Spatial-temporal Correlation Research Articles

RCCNet: A Spatial-Temporal Neural Network Model for Logistics Delivery Timely Rate Prediction

In logistics service, the delivery timely rate is a key experience indicator, which is highly essential to the competitive advantage of express companies. Prediction on it enables intervention on couriers with low predicted results in advance, thus ensuring employee productivity and customer satisfaction. Currently, few related works focus on couriers’ level delivery timely rate prediction, and there are complex spatial correlations between couriers and road districts in the express scenario, which makes traditional real-time prediction approaches hard to utilize. To deal with this, we propose a deep spatial-temporal neural network, RCCNet to model spatial-temporal correlations. Specifically, we adopt Node2vec, which can encode the road network-based graph directly to capture spatial correlations between road districts. Further, we calculate couriers’ historical time-series similarity to build a graph and employ graph convolutional networks to capture the correlation between couriers. We also leverage historical sequential information with long short-term memory networks. We conduct experiments with real-world express datasets. Compared with other competitive baseline methods widely used in industry, the experiment results demonstrate its superior performance over multiple baselines.

Crowd Flow Prediction: An Integrated Approach Using Dynamic Spatial–Temporal Adaptive Modeling for Pattern Flow Relationships

ABSTRACTPredicting crowd flows in smart cities poses a significant challenge for the intelligent transportation system (ITS). Traffic management and behavioral analysis are crucial and have garnered considerable attention from researchers. However, accurately and timely predicting crowd flow is difficult due to various complex factors, including dependencies on recent crowd flow and neighboring regions. Existing studies often focus on spatial–temporal dependencies but neglect to model the relationship between crowd flow in distant areas. In our study, we observe that the daily flow of each region remains relatively consistent, and certain regions, despite being far apart, exhibit similar flow patterns, indicating a strong correlation between them. In this paper, we proposed a novel Multiscale Adaptive Graph‐Gated Network (MSAGGN) model. The main components of MSAGGN can be divided into three major parts: (1) To capture the parallel periodic learning architecture through a layer‐wise gated mechanism, a layer‐wise functional approach is employed to modify gated mechanism, establishing parallel skip periodic connections to effectively manage temporal and external factor information at each time interval; (2) a graph convolutional‐based adaptive mechanism that effectively captures crowd flow traffic data by considering dynamic spatial–temporal correlations; and (3) we proposed a novel intelligent channel encoder (ICE). The task of this block is to capture citywide spatial–temporal correlation along external factors to preserve correlation for distant regions with external elements. To integrate spatio‐temporal flexibility, we introduce the adaptive transformation module. We assessed our model's performance by comparing it with previous state‐of‐the‐art models and conducting experiments using two real‐world datasets for evaluation.

Research on traffic flow forecasting based on interactive dynamic meta-graph learning

Traffic flow exhibits intricate dynamic spatial-temporal correlation. Aiming at the difficulty in capturing the long-term dependence and dynamic spatial correlations among concealed road nodes of in traffic flow, a new Interactive Dynamic forecasting model based on Meta-graph learning (Mega-ID) is proposed, which combines spatial-temporal transformer and interactive dynamic graph convolution (IDGCN) to optimize the Meta-graph module. Specifically, it optimizes a spatial-temporal meta-graph with memory and discrimination capabilities. The model introduces a Dynamic Graph Convolution (DGCN) embedded Interactive Learning structure, which simultaneously captures the hidden dynamic spatial correlations and long-term dependency of traffic flow. The experimental results demonstrate that the method proposed in this paper can capture the hidden dynamic spatial correlation and long-term dependence, leading to better forecasting performance compared to other baseline models.

Systemic and Anthropological Characteristics of Thinking

The article describes such systemic anthropological characteristics of thinking as multidimensionality, projectivity, inclusion in life relationships, transtemporality, and nonlinearity. On the one hand, these characteristics are inconvenient phenomenological "anomalies" for the classical psychology of thinking. On the other hand, in the context of progressive anthropologization of psychological cognition and interdisciplinary discourse, they make it possible to study thinking in a more holistic and human context, as well as to establish new conceptual links between the psychology of thinking and the psychology of human existence. Thinking is a multidimensional and nonlinear act that simultaneously unfolds in several registers of one’s mentality, capturing a variety of temporal, active, and noetic aspects of one’s being. The projective function of thinking consists in overcoming event uncertainty, building a semantic markup of living space, and producing semantic orientations in the activity and existential dimensions of the living environment. The fact that thinking is part of one’s life relationships ensures the integrity and transtemporal continuity of one’s life world. According to the polyphonic principle of mental dynamics, the dialectic unity of its spatial-temporal and modal correlations unfolds at each moment of thinking.

Spatial–Temporal-Correlation-Constrained Dynamic Graph Convolutional Network for Traffic Flow Forecasting

Accurate traffic flow prediction in road networks is essential for intelligent transportation systems (ITS). Since traffic data are collected from the road network with spatial topological and time series sequences, the traffic flow prediction is regarded as a spatial–temporal prediction task. With the powerful ability to model the non-Euclidean data, the graph convolutional network (GCN)-based models have become the mainstream framework for traffic forecasting. However, existing GCN-based models either use the manually predefined graph structure to capture the spatial features, ignoring the heterogeneity of road networks, or simply perform 1-D convolution with fixed kernel to capture the temporal dependencies of traffic data, resulting in insufficient long-term temporal feature extraction. To solve those issues, a spatial–temporal correlation constrained dynamic graph convolutional network (STC-DGCN) is proposed for traffic flow forecasting. In STC-DGCN, a spatial–temporal embedding encoder module (STEM) is first constructed to encode the dynamic spatial relationships for road networks at different time steps. Then, a temporal feature encoder module with heterogeneous time series correlation modeling (TFE-HCM) and a spatial feature encoder module with dynamic multi-graph modeling (SFE-DCM) are designed to generate dynamic graph structures for effectively capturing the dynamic spatial and temporal correlations. Finally, a spatial–temporal feature fusion module based on a gating fusion mechanism (STM-GM) is proposed to effectively learn and leverage the inherent spatial–temporal relationships for traffic flow forecasting. Experimental results from three real-world traffic flow datasets demonstrate the superior performance of the proposed STC-DGCN compared with state-of-the-art traffic flow forecasting models.

Unsupervised learning approach to quantum wave-packet dynamics from coupled temporal-spatial correlations

Unsupervised learning approach to quantum wave-packet dynamics from coupled temporal-spatial correlations

Cluster partition-fuzzy broad learning-based fast detection and localization framework for false data injection attack in smart distribution networks

The distributed renewable energy generations, as accessible and easily targets for attackers, introduce an extra false data injection attack (FDIA) threat in the smart distribution networks. Scattered attack points and complex attack features hinder the elimination of potential threats. In this context, an FDIA fast detection and pinpoint localization framework is proposed. This framework identifies abnormal signals and attacked nodes from the unique topology structure and status contiguity of smart distribution networks, namely, spatial-temporal correlations of power grids, by using a cluster partition-fuzzy broad learning system (CP-FBLS). Unlike most existing FDIA detection methods, which are dedicated to high accuracy but neglect the urgent need for rapid detection in smart distribution networks, the proposed CP-FBLS framework maintains the fast computational nature of a fuzzy broad learning system (FBLS), while avoiding the accuracy degradation caused by high-dimension of data in large-scale smart distribution networks. Moreover, the multi-layer structure of the proposed framework recognizes the location of FDIA, bridging the research gap of attack localization. To comprehensively evaluate the proposed strategy, datasets containing various FDIA types are constructed. Numerical simulations based on the above datasets in modified IEEE 34-bus and 123-bus distribution systems are implemented. The results of the case studies showed that the proposed method can achieve 98.43 % accuracy with 0.34 ms detection time, realizing rapid detection and localization of various FDIAs with satisfactory accuracy.

Long Term 5G Base Station Traffic Prediction Method Based on Spatial-Temporal Correlations

In the domain of 5G network management, accurately predicting traffic volumes at base stations remains a critical yet challenging endeavor, primarily due to the complexities inherent in the spatial and temporal data interactions. Current methods often fall short in effectively harnessing long-term trends and spatial interconnections among base stations. To bridge these gaps, this paper introduces the GCformer model, a novel approach that capitalizes on both spatial relationships and temporal patterns for multi-base station traffic prediction. Spatially, the proposed model employs graph convolutional networks to integrate diverse spatial information and construct insightful adjacency matrices that includes Euclidean distances and non-Euclidean distances (area types of base station locations and similarities in traffic flow among various stations), thereby enhancing the predictability of traffic dynamics. Temporally, the application of the Transformer's attention mechanism enables better capture of long-term relational dependencies in the temporal domain of 5G base station traffic data. Additionally, a time-variant optimization module is designed to establish diurnal cycle data for each base station's traffic, replacing the traditional positional encoding with a more nuanced model that improves the learning of historical data correlations. Empirical results from exhaustive case studies confirm the superiority of the GCformer model in forecasting traffic volumes. The GCformer exhibits a 4.01% improvement in mean squared error and a 3.37% enhancement in mean absolute error compared to the best-performing baseline model, showcasing its potential to significantly improve operational strategies in 5G networks.

Improved air traffic flow prediction in terminal areas using a multimodal spatial–temporal network for weather-aware (MST-WA) model

Accurately predicting air traffic flow in terminal areas is critical for balancing demand and capacity, particularly under challenging weather conditions. However, the complex interactions between weather patterns and air traffic make reliable predictions difficult. To address this issue, we propose a novel approach called the Multimodal Spatial-Temporal network for Weather-Aware prediction (MST-WA), designed to enhance air traffic flow prediction (ATFP) in terminal areas. Our method begins by constructing a spatial–temporal graph that captures the topology of the terminal area, including airports, routes, and fixes as nodes. A weather-aware module is then introduced, leveraging a Residual Network (ResNet) and attention mechanism to model the deep spatial–temporal correlations in the Weather Avoidance Field (WAF). The proposed model architecture integrates five key branches: arrival flow, departure flow, graph network topology, weather conditions, and flow constraint control, with predictions generated via an attention-based Long Short-Term Memory (LSTM) network. Experimental results using real-world data from Guangzhou Baiyun Airport, China, show that MST-WA outperforms baseline models in ATFP. Furthermore, a case study in convective weather scenarios demonstrates the model’s adaptability and effectiveness. We believe that the proposed model can serve as a valuable tool for air traffic controllers, enhancing decision-making and improving overall air traffic management.

NPFormer: Interpretable rotating machinery fault diagnosis architecture design under heavy noise operating scenarios

While existing models have achieved significant progress on fault diagnosis tasks, what interpretable temporal dependencies they capture to resist noise remains unclear. Previous studies have ignored the inherent non-stationary property of fault data, which is the basis for capturing reliable temporal dependencies under heavy noise operating scenarios. To tackle the aforementioned problems, we propose a non-stationary perception network (NPFormer). Firstly, we revisit the large kernel design in the early stage of the network, and we demonstrate that this can make the attention map more stable for modeling non-stationary series, which can be a more powerful paradigm with heavy noise interference. Secondly, an adaptive non-stationarity embedding block is developed, which breaks the raw data into multiple components and adaptively attenuates the non-stationarity of mechanical signals for better predictability. Finally, we design multi-scale fusion attention to capture fine-grained features with the global scope, while achieving localized spatial–temporal correlations. Extensive experiments have been conducted on three public datasets and the practical aero-engine engineering application, showing that NPFormer significantly outperforms existing state-of-the-art methods. The weight information is visualized to reveal how NPFormer works, which can demonstrate that the stable attention map is a clear clue of anti-noise. We quantitatively reveal the effect of noise on the model, and the model’s anti-noise clues, during model inference. Code and models will be available for further study.

Spatial–Temporal Similarity Fusion Graph Adversarial Convolutional Networks for traffic flow forecasting

Traffic flow forecasting is integral to the advancement of intelligent transportation systems and the development of smart cities. This paper introduces a novel model, the Spatial–Temporal Similarity Fusion Graphs Adversarial Convolutional Networks (STSF-GACN), which leverages advanced data preprocessing techniques to enhance the predictive accuracy and efficiency of traffic flow forecasting.The innovation of our approach lies in the meticulous construction of the spatial–temporal similarity matrix through the precise calculation of temporal and spatial similarities. This matrix forms the backbone of our model, serving as the generator in the integrated Generative Adversarial Network (GAN) architecture. The Spatial–Temporal Similarity Fusion Adaptive Graph Convolutional Network, developed as part of our GAN’s generator, utilizes cutting-edge techniques such as the Wasserstein distance and Dynamic Time Warping to optimize the adaptive adjacency matrix, enabling the model to capture latent spatial–temporal correlations with unprecedented depth and precision.The discriminator of the GAN further refines the model by evaluating the accuracy of the traffic predictions, ensuring that the generative model produces results that are not only accurate but also robust against varying traffic conditions. This cohesive integration of GAN into the model architecture allows for a significant improvement in prediction accuracy and convergence speed, moving beyond traditional forecasting methods.

Probing quantum causality with geometric asymmetry in spatial-temporal correlations

Probing quantum causality with geometric asymmetry in spatial-temporal correlations

UAV-Assisted Active Sparse Crowdsensing for Ground Signal Map Construction Based on 3-D Spatial–Temporal Correlation

UAV-Assisted Active Sparse Crowdsensing for Ground Signal Map Construction Based on 3-D Spatial–Temporal Correlation

Evaluation of spatial conflicts of land use and its driving factors in arid and semiarid regions: A case study of Xinjiang, China

The study of spatial conflicts of land use (SCLU) is an important breakthrough to reveal the evolution mechanism of regional human-land relationship. Accurate identification of SCLU and scientific exploration of its spatio-temporal characteristics of their driving factors are pivotal for regional sustainable development. However, the basic theoretical system of SCLU still needs to be improved, and the spatio-temporal characteristics of SCLU at different spatial scales and landforms as well as the spatio-temporal correlation effects of its driving factors need to be further studied. This study utilizes theoretical models for the identification of SCLU. Employing a comprehensive index approach involving ArcGIS, FRAGSTATS landscape pattern analysis, and the multi-scale geographically weighted regression model, the spatio-temporal characteristics of SCLU are analyzed across different administrative scales and Macro-landforms. The investigation unveils the intrinsic mechanisms of spatio-temporal differentiation driven by various factors from 2000 to 2020 in the arid and semiarid region of Xinjiang, China. Results indicated that: (1) SCLU is a sensitive indicator of human-land interaction, measurable using conflict risk and effect; (2) The SCLU in Xinjiang presents a spatial pattern of “severe conflict around the three mountains and mild conflict around the two basins” and the SCLU transitioned from an outbreak to a relief stage over the past two decades; (3) SCLU varies significantly among landform types, with conflict intensity in Plain > Mountains > Platform > Hill areas; (4) Elevation and slope in natural environmental factors, human footprint and distance of patches from water, from roads, and from railroads in social activity factors have inhibitory effects on SCLU, and GDP, population density, and human impact index in economic development factors have positive effects on SCLU, and focusing on controlling potentially high-conflict risk areas and enhancing the upgrading of ecological quality are key to mitigating SCLU. The results validate the proposed theoretical framework and provide theoretical and practical references for accurately identifying SCLU and optimizing national territory spatial patterns.

Graph Attention Informer for Long-Term Traffic Flow Prediction under the Impact of Sports Events.

Traffic flow prediction is one of the challenges in the development of an Intelligent Transportation System (ITS). Accurate traffic flow prediction helps to alleviate urban traffic congestion and improve urban traffic efficiency, which is crucial for promoting the synergistic development of smart transportation and smart cities. With the development of deep learning, many deep neural networks have been proposed to address this problem. However, due to the complexity of traffic maps and external factors, such as sports events, these models cannot perform well in long-term prediction. In order to enhance the accuracy and robustness of the model on long-term time series prediction, a Graph Attention Informer (GAT-Informer) structure is proposed by combining the graph attention layer and informer layer to capture the intrinsic features and external factors in spatial-temporal correlation. The external factors are represented as sports events impact factors. The GAT-Informer model was tested on real-world data collected in London, and the experimental results showed that our model has better performance in long-term traffic flow prediction compared to other baseline models.

WiVelo: Fine-grained Wi-Fi Walking Velocity Estimation

Passive human tracking using Wi-Fi has been researched broadly in the past decade. Besides straightforward anchor point localization, velocity is another vital sign adopted by the existing approaches to infer user trajectory. However, state-of-the-art Wi-Fi velocity estimation relies on Doppler-Frequency-Shift (DFS), which suffers from the inevitable signal noise incurring unbounded velocity errors, further degrading the tracking accuracy. In this article, we present WiVelo, which explores new spatial-temporal signal correlation features observed from different antennas to achieve accurate velocity estimation. First, we use subcarrier shift distribution (SSD) extracted from channel state information (CSI) to define two correlation features for direction and speed estimation, separately. Then, we design a mesh model calculated by the antennas’ locations to enable a fine-grained velocity estimation with bounded direction error. Finally, with the continuously estimated velocity, we develop an end-to-end trajectory recovery algorithm to mitigate velocity outliers with the property of walking velocity continuity. We implement WiVelo on commodity Wi-Fi hardware and extensively evaluate its tracking accuracy in various environments. The experimental results show our median and 90-percentile tracking errors are 0.47 m and 1.06 m, which are half and a quarter of state-of-the-art. The datasets and source codes are published through Github ( https://github.com/research-source/code ).

Traffic prediction based on spatial-temporal disentangled generative models

Understanding the underlying spatial-temporal patterns in a traffic network is crucial for accurate predictions. However, current deep learning techniques often struggle to effectively identify the generative factors hidden in spatial-temporal traffic data, leading to a lack of interpretability. To address this challenge, we have developed an innovative generative model called Spatial-Temporal Disentangled Neural Relational Inference (STDNRI) for traffic prediction and disentangling interpretable generative factors within observed traffic network data. In this study, we assume that spatial-temporal data is dominated by three generative factors: time-correlated factors, spatial-correlated factors and spatial-temporal joint correlation factors. Based on information bottleneck theory, we establish a novel objective function to maximize the disentanglement of different factors. To model the probability distributions of three factors, we propose three graph neural network encoders to represent time-correlated factors using a graph containing only self-loops, and denote spatial-correlated factors as well as spatial-temporal joint correlation factors adopting a fully-connected graph without self-loops. Then, graph neural network decoder with a spatial-temporal message passing mechanism is used to generate the final predictions. Several comprehensive experiments were conducted based on two real datasets from NYC Yellow Taxi and SHMetro. The results indicate that our STDNRI outperforms ten existing competitive models in terms of high prediction accuracy and provides excellent interpretability for generative factors. Furthermore, the disentangled interpretable factors can afford valuable insights for planners, urban developers, and other stakeholders to gain a deeper understanding of the dynamics of the traffic network, thus enabling more informed decision-making.

Decomposition with feature attention and graph convolution network for traffic forecasting

Traffic forecasting is a crucial task for enhancing the quality and efficiency of intelligent transportation systems. In recent years, several neural networks have been proposed to tackle this challenge. However, traffic data itself has complex composition and intertwined temporal patterns. Currently, the absence of effective methods to capture the inherent properties and overall profile of traffic data has become a bottleneck in improving the traffic prediction capabilities of models. To address these issues, we introduce a novel model, Decomposition with Feature Attention and Graph Convolution Network (DFAGCN), for Traffic Forecasting. To highlight the inherent properties of complicated traffic data, we present a unique Decomposition with Feature Attention (DFA) method to decompose the traffic data into seasonality, trend, and remainder. On the one hand, we introduce a new method to filter out noise and possible outliers from time series data. On the other hand, we employ an attention mechanism to make our decomposition method adaptively extract the trend. Subsequently, we utilize spatial–temporal attention to simulate the dynamic spatial–temporal correlations of traffic data. Finally, we use spatial–temporal convolution to extract local dependencies of traffic data. Experiments conducted on four real-world traffic forecasting datasets demonstrate that DFAGCN achieves state-of-the-art performance. The results show that the application of time series decomposition to traffic data significantly enhances the accuracy of traffic prediction.

IMD-MP: Imputation of Missing Data in IoT Based on Matrix Profile and Spatio-temporal Correlations

Data in the Internet of Things (IoT) domain may be missing due to connectivity errors, environmental extremes, sensor malfunctions, and human errors. Despite the many approaches for imputing missing values, the most significant difficulty in terms of imputation precision or compute complexity for larger missing sub-sequences in uni-variate series is still being explored. This work introduced IMD-MP (Imputation of Missing Data using Matrix Profile), a new technique that improves imputation accuracy for big data analysis in IoT applications based on spatial-temporal correlations using a novel distance metric Matrix Profile Distance (MPD). Our method preserves spatial correlation by grouping the sensors present in the network (using grouping algorithm-GA) to impute the missing data of the failed sensor node. After grouping, similar sensor nodes to the failed sensor node are identified using the Node Similarity Algorithm (NSF). From its similar sensor data, a certain number of sub-sequences that are most similar to the one preceding the failed node’s missing values are gathered. These sub-sequences heights are optimized to ensure temporal correlation in the imputed data. To find the optimal imputation sequence, the current research uses MPD and similarity scores. Numerical findings using sensor data from real-time environmental mon-itoring and Intel data sets demonstrate the algorithm’s effectiveness compared to other benchmarks.

Editorial

Dear Readers, It gives me great pleasure to announce the sixth regular issue of 2024. In this issue, 6 papers cover various topical aspects of computer science by 19 authors from 7 countries: Brazil, Chile, France, India, Saudi Arabia, Tunisia, and Turkey. In an ongoing effort to further strengthen our journal, I would like to expand the editorial board: If you are a tenured associate professor or above with a strong publication record, you are welcome to apply to join our editorial board. We are also interested in high-quality proposals for special issues on new topics and trends. As always, I would like to thank all authors for their sound research and the editorial board and our guest reviewers for their extremely valuable review effort and suggestions for improvement. These contributions, together with the generous support of the consortium members, help to maintain the quality of our journal.  In this regular issue, I am very pleased to introduce the following 6 accepted articles: George Marsicano, Edna Dias Canedo, Glauco V. Pedrosa, Cristiane S. Ramos, and Rejane M. da C. Figueiredo from Brazil look in their study into digital transformation of public services in a startup-based environment by 23 focus groups and 175 participants in total. Mauricio Solar and Pablo Aguirre from Chile discuss their research on 3D chest CT processes applying a ResNet-50 model to which a new dimension of information has been added, namely a simple autoencoder. In a collaborative work between researchers from Tunisia and France, Rakia Saidi, Fethi Jarray, and Didier Schwab propose in their article a cross-encoder neural network (Cross-BERT-GRU) to deal with the semantic similarity of Arabic sentences that benefits from both the strong contextual understanding of BERT and the sequential modeling capabilities of GRU. Also in a collaborative research between institutions from Tunisia and Saudi Arabia, Nozha Jlidi, Sameh Kouni, Olfa Jemai, and Tahani Bouchrika present their research on MediaPipe with GNN for human activity recognition. G.V.Vidya Lakshmi and S. Gopikrishnan from India look into missing values research for the IoT domain and in particular present IMD-MP technique that improves imputation accuracy for big data analysis in IoT applications based on spatial-temporal correlations. Last but not least, F. Didem Alay, Nagehan İlhan, and M. Tahir Güllüoğlu address in their article a comparative study of data mining methods for solar radiation and temperature forecasting models. Enjoy Reading Cordially,  Christian Gütl, Managing Editor Graz University of Technology, Graz, Austria