Temporal Flow Research Articles

Discovering spatiotemporal flow patterns: where the origin–destination map meets empirical orthogonal function decomposition

ABSTRACT Flows are usually represented as vector lines from origins to destinations and can reflect the movements of individuals or groups in space and time. Revealing and analyzing the spatiotemporal flow patterns are conducive to understanding information underlying movements. This paper proposes a new method called the OD – EOF (Origin – Destination – Empirical Orthogonal Function) to discover important spatiotemporal flow patterns on the premise of maintaining the pairwise connections between origins and destinations. We first construct a spatiotemporal flow matrix that contains connection information between origins and destinations and temporal flow information by adding a temporal dimension to the OD map. Then, we decompose the spatiotemporal flow matrix into spatial modes and corresponding time coefficients by EOF decomposition. The decomposition results depict the prominent spatial distribution of and temporal variation in flows, with most of the spatiotemporal characteristics highly concentrated into the first few spatial modes. The method is evaluated by five synthetic datasets and a user study and subsequently applied to analyze the impact of the COVID-19 pandemic on the spatiotemporal patterns of human mobility in China during the Spring Festival travel rush in 2020 and 2021. The results show the prominent spatiotemporal patterns of human mobility during these periods under the influence of the COVID-19 pandemic outbreak and the normalization of pandemic prevention and control.

Temporal changes and flow pattern analysis using Colwell indices in mountainous rivers

Temporal changes and flow pattern analysis using Colwell indices in mountainous rivers

The Independent Walking for Brain Health Intervention for Older Adults: Protocol for a Pilot Randomized Controlled Trial.

Extensive research suggests that physical activity (PA) is important for brain and cognitive health and may help to delay or prevent Alzheimer's disease and related dementias. Most PA interventions designed to improve brain health in older adults have been conducted in laboratory, gym, or group settings that require extensive resources and travel to the study site or group sessions. Research is needed to develop novel interventions that leverage mobile health (mHealth) technologies to help older adults increase their engagement in PA in free-living environments, reducing participant burden and increasing generalizability of research findings. Moreover, promoting engagement in moderate-to-vigorous PA (MVPA) may be most beneficial to brain health; thus, using mHealth to help older adults increase time spent in MVPA in free-living environments may help to offset the burden of Alzheimer's disease and related dementias and improve quality of life in older age. We developed a novel PA intervention that leverages mHealth to help older adults achieve more minutes of MVPA independently. This pilot study was a 12-week randomized controlled trial to investigate the feasibility of providing just-in-time (JIT) feedback about PA intensity during free-living exercise sessions to help older adults meet current PA recommendations (150 minutes per week of MVPA). Participants were eligible if they were cognitively healthy English speakers aged between 65 and 80 years without major cardiovascular, neurologic, or mental health conditions; could ambulate independently; and undergo magnetic resonance imaging. Enrollment occurred from October 2017 to March 2020. Participants randomized to the PA condition received an individualized exercise prescription and an mHealth device that provided heart rate-based JIT feedback on PA intensity, allowing them to adjust their behavior in real time to maintain MVPA during exercise sessions. Participants assigned to the healthy aging education condition received a reading prescription consisting of healthy aging topics and completed weekly quizzes based on the materials. In total, 44 participants were randomized to the intervention. A follow-up manuscript will describe the results of the intervention as well as discuss screening, recruitment, adverse events, and participants' opinions regarding their participation in the intervention. The long-term goal of this intervention is to better understand how MVPA affects brain and cognitive health in the real world and extend laboratory findings to everyday life. This pilot randomized controlled trial was conducted to determine the feasibility of using JIT heart rate zone feedback to help older adults independently increase time spent in MVPA while collecting data on the plausible mechanisms of change (frontal and medial temporal cerebral blood flow and cardiorespiratory fitness) that may affect cognition (memory and executive function) to help refine a planned stage 2 behavioral trial. ClinicalTrials.gov NCT03058146; https://clinicaltrials.gov/ct2/show/NCT03058146. DERR1-10.2196/42980.

A Qualitative Assessment of River Plumes Coupling SWAT Model Simulations and a Beach Optical Monitoring System

The study of plumes occurring at the mouth of small rivers of temporal flow is a challenging task due to the lack of sedimentological and flow data of appropriate spatiotemporal scales. The present contribution examined the case of a typical un-gauged intermittent Mediterranean stream located in Northern Crete (Xiropotamos river). The SWAT (soil and water assessment tool) model was used to simulate and reproduce the hydrological behavior of the adjacent intermittent (Giofyros) river discharging at the same beach, the basin of which has the same geomorphological and hydrological characteristics. The output of the calibrated SWAT model was used to simulate daily flow data for the year 2014. The results were then considered together with the results of the RGB analysis of optical datasets of high spatio-temporal resolution for the same period, derived from a beach optical monitoring system (BOMS). The RGB analysis of the optical (TIMEX) imagery was shown to be a useful technique to identify and classify coastal plumes by using the spatio-temporal variability of pixel properties. The technique was also shown to be useful for the (qualitative) validation of the SWAT output and could be further improved by the collection of ‘ground truth’ data.

Scalable timing-aware network design via lagrangian decomposition

This paper addresses instances of the temporal fixed-charge multi-commodity flow (tfMCF) problem that arise in a very large scale dynamic transportation application. We model the tfMCF as a discrete-time Resource Task Network (RTN) with cyclic schedule, and formulate it as a mixed-integer program. These problems are notoriously hard to solve due to their time-expanded nature, and their size renders their direct solution difficult. We exploit synergies between flows of certain commodities in the formulation to devise model condensation techniques that reduce the number of variables and constraints by a factor of 25%–50%. We propose a solution algorithm that includes balanced graph partitioning, Lagrangian decomposition and a linear programming filtering heuristic. Computational results show that the proposed algorithm allows the solution of previously intractable instances, and the primal solution obtained by the heuristic step is within 2% duality gap of the linear relaxation of the original problem.

Residual spatiotemporal convolutional networks for face anti-spoofing

Previous deep learning studies on Face Anti-Spoofing (FAS) systems have exploited many aspects of spatial data for face anti-spoofing detection, but few have used end-to-end spatiotemporal approaches to solving FAS problems. This paper aims to provide new perspectives for end-to-end spatiotemporal systems to deal with FAS problems, using five residual spatiotemporal convolutional models. This work analyzes and detects which network is the most appropriate for identifying spoofing on video-based identification systems. These five models were adapted to specific features of the FAS problem and its performance (accuracy and computational cost) were tested with OULU-NPU and SiW datasets. In addition, a cross-dataset validation was carried out. The experimentation shows the strengths and weaknesses of each model against the dependency on the temporal dimension, data initialization and different FAS environment conditions. According to experimentation, residual networks outperform the state-of-the-art, being the model based on decomposing spatial and temporal flow the best option.

Unanticipated protection failure scenarios optimistically bias reactor safety metrics

Risk management of hazardous technological processes is properly understood on filtered probability spaces where the temporal flow of engineering information is represented. The consequence of ignoring a filtration carrying the time evolution of engineering information includes computing risk metrics that overlook the possibility of unanticipated protection failure scenarios. Ignoring the possibility of as yet undiscovered failure scenarios clearly leads to an optimistic bias when computing risk metrics such as Core Damage Frequency (CDF) and Large Early Release Frequency (LERF) using Probabilistic Risk Assessment (PRA). When properly framed on a filtered probability space, it is impossible to quantify the optimistic bias of computed risk metrics.

Spatial–temporal dependence and similarity aware traffic flow forecasting

Traffic flow forecasting is the cornerstone of the development of intelligent transportation systems. Accurate forecasting is conducive to the control and management of urban traffic. However, it is still a challenge to extract the complex spatial–temporal relationships in the traffic flow. The existing methods based on Graph Convolutional Network (GCN) can effectively extract spatial–temporal dependence, but it is difficult to extract spatial–temporal similarity because of the limitation of the graph structure. In this paper, we propose a spatial–temporal dependence and similarity-aware method called Dependence and Similarity aware Temporal Graph Convolutional Network (DS-TGCN), which combines two modules to extract the complex spatial–temporal relationships and make traffic flow forecasting. One module is our Spatial–Temporal Similarity Feature module (STSF) newly designed for extracting the spatial–temporal similarity directly. Another module is the spatial–temporal convolution module with an attention mechanism, which can extract spatial–temporal dependence dynamically. The experiments on two types of datasets demonstrate that our proposed method outperforms the existing methods in terms of the effectiveness of traffic flow forecasting.

Investigation of the optic disc and retinal microvasculature by optical coherence tomography angiography in children with asthma.

To assess the optic disc and retinal microvasculature by optical coherence tomography angiography (OCTA) in asthmatic children. Thirty asthmatic children (asthma group) and 30 control age- and sex-matched healthy controls (control group) were included in this cross-sectional study. The asthma group was then divided into two subgroups according to the presence of inhaled steroid use. Demographic findings were noted. Retinal nerve fiber layer (RNFL) thickness and vessel density in different sections of the retina and optic nerve head were analyzed by OCTA. RNFL thickness for temporal quadrants and flow area for outer retina levels were significantly lower in the asthma group than the control group (72.58 ± 10.99 μm vs 77.73 ± 9.73μm, P = 0.015, and 0.60 ± 0.31mm2 vs. 0.72 ± 0.31mm2, P = 0.047, respectively). However, inside disc vascular densities were significantly higher in the asthma group when compared to controls (55.16% ± 3.71% vs. 52.08% ± 3.79%, P < 0.001). Inside disc vascular densities were also significantly higher, and RNFL thickness for temporal quadrants was significantly lower in the asthmatic patients without steroid use subgroup when compared to others (P < 0.001, P = 0.012, respectively). Lower values of temporal quadrant RNFL, and flow area for outer retina, but higher levels of inside disc vascular density seem to be associated with asthmatic children. OCTA findings in asthmatic children appear to be regardless of inhaled steroid use.

Debris Flow Hazard Mapping Along Linear Infrastructure: An Agent Based Model and GIS Approach

Often linear infrastructure, including rail, highways, and pipelines, span large geographic areas intersecting a variety of terrain, predisposing infrastructure to a higher likelihood of geohazard interaction. Debris flow models can be particularly advantageous in remote hazard and risk mapping along linear infrastructure as runout from susceptible slopes may extend considerable distance downslope to a receptor. In this sentiment, a method is developed using an agent-based model, DebrisFlow Predictor, in combination with geographic information software (GIS), to produce regional debris flow hazard and risk profiles along widespread corridors. Thousands of debris flows upslope of a receptor(s) are simulated in the model environment (i.e., model scenarios). Outputs of the modelled scenarios provide probabilistic spatial attributes of debris flow runout and depth across a digital elevation model at a 5m resolution. The outputs of multiple scenarios are mosaiced and corrected to in-situ temporal and spatial debris flow initiation conditions in GIS. The corrected scenario outputs provide a comprehensive hazard profile along the infrastructure alignment, that in turn can facilitate quantified vulnerability and risk calculations. Thousands of modelled debris flows throughout several physiographic regions of Canada and the United States of America, calibrated to local conditions, provide substantive support for a novel methodology to identify key hazard and risk locations to major linear infrastructure.

Fluxo Temporal da Capacidade de Transferência Tecnológica: Além do Ciclo de Vida

ABSTRACT Objective: to analyze the behavior of technology transfer capability over time in Brazilian public institutions of higher education. Method: the extended case method was adopted in two national institutions of reference in technology transfer management. Interviews, observations, and document analysis were the main techniques for data collection. The analysis was performed using also the techniques of the extended case method. Results: became evident that the behavior of a capacity, during its existence, may not obey a linear curve, but vary over different stages and undergo different mutations. Conclusions: it was concluded that the lifecycle of a capability would not be exactly equivalent or limited to the heuristic of a biological lifecycle with predictable entropy until the total collapse. Unlike this, considering the environment and the conjuncture, a capability can come back to exist as long as it is understood as opportune and feasible. The findings advance the understanding of the technology transfer process that now has the explanatory potential of dynamic capabilities.

Temporal Flow of Technology Transfer Capability: Beyond the Lifecycle

RESUMO Objetivo: analisar o comportamento da capacidade de transferência tecnológica ao longo do tempo em instituições públicas brasileiras de ensino superior. Método: adotou-se o método de caso estendido em duas instituições nacionais de referência em gestão da transferência tecnológica. Entrevistas, observações e análise de documentos foram as principais técnicas para a coleta de dados. A análise também ocorreu por meio das técnicas do método de caso estendido. Resultados: evidenciou-se que o comportamento de uma capacidade, durante a sua existência, pode não obedecer a uma curva linear, mas sim variar ao longo de estágios distintos e sofrer diferentes mutações. Conclusões: conclui-se que o ciclo de vida de uma capacidade não seria exatamente equivalente ou limitado à heurística de um ciclo de vida biológico dotado de previsível entropia até o total colapso. Diferente disso, considerando o ambiente e a conjuntura, uma capacidade pode voltar a existir desde que se entenda isso como oportuno e exequível. Os achados avançam na compreensão do processo de transferência tecnológica que passa a contar com o potencial explicativo das capacidades dinâmicas.

Phosphorus and iron-oxide transport from a hydrologically isolated grassland hillslope

Dissolved reactive phosphorus (DRP) loss from agricultural soils can negatively affect water quality. Shallow subsurface pathways can dominate P losses in grassland soils, especially in wetter months when waterlogging is common. This study investigated the processes controlling intra- and inter-event and seasonal DRP losses from poorly drained permanent grassland hillslope plots. Temporal flow related water samples were taken from surface runoff and subsurface (in-field pipe) discharge, analysed, and related to the likelihood of anaerobic conditions and redoximorphic species including nitrate (NO3−) over time. Subsurface drainage accounted for 89% of total losses. Simple linear regression and correlation matrices showed positive relationships between DRP and iron and soil moisture deficit; and negative relationships between these three factors and NO3− concentrations in drainage. These data indicate that waterlogging and low NO3− concentrations control the release of P in drainage, potentially via reductive dissolution. The relationship between DRP and metal release was less obvious in surface runoff, as nutrients gathered from P-rich topsoil camoflaged redox reactions. The data suggest a threshold in NO3− concentrations that could exacerbate P losses, even in low P soils. Knowledge of how nutrients interact with soil drainage throughout the year can be used to better time soil N and P inputs via, for example, fertiliser or grazing to avoid to excessive P loss that could harm water quality.

Fine-Grained Vessel Traffic Flow Prediction With a Spatio-Temporal Multigraph Convolutional Network

The accurate and robust prediction of vessel traffic flow is gaining importance in maritime intelligent transportation system (ITS), such as vessel traffic services, maritime spatial planning, and traffic safety management, etc. To achieve fine-grained vessel traffic flow prediction, we will first generate the maritime traffic network (which is essentially a graph), and then propose a graph-driven neural network. In particular, to represent various correlations among spatio-temporal vessel traffic flow, we tend to extract the feature points (i.e., starting, way and ending points) by utilizing the knowledge of vessel positioning data. These feature points are essentially related to the geometrical structures of massive vessel trajectories collected from massive automatic identification system (AIS) records, contributing to the generation of maritime traffic network. We then propose a spatio-temporal multi-graph convolutional network (STMGCN)-based vessel traffic flow prediction method by exploiting multiple types of inherent correlations in the generated maritime graph. The proposed STMGCN mainly contains one spatial multi-graph convolutional layer and two temporal gated convolutional layers, beneficial for extracting spatial and temporal traffic flow patterns. The main benefit of our graph-driven prediction method is that it takes full advantage of the maritime graph and multi-graph learning. Comprehensive experiments have been implemented on realistic AIS dataset to compare our method with several state-of-the-art prediction methods. The fine-grained prediction results have demonstrated our superior performance in terms of both accuracy and robustness.

Distribution Prediction for Reconfiguring Urban Dockless E-Scooter Sharing Systems

Dockless E-scooter Sharing (DES) has become a popular means of last-mile commute for many smart cities. As e-scooters are getting deployed dynamically and flexibly across city regions that expand and/or shrink, accurate prediction of the e-scooter distribution given the reconfigured regions becomes essential for city planning. We present <bold/> <monospace><b>GCScoot</b></monospace> <bold/> , a novel flow distribution prediction approach for reconfiguring urban DES systems. Based on real-world datasets with reconfiguration, we analyze e-scooter distribution features and flow dynamics for the data-driven designs. We propose a novel spatio-temporal graph capsule neural network within <monospace>GCScoot</monospace> to predict future dockless e-scooter flows given the reconfigured regions. <monospace>GCScoot</monospace> pre-processes historical spatial e-scooter distributions into flow graph structures, where discretized city regions are considered as nodes and inter-region flows as edges. To facilitate initial training, we cluster the regions and generate virtual data for new deployment regions based on their peers in the same cluster. Given above designs, the region-to-region correlations embedded within the temporal flow graphs are captured via the multi-graph capsule convolutional neural network which accurately predicts the DES flows. Extensive studies upon four e-scooter datasets (total <inline-formula><tex-math notation="LaTeX">$&gt;$</tex-math></inline-formula> 3.4 million rides) in four populous US cities have corroborated accuracy and effectiveness of <monospace>GCScoot</monospace> in predicting the e-scooter distributions.

“What does it mean to make a picture of the now?”

“What does it mean to make a picture of the now?”

Investigation of the temporal and spatial flow features within the high‐shear mixer by modal decomposition techniques

AbstractAn insightful comprehension of hydrodynamics in the high‐shear mixer (HSM) is crucial for its design and optimization. Therefore, the energetical and dynamical dominant features of inline HSM's velocity fields are extracted by proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD), of which the sampling spaces are generated through Large‐Eddy Simulation. POD results reveal that the significant flow structures located in the shear gap and the adjacent regions of rotor/stator slot inlets comprise various energy levels. The strip‐shaped structures at the rotor/stator leading edges, wake and large‐scale vortices within the outflow region, and circulation inside the rotor slot are also visualized. DMD results emphasize the importance of flow patterns oscillating lower than rotor teeth passage frequency and reveal that the coherent structures shift and expand as their frequencies decrease. The POD and DMD analyses provide an insightful perspective of the spatial and temporal behaviors of the flow in HSM.

Node Centrality Comparison between Bus Line and Passenger Flow Networks in Beijing

In recent decades, complex network theory has become one of the most important approaches for exploring the structure and dynamics of traffic networks. Most studies mainly focus on the static topology features of the traffic networks, and there are also increasing literature focusing on passenger flow networks. However, not much work has been completed on comparing the static networks with dynamic flow networks from the perspective of supply and demand. Therefore, this study aimed to apply the complex network approach to explore the spatial relationship between bus line organization and bus flows in Beijing. Based on the bus route data and the passenger flow data obtained from the Beijing smart bus card, this study investigated the spatial characteristics of the bus line network and the temporal bus flow networks, and presented a comparison analysis on the spatial relationship between them by using the node centrality indices, namely degree centrality, betweenness centrality and closeness centrality. The results show that the overall spatial patterns of node centralities between the bus line network and the bus flow network were similar, while there were also some differences. For weekdays, the correlation between them is higher, as calculated by the degree of centrality. For weekends, the two networks have a greater correlation measured by degree centrality and betweenness centrality. The highest coefficients of correlation between the line network and traffic network appear in the morning peak, which implies that the congestion issues during the morning peak hours might receive the highest priority in Beijing’s bus-line network planning. Our study can provide implications for policymakers to improve the public urban transport network, and thus enhance residents’ happiness.

Object Interaction-Based Localization and Description of Road Accident Events Using Deep Learning

Detection and localization of road accidents in real-time is an integral part of the Intelligent Transportation System (ITS). Even though the existing road accident detection methods show promising results, the process suffers from some drawbacks. For example, existing methods require a large number of sample videos for feature learning. Moreover, features such as temporal gradients or flow fields are time-consuming. To address these issues, we introduce a new method that uses objects and their positions to detect accidents in real-time. Apart from localization of the accident events in videos, we perform a high-level post processing to describe the severity and context of an accident. Firstly, we divide an input video into pre-accident, accident and post-accident stages to extract object interactions. These interaction proposals are then filtered using a refinement algorithm. We then adopt an iterative training procedure to classify normal and accident interactions. We also highlight the damaged zone using heat maps. Finally, we generate high-level textual descriptions to quantify the context and severity of an accident. We have trained the proposed model using offline setups. However, it can be deployed online to detect road accident events in real-time by taking the video inputs directly from the CCTV camera. Moreover, with a minimal supervision, the model can be retrained for online surveillance. Extensive experiments carried out on UCF Crime and CADP datasets reveal that the proposed framework achieves state-of-the-art performance when compared with the recently proposed accident event detection methods in terms of AUC (UCF Crime: 69.70% and CADP: 72.59%) and FAR (UCF Crime: 0.8 and CADP: 2.2). The high-level description of the accident is an added advantage that will certainly help the traffic police to react in a timely manner.

A non-intrusive reduced order model with transformer neural network and its application

In this paper, a novel method to construct non-intrusive reduced order model (ROM) is proposed. The method is based on proper orthogonal decomposition and transformer neural network. Proper orthogonal decomposition is used to generate the basis functions of the low-dimensional flow field, and the coefficients are taken as low-dimensional flow field features. Transformer network is used to extract temporal feature relationships from low-dimensional features. Compared with recurrent neural network and convolutional neural network, transformer network can better capture flow dynamics. At online stage, the input temporal flow sequences are calculated in parallel and can effectively reduce online calculation time. The model proposed in this paper has been verified in two scenarios: two-dimensional flow past a cylinder and two-dimensional flow past a building group. Experimental results show that our model can better capture the flowing change details and has higher accuracy. Compared with the ROM based on long short-term memory and temporal convolutional network, the prediction error is reduced by 35% and 60%, and the time cost is reduced by 65% and 60%. Finally, we apply the ROMs to a practical three-dimensional complicated scenario, flow past London South Bank University, and discuss future development of ROMs.