Spatio-temporal Trajectories Research Articles

A general modeling scheme for spatiotemporal DGGS with emphasis on encoding and operating multiscale time grids

AbstractOne of the basic scientific problems concerning geographic information science is how to rapidly organize, query, and compute spatiotemporal big data. The spatiotemporal discrete global grid system (DGGS) provides a homogenized discrete structure for processing multiscale and multitype spatiotemporal data. To date, most research in spatiotemporal DGGS has focused on spatial discretization while neglecting temporal discretization. Here, we propose a general modeling scheme for spatiotemporal DGGS with emphasis on encoding and operating multiscale time grids. We subdivide continuous time into multiscale temporal grids, which are then encoded as integers. Moreover, we designed integer code operations, including hierarchical traversal, neighborhood finding, and temporal relationship calculations. Compared to the multiscale time segment integer coding (MTSIC) approach, the proposed method resulted in 22% higher encoding efficiency, 10.92 times faster decoding, 2.81 times better parent code finding efficiency, 41% improved efficiency, 100% accuracy in finding children codes (compared to less than 100% with MTSIC), and a 62% enhancement in temporal relationship calculation efficiency. The application of querying spatiotemporal trajectory data validates the feasibility and practicality of substituting conventional string‐based time and floating‐point location coordinates with spatiotemporal integer codes to query data. The time encoding and operation methods proposed here indicate high efficiency, superior accuracy, and broad application prospects.

Neutron scattering and neural-network quantum molecular dynamics investigation of the vibrations of ammonia along the solid-to-liquid transition

Vibrational spectroscopy allows us to understand complex physical and chemical interactions of molecular crystals and liquids such as ammonia, which has recently emerged as a strong hydrogen fuel candidate to support a sustainable society. We report inelastic neutron scattering measurement of vibrational properties of ammonia along the solid-to-liquid phase transition with high enough resolution for direct comparisons to ab-initio simulations. Theoretical analysis reveals the essential role of nuclear quantum effects (NQEs) for correctly describing the intermolecular spectrum as well as high energy intramolecular N-H stretching modes. This is achieved by training neural network models using ab-initio path-integral molecular dynamics (PIMD) simulations, thereby encompassing large spatiotemporal trajectories required to resolve low energy dynamics while retaining NQEs. Our results not only establish the role of NQEs in ammonia but also provide general computational frameworks to study complex molecular systems with NQEs.

Mitochondria Regulate Spatiotemporal Ca2+ Dynamics Between Cellular Compartments Affecting the Intrinsic Excitability of Vasopressin Neurons

Calcium (Ca2+) is a critical secondary messenger in all neurons that coordinates important mechanisms of cell physiology. In vasopressin (VP) neurons of supraoptic nucleus (SON) it’s required for somatodendritic release of VP and the slow afterhyperpolarization (sAHP), both of which coordinate neuronal output and thus control stimulus-secretion coupling. Recently, our lab demonstrated that VP sAHPs require endoplasmic reticulum (ER) Ca2+ for activation and that mitochondrial Ca2+ buffering shapes the spatiotemporal trajectory. We also have recent data demonstrating electrotonic segregation between cell compartments. Given the Ca2+ dependence of somatodendritic release and sAHPs, delineation of somatodendritic release from axonal release, and the decoupling of somatic and dendritic electrontonic properties, we hypothesized that calcium dynamics also may be compartmentally segregated as well. Utilizing patch clamp electrophysiology, live calcium imaging, and focal UV uncaging, we probed the spatiotemporal trajectory of rapidly evoked Ca2+ responses in VP neuron somas and dendrites with corresponding membrane potential. UV Ca2+ uncaging at the soma evokes a membrane sAHP and robust increase in cytosolic Ca2+ that propagates into the dendrites. Conversely, Ca2+ uncaged in dendrites propagates bidirectionally from the UV flash but does not penetrate the soma; no membrane hyperpolarization was observed under these conditions. Disabling mitochondrial Ca2+ buffering with Ru360 in the pipette amplifies Ca2+ signasl and sAHPs in somas and dendrites; Ca2+ uncaging in dendrites penetrates soma and can evoke sAHPs under these circumstances. Together, these results suggest that under normal conditions, Ca2+ movement between compartments in VP neurons is unidirectional (soma to dendrite), and that sAHP channels require somatic Ca2+ increases. Moreover, Ca2+ uptake by mitochondria is a critical mechanism that tightly controls the magnitude and progagation of dendritic Ca2+ signals between dendritic and somatic compartments, thus playing an important role in regulating intrinsic mechanisms and ultimately, their neuronal output. NIH K99HL168434 (M.K.K.); R01 HL162575-01 (J.E.S.). This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Conflict Detection and Separation Configuration Method Based on Uncertain Flight Trajectory

Aiming at two aircraft conflict scenario in the pre-tactical stage, by converting the uncertain flight trajectory of the target aircraft into a spatio-temporal trajectory under its performance constraints, a conflict detection model based on truncated normal distribution was proposed,and influencing factors affecting the overall conflict probability were analysed by numerical simulation. For the conflict scenario, nonlinear particle swarm optimisation (NPSO) algorithm was applied to solve the optimal separation configuration strategy for the ownship. The simulation results show that, in comparison to conventional PSO algorithm, the improved NPSO algorithm improves the optimal value by 14.88% and decreases the maximum velocity change by 19.84%. The simulation also shows that the algorithm can maintain the minimum interval requirements under different initial parameters, demonstrating its strong adaptability.

Metal-for-Amber in the European Bronze Age

Abstract Baltic amber is often considered the principal Scandinavian commodity exchanged for metal from ore-rich regions in Europe. If correct, this may explain the astonishing metal wealth of the Nordic Bronze Age and the modest social consumption of amber locally. The hypothesis of a metal-for-amber principle behind the trade is here for the first time assessed on scales from micro to macro. Amber finds were charted across Europe, and the result was then compared to evidence for regular shifts in copper ore preferences/availability, as found in the systematically changing isotopic/elemental composition of Nordic metal objects in c. 2100–1200 BC. Comparisons indicate that amber and metal followed similar spatiotemporal trajectories with major reorganizations at the turn from the Early to the Middle Bronze Age. Shifts in amber distribution correlate with the geography of metal sources used in both periods and flows of metal to Scandinavia and amber to Europe appear to be contingent even in subperiods. Major European amber tracks – and the crossroads hubs controlling them – were identified for the transfer of goods, yet again revealing major changes at the transition to the Middle Bronze Age. The social roles and meanings of amber among Nordic communities were likewise examined, suggesting that bearers of amber played a role in exercising social control of this resource. It is concluded that amber almost certainly was exchanged for metal.

Single-cell and spatial RNA sequencing reveal the spatiotemporal trajectories of fruit senescence

The senescence of fruit is a complex physiological process, with various cell types within the pericarp, making it highly challenging to elucidate their individual roles in fruit senescence. In this study, a single-cell expression atlas of the pericarp of pitaya (Hylocereus undatus) is constructed, revealing exocarp and mesocarp cells undergoing the most significant changes during the fruit senescence process. Pseudotime analysis establishes cellular differentiation and gene expression trajectories during senescence. Early-stage oxidative stress imbalance is followed by the activation of resistance in exocarp cells, subsequently senescence-associated proteins accumulate in the mesocarp cells at late-stage senescence. The central role of the early response factor HuCMB1 is unveiled in the senescence regulatory network. This study provides a spatiotemporal perspective for a deeper understanding of the dynamic senescence process in plants.

Research on the Application of Computer Big Data Technology in Smart Travel

This paper mainly focuses on the algorithms related to local path planning and path tracking control of unmanned vehicles in the process of obstacle avoidance. By introducing the temporal dimension as a reference, the perceptual results are projected onto the 3D spatio-temporal navigation map by combining the multi-target behavior prediction and other means; by increasing the temporal dimension, the static obstacles and dynamic obstacles are unified into the same parameter space. Under this parameter space, the front-end A* path search initializes the unified B spline curve control points, designs the trajectory cost function and performs nonlinear optimization to generate a spatio-temporal trajectory that satisfies the safety collision-free and vehicle motion constraints (speed and acceleration limits), thus transforming the decision and planning problem under the two-dimensional fence dynamic physical space into a static scene decision and planning problem under the three-dimensional spatio-temporal space. Through simulation verification, the whole process of the proposed trajectory planning method takes 51.27ms on average, which meets the driving requirements of driverless cars. In addition, by adjusting the search conditions of the A* algorithm, its overall planning efficiency is improved by 27.86% compared with the search speed of the traditional algorithm. The actual feeling and data results from the real vehicle experiments show its good tracking effect, which verifies the effectiveness and practicality of the algorithm proposed in this paper.

ODTrack: Online Dense Temporal Token Learning for Visual Tracking

Online contextual reasoning and association across consecutive video frames are critical to perceive instances in visual tracking. However, most current top-performing trackers persistently lean on sparse temporal relationships between reference and search frames via an offline mode. Consequently, they can only interact independently within each image-pair and establish limited temporal correlations. To alleviate the above problem, we propose a simple, flexible and effective video-level tracking pipeline, named ODTrack, which densely associates the contextual relationships of video frames in an online token propagation manner. ODTrack receives video frames of arbitrary length to capture the spatio-temporal trajectory relationships of an instance, and compresses the discrimination features (localization information) of a target into a token sequence to achieve frame-to-frame association. This new solution brings the following benefits: 1) the purified token sequences can serve as prompts for the inference in the next video frame, whereby past information is leveraged to guide future inference; 2) the complex online update strategies are effectively avoided by the iterative propagation of token sequences, and thus we can achieve more efficient model representation and computation. ODTrack achieves a new SOTA performance on seven benchmarks, while running at real-time speed. Code and models are available at https://github.com/GXNU-ZhongLab/ODTrack.

Understanding climate changes in East Asia and Europe based on spatial climate analogs

Spatial climate analogs effectively illustrate how a location’s climate may become more similar to that of other locations from the historical period to future projections. Also, novel climates (emerging climate conditions significantly different from the past) have been analyzed as they may result in significant and unprecedented ecological and socioeconomic impacts. This study analyzes historical to future spatial climate analogs across East Asia and Europe, in the context of climatic impacts on ecology and human health, respectively. Firstly, the results of climate analogs analysis for ecological impacts indicate that major cities in East Asia and Europe have generally experienced novel climates and climate shifts originating from southern/warmer regions from the early 20th century to the current period, primarily attributed to extensive warming. In future projections, individual cities are not expected to experience additional significant climate change under a 1.5 °C global warming (warming relative to pre-industrial period), compared to the contemporary climate. In contrast, robust local climate change and climate shifts from southern/warmer regions are expected at 2.0 °C and 3.0 °C global warming levels. Specially, under the 3.0 °C global warming, unprecedented (newly emerging) climate analogs are expected to appear in a few major cities. The climate analog of future projections partially align with growing season length projections, demonstrating important implications on ecosystems. Human health-relevant climate analogs exhibit qualitatively similar results from the historical period to future projections, suggesting an increasing risk of climate-driven impacts on human health. However, distinctions emerge in the specifics of the climate analogs analysis results concerning ecology and human health, emphasizing the importance of considering appropriate climate variables corresponding to the impacts of climate change. Our results of climate analogs present extensive information of climate change signals and spatiotemporal trajectories, which provide important indicators for developing appropriate adaptaion plans as the planet warms.

Entangled brains and the experience of pains.

The International Association for the Study of Pain (IASP) revised its definition of pain to "an unpleasant sensory and emotional experience." Three recent recommendations for understanding pain if there are no clear brain correlates include eliminativism, multiple realizability, and affordance-based approaches. I adumbrate a different path forward. Underlying each of the proposed approaches and the new IASP definition is the suspicion that there are no specific correlates for pain. I suggest that this basic assumption is misguided. As we learn more about brain function, it is becoming clear that many areas process many different types of information at the same time. In this study, I analogize how animal brains navigate in three-dimensional space with how the brain creates pain. Underlying both cases is a large-scale combinatorial system that feeds back on itself through a diversity of convergent and divergent bi-directional connections. Brains are not like combustion engines, with energy driving outputs via the structure of the machine, but are instead more like whirlpools, which are essentially dynamic patterns in some substrates. We should understand pain experiences as context-dependent, spatiotemporal trajectories that reflect heterogeneous, multiplex, and dynamically adaptive brain cells.

Data-driven typhoon case retrieval method based on spatiotemporal trajectory similarity and risk preferences of decision-makers

The results of typhoon case retrieval are important for decision-making during emergency situations. Therefore, this study proposes a new typhoon case retrieval method to improve the accuracy of the retrieval results based on the spatiotemporal trajectory similarity and the risk preferences of decision-makers. First, a spatiotemporal trajectory similarity measurement is proposed to generate the path similarity case set. Next, attribute similarity and case similarity measurements are applied to obtain the set of similar cases. Subsequently, a group decision-making model based on fuzzy evidence reasoning is constructed to obtain the risk utility value according to the assessment information and risk preferences of the decision-makers. The risk utility and case similarity are aggregated to obtain the comprehensive utility value and the most appropriate historical typhoon case, thereby generating the emergency alternative. Finally, the effectiveness and feasibility of the proposed method are demonstrated using a typhoon disaster example. The advantages of the proposed method are illustrated through comparative analyses with other methods.

Person group detection with global trajectory extraction in a disjoint camera network

Person group detection refers to the grouping of people with similar spatio-temporal trajectories. In this work, we address the problem of automatically detecting groups of people from disjoint camera views, which has an essential application in public safety but has not been seriously studied. The main challenge of this task is that the sparse distribution of cameras in a large surveillance area makes it difficult to infer and match people’s trajectories across cameras. To address this challenge, we propose a CCRF (Cyclic Conditional Random Fields) based model for cross-camera trajectory extraction, which takes both visual appearance and heterogeneous spatio-temporal information (including camera locations, video capture times, and the map of the surveillance area) as input and infers multiple candidate cross-camera trajectories for each person. Then, for each pair of people, we propose to use a dynamic trajectory warping (DTW) method to measure the similarity of their trajectories. DTW uses visual features to optimize the selection of trajectories and addresses the problem of trajectory length matching. Since there is no existing dataset that can directly support our research, we enrich our previously built Person Trajectory Dataset by adding the person group annotation and then verify the effectiveness of the proposed method on this dataset. The dataset and code are released at https://github.com/zhangxin1995/PTD_GROUP.

Road traffic noise exposure assessment based on spatiotemporal data fusion

The current static assessment methods for road traffic noise exposure have limitations in representing spatiotemporal variations and only account for the noise exposure of registered residents. To address these issues, this study proposes an assessment method by fusing spatiotemporal noise and population distributions. This approach estimates spatiotemporal noise distributions through real-time noise monitoring data and an update model of noise source intensity calibrated by historical noise monitoring data and road segment speed data. Meanwhile, spatiotemporal population distributions are generated by aggregating standardized spatiotemporal trajectories of users extracted from mobile phone data. The proposed method is applied to evaluate the population exposure to traffic noise in a Chinese city in different hourly periods. The results demonstrate that this method is a quantitative tool for assessing road traffic noise exposure dynamically, providing policymakers with valuable information to locate hotspots of traffic noise over short periods of time.

Spatiotemporal Trajectory Planning for Autonomous Vehicle based on Reachable Set and Iterative LQR

Spatiotemporal Trajectory Planning for Autonomous Vehicle based on Reachable Set and Iterative LQR

Spatio-Temporal Trajectory Design for UAVs: Enhancing URLLC and LoS Transmission in Communications

Spatio-Temporal Trajectory Design for UAVs: Enhancing URLLC and LoS Transmission in Communications

MMCPP: A MULTI-MODAL CONTRASTIVE PRE-TRAINING MODEL FOR PLACE REPRESENTATION BASED ON THE SPATIO-TEMPORAL FRAMEWORK

Abstract. The concept of "place" is crucial for understanding geographical environments from a human perspective. Place representation learning involves converting places into numerical low-dimensional dense vectors and is a fundamental procedure for artificial intelligence in geography (GeoAI). However, most studies ignore multi-level distance constraints and spatial proximity interactions that enable behavioral interactions between places. Furthermore, representing the temporal characteristics of these interactions in trajectory sequences poses a challenge for natural language processing and other field techniques. In addition, most existing methods rely on all modalities from inputs as they use joint training to integrate multiple modalities. To address these issues, we propose a Multi-Modal Contrastive Pre-training model for Place representation (MMCPP). Our model consists of three encoders that capture corresponding place attributes across different modalities, including point of interests (POIs), images, and trajectories. The trajectory encoder, named RodtFormer, takes fine-grained spatio-temporal trajectories as input and leverages self-attention with rotary temporal interval position embedding to simulate dynamic spatial and behavioral proximity interactions between places. By using a coordinated pre-training framework, MMCPP independently encodes place representations across different modalities and improves model reusability. We verify the effectiveness of our model on a taxi trajectory dataset using the location prediction task at next n seconds, including 30 seconds(s), 180(s), 300(s). Our results demonstrate that compared to existing embedding methods, our model is capable of learning higher-quality position representations during pre-training, leading to improved performance on downstream tasks.

Dynamic trajectory partition optimization method based on historical trajectory data

Partitioning dynamic trajectory data can improve the efficiency and accuracy of trajectory data processing, provide a foundation for trajectory data mining and analysis. However, with the continuous growth of trajectory data scales and the urgent demand for trajectory query efficiency and accuracy, partitioning methods have become crucial. The partitioning method of dynamic trajectory data faces significant challenges in terms of spatiotemporal trajectory locality, partition load balancing, and partition time. To address these challenges, we propose a method based on historical trajectory pre-partitioning, which can store data more effectively in distributed systems. We partition similar historical trajectory data to achieve preliminary partitioning of the data. In addition, we also construct a cost model to ensure that the workload of each partition is close to consistency. Extensive experiments have demonstrated the excellent partitioning efficiency and query efficiency achieved by our design compared to other partitioning methods.

Reactive mobile manipulation based on dynamic dual-trajectory tracking

The letter presents an online obstacle avoidance strategy for a nonholonomic mobile manipulator, including dual-trajectory generation for the mobile base(MB) and end effector(EE), as well as whole-body reactive obstacle avoidance. The feasible region of the manipulator’s workspace is generated by traversing the path of the MB, and the spatio-temporal trajectory of the EE is generated by quadratically constrained quadratic programming(QCQP) planning, satisfying the physical constraints and obstacle avoidance constraints. The reactive obstacle avoidance strategy is based on quadratic programming(QP), using singularity avoidance and EE trajectory tracking tasks as soft constraints with different priorities. Considering the nonholonomic constraints and motion differences of MB, we employ a dynamic tracking approach to avoid obstacles of MB. Additionally, an adaptive weight method is utilized to adjust the priority of MB path tracking task, effectively preventing joint velocity jitter during the insertion process. Our strategy has been proven to generate obstacle avoidance trajectories for EE in real-time and ensure safe joint velocities for MB and the manipulator through simulation and real-world scenarios.

GAN-BiLSTM network for field-road classification on imbalanced GNSS recordings

Field-road classification is a key task in processing spatio-temporal trajectories in the agricultural field. This task involves classifying agricultural machinery trajectories into field operation and road driving trajectories. Accurately classifying these trajectories is essential for precision agriculture. However, the existing methods for field-road classification do not address the issue of data imbalance between the numbers of field and road points in trajectories. To improve the accuracy of field-road classification and solve the data imbalance problem in agricultural machinery trajectories, a generative adversarial network-bidirectional long short-term memory network (GAN-BiLSTM) field-road classification model is proposed in this paper. First, we use a GAN for data augmentation to obtain a balanced dataset. Second, we propose a BiLSTM with attention mechanism (Att-BiLSTM) to capture data features. Finally, we train the model using focal loss to further learn points that are difficult to classify for field-road classification. To evaluate the effectiveness of our proposed model, we conducted experiments and compared our results with those of the current field-road classification methods on 160 agricultural trajectory samples. Our model achieved 92.3% accuracy and a 92.1% F1 score, exhibiting improvements of 5.9% in accuracy and 6.4% in F1 score compared with the current state-of-the-art method. The source code is publicly available at https://github.com/hysyyds/GAN-BiLSTM.

Robust mapping of spatiotemporal trajectories and cell–cell interactions in healthy and diseased tissues

Spatial transcriptomics (ST) technologies generate multiple data types from biological samples, namely gene expression, physical distance between data points, and/or tissue morphology. Here we developed three computational-statistical algorithms that integrate all three data types to advance understanding of cellular processes. First, we present a spatial graph-based method, pseudo-time-space (PSTS), to model and uncover relationships between transcriptional states of cells across tissues undergoing dynamic change (e.g. neurodevelopment, brain injury and/or microglia activation, and cancer progression). We further developed a spatially-constrained two-level permutation (SCTP) test to study cell-cell interaction, finding highly interactive tissue regions across thousands of ligand-receptor pairs with markedly reduced false discovery rates. Finally, we present a spatial graph-based imputation method with neural network (stSME), to correct for technical noise/dropout and increase ST data coverage. Together, the algorithms that we developed, implemented in the comprehensive and fast stLearn software, allow for robust interrogation of biological processes within healthy and diseased tissues.