental health by promoting greenspace exposure behavior. Therefore, using typical urban parks in Fuzhou as a case study, indicators of greenspace availability, accessibility, and visibility were identified from objective park characteristics and subjective crowd evaluation. The factors influencing greenspace exposure behavior were analyzed using bivariate correlation tests and multivariate linear regression analysis. The results indicated that, among objective park characteristics, the per capita green park area negatively impacted greenspace exposure behavior, while the green view index positively influenced it (p < 0.05). Regarding subjective crowd evaluation, subjective indicators positively impacted greenspace exposure behavior except for the condition of activity areas. In addition, subjective factors, especially subjective visibility indicators, are more predictive of greenspace exposure behavior than objective factors. The theoretical contribution of this study lies in further refining the research framework for quantifying and evaluating park greenspace exposure, and enriching the theoretical connotation of research on park greenspace exposure behavior. The research results suggested park greening strategies for the relevant departments, enhanced the greenspace exposure behavior, and improved people’s physical and mental health. Full article

Finding a parking space is a major concern in megacities, causing drivers to lose time and money while contributing to atmospheric pollution and global warming. This study proposes a method to predict parking slot availability by comparing Long Short-Term Memory (LSTM) and Multi-Head Attention (MHA) methods using the CityPulse Smart City Datasets. The initial experiments assessed the impact of pollution and time features on prediction accuracy. In a subsequent experiment, the dataset was expanded by incorporating weather-related features and a broader time range while excluding pollution and time features, as informed by the initial results. Various experiments were conducted with different parameters, such as model depth and activation functions. The results demonstrated that MHA outperformed LSTM in predicting occupancy rates, achieving a Mean Absolute Error (MAE) score of 0.0589 on the extended dataset. This study marks a pioneering effort in using MHA for real-time parking occupancy prediction, showcasing significant success with fewer parameters and a smaller model size. Full article

This study evaluated the possibility of using two complementary electronic sensors (rising plate meter (RPM) and active optical sensor (AOS)) to obtain a global indicator, pasture crude protein (CP) in kg ha⁻¹. This parameter simultaneously integrates two essential dimensions: pasture dry matter availability (dry matter (DM) in kg ha⁻¹) measured by RPM, and pasture quality (measured by AOS), and supports management decisions, particularly those related to the stocking rates, supplementation, or rotation of animals between grazing parks. The experimental work was carried out on a dryland biodiverse and representative pasture, and consisted of sensor measurements, followed by the collection of a total of 144 pasture samples, distributed between three dates of the pasture vegetative cycle of 2023/2024 (Autumn—December 2023; Winter—February 2024; and Spring—May 2024). These samples were subjected to laboratory reference analysis to determine DM and CP. Sensor measurements (compressed height (H_RPM) in the case of RPM, and normalized difference vegetation index (NDVI) in the case of AOS) and the results of reference laboratory analysis were used to develop prediction models. The best correlations between CP (kg ha⁻¹) and “H_RPM × NDVI” were obtained in the initial and intermediate phases of the cycle (autumn: R² = 0.86 and LCC = 0.80; and Winter; R² = 0.74 and LCC = 0.81). In the later phase of the cycle (spring), the accuracy of the forecasting model decreased dramatically (R² = 0.28 and LCC = 0.42), a trend that accompanies the decrease in the pasture moisture content (PMC) and CP. The results of this study show not only the importance of extending the database to other pasture types in order to enhance the process of feed supplement determination, but also the potential for the research and development of proximal and remote sensing tools to support pasture monitoring and animal production management. Full article

Visitor attraction dynamics lead tourism industry paths. A complex artificial neural network model was built to predict the incoming tourism flow in the Apulia region of Southern Italy as a function of the heterogeneity of the tourism supply available in this area. Open data from the Regional Tourism Observatory were targeted. Information on the distribution of facilities and activities that attract regional tourist flows was collected and grouped by municipality. An artificial neural network model was built with total tourist attendance as the dependent variable and tourist attractions as regressors. The Root Mean Square Error (RMSE) was used to select the optimal model using the lowest value. The final model was run with a hidden layer consisting of three neurons and a decay value of 0.01. A Multi-Objective Counterfactual model (MOC) was then constructed using a randomly selected row of normalized data frame to validate a useful tool in increasing total tourist attendance by 20% over that of the randomly selected municipality. A Garson’s variables importance plot indicated natural landscapes such as beaches, sea caves, and natural parks have a primary role expressed in terms of variable importance in the AI algorithm when used as an innovative methodology for evaluating tourism flows in the Apulia region. A further MOC model built using a randomly selected row of normalized data frame showed convents, libraries, historical buildings, public gardens, and museums as the top five features most modified to improve total attendance in a randomly selected municipality. Use of AI modeling revealed that the implementation of nature-based solutions may speed up the flow of tourism in the Apulia region while also promoting sustainable social development. Full article

The prediction of vacant parking spaces (VPSs) can reduce the time drivers spend searching for parking, thus alleviating traffic congestion. However, previous studies have mostly focused on modeling the temporal features of VPSs using historical data, neglecting the complex and extensive spatial characteristics of different parking lots within the transportation network. This is mainly due to the lack of direct physical connections between parking lots, making it challenging to quantify the spatio-temporal features among them. To address this issue, we propose a dynamic spatio-temporal adaptive graph convolutional recursive network (DSTAGCRN) for VPS prediction. Specifically, DSTAGCRN divides VPS data into seasonal and periodic trend components and combines daily and weekly information with node embeddings using the dynamic parameter-learning module (DPLM) to generate dynamic graphs. Then, by integrating gated recurrent units (GRUs) with the parameter-learning graph convolutional recursive module (PLGCRM) of DPLM, we infer the spatio-temporal dependencies for each time step. Furthermore, we introduce a multihead attention mechanism to effectively capture and fuse the spatio-temporal dependencies and dynamic changes in the VPS data, thereby enhancing the prediction performance. Finally, we evaluate the proposed DSTAGCRN on three real parking datasets. Extensive experiments and analyses demonstrate that the DSTAGCRN model proposed in this study not only improves the prediction accuracy but can also better extract the dynamic spatio-temporal characteristics of available parking space data in multiple parking lots. Full article