Spatial-temporal Constraints Research Articles

A traffic incident detection method of highway based on spatial-temporal characteristics

In this study, we propose a method for detecting traffic incidents by leveraging tensor decomposition in conjunction with spatial-temporal constraints. The method comprises three main stages. Firstly, we develop an initialization and noise reduction procedure for the traffic data pre-processing model, which includes Tucker decomposition and truncating higher-order Singular Value Decomposition (SVD). Subsequently, we construct spatial and temporal coefficient matrices based on the Pearson test and introduce a Laplace penalty term to quantify the disparity between predicted and actual data. Next, we validate the method using traffic loop detector data and incident records from I90 in Seattle, USA, in 2015, comparing its performance with seven other traffic prediction methods. The results demonstrate the superior prediction accuracy, correct detection rate, and low false alarm rate of our traffic incident detection method. Specifically, the mean square error of speed prediction is 5.22 km / h , the average relative error is 6.88 % , and the detection rate reaches 98.92 % . Our method effectively evaluates the spatial-temporal characteristics of traffic data, enabling accurate prediction and detection of traffic incidents. Its technical applicability holds promise for enhancing the capacity and efficiency of future traffic control systems.

Distributed Computing Optimization in Unmanned Aerial Vehicle Swarm Cooperative Networks

The Unmanned Aerial Vehicle (UAV) swarm cooperative networks across diverse domains present significant challenges in management and control to maximize collaborative efficiency, particularly in applications for large-scale and dynamic environments where real-time coordination is essential. This challenge has led to the development of a UAV swarm cooperative network flow prediction model, facilitating the strategic anticipation of network dynamics. A novel algorithm for distributed, finite-time cooperative control is introduced, grounded in this model. This algorithm optimizes the operational efficiency and control precision of UAV systems, while simultaneously augmenting the collaborative efficiency and stability of the UAV swarms. It marks a significant advancement in distributed computing strategies, offering a viable solution for the real-time coordination challenges in extensive UAV swarm cooperative networks. The proposed approach leverages multi-agent technology, integrating spatial-temporal constraints, location limitations, and path selection in cooperative behavior for dynamic and effective network management. This study not only enhances the understanding of UAV swarm dynamics but also contributes to the practical application of UAV swarms in areas requiring precise coordination and robust network stability.

Multiple-stage spatial–temporal cooperative guidance without time-to-go estimation

This paper investigates the spatial–temporal cooperative guidance problem for multiple flight vehicles without relying on time-to-go information. First, a two-stage cooperative guidance strategy, namely the cooperative guidance and the Proportional Navigation Guidance (PNG) stage strategy, is developed to realize the spatial–temporal constraints in two dimensions. At the former stage, two controllers are designed and superimposed to satisfy both impact time consensus and impact angle constraints. Once the convergent conditions are satisfied, the flight vehicles will switch to the PNG stage to ensure zero miss distance. To further extend the results to three dimensions, a planar pursuit guidance stage is additionally imposed at the beginning of guidance. Due to the independence of time-to-go estimation, the proposed guidance strategy possesses great performance in satisfying complex spatial–temporal constraints even under flight speed variation. Finally, several numerical simulations are implemented to verify the effectiveness and advantages of the proposed results under different scenarios.

Spatial-temporal constraints for surface soil moisture mapping using Sentinel-1 and Sentinel-2 data over agricultural regions

Surface soil moisture (SSM) information could have important applications in agricultural and regional water management. Remote sensing, particularly synthetic aperture radar (SAR), is an important technology for the estimation of spatial–temporal SSM over larger areas. Using Sentinel-1 and Sentinel-2 data, this research developed a general spatial–temporal constrained machine-learning-based method for surface soil moisture mapping over agricultural regions. Central to this method is the construction of spatial and temporal constraints and their implementation in machine-learning models. We first defined the spatial and temporal constraints for SSM estimation by investigating the spatial division of cultivated crop types and the temporal division of cumulative precipitation. Second, under the presumption that the SSM and associated variables are smoothly changing, we extracted the temporal difference variables from the multi-temporal remote sensing data. Finally, we incorporated two constraints as categorical features and temporal differences into a CatBoost-based model to improve surface soil moisture mapping. We verified the proposed model in a Spain study area with multi-temporal remote sensing observations. The experimental results after incorporating the spatial–temporal constraints demonstrate the efficacy of the proposed model for mapping surface soil moisture over agricultural regions, with significantly improved R2 = 0.7328, RMSE = 0.0451 vol, and MAE = 0.0351 vol This study also concluded that using multiple polarization in the machine-learning-based method could reliably and accurately estimate surface soil moisture.

Coupling travel characteristics identifying and deep learning for demand forecasting on car‐hailing tourists: A case study of Beijing, China

AbstractOnline 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.

Modeling Spatial–Temporal Constraints and Spatial-Transfer Patterns for Couriers’ Package Pick-up Route Prediction

Modeling Spatial–Temporal Constraints and Spatial-Transfer Patterns for Couriers’ Package Pick-up Route Prediction

Exploring the endogenous effects among car dependency, work arrangement choice, and daily travel using the 2017 NHTS data

The paper presents an effort in investigating the cause-effect relationships among telecommuting, car dependency, and trip making behavior. Data from the 2017 National Household Travel Survey (NHTS) was used to develop a structural equations model (with latent constructs) to examine the endogenous effects between the decision to telework, the degree of car dependency, and the number of daily trips. Using confirmatory factor analysis, car dependency was measured by vehicle ownership, annual mileage, and car use frequency. The model provided interesting insights. Car dependency had a negative (both direct and total) impact on telework, an inference that confirms the role of car dependency as a resistant factor against alternative work arrangements and potentially any other new tech-based policies. In the opposite direction and surprisingly, telework encouraged car dependency, which might indicate that teleworkers tended to utilize the relaxed temporal-spatial constraints for non-work trips, and they were unlikely to give up private vehicle ownership simply because of their option to telecommute. Second, number of daily trips tended to increase the probability to telework, confirming the positive endogenous role of daily activity patterns on work arrangement decisions in a daily framework. Lastly, the significant negative impact of telework on daily trips was positively mediated through car dependency, leading to a smaller negative total impact on daily trips. Our study confirms that the association between telework and trip generation is quite a complex and multi-faceted relationship, leaving the argument regarding whether or not telework increases the number of daily trips as a debatable topic that calls for further exploration.

Spatial-temporal simulation for hospital infection spread and outbreaks of Clostridioides difficile

Validated and curated datasets are essential for studying the spread and control of infectious diseases in hospital settings, requiring clinical information on patients’ evolution and their location. The literature shows that approaches based on Artificial Intelligence (AI) in the development of clinical-support systems have benefits that are increasingly recognized. However, there is a lack of available high-volume data, necessary for trusting such AI models. One effective method in this situation involves the simulation of realistic data. Existing simulators primarily focus on implementing compartmental epidemiological models and contact networks to validate epidemiological hypotheses. Nevertheless, other practical aspects such as the hospital building distribution, shifts or safety policies on infections has received minimal attention. In this paper, we propose a novel approach for a simulator of nosocomial infection spread, combining agent-based patient description, spatial-temporal constraints of the hospital settings, and microorganism behavior driven by epidemiological models. The predictive validity of the model was analyzed considering micro and macro-face validation, parameter calibration based on literature review, model alignment, and sensitive analysis with an expert. This simulation model is useful in monitoring infections and in the decision-making process in a hospital, by helping to detect spatial-temporal patterns and predict statistical data about the disease.

Recharge and vulnerability assessment of groundwater resources in North west India: Insights from isotope-geospatial modelling approach

Recent studies indicate dwindling groundwater quantity and quality of the largest regional aquifer system in North West India, raising concern over freshwater availability to about 182 million population residing in this region. Widespread agricultural activities have resulted severe groundwater pollution in this area, demanding a systematic vulnerability assessment for proactive measures. Conventional vulnerability assessment models encounter drawbacks due to subjectivity, complexity, data-prerequisites, and spatial–temporal constraints. This study incorporates isotopic information into a weighted-overlay framework to overcome the above-mentioned limitations and proposes a novel vulnerability assessment model. The isotope methodology provides crucial insights on groundwater recharge mechanisms (18O and 2H) and dynamics (3H) - often ignored in vulnerability assessment. Isotopic characterisation of precipitation helped in establishing Local Meteoric Water Line (LMWL) as well as inferring contrasting recharge mechanisms operating in different aquifers. Shallow aquifer (depth < 60 m) showed significant evaporative signature with evaporation loss accounting up to 18.04% based on Rayleigh distillation equations. Inter-aquifer connections were apparent from Kernel Density Estimate (KDE) and isotope correlations. A weighted overlay isotope-geospatial model was developed combining 18O, 3H, aquifer permeability, and water level data. The central and northern parts of study area fall under least (0.29%) and extremely (1.79%) vulnerable zones respectively, while majority of the study area fall under moderate (42.71%) and highly vulnerable zones (55.20%). Model validation was performed using groundwater NO3– concentration, which showed an overall accuracy up to 82%. Monte Carlo Simulation (MCS) was performed for sensitivity analysis and permeability was found to be the most sensitive input parameter, followed by 3H, 18O, and water level. Comparing the vulnerability map with Land Use Land Cover (LULC) and population density maps helped in precisely identifying the high-risk sites, warranting a prompt attention. The model developed in this study integrates isotopic information with vulnerability assessment and resulted in model output with good accuracy, scientific basis, and widespread relevance, which highlights its crucial role in formulating proactive water resource management plans, especially in less explored data-scarce locations.

Tracking multiple paleo oil charges in the Precambrian dolomite reservoirs of the central Sichuan Basin, China

In the Precambrian dolomites in the central Sichuan Basin, a substantial paleo-oil system previously existed, which has since evolved into a dry gas system that is currently in place. The genesis of this paleo-oil system remained poorly understood, leaving uncertainties regarding the timing of oil charging and associated source rocks. Integration of Re–Os geochronometry with basin modeling allows temporal-spatial constraints on the ancient petroleum system to be established.Two texturally distinct generations of reservoir bitumen were identified. Late-generation bitumen yielded a geologically meaningful Re–Os age of 189 ± 20 Ma (MSWD = 10.4, 2σ), distinct from the previously documented Re–Os ages such as the 414 ± 44 Ma reported by Shi et al. (2020). This new age, along with the ∼414 Ma, aligns within uncertainties with the two relative timings derived from our independent basin model (ca. 450–390 Ma and 260–180 Ma), revealing two temporally distinct oil generation-migration events. The date of ∼189 Ma also lends support to the previous oil charge timing inferred from fluid inclusions. We evaluated the imprecisions of these two Re–Os ages and considered them to result from a narrow spread in 187Re/188Os, and variations in initial Os isotopic compositions (Osi) due to the giant size of the petroleum system and a long duration of oil generation from source kitchen over a large geographical area in this play. Effects of asphaltene precipitation, due to uplift-induced decompression of the migrating oils, may also have contributed to the scatter. Separating the samples based on Osi produced a higher precision age (184.1 ± 5.7 Ma, Model 1) with an MSWD of 0.97.The first oil charge is relatively weak as shown by the basin model and observed bitumen volumes, and it had not been well preserved due to significant reservoir exhumation by Caledonian tectonism. Consequently, the younger oil charge is considered to be the principal precursor to the current dry gas accumulations. Os isotope histories for four potential source units were constructed using publicly available Re–Os data. Comparisons between bitumen and rock Os compositions tend to support the lower Cambrian Qiongzhusi Formation as the predominant source unit for the two major paleo-oil charges. However, contributions from the Duoshantuo, Dengying, and Maidiping source rocks can not be precluded. Overall, these new observations allow temporal-spatial constraints to be placed on the complex evolution, from paleo-oil generation to destruction and dry gas generation.

Three-Dimensional Spatial–Temporal Cooperative Guidance Without Active Speed Control

This paper investigates the three-dimensional (3D) spatial–temporal cooperative guidance problem for multiple missiles with time-varying speeds, which is addressed by a progressive design strategy. First, the zero miss distance and desired impact directions are realized by a 3D vector impact angle control guidance (IACG) law whose time-to-go is predicted accurately and efficiently by a numerical algorithm. Then, by introducing an implicit trajectory-to-go function, the time-to-go control mechanism is clearly revealed under missile speed variation. Accordingly, a coordinated biased term is added to the 3D vector IACG law to ensure a simultaneous attack. Finally, considering the seeker’s field-of-view (FOV) limit, the resultant cooperative guidance law is further augmented by another biased term to prevent the lead angle from exceeding its upper bound. Unlike similar existing results, the proposed one can satisfy multiple complex spatial–temporal constraints without active speed control, thereby manifesting great practical significance. Several numerical simulations are provided to show the effectiveness and advantages of the proposed cooperative guidance law.

Cross-Camera Multi-Object Tracking based on Person Re-Identification and Spatial-Temporal Constraints

In order to reduce the influence of occlusion on the overall feature representation of tracks and improve the accuracy of track correlation between cameras, this paper proposes a cross camera multi-target tracking method based on person appearance and spatial-temporal constraints: a new cross-camera multi-object tracking framework is constructed. Then the person spatial-temporal probability model is established. Finally, the spatial-temporal probability model and the person appearance similarity are jointly measured and the person trajectory correlation under cross-camera is completed by using data correlation; Comparative experiments on the dataset proved that the method is effective.

Recognizing Real-Time Transfer Patterns between Metro and Bus Systems Based on Spatial–Temporal Constraints

Recognizing Real-Time Transfer Patterns between Metro and Bus Systems Based on Spatial–Temporal Constraints

Dynamic Delayed-Decision Task Assignment Under Spatial-Temporal Constraints in Mobile Crowdsensing

Task assignment is a research focus of Mobile Crowd Sensing(MCS). On the arrival of a sensing task, most researches use the way of immediate recruitment to assign the task, ignoring the impact of the task assignment time point. However, the arrival time and start time of a task are often different, hence an earlier assignment decision leads to rough predictions of users reaching the destination of task, which reduces task completion ratio and budget utilization ratio. In order to solve this problem, in this paper we propose a dynamic delayed-decision task assignment method. Firstly, we formalize a new task assignment problem considering the time point of task assignment, in which each selected user can complete as many tasks as possible within the given spatial-temporal constraints, rather than only one task, and propose a method of decision time point selection using a delayed-decision strategy to deal with the tasks of dynamically arriving at the MCS platform. Secondly, we propose two mobility prediction methods to efficiently compute the probabilities of users reaching the destinations before the end time of the sensing tasks, and then propose the related task assignment algorithms, namely with TDMar and TDMeta, which use the proposed semi-Markov prediction and meta-path prediction, respectively. Finally, by using large-scale real data sets, we evaluate the two algorithms and compare them with the baseline methods. The results show that using delayed-decision strategy could evidently improve the task completion ratio and budget utilization ratio, and also decrease the user singleness.

Constrained Gaussian Condensation Filter for Cooperative Target Tracking

Real-time high-precision navigation has many applications, such as pedestrian navigation, emergency rescue, and vehicle networks. In practice, the measurement models are often nonlinear, and sequential Bayesian filters, such as Kalman and particle filter, suffer from accumulative errors, which cannot provide long-time high-precision services for localization. To solve arbitrary noise distribution, this article proposes a Gaussian condensation filter (GCF) algorithm to achieve high-precision localization in a non-Gaussian noise environment. To this end, we proposed an error-ellipse resampling (EER)-based GCF (EER-GCF), which establishes error ellipses with different confidence probabilities and implements a resampling algorithm based on the sampling points’ geometrical positions. Furthermore, a cooperative EER-based GCF (CEER-GCF) is proposed to enhance information fusion in the multitarget network. This study accomplishes cooperative tracking based on spatial–temporal constraints to enhance error correction. The experimental results show that CEER-GCF can effectively eliminate the accumulative error and optimize state estimation, which outperforms state of the arts, such as unscented Kalman filter and particle filter.

DeepRoute+: Modeling Couriers’ Spatial-temporal Behaviors and Decision Preferences for Package Pick-up Route Prediction

Over 10 billion packages are picked up every day in China. A fundamental task raised in the emerging intelligent logistics systems is the couriers’ package pick-up route prediction, which is beneficial for package dispatching, arrival-time estimation and overdue-risk evaluation, by leveraging the predicted routes to improve those downstream tasks. In the package pick-up scene, the decision-making of a courier is affected by strict spatial-temporal constraints (e.g., package location, promised pick-up time, current time, and courier’s current location). Furthermore, couriers have different decision preferences on various factors (e.g., time factor, distance factor, and balance of both), based on their own perception of the environments and work experience. In this article, we propose a novel model, named DeepRoute+, to predict couriers’ future package pick-up routes according to the couriers’ decision experience and preference learned from the historical behaviors. Specifically, DeepRoute+ consists of three layers: (1) The representation layer produces experience- and preference-aware representations for the unpicked-up packages, in which a decision preference module can dynamically adjust the importance of factors that affects the courier’s decision under the current situation. (2) The transformer encoder layer encodes the representations of packages while considering the spatial-temporal correlations among them. (3) The attention-based decoder layer uses the attention mechanism to generate the whole pick-up route recurrently. Experiments on a real-world logistics dataset demonstrate the state-of-the-art performance of our model.

MAT: Motion-aware multi-object tracking

Modern multi-object tracking (MOT) systems usually build trajectories through associating per-frame detections. However, facing the challenges of camera motion, fast motion, and occlusion, it is difficult to ensure the quality of long-range tracking or even the tracklet purity, especially for small objects. Most of tracking frameworks depend heavily on the performance of re-identification (ReID) for the data association. Unfortunately, the ReID-based association is not only unreliable and time-consuming, but still cannot address the false negatives for occluded and blurred objects, due to noisy partial-detections, similar appearances, and lack of temporal-spatial constraints. In this paper, we propose an enhanced MOT paradigm, namely Motion-Aware Tracker (MAT). Our MAT is a plug-and-play solution, it mainly focuses on high-performance motion-based prediction, reconnection, and association. First, the nonrigid pedestrian motion and rigid camera motion are blended seamlessly to develop the Integrated Motion Localization (IML) module. Second, the Dynamic Reconnection Context (DRC) module is devised to guarantee the robustness for long-range motion-based reconnection. The core ideas in DRC are the motion-based dynamic-window and cyclic pseudo-observation trajectory filling strategy, which can smoothly fill in the tracking fragments caused by occlusion or blur. At last, we present the 3D Integral Image (3DII) module to efficiently cut off useless track-detection association connections using temporal-spatial constraints. Extensive experiments are conducted on the MOT16&17 challenging benchmarks. The results demonstrate that our MAT can achieve superior performance and surpass other state-of-the-art trackers by a large margin with high efficiency.

A novel quality control model of rainfall estimation with videos – A survey based on multi-surveillance cameras

The widespread use of surveillance cameras has become an emerging means for rainfall observations. With the advantages of high spatial–temporal resolution, rainfall information obtained from surveillance videos is highly suitable for meteorological-related research and has bright prospects. However, due to the complex and variable monitoring scenarios, the quality of the rainfall data estimated by each camera is always inconsistent, resulting in low practical value. Dense ground-level surveillance cameras have temporal and spatial correlations that can be used to improve the accuracy of rainfall estimation through mutual verification. In this study, we first introduce camera parameters to refine the spatial volume of rainfall (SVoR)11Most acronyms used in this study appear in the Appendix 1. perceived by cameras to improve the accuracy of rainfall intensity (RI) estimation. Next, a novel quality control (QC) model of rainfall estimation with multi-surveillance camera collaboration that takes the rainfall observations of all cameras as input is proposed. (i) We build a reliability evaluation (RE) model for the estimation of RI in accordance with raindrop imagery features to provide a reference for the subsequent correction of RI estimation; (ii) inspired by the first law of geography (Tobler, 1970), we then construct a spatial–temporal consistency filter and a situation consistency filter by using the spatial–temporal constraints between cameras to coarsely evaluate the RI values; and (iii) the correlation between cameras is calculated based on the fuzzy method to further build a correlation filter for the fine-grained correction of RI values. Experiments show that our method can effectively eliminate RI outliers and improve the accuracy and reliability of rainfall estimation results. Moreover, our method is highly suitable for heavy and violent rainfall application scenarios and can provide high-resolution rainfall data support for flooding warnings and simulations in urban areas.

Internal and external spatial–temporal constraints for person reidentification

Spatial–temporal information is easy to achieve in a practical surveillance scene, but it is often neglected in most current person reidentification (ReID) methods. Employing spatial–temporal information as a constrain has been verified as beneficial for ReID. However, there is no effective modeling according to the pedestrian movement law. In this paper, we present a ReID framework with internal and external spatial–temporal constraints, termed as IESC-ReID. A novel residual spatial attention module is proposed to build a spatial–temporal constraint and increase the robustness to partial occlusions or camera viewpoint changes. A Laplace-based spatial–temporal constraint is also introduced to eliminate irrelevant gallery images, which are gathered by the internal learning network. IESC-ReID constrains the attention within the functioning range of the channel space, and utilizes additional spatial–temporal constrains to further constrain results. Intensive experiments show that these constraints consistently improve the performance. Extensive experimental results on numerous publicly available datasets show that the proposed method outperforms several state-of-the-art ReID algorithms. Our code is publicly available at https://github.com/jiaming-wang/IESC.

Would “No More Counting Dollars Be Counting Stars”?: CEO Compensation and Spatial-Temporal Cognition

Prior research on corporate governance has paid little attention to the cognitive consequences of compensation for CEOs. This paper studies how compensation affects the most basic aspect of cognition, i.e., spatial-temporal cognition of CEOs. Drawing from prior theory and research, it is argued that money reduces the relevance of spatial-temporal constraints on CEOs’ cognition of reality by increasing the sense of control over space-time. Further, this main effect reduces as CEO compensation becomes more contingent upon firm performance. Using a content-intensive sample of 713 CEO-year observations of public corporations, and through content-analysis and simultaneous equation modeling, the hypotheses are tested. The results provide support for the theory put forward in the present paper. The moderating effect somewhat but partially alter the main effect. Most interestingly, these findings point to a potential paradox in that compensation that is long believed to be essential for aligning the interests of CEOs with the owners’ interests, acts against such alignment by biasing CEOs’ cognitive representation of reality. The implications of the paper’s findings are discussed for the research on CEO compensation, CEO cognition, incentive alignment and diversification/merger/acquisition.