Trajectory Database Research Articles

Experimental Visualization and Modeling of the Transport Behaviors of Monofilament Microplastic Fibers Through an Idealized Porous Media

AbstractMicroplastic fibers (MPF) are the largest fraction of microplastics in the environment by mass. The endpoints of these contaminants' movement is generally known at large‐scale (i.e., their origins and where they end up), but the mechanics of how they get to those sinks remains poorly understood. The objective of this work was to improve understanding of the mechanisms driving MPF migration through terrestrial systems by directly imaging their motion through idealized representations of porous media. Monofilament line with 0.3 mm diameter was passed through a bench‐scale, pseudo‐2d flow cell to capture trajectories of MPFs of three different lengths and trajectories of passive micro‐bead tracers were also captured. Video processing and automated image analysis converted the video of the experiments into a database of trajectories, allowing comparison of the experimental data to various numerical models. Simple advection‐dispersion models were adequate for modeling the passive tracer but did not provide a good description of MPF transport. A physics‐based, distributed model was able to generate realistic trajectories through the domain, but the speeds of the fibers in the initial simulation were too fast, despite working well for the passive tracer. Adding a delay (waiting time) process resulted in good description of the trajectories and travel times. The specifics of the delay process could not be deduced from these experiments, but its overall impact on transport provides mechanistic insights. These direct observation of the trajectories and speeds of MPFs moving through porous media show that MPFs likely have strong interactions with their surroundings.

Industrial robot energy consumption model identification: A coupling model-driven and data-driven paradigm

Due to wide distribution and low energy efficiency, the energy-saving in industrial robots (IRs) is attracting extensive attention. Accurate energy consumption (EC) models of IRs lay the foundation for energy-saving. However, most dynamic and electrical parameters of IRs are not disclosed by manufacturers, which leads to the invalidity of most model-based EC prediction methods. To bridge this gap, a mechanism-data hybrid-driven method is proposed to predict the EC of IRs in this paper. First, a joint torque prediction model integrating a hybrid-driven parameter identification is developed based on deep reinforcement learning (DRL). The framework for DRL-based parameter identification is constructed through tailored design of interfaces and training mechanisms, wherein the DRL agent can learn to identify the dynamic parameters from the trajectory database. And a deep neural network based on long short-term memory (LSTM) is proposed to predict the EC of IRs according to the joint torques and velocities. The nonlinear item, which is not modeled in the robot dynamic equation, are also encapsulated in the deep neural network with one-dimensional convolutional neural network (1D-CNN) layers to improve the prediction accuracy. To validate the accuracy and efficacy of the proposed method, experiments are conducted on a KUKA KR60-3 industrial robot with different loads. The results demonstrate that the proposed method can predict EC with a mean absolute percentage error of less than 2% under a fixed load and less than 3% under loads not used for agent training.

Intelligent Trajectory Prediction Algorithm for Hypersonic Vehicle Based on Sparse Associative Structure Model

The Hypersonic Glide Vehicle (HGV) has become a focal point in military competitions among nations. Predicting the real-time trajectory of an HGV is of significant importance for aerospace defense interception and assessing enemy combat intentions. Existing prediction methods are limited by the need for large data samples and poor general applicability. To address these challenges, this paper presents a novel trajectory forecasting approach based on the Sparse Association Structure Model (SASM). The SASM can uncover the relationship among known data, transfer associative relationships to unknown data, and improve the generalization of the model. Firstly, a trajectory database is established for different maneuvering modes based on the six-degree-of-freedom motion equations and models of attack and bank angles of the HGV. Subsequently, three trajectory parameters are selected as prediction variables according to the maneuvering characteristics of the HGV. A parameters prediction model based on the SASM is then constructed to predict trajectory parameters. The SASM model demonstrates high accuracy and generalization in forecasting the trajectories of three different HGV types. Experimental results show a 50.35% reduction in prediction error and a 48.7% decrease in average processing time compared to the LSTM model, highlighting the effectiveness of the proposed method for real-time trajectory forecasting.

A spatiotemporal co-occurrence pattern mining algorithm based on ship trajectory data

Finding the potential spatiotemporal co-occurrence behavior patterns of large groups of ships while sailing is a challenging problem of great importance in many real-world applications. Through spatiotemporal data mining of ship trajectory data, route rules, navigation behavior, and potential anomalies can be mined, providing important support for maritime management, navigation safety, and emergency response. With the analysis and mining of ship trajectory data in some hotspot sea areas, this paper introduced a ship spatiotemporal co-occurrence pattern mining algorithm based on association rules. Based on the research of data model and the judgment criterion of spatio-temporal co-occurrence law, such concepts as candidate set, frequency set, and instance set are introduced together with the key procedure of algorithm, including pruning and pasting of candidate sets, screening of instance sets, definition of association reasoning, and association rule mining. Subsequently, the process of implementing the spatiotemporal co-occurrence pattern mining algorithm is devised. In the end, the algorithm is verified by taking the automatic identification system data of ships in hotspot sea areas as the source data. The proposed algorithm can find several ship combinations with spatiotemporal co-occurrence regularity in these hotspot sea areas, and the association rules on the co-occurrence of several ships. The performance of the proposed algorithms is illustrated on a real-world ship trajectory database and made a detailed comparative analysis. The results are very promising in terms of computational time. The experimental results show that our algorithm can effectively identify the motion patterns and behavior characteristics of ships, which provides an important reference and support for Marine traffic management, ship safety and Marine environment protection. The research results of this paper are of great significance for improving the efficiency and safety of maritime traffic, and also provide new ideas and methods for further research in related fields.

On optimal three-impulse Earth–Moon transfers in a four-body model

Within the emerging age of lunar exploration, optimizing transfer trajectories is a fundamental measure toward achieving more economical and efficient lunar missions. This study addresses the possibility of reducing the fuel cost of two-impulse Earth–Moon transfers in a four-body model with the Earth, the Moon, and the Sun as primaries. Lawden’s primer vector theory is applied within this framework to derive a set of necessary conditions for a fuel-optimal trajectory. These conditions are used to identify which trajectories from an existing database could benefit from the insertion of an additional intermediate impulse. More than 10,000 three-impulse transfers are computed with a direct numerical optimization method. These solutions contribute to enriching the database of impulsive trajectories, useful to perform trade-off analyses. While the majority of trajectories exhibit only marginal improvements, a significant breakthrough emerges for transfers featuring an initial gravity assist at the Moon. Implementing a corrective maneuver after the lunar encounter yields substantial reductions in fuel costs.

ThermoPCD: a database of molecular dynamics trajectories of antibody-antigen complexes at physiologic and fever-range temperatures.

Progression of various cancers and autoimmune diseases is associated with changes in systemic or local tissue temperatures, which may impact current therapies. The role of fever and acute inflammation-range temperatures on the stability and activity of antibodies relevant for cancers and autoimmunity is unknown. To produce molecular dynamics (MD) trajectories of immune complexes at relevant temperatures, we used the Research Collaboratory for Structural Bioinformatics (RCSB) database to identify 50 antibody:antigen complexes of interest, in addition to single antibodies and antigens, and deployed Groningen Machine for Chemical Simulations (GROMACS) to prepare and run the structures at different temperatures for 100-500ns, in single or multiple random seeds. MD trajectories are freely available. Processed data include Protein Data Bank outputs for all files obtained every 50ns, and free binding energy calculations for some of the immune complexes. Protocols for using the data are also available. Individual datasets contain unique DOIs. We created a web interface, ThermoPCD, as a platform to explore the data. The outputs of ThermoPCD allow the users to relate thermally-dependent changes in epitopes:paratopes interfaces to their free binding energies, or against own experimentally derived binding affinities. ThermoPCD is a free to use database of immune complexes' trajectories at different temperatures that does not require registration and allows for all the data to be available for download. Database URL: https://sites.google.com/view/thermopcd/home.

APPLYING THE CARE SETTING TRAJECTORY DATABASE TO STUDY END-OF-LIFE OUTCOMES IN PERSONS WITH DEMENTIA

Abstract This study highlights an application of the care setting trajectory database and our new, harmonized, self-reported race/ethnicity variable to evaluate the impact of home health care on later use of hospice and place of death in a large cohort of Medicare decedents. This study evaluated home health care use during the last 3 years of life as a predictor of death in the care of hospice for a cohort of 895,526 Medicare beneficiaries with dementia who died in 2019. The cohort was limited to beneficiaries continuously enrolled for at least 5 years, who were age 40 and older, and who lived in the U.S. fifty states, District of Columbia, or Puerto Rico at death. Co-variates included number of acute hospitalizations during the last 3 years of life, age at death, sex, race/ethnicity, comorbidities, insurance, rural/urban area, and state of residence. 54.5% of the cohort used home health care at any point during the last 3 years of life. Compared to beneficiaries who did not use home health care, those who received home health care were more likely to die at home (64.5% vs. 60%, OR 1.08) or in the care of hospice (65% vs. 58%, OR 1.05). Among beneficiaries who died with hospice, use of home hospice was more prevalent for those who had previously used home health care (53% vs. 49%). This study provides compelling evidence of the value of home health care as a strategy to improve end-of-life care quality among Medicare beneficiaries living with dementia.

APPLIED METHODS FOR HEALTH EQUITY AND END-OF-LIFE OUTCOMES RESEARCH USING MEDICARE DATA

Abstract This symposium brings together four related and complementary projects on the development of new data sources and methods to analyze the new data to study health equity and end-of-life outcomes research. The first abstract presents the development of the Care Setting Trajectory Database, which integrates data from various CMS data sources for Medicare beneficiaries to create a longitudinal care experience for older adults across healthcare settings. The second abstract describes the creation of a new race/ethnicity variable to augment the Medicare administrative data for minority populations using harmonized, self-reported race/ethnicity data. The third abstract describes the development of harmonized datasets to study geriatric syndromes’ prevalence, cost, and health outcomes over time. The fourth abstract identifies and predicts place of care trajectories during the last three years of life among Medicare beneficiaries, identifying dual trajectories of inpatient/institutional care and skilled home healthcare/home hospice. Finally, the fifth abstract includes an application of the care setting trajectory database and our new, harmonized, self-reported race/ethnicity variable to evaluate the impact of home health care on later use of hospice and place of death in a large cohort of Medicare decedents. This symposium introduces innovative approaches for harmonizing and merging CMS data sources for Medicare beneficiaries. The methodological advances described in this symposium aim to support health services researchers and policymakers to generate and evaluate research improving care coordination, continuity, and quality of life for older adults with serious illnesses.

Remaining useful life prediction based on a PCA and similarity methods

Aircraft engine failures or damages not only incur substantial financial losses but also present risks of injuries or even fatalities. Hence, it is of utmost importance to devise an effective method to predict potential failures in advance, thereby mitigating accidents and minimizing losses. This paper proposes a novel approach that combines a principal component analysis (PCA) with similarity methods to establish a degradation trajectory database and predict the remaining useful life (RUL) of new engines by identifying similar trajectories. Firstly, the data dimensionality is reduced using a PCA to create a health indicator. The validity of the reduced data is confirmed by calculating the Spearman correlation coefficient between the health indicator and the system RUL. During the similarity comparison process, the Manhattan distance is employed for the similarity calculation, and parameter optimization is performed on the length of selected time segments and the number of chosen similar trajectories to optimize the similarity of RUL prediction model, resulting in the optimal prediction results among all engine test sets. Notably, this paper introduces the feasibility of employing the Manhattan distance in similarity method-based prediction, which is superior to the commonly used Euclidean distance calculation method found in most literature. This finding offers innovative ideas and perspectives for advancing RUL prediction methodologies. By adopting the proposed approach, the occurrence of accidents and losses associated with aircraft engine failures can be substantially reduced, leading to enhanced safety and economic benefits.

A High-Precision Car-Following Model with Automatic Parameter Optimization and Cross-Dataset Adaptability

Despite the significant impact of network hyperparameters on deep learning car-following models, there has been relatively little research on network hyperparameters of deep learning car-following models. Therefore, this study proposes a car-following model that combines particle swarm optimization (PSO) and gated recurrent unit (GRU) networks. The PSO-GRU car-following model is trained and tested using data from the natural driving database. The results demonstrate that compared to the intelligent driver model (IDM) and the GRU car-following model, the PSO-GRU car-following model reduces the mean squared error (MSE) for the speed simulation of following vehicles by 88.36% and 72.92%, respectively, and reduces the mean absolute percentage error (MAPE) by 64.81% and 50.14%, respectively, indicating a higher prediction accuracy. Dataset 3 from the drone video trajectory database of Southeast University and NGSIM’s I-80 dataset are used to study the car-following model’s cross-dataset adaptability, that is, to verify its transferability. Compared to the GRU car-following model, the PSO-GRU car-following model reduces the standard deviation of the test results by 60.64% and 32.89%, highlighting its more robust prediction stability and better transferability. Verifying the ability of the car-following model to produce the stop-and-go phenomenon can evaluate its transferability more comprehensively. The PSO-GRU car-following model outperforms the GRU car-following model in creating stop-and-go sensations through platoon simulation tests, demonstrating its superior transferability. Therefore, the proposed PSO-GRU car-following model has higher prediction accuracy and cross-dataset adaptability compared to other car-following models.

A repository of COVID-19 related molecular dynamics simulations and utilisation in the context of nsp10-nsp16 antivirals

The coronavirus disease 2019 (COVID-19) pandemic highlighted the importance of establishing systems and infrastructure to develop vaccines, antiviral drugs, and therapeutic antibodies against emerging pathogens. Typical drug discovery processes involve targeting suitable proteins to effect pathogen replication or to attenuate host responses, by examining either large chemical databases or protein-protein interactions. Following initial screens, molecular dynamics (MD) simulations are critical for gaining further insight into molecular interactions. During the COVID-19 pandemic, many research groups made their simulations widely available, as highlighted by the comprehensive D.E. Shaw Research trajectory database. To investigate protein target sites and evaluate potential lead compounds, we performed over 300 MD simulations relating to COVID-19. We organised our simulations into a repository, which is publicly available at https://epimedlab.org/trajectories/. The trajectories cover a large part of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) proteome, and the majority of our MD simulations focused on the identification of potential antivirals. For example, we focused on the S-adenosyl-l-methionine binding site of the nsp10-nsp16 complex, a critical component of viral replication, revealing verbascoside as a potential lead. Moreover, we utilised MD trajectories to explore the interface between the spike protein receptor binding domain and human angiotensin-converting enzyme 2 receptor, with the ultimate aim being investigation of new variants in real-time. Overall, MD simulations are a critical component of the in silico drug discovery process and as highlighted throughout the pandemic, data sharing enables accelerated progress. We have organised our extensive collection of COVID-19 related MD trajectories into an easily accessible repository.

Uncertainty analysis and interval prediction of LEDs lifetimes

Lifetime analyses are crucial for ensuring the durability of new Light-emitting Diodes (LEDs) and uncertainty quantification (UQ) is necessary to quantify a lack of usable failure and degradation data. This work presents a new framework for predicting the lifetime of LEDs in terms of lumen maintenance, effectively quantifying the natural variability of lifetimes (aleatory) as well as the reducible uncertainty resulting from data scarcity (epistemic). Non-parametric survival models are employed for UQ of low-magnitude failures, while a new parametric interval prediction model (IPM) is introduced to characterize the uncertainty in high-magnitude lumen depreciation events and long-term extrapolated lifetimes. The width of interval-valued predictions reflects the inherent variability in degradation paths whilst the epistemic uncertainty, arising from data scarcity, is quantified by a statistical bound on the probability of the prediction errors for future degradation trajectories. A modified exponential flux decay model combined with the Arrhenius equation equips the IPM with physical information on the physics of LED luminous flux degradation. The framework is tested and validated on a novel database of LED degradation trajectories and in comparison to well-established probabilistic predictors. The results of this study support the validity of the proposed approach and the usefulness of the additional UQ capabilities.

A privacy-preserving trajectory data synthesis framework based on differential privacy

Trajectory data contains spatio-temporal information about a large number of moving objects, and analysis of this data can yield a lot of valuable information to support a variety of application requirements related to moving objects. However, publishing unprotected trajectory data carries the risk of revealing personal privacy. In the existing approaches, the utility of published trajectory data is sacrificed for privacy protection. In this paper, we propose a differential privacy mechanism DP-Std for data publication in synthetic trajectory databases. first, the DP-Std mechanism constructs a density- and distance-aware adaptive grid structure for discrete geographic regions, and adds noise in the process to satisfy differential privacy. Second, DP-Std extracts feature distributions to ensure the utility of synthetic trajectories. Third, DP-Std constructs a mobility model that samples intermediate location points. And then the trajectory dataset is synthesized by differential privacy distribution and mobility model. Finally, we conduct experiments on two datasets to evaluate our framework. The results show that DP-Std outperforms other feature-based trajectory synthesis methods in terms of data utility and achieves a trade-off between privacy and utility with strict privacy protection.

Efficient and scalable DBSCAN framework for clustering continuous trajectories in road networks

Clustering the trajectories of vehicles moving on road networks is a key data mining technique for understanding human mobility patterns, as well as their interactions with urban environments. The development of efficient and scalable trajectory clustering algorithms, however, still faces challenges because of the computational costs when measuring similarities among a large number of network-constrained trajectories. To address this problem, a novel trajectory clustering framework based on the well-developed Density-Based Spatial Clustering of Applications with Noise (DBSCAN) approach is proposed. This proposed framework accurately quantifies similarities using a trajectory representation of continuous polylines in the space and time dimensions, and does not require trajectory discretization. Further, the proposed framework utilizes the space-time buffering concept to formulate ε -neighborhood queries that directly retrieve the ε -neighbors of trajectories and thus avoids computing a trajectory similarity matrix. State-of-the-art trajectory databases and index structures are incorporated to further improve trajectory clustering performance. A comprehensive case study was carried out using an open dataset of 20,161 trajectories. Results show that the proposed framework efficiently executed trajectory clustering on the large test dataset within 3 min. This was approximately 2,700 times faster than existing DBSCAN algorithms.

Lane-changing trajectory control strategy on fuel consumption in an iterative learning framework

A novel lane-changing trajectory control strategy in an iterative learning framework is proposed and its impact on fuel consumption of the off-ramp traffic system is analyzed in this paper. The iterative learning framework includes three layers. In the calibration layer, the lane-changing decision process is modelled by probabilistic neural network while a back-propagation neural network is designed to imitate the lane-changing trajectory. Moreover, we use a cost function and numerical simulation in the optimization layer to optimize the trajectory database and calibrate the proposed framework, respectively. In the application layer, simulation experiments are conducted to examine the fuel consumption of individuals and systems. The results indicate that connected automated vehicles (CAVs) can dissipate quickly after congestion is formed and complete lane-change and avoidance behavior in a more stable state after iterative learning. CAVs can reduce fuel consumption by 35% compared with human-driven vehicles.

Geometric Analysis of Sun-Assisted Lunar Transfer Trajectories in the Planar Bicircular Four-Body Model

This research presents a geometric analysis of Sun-assisted low-energy lunar transfers and several convenient tools that enable the systematic trajectory design in the framework of the planar bicircular restricted four-body problem. By analogy with the patched conic approximation approach for high-energy transfers, a Sun-assisted low-energy trajectory is divided into three legs. Two interior legs, departing and arriving, are located inside the Earth–Moon region of prevalence and designed in the Earth–Moon circular restricted three-body problem, whereas the exterior leg lies outside the region of prevalence and is calculated in the Earth–Moon–Sun bicircular restricted four-body model. The whole trajectory is obtained by smoothly patching the three legs on the boundary of the region of prevalence. The arrival conditions are met by targeting a specific point in the L2 lunar gateway. The interior legs are easily adjustable to the four-body dynamics. The database of planar lunar transfer trajectories can be used to select an initial guess for the multiple-shooting procedure of designing a three-dimensional Sun-assisted lunar transfer in high-fidelity dynamical models.

A Novel Fault-Tolerant Air Traffic Management Methodology Using Autoencoder and P2P Blockchain Consensus Protocol

This paper presents a methodology for designing a highly reliable Air Traffic Management and Control (ATMC) methodology using Neural Networks and Peer-to-Peer (P2P) blockchain. A novel data-driven algorithm was designed for Aircraft Trajectory Prediction (ATP) based on an Autoencoder architecture. The Autoencoder was considered in this study due to its excellent fault-tolerant ability when the input data provided by the GPS is deficient. After conflict detection, P2P blockchain was used for securely decentralized decision-making. A meta-controller composed of this Autoencoder, and P2P blockchain performed the ATMC task very well. A comprehensive database of trajectories constructed using our UAS-S4 Ehécatl was used for algorithms validation. The accuracy of the ATP was evaluated for a variety of data failures, and the high-performance index confirmed the excellent efficiency of the autoencoder. Aircraft were considered in several local encounter scenarios, and their trajectories were securely managed and controlled using our in-house Smart Contract software developed on the Ethereum platform. The Sharding approach improved the P2P blockchain performance in terms of computational complexity and processing time in real-time operations. Therefore, the probability of conflicts among aircraft in a swarm environment was significantly reduced using our new methodology and algorithm.

SoK: Differentially Private Publication of Trajectory Data

Trajectory analysis holds many promises, from improvements in traffic management to routing advice or infrastructure development. However, learning users' paths is extremely privacy-invasive. Therefore, there is a necessity to protect trajectories such that we preserve the global properties, useful for analysis, while specific and private information of individuals remains inaccessible. Trajectories, however, are difficult to protect, since they are sequential, highly dimensional, correlated, bound to geophysical restrictions, and easily mapped to semantic points of interest. This paper aims to establish a systematic framework on protective masking and synthetic-generation measures for trajectory databases with syntactic and differentially private (DP) guarantees, including also utility properties, derived from ideas and limitations of existing proposals. To reach this goal, we systematize the utility metrics used throughout the literature, deeply analyze the DP granularity notions, explore and elaborate on the state of the art on privacy-enhancing mechanisms and their problems, and expose the main limitations of DP notions in the context of trajectories.

Privacy-Preserving Public Route Planning Based on Passenger Capacity

Precise route planning needs huge amounts of trajectory data recorded in multimedia devices. The data, including each user’s location privacy, are stored as cipher text. The ability to plan routes on an encrypted trajectory database is an urgent necessity. In this paper, in order to plan a public route while protecting privacy, we design a hybrid encrypted random bloom filter (RBF) tree on encrypted databases, named the encrypted random bloom filter (eRBF) tree, which supports pruning and a secure, fast k nearest neighbor search. Based on the encrypted random bloom filter tree and secure computation of distance, we first propose a reverse k nearest neighbor trajectory search on encrypted databases (RkNNToE). It returns all transitions, in which each takes the query trajectory as one of its k nearest neighbor trajectories on the encrypted database. The results can be the indicator of a new route’s capacity in route planning. The security of the trajectory and query is proven via the simulation proof technique. When the number of points in the trajectory database and transition database are 1174 and 18,670, respectively, the time cost of an R2NNToE is about 1200 s.

A deep learning-based approach to time-coordination entry guidance for multiple hypersonic vehicles

AbstractA multiple-vehicles time-coordination guidance technique based on deep learning is suggested to address the cooperative guiding problem of hypersonic gliding vehicle entry phase. A dual-parameter bank angle profile is used in longitudinal guiding to meet the requirements of time coordination. A vehicle trajectory database is constructed along with a deep neural network (DNN) structure devised to fulfill the error criteria, and a trained network is used to replace the conventional prediction approach. Moreover, an extended Kalman filter is constructed to detect changes in aerodynamic parameters in real time, and the aerodynamic parameters are fed into a DNN. The lateral guiding employs a logic for reversing the sign of bank angle, which is based on the segmented heading angle error corridor. The final simulation results demonstrate that the built DNN is capable of addressing the cooperative guiding requirements. The algorithm is highly accurate in terms of guiding, has a fast response time, and does not need inter-munition communication, and it is capable of solving guidance orders that satisfy flight requirements even when aerodynamic parameter disruptions occur.