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Student behavior research can improve learning efficiency, provide decision evidences for infrastructure management. Existing campus-scale behavioral analysis work have not taken into account the students characteristics and spatiotemporal pattern. Moreover, the visualization methods are weak in wholeness, intuitiveness and interactivity perspectives. In this paper, we design a geospatial dashboard-based student behavior analysis and visualization system considering students characteristics and spatiotemporal pattern. This system includes four components: user monitoring, data mining analysis, behavior prediction and spatiotemporal visualization. Furthermore, a deep learning model based on LSTNet to predict student behaviour. Our work takes WiFi log data of a university in Beijing as dataset. The results show that this system can identify student behavior patterns at a finer granularity by visualization method, which is helpful in improving learning and living efficiency. [ABSTRACT FROM AUTHOR]

With the rapid development of the energy industry and the growth of the global energy demand in recent years, the development of the photovoltaic industry has become increasingly significant. However, the development of the PV industry is constrained by high land costs, and land in central cities and industrial areas is often very expensive and unsuitable for the installation of PV equipment in large areas. With this background knowledge, the key to evaluating the PV potential is by counting the rooftop information of buildings, and an ideal solution for extracting building rooftop information is from remote sensing satellite images using the deep learning method; however, the deep learning method often requires large-scale labeled samples, and the labeling of remote sensing images is often time-consuming and expensive. To reduce the burden of data labeling, models trained on large datasets can be used as pre-trained models (e.g., ImageNet) to provide prior knowledge for training. However, most of the existing pre-trained model parameters are not suitable for direct transfer to remote sensing tasks. In this paper, we design a pseudo-label-guided self-supervised learning (PGSSL) semantic segmentation network structure based on high-resolution remote sensing images to extract building information. The pseudo-label-guided learning method allows the feature results extracted by the pretext task to be more applicable to the target task and ultimately improves segmentation accuracy. Our proposed method achieves better results than current contrastive learning methods in most experiments and uses only about 20–50% of the labeled data to achieve comparable performance with random initialization. In addition, a more accurate statistical method for building density distribution is designed based on the semantic segmentation results. This method addresses the last step of the extraction results oriented to the PV potential assessment, and this paper is validated in Beijing, China, to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Short-term origin–destination (termed as OD) prediction is crucial to improve the operation of urban rail transit (termed as URT). The latest research results show that deep learning can effectively improve the performance of short-term OD prediction and meet the real-time requirements. However, many advanced neural network design ideas have not been fully applied in the field of short-term OD prediction in URT. In this paper, a novel parallel interactive attention network (termed as PIANet) for short-term OD prediction in URT is proposed to further improve the short-term OD prediction accuracy. In the proposed PIANet, a novel omnidirectional attention module (termed as OAM) is proposed to improve the representational power of the network by calculating the feature weights in the channel–spatial dimension. Moreover, a simple yet effective feature interaction is proposed to improve the feature utilization. Based on the two real-world datasets from the Beijing subway, the comparative experiments demonstrate that the proposed PIANet outperforms the state-of-the-art deep learning methods for short-term OD prediction in URT, and the ablation studies demonstrate that the proposed OAMs and feature interaction play an important role in improving the short-term OD prediction accuracy. [ABSTRACT FROM AUTHOR]

Accurate air quality prediction is of great significance for pollution prevention and disaster prevention. Effective and reliable prediction models are needed not only for short time prediction, but are even more important for long time-series future predictions. In the long time series, most of the current models might not function as accurately as in the short period and thus a new model is required. In this paper, the new PM2.5 predictor is proposed to achieve accurate long time series PM2.5 prediction in Beijing. The predictor simplifies the input parameters through Spearman correlation analysis and implements the long time series prediction through Informer. The results show that AQI, CO, NO2, and PM10 concentrations are selected from the air quality data, and Dew Point Temperature (DEWP) and wind speed are incorporated from two meteorological data to better improve the prediction efficiency by almost 27%. By comparing with LSTM and attention-LSTM models, the model constructed in this paper performs well in different prediction time periods, with at least 21%, 19%, 28%, and 35% improvement in accuracy in four prediction time series: 48 h, 7 days, 14 days, and 30 days. In conclusion, the proposed model is proved to solve the problem of predicting long time series PM2.5 concentrations in the future, which can make up for the shortcomings of the currently existing models and have good application value. [ABSTRACT FROM AUTHOR]

Accurate forecasting of PM2.5 concentrations serves as a critical tool for mitigating air pollution. This study introduces a novel hybrid prediction model, termed MIC-CEEMDAN-CNN-BiGRU, for short-term forecasting of PM2.5 concentrations using a 24-hour historical data window. Utilizing the Maximal Information Coefficient (MIC) for feature selection, the model integrates Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Convolutional Neural Network (CNN), and Bidirectional Recurrent Gated Neural Network (BiGRU) to optimize predictive accuracy. We used 2016 PM2.5 monitoring data from Beijing, China as the empirical basis of this study and compared the model with several deep learning frameworks. RNN, LSTM, GRU, and other hybrid models based on GRU, respectively. The experimental results show that the prediction results of the hybrid model proposed in this question are more accurate than those of other models, and the R2 of the hybrid model proposed in this paper improves the R2 by nearly 5 percentage points compared with that of the single model; reduces the MAE by nearly 5 percentage points; and reduces the RMSE by nearly 11 percentage points. The results show that the hybrid prediction model proposed in this study is more accurate than other models in predicting PM2.5. [ABSTRACT FROM AUTHOR]

The effective refuge area is a key indicator in the study of emergency shelters. Accurately extracting the effective refuge area and analyzing the effectiveness of emergency shelters are of great significance for site selection, spatial distribution, and the evaluation of suitability. Beijing is one of only three capitals in the world located in a high-seismic-intensity zone of magnitude 8. The fast and accurate monitoring of effective refuge areas and an analysis of the effectiveness of emergency shelters are conducive to evacuation planning and disaster prevention and mitigation, and they promote the construction of a resilient city. However, the extraction of effective refuge areas in existing studies is not only a time-consuming and labor-intensive task but also has accuracy and efficiency problems, resulting in less precise validity analyses. In this paper, a remote sensing monitoring technology system for the effective refuge areas of emergency shelters is proposed based on multi-source data. Different methods were used to extract various land features, such as buildings and collapsed areas, water, dense areas of understory vegetation, and steep slope areas that cannot be evacuated, to obtain the effective refuge area at a detailed scale, in combination with the service radius of emergency shelters, the population distribution, and the actual road network, the criteria for effectiveness analysis were established for the effective open space ratio, capacity, per capita accessible effective refuge area, and population allocation gap. Taking the area within the Fifth Ring Road of Beijing as an example, the effectiveness of emergency shelters was analyzed at both the whole scale and a local scale. The results show that the effective refuge areas of different emergency shelters in Beijing vary significantly, with the smallest effective refuge area being located in Rings 2–3 and the largest one being located in Rings 4–5; between different regions, there are differences in the effectiveness. This study provides a feasible method for the fast, accurate, and detailed extraction of the effective refuge areas of emergency shelters and also provides a reference for emergency planning for disaster prevention and mitigation. [ABSTRACT FROM AUTHOR]

Air quality plays a vital role in people's health, and air quality forecasting can assist in decision making for government planning and sustainable development. In contrast, it is challenging to multi-step forecast accurately due to its complex and nonlinear caused by both temporal and spatial dimensions. Deep models, with their ability to model strong nonlinearities, have become the primary methods for air quality forecasting. However, because of the lack of mechanism-based analysis, uninterpretability forecasting makes decisions risky, especially when the government makes decisions. This paper proposes an interpretable variational Bayesian deep learning model with information self-screening for PM2.5 forecasting. Firstly, based on factors related to PM2.5 concentration, e.g., temperature, humidity, wind speed, spatial distribution, etc., an interpretable multivariate data screening structure for PM2.5 forecasting was established to catch as much helpful information as possible. Secondly, the self-screening layer was implanted in the deep learning network to optimize the selection of input variables. Further, following implantation of the screening layer, a variational Bayesian gated recurrent unit (GRU) network was constructed to overcome the complex distribution of PM2.5 and achieve accurate multi-step forecasting. The high accuracy of the proposed method is verified by PM2.5 data in Beijing, China, which provides an effective way, with multiple factors for PM2.5 forecasting determined using deep learning technology. [ABSTRACT FROM AUTHOR]

The profound impacts of severe air pollution on human health, ecological balance, and economic stability are undeniable. Precise air quality forecasting stands as a crucial necessity, enabling governmental bodies and vulnerable communities to proactively take essential measures to reduce exposure to detrimental pollutants. Previous research has primarily focused on predicting air quality using only time-series data. However, the importance of remote-sensing image data has received limited attention. This paper proposes a new multi-modal deep-learning model, Res-GCN, which integrates high spatial resolution remote-sensing images and time-series air quality data from multiple stations to forecast future air quality. Res-GCN employs two deep-learning networks, one utilizing the residual network to extract hidden visual information from remote-sensing images, and another using a dynamic spatio-temporal graph convolution network to capture spatio-temporal information from time-series data. By extracting features from two different modalities, improved predictive performance can be achieved. To demonstrate the effectiveness of the proposed model, experiments were conducted on two real-world datasets. The results show that the Res-GCN model effectively extracts multi-modal features, significantly enhancing the accuracy of multi-step predictions. Compared to the best-performing baseline model, the multi-step prediction's mean absolute error, root mean square error, and mean absolute percentage error increased by approximately 6%, 7%, and 7%, respectively. [ABSTRACT FROM AUTHOR]

Geological hazards often occur in mountainous areas and are sudden and hidden, so it is important to identify and assess geological hazards. In this paper, the western mountainous area of Beijing was selected as the study area. We conducted research on landslides, collapses, and unstable slopes in the study area. The surface deformation of the study area was monitored by multi-temporal interferometric synthetic aperture radar (MT-InSAR), using a combination of multi-looking point selection and permanent scatterer (PS) point selection methods. Random forest (RF), support vector machine (SVM), convolutional neural network (CNN), and recurrent neural network (RNN) models were selected for the assessment of geological hazard susceptibility. Sixteen geological hazard-influencing factors were collected, and their information values were calculated using their features. Multicollinearity analysis with the relief-F method was used to calculate the correlation and importance of the factors for factor selection. The results show that the deformation rate along the line-of-sight (LOS) direction is between −44 mm/year and 28 mm/year. A total of 60 geological hazards were identified by combining surface deformation with optical imagery and other data, including 7 collapses, 25 unstable slopes, and 28 landslides. Forty-eight of the identified geological hazards are not recorded in the Beijing geological hazards list. The most effective model in the study area was RF. The percentage of geological hazard susceptibility zoning in the study area is as follows: very low susceptibility 27.40%, low susceptibility 28.06%, moderate susceptibility 21.19%, high susceptibility 13.80%, very high susceptibility 9.57%. [ABSTRACT FROM AUTHOR]

Day by day pollution in cities is increasing due to urbanization. One of the biggest challenges posed by the rapid migration of inhabitants into cities is increased air pollution. Sustainable Development Goal 11 indicates that 99 percent of the world's urban population breathes polluted air. In such a trend of urbanization, predicting the concentrations of pollutants in advance is very important. Predictions of pollutants would help city administrations to take timely measures for ensuring Sustainable Development Goal 11. In data engineering, imputation and the removal of outliers are very important steps prior to forecasting the concentration of air pollutants. For pollution and meteorological data, missing values and outliers are critical problems that need to be addressed. This paper proposes a novel method called multiple iterative imputation using autoencoder-based long short-term memory (MIA-LSTM) which uses iterative imputation using an extra tree regressor as an estimator for the missing values in multivariate data followed by an LSTM autoencoder for the detection and removal of outliers present in the dataset. The preprocessed data were given to a multivariate LSTM for forecasting PM2.5 concentration. This paper also presents the effect of removing outliers and missing values from the dataset as well as the effect of imputing missing values in the process of forecasting the concentrations of air pollutants. The proposed method provides better results for forecasting with a root mean square error (RMSE) value of 9.8883. The obtained results were compared with the traditional gated recurrent unit (GRU), 1D convolutional neural network (CNN), and long short-term memory (LSTM) approaches for a dataset of the Aotizhonhxin area of Beijing in China. Similar results were observed for another two locations in China and one location in India. The results obtained show that imputation and outlier/anomaly removal improve the accuracy of air pollution forecasting. [ABSTRACT FROM AUTHOR]

Geospatial artificial intelligence (GeoAI) has emerged as a subfield of GIScience that uses artificial intelligence approaches and machine learning techniques for geographic knowledge discovery. The non-regularity of data structures has recently led to different variants of graph neural networks in the field of computer science, with graph convolutional neural networks being one of the most prominent that operate on non-euclidean structured data where the numbers of nodes connections vary and the nodes are unordered. These networks use graph convolution – commonly known as filters or kernels – in place of general matrix multiplication in at least one of their layers. This paper suggests spatial regression graph convolutional neural networks (SRGCNNs) as a deep learning paradigm that is capable of handling a wide range of geographical tasks where multivariate spatial data needs modeling and prediction. The feasibility of SRGCNNs lies in the feature propagation mechanisms, the spatial locality nature, and a semi-supervised training strategy. In the experiments, this paper demonstrates the operation of SRGCNNs with social media check-in data in Beijing and house price data in San Diego. The results indicate that a well-trained SRGCNN model is capable of learning from samples and performing reasonable predictions for unobserved locations. The paper also presents the effectiveness of incorporating the idea of geographically weighted regression for handling heterogeneity between locations in the model approach. Compared to conventional spatial regression approaches, SRGCNN-based models tend to generate much more accurate and stable results, especially when the sampling ratio is low. This study offers to bridge the methodological gap between graph deep learning and spatial regression analytics. The proposed idea serves as an example to illustrate how spatial analytics can be combined with state-of-the-art deep learning models, and to enlighten future research at the front of GeoAI. [ABSTRACT FROM AUTHOR]

Passenger flow prediction is a critical approach to ensure the effective functioning of urban rail transit. However, there are few studies that combine multiple influencing factors for short-term passenger flow prediction. It is also a challenge to accurately predict passenger flow at all stations in the line at the same time. To overcome the above limitations, a deep learning-based method named ST-RANet is proposed, which consists of three spatio-temporal modules and one external module. The model is capable of predicting inbound and outbound passenger flow for all stations within the network simultaneously. We model the spatio-temporal data in terms of three temporal characteristics, including closeness, period, and trend. For each characteristic, we construct a spatio-temporal module that innovatively integrates the attention mechanisms into the middle of residual units and convolutional neural networks (CNNs) to extract and learn spatio-temporal features. Subsequently, the results of the three modules are integrated using a parameter matrix method, which allows for dynamic aggregation based on data. The integration results are further combined with external factors, such as holidays and meteorological information, to obtain passenger flow prediction values for each station. The proposed model is validated using real data from Beijing Subway, and optimized parameters are applied for 30-min granularity passenger flow predictions. Comparing the performance against 5 baseline models and verifying with data from multiple lines, the results indicate that the proposed ST-RANet model shows the best results. It is demonstrated that the method proposed in this paper has high prediction accuracy and good applicability. [ABSTRACT FROM AUTHOR]

The accurate prediction of flight delays is of great significance to airports, airlines and passengers. This paper presents a causal flight delay prediction model developed for a single airport. A long short‐term memory network of delay prediction with an attention mechanism (LSTM‐AM) is established to predict flight delays and analyse their primary causes. In this model, the direct and indirect factors related to delays are comprehensively considered. LSTM‐AM can focus on input data combined with the attention vector to capture the critical time points, which can make the prediction more accurate. The model's performance is verified by actual operational data of Beijing International Airport, one of the busiest airports in the world. Experimental results show that LSTM‐AM has better prediction accuracy than baseline algorithms such as some machine learning methods and deep learning methods. The mean absolute error of LSTM‐AM is about 8.15 min on the test dataset. The study found that using the predicted results of this paper to release delayed information in advance can effectively alleviate the nervousness of passengers. The critical time point captured by LSTM‐AM combined with runway and apron flow control can reduce or eliminate delays of one flight. [ABSTRACT FROM AUTHOR]

The application of deep learning in time-series prediction has developed gradually. In this paper, we propose a deep generative network model for feature extraction of multivariate time series, namely, mutual information variational autoencoders (MI-VAE). In the architecture of the proposed model, we use the latent space of VAE for feature learning, which can extract the essential features of multivariate time-series data effectively. The latent space employed directly as a feature extractor can avoid poor interpretability of model. In addition, we introduce a mutual information term into the loss function, which improves the expression capability and accuracy of model. The proposed model, combining the merits of VAE and mutual information, extracts features for multivariate time-series data from a new perspective. The Lorenz system and Beijing air quality time series are used to test performance of the proposed model and comparative models. Results show that the proposed model is superior to other similar models in terms of accuracy and expression capability of latent space. [ABSTRACT FROM AUTHOR]

Under the Beijing-Tianjin regional comprehensive transportation system, the flow of air passengers between multiple airports in the region is more frequent. The fundamental reason for the flow of air passengers is that there are differences in the level of service quality provided by airports and airlines in the region. Passengers' choice intention is the consumption and purchase decision of passengers on aviation services. By constructing a Logit model, this paper analyzes the degree of influence on the travel choice intention of air passengers in the Beijing-Tianjin region from five aspects: individual passenger demographic characteristics, travel purpose, ground transportation characteristics, airport operation capacity, and airport soft power. Passengers can effectively predict the choice of air travel mode in the Beijing-Tianjin region. The results show that Beijing Capital Airport is favored by business travelers; Beijing Daxing International Airport is favored by travelers because of its fast security check-through speed; for Tianjin Binhai International Airport, the convenience of getting in and out of the airport by car and the speed of airport security check-through are two significant factors. Indicators do not affect the selection of airports; reasonable follow-up arrangements when airport flights are delayed are the only significant but negatively correlated factor; the research design results provide new ideas for the analysis of passenger travel mode selection behavior in multiple airport areas, enriching the data-driven research on transportation choices. [ABSTRACT FROM AUTHOR]

Policy decision of curling refers to providing strategy suggestions for curling competition with the help of computers. Existing curling agents have achieved good results in the digital scenarios, but cannot make correct decisions when applied to actual competition and training scenes. In this paper, a strategies decision agent in the real scene has been proposed. The competition situation was acquired by a Situation-Aware Network and mapped by a Digital Extraction module. We designed Curling MCTS to explore the best strategy in continuous space. The effectiveness of our framework has been verified by experiments and evaluated by China's wheelchair curling team at China Disabled Sports Management Center. With the help of our system, China's wheelchair curling team trained effectively and won the championship in the XIII Paralympic Winter Games (2022, Beijing). In addition, a new curling target detection dataset was provided. [ABSTRACT FROM AUTHOR]

The predictions from time series data can help us sense development trends and make scientific decisions in advance. The commonly used forecasting methods with backpropagation consume a lot of computational resources. The deep echo state network (DeepESN) is an advanced prediction method with a deep neural network structure and training algorithm without backpropagation. In this paper, a Bayesian optimization algorithm (BOA) is proposed to optimize DeepESN to address the problem of increasing parameter scale. Firstly, the DeepESN was studied and constructed as the basic prediction model for the time series data. Secondly, the BOA was reconstructed, based on the DeepESN, for optimal parameter searching. The algorithm is proposed within the framework of the DeepESN. Thirdly, an experiment was conducted to verify the DeepESN with a BOA within three datasets: simulation data generated from computer programs, a real humidity dataset collected from Beijing, and a power load dataset obtained from America. Compared with the models of BP (backpropagation), LSTM (long short-term memory), GRU (gated recurrent unit), and ESN (echo state network), DeepESN obtained optimal results, which were 0.0719, 18.6707, and 764.5281 using RMSE evaluation. While getting better accuracy, the BOA optimization time was only 323.4 s, 563.2 s, and 9854 s for the three datasets. It is more efficient than grid search and grey wolf optimizer. [ABSTRACT FROM AUTHOR]

Finding students at high risk of poor academic performance as early as possible plays an important role in improving education quality. To do so, most existing studies have used the traditional machine learning algorithms to predict students' achievement based on their behavior data, from which behavior features are extracted manually thanks to expert experience and knowledge. However, owing to an increase in the varieties and overall volume of behavioral data, it has become more and more challenging to identify high-quality handcrafted features. In this paper, we propose an end-to-end deep learning model that automatically extracts features from students' multi-source heterogeneous behavior data to predict academic performance. The key innovation of this model is that it uses long short-term memory networks to capture inherent time-series features for each type of behavior, and it takes two-dimensional convolutional networks to extract correlation features among different behaviors. We conducted experiments with four types of daily behavior data from students of the university in Beijing. The experimental results demonstrate that the proposed deep model method outperforms several machine learning algorithms. [ABSTRACT FROM AUTHOR]

Air pollution has become a critical factor affecting the health of human beings. Forecasting the trend of air pollutants will be of considerable help to public health, including improving early-warning systems. The article designs a novel hybrid deep learning framework FPHFA (FPHFA is the abbreviation of the title of this paper) for PM2.5 concentration forecasting is proposed, which learns spatially correlated features and long-term dependencies of time series data related to PM2.5. Owing to the complex nonlinear dynamic and spatial features of pollutant data, the FPHFFA model combines multi-channel one-dimensional convolutional neural networks, bi-directional long short-term memory neural networks, and attention mechanisms for the first time. Multi-channel 1D CNNs are applied to capture trend features between some sites and overall spatial characteristics of PM2.5 concentration, Bi LSTMs are used to learn the temporal correlation of PM2.5 concentration, and the attention mechanism is used to focus more effective information at different moments. We carried out experimental evaluations using the Beijing dataset, and the outcomes show that our proposed model can effectively handle PM2.5 concentration prediction with satisfactory accuracy. For the prediction task from 1 to 12 h, our proposed prediction model performs well. The FPHFA also achieves satisfactory results for prediction tasks from 13 to 96 h. [ABSTRACT FROM AUTHOR]

Air pollution is a growing problem and poses a challenge to people's healthy lives. Accurate prediction of air pollutant concentrations is considered the key to air pollution warning and management. In this paper, a novel PM2.5 concentration prediction model, CBAM-CNN-Bi LSTM, is constructed by deep learning techniques based on the principles related to spatial big data. This model consists of the convolutional block attention module (CBAM), the convolutional neural network (CNN), and the bi-directional long short-term memory neural network (Bi LSTM). CBAM is applied to the extraction of feature relationships between pollutant data and meteorological data and assists in deeply obtaining the spatial distribution characteristics of PM2.5 concentrations. As the output layer, Bi LSTM obtains the variation pattern of PM2.5 concentrations from spatial data, overcomes the problem of long-term dependence on PM2.5 concentrations, and achieves the task of accurately forecasting PM2.5 concentrations at multiple sites. Based on real datasets, we perform an experimental evaluation and the results show that, in comparison to other models, CBAM-CNN-Bi LSTM improves the accuracy of PM2.5 concentration prediction. For the prediction tasks from 1 to 12 h, our proposed prediction model performs well. For the 13 to 48 h prediction task, the CBAM-CNN-Bi LSTM also achieves satisfactory results. [ABSTRACT FROM AUTHOR]

In the curling sport, the coefficient of friction between the curling stone and pebbled ice is crucial to predict the motion trajectory. However, the theoretical and experimental investigations on stone–ice friction are limited, mainly due to the limitations of the field measurement techniques and the inadequacy of the experimental data from professional curling rinks. In this paper, on-site measurement of the stone–ice friction coefficient in a prefabricated ice rink for the Beijing Winter Olympics curling event was carried out based on computer vision technology. Firstly, a procedure to determine the location of the curling stone was proposed using YOLO-V3 (You Only Look Once, Version 3) deep neural networks and the CSRT Object tracking algorithm. Video data was recorded during the curling stone throwing experiments, and the friction coefficient was extracted. Furthermore, the influence of the sliding velocity on the friction coefficient was discussed. Comparison with published experimental data and models and verification of the obtained results, using a sensor-based method, were conducted. Results show that the coefficient of friction (ranging from 0.006 to 0.016) decreased with increasing sliding velocity, due to the presence of a liquid-like layer. Our obtained results were consistent with the literature data and the friction model of Lozowski. In addition, the experimental results of the computer vision technique method and the accelerometer sensor method showed remarkable agreement, supporting the accuracy and reliability of our proposed measurement procedure based on deep learning. [ABSTRACT FROM AUTHOR]

Nowadays, the autonomous driving transportation systems are at the heart of both academic and industry research for the distinguished advantages including increased network capacity, enhanced punctuality, greater flexibility and improved overall safety level. With the responsibility of transporting passengers in a safe, comfortable and efficient way, the decision making method plays a critical position in the autonomous driving of high-speed trains. Focusing on solving the autonomous decision making problem, this paper proposes a novel learning based framework by combining the deep learning technology with the distributed tracking control approach. To cope with the data insufficiency problem in training the deep learning network, a generative adversarial network (GAN) based data argumentation scheme is proposed to generate data samples that have the same distribution with actual data samples, and a hybrid learning network is constructed to predict the speed trajectory from the multi-attribute data with both temporal sequences and static features. Then, based on the model predictive control (MPC) scheme, a distributed tracking control model is formulated to minimize the tracking deviations and balance the performance of punctuality, energy-efficiency and riding comfort. Further, the dual decomposition technique is adopted to deal with the coupling constraints for the safe distance headway such that the separation for the autonomous driving of high-speed trains is achieved. Finally, simulation experiments based on actual scenarios of the Beijing-Shanghai high-speed railway are conducted to illustrate the effectiveness of our methods. [ABSTRACT FROM AUTHOR]

House price is closely associated with the development of the national economy and people's daily life. Understanding the spatial distribution characteristics and influencing factors of the house price is of great practical significance. Although a lot of attention has been paid to modeling the house price from structure and location attributes, limited work has considered the impact of visual attributes. Intuitively, a better visual environment may raise the surrounding house price. When aggregating multiple factors that influence house price, the multiscale geographically weighted regression (MGWR) provides a suitable solution. Specifically, the MGWR assigns each factor a bandwidth to model the spatial heterogeneity, e.g., a factor may have different influences at different places. In this paper, we introduce the visual environment factors into the MGWR method. In detail, we extract ten visual elements, e.g., sky, vegetation, road, from the Baidu street view (BSV) images, using a deep learning framework. We further define six visual environment factors to investigate their influence on house price. Based on the data from two representative Chinese cities, i.e., Beijing and Chongqing, we reveal the influence degree and spatial scale difference of six visual indexes on the house price in two cities. Results show that: (1) the influence intensity of our proposed six visual environment factors on the house price in different regions of the city can be identified, and the green view index (GVI) is the most important visual environmental factor; and (2) the influence of these view indexes changes significantly or even reversely depends on different areas. [ABSTRACT FROM AUTHOR]

Over the past few decades, due to human activities, industrialization, and urbanization, air pollution has become a life-threatening factor in many countries around the world. Among air pollutants, Particulate Matter with a diameter of less than 2.5 μ m ( P M 2.5 ) is a serious health problem. It causes various illnesses such as respiratory tract and cardiovascular diseases. Hence, it is necessary to accurately predict the P M 2.5 concentrations in order to prevent the citizens from the dangerous impact of air pollution beforehand. The variation of P M 2.5 depends on a variety of factors, such as meteorology and the concentration of other pollutants in urban areas. In this paper, we implemented a deep learning solution to predict the hourly forecast of P M 2.5 concentration in Beijing, China, based on CNN-LSTM, with a spatial-temporal feature by combining historical data of pollutants, meteorological data, and P M 2.5 concentration in the adjacent stations. We examined the difference in performances among Deep learning algorithms such as LSTM, Bi-LSTM, GRU, Bi-GRU, CNN, and a hybrid CNN-LSTM model. Experimental results indicate that our method "hybrid CNN-LSTM multivariate" enables more accurate predictions than all the listed traditional models and performs better in predictive performance. [ABSTRACT FROM AUTHOR]

Short-term prediction of origin–destination (OD) flow is a primary but complex assignment to urban rail companies, which is the basis of intelligent and real-time urban rail transit (URT) operation and management. The short-term prediction of URT OD flow has three special characteristics: data lag, data dimensionality, and data malconformation, distinguishing it from other short-term prediction tasks. It is essential to propose a novel prediction algorithm that considers the special characteristics of the URT OD flow. For this purpose, based on deep learning methods and multi-source big data, a modified spatial–temporal long short-term memory (ST-LSTM) model is established. The proposed model comprises four components: (1) a temporal feature extraction module is devised to extract time information within network-wide historical OD data; (2) a spatial correlation learning module is introduced to address the data malconformation and data dimensionality problems, which provides an interpretable spatial correlation quantization method; (3) an input control-gated mechanism is originally proposed to solve the data lag problem, which combines the processed available OD flow and real-time inflow/outflow; (4) a fusion module combines historical spatial–temporal features with real-time information to achieve accurate OD flow prediction. We also further discuss the interpretability of the model in detail. The ST-LSTM model is evaluated by sufficient experiments on two large-scale actual subway datasets from Nanjing and Beijing, and the experimental results demonstrate that it can better learn the spatial–temporal correlations and exceed the rest benchmarking methods. [ABSTRACT FROM AUTHOR]

Existing water gauge reading approaches based on image analysis have problems such as poor scene adaptability and weak robustness. Here, we proposed a novel water level measurement method based on deep learning (YOLOv5s, convolutional neural network) to overcome these problems. The proposed method uses the YOLOv5s to extract the water gauge area and all scale character areas in the original video image, uses image processing technology to identify the position of the water surface line, and then calculates the actual water level elevation. The proposed method is validated with a video monitoring station on a river in Beijing, and the results show that the systematic error of the proposed method is only 7.7 mm, the error is within 1 cm/the error is between 1 cm and 3 cm, and the proportion of the number of images is 95%/5% (daylight), 98%/2% (infrared lighting at night), 97%/2% (strong light), 45%/44% (transparent water body), 91%/9% (rainfall), and 90%/10% (water gauge is slightly dirty). The results demonstrate that the proposed method shows good performance in different scenes, and its effectiveness has been confirmed. At the same time, it has a strong robustness and provides a certain reference for the application of deep learning in the field of hydrological monitoring. [ABSTRACT FROM AUTHOR]

While it is widely known that the greening landscape spatial quality plays a vital role in promoting the level of highway landscape design, there is still no consensus on how to measure it quantitatively on a large scale. Recent advancements in computer vision tasks under deep learning, along with esmerging datasets such as street view pictures, have shown the potential to overcome previous limitations, leading to a paradigm shift in research. This paper proposes a novel digital measurement technique that automatically identifies elements and greening landscape spatial features from a partial panoramic and spherical view field, considering the driver and passengers' environmental perspectives. To achieve this, we have developed a spatial quality parameter index system consisting of three dimensions and nine indicators, with two regions dedicated to plains and mountainous regions. We have also created a visualization atlas that illustrates the landscape of highway greening in both vertical and horizontal dimensions. We applied this method to the Beijing section of the Beijing-Tibet highway to guide targeted improvement strategies from different perspectives. Our findings provide strategies for enhancing scientific performance and tools for quantitative analysis in digitizing, engineering, and refining highway landscape design. • Propose a high-precision training model Panoptic-DeepLab. • Propose a digital measurement indicator system of highway greening landscape spatial quality. • Gives an atlas representation method of highway greening landscape spatial quality. [ABSTRACT FROM AUTHOR]

The provision of accurate travel time information holds paramount significance for optimizing traffic operations and management. However, existing approaches typically address multiple travel time learning tasks independently, neglecting the correlation of tasks and necessitating the construction of separate models for each task. As the scale of the road network expands, this practice becomes increasingly infeasible. To bridge this gap, this paper presents an adaptive deep multi-task learning model to solve multiple travel time estimation tasks collaboratively from a citywide perspective. The novel model seamlessly integrates geographical, environmental, and spatial-correlated features, capturing the complex non-linear characteristics of travel time in the urban road network through a deep network learning feature representations layer by layer from the input data. Through the paradigm of deep multi-task collaborative learning, the knowledge learned from one task serves as an inductive bias for other tasks, thereby facilitating the learning of related tasks. By modeling the uncertainty of travel time with a probabilistic model, the weights of jointly learned tasks are adaptively fine-tuned in a principled way. Additionally, a novel algorithm is devised to fully leverage the information embedded within the data with partial information. The proposed methodology is evaluated through a case study on the citywide road network in Beijing, China. Empirical results of extensive experiments demonstrate the adeptness of the proposed model in effectively harnessing information transferred from parallel tasks, adaptively fine-tuning the weights of joint learning tasks, and proficiently exploiting data characterized by incomplete labels, therefore leading to superior performance compared to competing methods. The average estimation error of the four jointly learned travel time estimation tasks is about 28.48%, yielding an improvement of over 4% compared to single-task learning models. The results verify the effectiveness and robustness of the proposed model for travel time estimation in the urban road network. [ABSTRACT FROM AUTHOR]

In deep learning based synthetic aperture radar (SAR) change detection, selecting samples of high quality is a crucial step. In this work, we have proposed a refined sample selection algorithm for unsupervised SAR change detection. The propose and incorporation of volume control factors and multi-hierarchical fuzzy c-means (MH-FCM) algorithm generate samples of large diversity and high confidence, thus satisfying the needs for high quality samples. The method includes two phases: firstly, an enhanced difference image is constructed according to the difference consistency between single pixels and their neighbourhoods, and a triangular threshold segmentation method is then proposed to determine the volume control factors for sample selection. MH-FCM is developed to classify the log mean ratio difference image into 4 classes. Secondly, a dual-channel convolution neural network with an adaptive weighted loss is adopted to learn and predict the input and to obtain the change detection result. Experimental results of the Gaofen-3 dataset in Beijing have validated the effectiveness and usefulness of the proposed method. [ABSTRACT FROM AUTHOR]

The rise of online‐to‐offline (O2O) e‐commerce business has brought tremendous opportunities to the logistics industry. In the online‐to‐offline logistics business, it is essential to detect anomaly merchants with fraudulent shipping behaviours, such as sending other merchants' packages for profit with their low discounts. This can help reduce the financial losses of platforms and ensure a healthy environment. Existing anomaly detection studies have mainly focused on online fraud behaviour detection, such as fraudulent purchase and comment behaviours in e‐commerce. However, these methods are not suitable for anomaly merchant detection in logistics due to the more complex online and offline operation of package‐sending behaviours and the interpretable requirements of offline deployment in logistics. MultiDet, a semi‐supervised multi‐view fusion‐based Anomaly Detection framework in online‐to‐offline logistics is proposed, which consists of a basic version SemiDet and an attention‐enhanced multi‐view fusion model. In SemiDet, pair‐wise data augmentation is first conducted to promote model robustness and address the challenge of limited labelled anomaly instances. Then, SemiDet calculates the anomaly scoring of each merchant with an auto‐encoder framework. Considering the multi‐relationships among logistics merchants, a multi‐view attention fusion‐based anomaly detection network is further designed to capture merchants' mutual influences and improve the anomaly merchant detection performance. A post‐hoc perturbation‐based interpretation model is designed to output the importance of different views and ensure the trustworthiness of end‐to‐end anomaly detection. The framework based on an eight‐month real‐world dataset collected from one of the largest logistics platforms in China is evaluated, involving 6128 merchants and 16 million historical order consignor records in Beijing. Experimental results show that the proposed model outperforms other baselines in both AUC‐ROC and AUC‐PR metrics. [ABSTRACT FROM AUTHOR]

Accurate traffic congestion estimation and prediction are critical building blocks for smart trip planning and rerouting decisions in transportation systems. Over the decades, there have been many studies focusing on traffic congestion estimation and prediction with different statistical approaches (e.g., Markov chain) and machine learning models (e.g., clustering, Bayesian networks, and artificial neural networks). However, there is a lack of a unified framework to address the mechanisms of different models and integrate the advantages of different methods through combinations. This paper introduces the FD-Markov-LSTM model, a hybrid interpretable approach that combines the fundamental diagram (FD), Markov chain, and long short-term memory (LSTM). The aim is to estimate and predict traffic states by integrating statistical data in both congested and uncongested scenarios. The FD-Markov-LSTM model leverages the FD to identify hierarchical traffic states and utilizes the Markov process to capture the probabilistic transitions between these states. We employ the LSTM model to further capture the residual time series produced by the Markov chain model (assuming a memoryless property) to enhance the estimation and prediction performance. The proposed model's accuracy in estimating and predicting traffic flow is evaluated using empirical data from three case studies conducted in Beijing and Los Angeles. The results highlight a significant improvement in accuracy compared to classical benchmark models such as the Markov model, ARIMA model, k-Nearest Neighbor model, Random Forest model, and LSTM. Specifically, the FD-Markov-LSTM model achieves reductions of over 39% in mean absolute error, 35% in root mean squared error, and 7.4% in mean absolute percentage error. These results clearly demonstrate that the FD-Markov-LSTM model outperforms the benchmark models, enabling more precise predictions of traffic flow. [ABSTRACT FROM AUTHOR]

Road damage is a great threat to the service life and safety of roads, and the early detection of pavement damage can facilitate maintenance and repair. Street view images serve as a new solution for the monitoring of pavement damage due to their wide coverage and regular updates. In this study, a road pavement damage dataset, the Street View Image Dataset for Automated Road Damage Detection (SVRDD), was developed using 8000 street view images acquired from Baidu Maps. Based on these images, over 20,000 damage instances were visually recognized and annotated. These instances were distributed in five administrative districts of Beijing City. Ten well-established object detection algorithms were trained and assessed using the SVRDD dataset. The results have demonstrated the performances of these algorithms in the detection of pavement damages. To the best of our knowledge, SVRDD is the first public dataset based on street view images for pavement damages detection. It can provide reliable data support for future development of deep learning algorithms based on street view images. [ABSTRACT FROM AUTHOR]

Online car‐hailing, with its advantages of convenience and efficiency, has quickly become popular among tourists, playing a crucial role in the accessibility of scenic spots. Due to the particularities of tourist travel behaviour and the complexity of travel supply and demand around scenic spots, research on car‐hailing tourists is relatively lacking at this stage. Based on multi‐source data, this study couples the identifying of travel characteristics, by introducing the concept of service dependency degree, with a Bayesian optimization–long short‐term memory–convolutional neural network (BO‐LSTM‐CNN) method to conduct multi‐task online car‐hailing demand forecasting. The evaluation of the dependency degree primarily encompasses the establishment of evaluation indices and the application of the entropy weight method and natural breakpoint method. The BO‐LSTM‐CNN model utilizes Bayesian optimization for hyperparameter tuning, LSTM for temporal variable processing, and CNN for the fusion of multi‐source information related to weather, space, and online car‐hailing attributes. Extracting online car‐hailing tourist travel orders based on spatial–temporal constraints, the proposed methods are applied to 72 scenic spots in Beijing, China. According to their dependency degree, Beijing's scenic spots are categorized into three levels of dependency on online car‐hailing services, from high to low. The outstanding forecasting efficacy of the proposed model for various scenic spots is verified through comparison tests with several benchmark models. Consequent to these findings, mobility service improvement strategies are specifically proposed for each class of scenic spots, which can provide valuable insights for the relevant tourism traffic management personnel. [ABSTRACT FROM AUTHOR]

The fast and accurate extraction of plastic greenhouses over large areas is important for environmental and agricultural management. Traditional spectral index methods and object-based methods can suffer from poor transferability or high computational costs. Current deep learning-based algorithms are seldom specifically aimed at extracting plastic greenhouses at large scales. To extract plastic greenhouses at large scales with high accuracy, this study proposed a new deep learning-based network, U-FDS Net, specifically for plastic greenhouse extraction over large areas. U-FDS Net combines full-scale dense connections and adaptive deep supervision and has strong future fusion capabilities, allowing more accurate extraction results. To test the extraction accuracy, this study compiled new greenhouse datasets covering Beijing and Shandong with a total number of more than 12,000 image samples. The results showed that the proposed U-FDS net is particularly suitable for complex backgrounds and reducing false positive conditions for nongreenhouse ground objects, with the highest mIoU (mean intersection over union) an increase of ~2%. This study provides a high-performance method for plastic greenhouse extraction to enable environmental management, pollution control and agricultural plans. [ABSTRACT FROM AUTHOR]

Groundwater depletion is adversely affecting Beijing's ecology and environment. However, the effective execution of the South-to-North Water Diversion Project's middle route (SNDWP-MR) is anticipated to mitigate Beijing's groundwater depletion. Here, we propose a robust hybrid statistical downscaling method aimed at enhancing the capability of the Gravity Recovery and Climate Experiment (GRACE) to detect the small-scale groundwater storage anomaly (GWSA) in Beijing. We used three deep learning (DL) methods to reconstruct the 0.5° × 0.5° terrestrial water storage anomaly (TWSA) between 2004 and 2021. Moreover, multiple processing strategies were used to downscale the GWSA to 0.25° from 2004 to 2021 by integrating wells and GRACE/GRACE follow-on data from the optimal DL model. Additionally, we analyzed the spatiotemporal evolution trends of GW in Beijing before and after the implementation of the SNDWP-MR. The results show that the long short-term memory model delivers optimal performance in the TWSA reconstruction of Beijing, with the correlation coefficient (CC), Nash–Sutcliffe coefficient (NSE), and root mean square error (RMSE) being 0.98, 0.96, and 10.19 mm, respectively. The GWSA before and after downscaling is basically consistent with wells data, but the CC and RMSE of downscaling the GWSA from 2004 to 2021 are improving by 34% and 31%, respectively. Before the SNDWP-MR (2004–2014), the trend of GWSA in Beijing was − 17.68 ± 4.46 mm/y, with a human contribution of 69.30%. After SNDWP-MR (2015–2021), GWSA gradually increased by 10.00 mm per year, with the SNDWP-MR accounting for 18.30%. This study delivers a technical innovation reference for dynamically monitoring a small-scale GWSA from GRACE/GRACE-FO data. [ABSTRACT FROM AUTHOR]

Station parking accuracy is an important indicator for the automatic control of high‐speed trains; however, it is subject to many influencing factors, such as the characteristics of high nonlinearity and large time delays in the train control model, time‐variant humidity, and uncertain weather conditions, leading to unsatisfying performance with existing feedback control algorithms. This study first proposes an intelligent train stopping control approach based on deep learning for high‐speed railways. By collecting a large amount of historical train operation data from the Beijing–Shenyang high‐speed railway, three data‐driven models are developed for train stopping control. The first model is based on a deep‐layered feedforward neural network (DNN), which predicts the exact train stopping position with dynamic train running states (position, velocity etc.) as input. Taking advantage of the physical train control models used in practice, the DNN to a convolutional neural network (CNN) is then improved by customizing the convolutional layers of the CNN. To overcome the issues arising from the incompleteness of data samples, a few‐shot convolutional neural network (FSCNN) is further developed to enhance the prediction performance of the CNN. Compared with that of the current method used in practice, the simulation experiments show that the train station parking error can be decreased by 38.6%, 42.8%, and 49.2% by our developed DNN, CNN, and FSCNN, respectively. [ABSTRACT FROM AUTHOR]

Rail transit has many advantages, such as large passenger capacity, convenience, safety, and environmental protection, making it the preferred travel mode for most passengers. Deep learning has become an effective method for short‐term rail passenger flow prediction. A deep learning model which combines a graph convolutional network and a three‐dimensional Convolutional Neural Network improved by a residual module and an attention mechanism (ARConv‐CGN) is proposed. First, historical passenger inflow and outflow data are aggregated into three patterns: recent pattern, daily pattern, and weekly pattern separately. The GCN is applied to capture the spatiotemporal and topological information of passenger flows in each pattern. Second, a three‐dimensional convolutional neural network is used to deeply integrate three patterns of passenger flow information. Additionally, a residual module is used to increase the number of neural network layers and prevent the gradient of the deep neural network from disappearing. Finally, an attention mechanism is also introduced to adjust the importance of passenger flow in different pattern, so that improving the performance of the model. Training with passenger flow data from automatic fare collection on weekdays in Beijing and Xiamen provides a good demonstration of the superiority of ARConv‐CGN in metro passenger flow forecasting. [ABSTRACT FROM AUTHOR]

• A deep learning approach to predicting the electric vehicle (EV) charging demand. • A real-world large EV trajectory dataset was used for model training and validation. • The deep learning approach outperformed traditional time series prediction models. • Input data structures, and sample sizes were not influential to prediction accuracy. Short-term prediction of the Electric Vehicle (EV) charging demand is of great importance to the operation of EV fleets and charging stations. This paper develops a Long Short-Term Memory (LSTM) neural network to predict the EV charging demand at the station level for the next few hours (e.g., 1–5 h), using a unique trajectory dataset containing over 76,000 private EVs in Beijing in January 2018. To explore the performance of the LSTM model, we set up four scenarios by 1) comparing LSTM against two typical time series prediction models, i.e., the Auto-Regressive Moving Average model (ARIMA), and the Multiple Layer Perceptron model (MLP), 2) and investigating how different input data structures, sample sizes, and time spans and intervals would influence model accuracy. The results suggest that the LSTM model outperformed the ARIMA, and MLP models, and their MAPE1 values are 6.83 %, 21.58 %, and 18.31 %, respectively. In addition, we find that the time span and interval tend to be more influential to the LSTM model's prediction accuracy than input data structures, and sample sizes. In general, the LSTM model with a shorter time span or interval (e.g., 1 h) would perform better. [ABSTRACT FROM AUTHOR]

The physical presence of a street, called the "street view", is a medium through which people perceive the urban form. A street's spatial ratio is the main feature of the street view, and its measurement and quality are the core issues in the field of urban design. The traditional method of studying urban aspect ratios is manual on-site observation, which is inefficient, incomplete and inaccurate, making it difficult to reveal overall patterns and influencing factors. Street view images (SVI) provide large-scale urban data that, combined with deep learning algorithms, allow for studying street spatial ratios from a broader space-time perspective. This approach can reveal an urban forms' aesthetics, spatial quality, and evolution process. However, current streetscape research mainly focuses on the creation and maintenance of spatial data infrastructure, street greening, street safety, urban vitality, etc. In this study, quantitative research of the Beijing street spatial ratio was carried out using street view images, a convolution neural network algorithm, and the classical street spatial ratio theory of urban morphology. Using the DenseNet model, the quantitative measurement of Beijing's urban street location, street aspect ratio, and the street symmetry was realized. According to the model identification results, the law of the gradual transition of the street spatial ratio was depicted (from the open and balanced type to the canyon type and from the historical to the modern). Changes in the streets' spatiotemporal characteristics in the central area of Beijing were revealed. Based on this, the clustering and distribution phenomena of four street aspect ratio types in Beijing are discussed and the relationship between the street aspect ratio type and symmetry is summarized, selecting a typical lot for empirical research. The classical theory of street spatial proportion has limitations under the conditions of high-density development in modern cities, and the traditional urban morphology theory, combined with new technical methods such as streetscape images and deep learning algorithms, can provide new ideas for the study of urban space morphology. [ABSTRACT FROM AUTHOR]

As an essential material carrier of cultural heritage, the accurate identification and effective monitoring of buildings in traditional Chinese villages are of great significance to the sustainable development of villages. However, along with rapid urbanization in recent years, many towns have experienced problems such as private construction, hollowing out, and land abuse, destroying the traditional appearance of villages. This study combines deep learning technology and UAV remote sensing to propose a high-precision extraction method for conventional village architecture. Firstly, this study constructs the first sample database of traditional village architecture based on UAV remote sensing orthophotos of eight representative villages in Beijing, combined with fine classification; secondly, in the face of the diversity and complexity of the built environment in traditional villages, we use the Mask R-CNN instance segmentation model as the basis and Path Aggregate Feature Pyramid Network (PAFPN) and Atlas Space Pyramid Pool (ASPP) as the main strategies to enhance the backbone model for multi-scale feature extraction and fusion, using data increment and migration learning as auxiliary means to overcome the shortage of labeled data. The results showed that some categories could achieve more than 91% accuracy, with average precision, recall, F1-score, and Intersection over Union (IoU) values reaching 71.3% (+7.8%), 81.9% (+4.6%), 75.7% (+6.0%), and 69.4% (+8.5%), respectively. The application practice in Hexi village shows that the method has good generalization ability and robustness, and has good application prospects for future traditional village conservation. [ABSTRACT FROM AUTHOR]

Complete and high-quality deformation monitoring data are critical for shield tunnel construction safety and quality. In engineering practices, data missing frequently occurs during instrumentation, adversely impacting further analysis and decision making. Existing imputation methods either ignore the crucial interactions between different parameters during shield tunneling or focus on the global characteristics of deformation data while neglect their local difference. This paper proposes a novel hybrid model, MCCB, combining multi-view matrix completion algorithms, convolutional neural network (CNN), and bidirectional long short-term memory (BiLSTM) algorithms to impute missing deformation values in shield tunnel monitoring data. The performance of the proposed method is verified using bridge deformation data from a practical project in Beijing. Different missing patterns of the bridge deformation data are filled. The experiment results show that the proposed model can effectively learn the various characteristics of the deformation data and outperforms the four selected models and its two sub-models, and can be used to improve the accuracy of the deformation prediction through data imputation. The novelty of this study includes two aspects. First is that the complicated interaction between different parameters and local difference of the data are considered simultaneously. They have not been addressed before by existing studies. Second is that the innovative combination of matrix completion and deep learning algorithm for application in missing deformation values imputation. To our best knowledge, no research on engineering construction has implemented this technique before. [ABSTRACT FROM AUTHOR]

Long short-term memory (LSTM), especially vanilla LSTM (VLSTM), has been widely used in air quality prediction field. However, VLSTM has many more parameters, thereby making training slow and prediction performance unstable. The VLSTM network input data have not been selected for better efficiency. In this paper, we propose an air quality prediction model based on the improved VLSTM with multichannel input and multiroute output (IVLSTM-MCMR). The proposed model includes the IVLSTM and MCMR modules. The proposed IVLSTM module is developed by improving the VLSTM inner structure of VLSTM in order to reduce the number of parameters that help to accelerate the convergence. A new historical information usage approach is further proposed to obtain a stable training process. For the MCMR module, a multichannel data input model (MC) with an improved linear similarity dynamic time warping is introduced to choose the valid data as the input of IVLSTM. A multiroute output model (MR) is designed to integrate the results from MC, in which the results of different target stations with different features are output by different routes. We evaluate the proposed model with the collected data from Beijing, China, and the experimental results show that our model achieves improvements regarding the predication performance. • An air quality prediction model IVLSTM-MCMR is proposed based on deep learning. • The number of parameters in IVLSTM-MCMR is reduced to accelerate the convergence. • The improved linear similarity dynamic time warping is introduced in IVLSTM-MCMR. • The integration of multi-channel data input and multi-route output is designed. [ABSTRACT FROM AUTHOR]

[Display omitted] • The measurement errors in long-time series remote sensing images are corrected by statistical and spatio-temporal prediction model. • The ecological degradation of the Miyun Reservoir basin from 1985 to 2021 is mainly caused by anthropogenic factors. • Through statistical theory, the ecological development trend in the experimental area is analyzed, and the conclusion is verified by land use evolution. Miyun Reservoir, located in the Miyun District, Beijing, China, is the largest comprehensive water conservancy project and is an important ecological protection area in the North China region. Changes within the basin are the driving factors affecting the ecosystem in the watershed; therefore, it is important to analyze the changes in the ecological environment of Miyun Reservoir. For the analysis of a long time series of image data remotely sensed by satellite, the outliers caused by atmospheric, lighting, and sensor measurement errors are significant, and it is difficult for traditional algorithms to effectively recover the true image value. To address this, this paper proposes a theoretical model for predicting spatio-temporal variation based on deep learning to identify and correct invalid and anomalous values in extended time series data. This study corrected and analyzed the results of Remote Sensing based Ecological Index inversion of Landsat data of the Miyun Reservoir watershed from 1985 to 2021. The findings and conclusions of this study are important for the analysis of long time series image data from satellite remote sensing and for improving regional ecological evaluation and sustainable development planning. [ABSTRACT FROM AUTHOR]

Many Chinese cities have severe air pollution due to the rapid development of the Chinese economy, urbanization, and industrialization. Particulate matter (PM2.5) is a significant component of air pollutants. It is related to cardiopulmonary and other systemic diseases because of its ability to penetrate the human respiratory system. Forecasting air PM2.5 is a critical task that helps governments and local authorities to make necessary plans and actions. Thus, in the current study, we develop a new deep learning approach to forecast the concentration of PM2.5 in three major cities in China, Beijing, Shijiazhuang, and Wuhan. The developed model is based on the Informer architecture, where the attention distillation block is improved with a residual block-inspired structure from efficient networks, and we named the model ResInformer. We use air quality index datasets that cover 98 months collected from 1 January 2014 to 17 February 2022 to train and test the model. We also test the proposed model for 20 months. The evaluation outcomes show that the ResInformer and ResInformerStack perform better than the original model and yield better forecasting results. This study's methodology is easily adapted for similar efforts of fast computational modeling. [ABSTRACT FROM AUTHOR]

Obtaining crowd flow distribution with recognized human intention is extremely valuable for a series of applications for metropolitan cities. Previous solutions look at spatial correlation and temporal periodicity based on historical crowd flow information to calculate future crowd flow distribution. However, these mechanisms cannot recognize the intention behind crowd flow. We address this problem by leveraging a key insight – people's intention behind their movement is highly correlated with the point-of-interest (POI) distribution of the corresponding regions and adjacent regions. Therefore, we propose DeepFlowGen to model the complicated relationship between crowd flow, POI, check-ins, and time to generate intention-aware crowd flow. Specifically, we solve the conflict between dynamic crowd flow and static POI distribution by fusing the information in both time and POI domains. Besides, we employ a sequence of residual blocks in DeepFlowGen to address the challenges of modeling the diverse temporal rhythms and heterogeneous influence of POI. Furthermore, we examine the generated intention-aware crowd flow from two aspects to substantiate the reasonability of DeepFlowGen. Extensive experiments demonstrate that our model outperforms the state-of-the-art solutions by at most 30 percent in terms of NRMSE of total crowd flow. Moreover, the correlation between the generated intention-aware crowd flow and the check-in distribution across different categories of POIs is as high as 0.90 and 0.80 in Beijing and Shanghai. Combined with extensive case studies, we demonstrate the strong ability of our model in generating intention-aware crowd flow. [ABSTRACT FROM AUTHOR]

With the advent of Big Data technology and the Internet of Things, Intelligent Transportation Systems (ITS) have become inevitable for future transportation networks. Travel time prediction (TTP) is an essential part of ITS and plays a pivotal role in congestion avoidance and route planning. The novel data sources such as smartphones and in-vehicle navigation applications allow traffic conditions in smart cities to be analyzed and forecast more reliably than ever. Such a massive amount of geospatial data provides a rich source of information for TTP. Gated Recurrent Unit (GRU) has been successfully applied to traffic prediction problems due to its ability to handle long-term traffic sequences. However, the existing GRU does not consider the relationship between various historical travel time positions in the sequences for traffic prediction. We propose an attention-based GRU model for short-term travel time prediction to cope with this problem enabling GRU to learn the relevant context in historical travel time sequences and update the weights of hidden states accordingly. We evaluated the proposed model using FCD data from Beijing. To demonstrate the generalization of our proposed model, we performed a robustness analysis by adding noise obeying Gaussian distribution. The experimental results on test data indicated that our proposed model performed better than the existing deep learning time-series models in terms of Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R2). [ABSTRACT FROM AUTHOR]

• A novel deep learning model is proposed for PM 2.5 prediction. • A new correlation calculation method is proposed to construct dynamic adjacency matrices. • Two real-world datasets are selected to verify the performance of models. Air pollution is a serious threat to both the ecological environment and the physical health of individuals. Therefore, accurate air quality prediction is urgent and necessary for pollution mitigation and residents' travel. However, few existing models are established based on the dynamic spatiotemporal correlation of air pollutants to predict air quality. In this paper, a novel deep learning model combining the dynamic graph convolutional network and the multi-channel temporal convolutional network (DGC-MTCN) is proposed for air quality prediction. To efficiently represent the time-varying spatial dependencies, a new spatiotemporal dynamic correlation calculation method based on gray relation analysis is proposed to construct dynamic adjacency matrices. Then, the spatiotemporal features are sufficiently extracted by the graph convolutional network and the multi-channel temporal convolutional network. Two real-world air quality datasets collected from Beijing and Fushun are applied to verify the performance of our proposed model. The experimental results show that compared with other baselines, the DGC-MTCN model has excellent prediction accuracy. Especially for the prediction of multi-step and different stations, our model performs better temporal stability and generalization ability. [ABSTRACT FROM AUTHOR]

Accurate mapping of dams can provide useful information about geographical locations and boundaries and can help improve public dam datasets. However, when applied to disaster emergency management, it is often difficult to completely determine the distribution of dams due to the incompleteness of the available data. Thus, we propose an automatic and intelligent extraction method that combines location with post-segmentation for dam detection. First, we constructed a dataset named RSDams and proposed an object detection model, YOLOv5s-ViT-BiFPN (You Only Look Once version 5s-Vision Transformer-Bi-Directional Feature Pyramid Network), with a training method using deep transfer learning to generate graphical locations for dams. After retraining the model on the RSDams dataset, its precision for dam detection reached 88.2% and showed a 3.4% improvement over learning from scratch. Second, based on the graphical locations, we utilized an improved Morphological Building Index (MBI) algorithm for dam segmentation to derive dam masks. The average overall accuracy and Kappa coefficient of the model applied to 100 images reached 97.4% and 0.7, respectively. Finally, we applied the dam extraction method to two study areas, namely, Yangbi County of Yunnan Province and Changping District of Beijing in China, and the recall rates reached 69.2% and 81.5%, respectively. The results show that our method has high accuracy and good potential to serve as an automatic and intelligent method for the establishment of a public dam dataset on a regional or national scale. [ABSTRACT FROM AUTHOR]

The increasing free access of the Internet provides us with favorable circumstances to investigate search engine index reflecting more and more personal behavior information. Part of valuable travel search information can assist us to achieve more robust and reliable prediction of metro passenger flow. Inspired by this, the paper develops a new multi-source time series fusion and direct interval prediction approach to grasp the dynamic law of metro passenger flow effectively. Multi-source index regarding metro travel from three major search engines (Baidu, Sogou and 360) in China are screened out and fused into the powerful predictors. By integrating an optimized multivariate mode decomposition strategy and long short-term memory model, lower and upper bounds of prediction interval are estimated directly by a multi-objective framework that combines the advantages of both the deep learning models long short-term memory and the ensemble learning approach. Especially, two sets of real experiment data of Beijing and Shanghai metro systems are employed to test our approach. Findings show that fusion of multi-source index information promotes the predictability of metro passenger flow, contributing to improving operation management and service quality. [Display omitted] • Fusing multi-source search data from three search engines based on query popularity. • Developing a new multivariate preprocessing technique MOHHOMVMD. • Proposing a direct multi-objective interval prediction approach LSTM-based LUBE. • Verifying the superiority of our approach over six benchmark models in two cases. [ABSTRACT FROM AUTHOR]

Pollen allergies are seasonal epidemic diseases that are accompanied by high incidence rates, especially in Beijing, China. With the development of deep learning, key progress has been made in the task of automatic pollen grain classification, which could replace the time-consuming and laborious manual identification process using a microscope. In China, few pioneering works have made significant progress in automatic pollen grain classification. Therefore, we first constructed a multi-class and large-scale pollen grain dataset for the Beijing area in preparation for the task of pollen classification. Then, a deblurring pipeline was designed to enhance the quality of the pollen grain images selectively. Moreover, as pollen grains vary greatly in size and shape, we proposed an easy-to-implement and efficient multi-scale deep learning architecture. Our experimental results showed that our architecture achieved a 97.7% accuracy, based on the Resnet-50 backbone network, which proved that the proposed method could be applied successfully to the automatic identification of pollen grains in Beijing. [ABSTRACT FROM AUTHOR]