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Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Trajectory prediction for moving objects is a critical task for intelligent transportation with numerous applications, such as route planning, traffic management, congestion alleviation, etc. In this paper, we propose a novel framework that integrates sequence modeling, trajectory clustering and topology extraction to improve the accuracy of trajectory prediction. By incorporating self-attention for sequence modeling, we are able to effectively capture the temporal dependencies in trajectory data. Additionally, by taking into account the clustering information via a variational auto-encoder and the topological information based on a graphical neural network (GNN), we can further improve the accuracy of trajectory prediction. Furthermore, integrating a GNN facilitates our framework to handle diverse characteristics of road networks, such as road distance and traffic status, thereby making the proposed approach adaptive to different practical scenarios. As demonstrated by the experimental results on two publicly available datasets, our proposed method improves the accuracies by up to 0.5% and 3.8% for 1-step and 15-step predictions respectively, compared to the state-of-the-art method. [ABSTRACT FROM AUTHOR]

Recognizing common travel paths of crowds in a road network is valuable for understanding human mobility patterns and developing intelligent ride-sharing services. To achieve this, it is critical to measure the similarity of their trajectories. Although many measures have been proposed in the past decades, they often ignore movement consistency, exhibit one or more deficiencies in the face of noise and misaligned trajectories, or require extra parameters to tune predictions. In this paper, we propose an improved similarity measure called the directed segment path distance (DSPD), which considers the spatial proximity and movement consistency of trajectories. By integrating the spatial proximity distance and moving direction similarity between trajectories, the DSPD is a competitive parameter-free similarity measure that can effectively distinguish trajectories with different movement characteristics. To verify the effectiveness of the DSPD, we conducted a quantitative comparative study between the DSPD measure and 11 state-of-the-art trajectory similarity measures on six simulated trajectory datasets and applied the DSPD to two typical application scenarios: trajectory clustering for road network generation and retrieving common trajectories for ride-sharing path planning. The results demonstrate the effectiveness, robustness, and superiority of the DSPD and its great potential in trajectory search, clustering, and classification. [ABSTRACT FROM AUTHOR]

This study addresses the challenges of maritime traffic management in the western waters of Taiwan, a region characterized by substantial commercial shipping activity and ongoing environmental development. Using 2023 Automatic Identification System (AIS) data, this study develops a robust feature extraction framework involving data cleaning, anomaly trajectory point detection, trajectory compression, and advanced processing techniques. Dynamic Time Warping (DTW) and the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithms are applied to cluster the trajectory data, revealing 16 distinct maritime traffic patterns, key navigation routes, and intersections. The findings provide fresh perspectives on analyzing maritime traffic, identifying high-risk areas, and informing safety and spatial planning. In practical applications, the results help navigators optimize route planning, improve resource allocation for maritime authorities, and inform the development of infrastructure and navigational aids. Furthermore, these outcomes are essential for detecting abnormal ship behavior, and they highlight the potential of route extraction in maritime surveillance. [ABSTRACT FROM AUTHOR]

The benchmarking of CO2 emissions serves as the foundation for the accurate assessment of the environmental impact of air traffic. To calculate the environmental benchmarks of arrival traffic flows with Point Merge System (PMS) patterns, this study proposes a 4D trajectory optimization method that combines data-driven and optimal control models. First, the predominant arrival routes of traffic flows are identified using the trajectory spectral clustering method, which provides the horizontal reference for 4D trajectory optimization. Second, an optimal control model for vertical profiles with point merging topology is established, with the objective of minimizing the fuel–time cost. Finally, considering the complex structure of the PMS, a flexible and adaptable genetic algorithm-based vertical profile nonlinear optimization model is created. The experimental results demonstrate that the proposed method is adaptable to variations in aircraft type and cost index parameters, enabling the generation of different 4D trajectories. The results also indicate an environmental efficiency gap of approximately 10% between the actual CO2 emissions of the arrival traffic flow example and the obtained benchmark. With this benchmark trajectory generation methodology, the environmental performance of PMSs and associated arrival aircraft scheduling designs can be assessed on the basis of reliable data. [ABSTRACT FROM AUTHOR]

Ship collision avoidance has always been a concern and it is crucial for achieving safe navigation of ships at sea. There are many studies on ship collision avoidance in open water, but less attention on coastal waters considering the uncertainty of target ships due to the complexity of the environment and traffic flow. In this paper, collision avoidance decision-making research in coastal waters considering the uncertainty of target ships was proposed. Firstly, accurate ship trajectories are obtained by preprocessing the raw Automatic Identification System (AIS) data. Subsequently, the processed trajectories are clustered using the Ordering Points to Identify the Clustering Structure (OPTICS) algorithm and Hausdorff distance, acquiring a dataset for trajectory prediction of target ships. Then, a mixed Gaussian model is utilized to calculate the prior probability distribution of the prediction model, thus establishing a trajectory prediction model that considers the uncertainty of the target ship. Finally, ship maneuverability is simulated using the Mathematical Model Group (MMG) and Proportion Integration Differentiation (PID) models, and a collision avoidance decision-making model for ships is constructed. The proposed algorithm has been tested and verified in a case study. The results show that the approach effectively predicts the trajectory of the target ship and facilitates informed collision avoidance decision-making. [ABSTRACT FROM AUTHOR]

Vessel trajectory prediction is the key to maritime applications such as traffic surveillance, collision avoidance, anomaly detection, and so on. Making predictions more precisely requires a better understanding of the moving trend for a particular vessel since the movement is affected by multiple factors like marine environment, vessel type, and vessel behavior. In this paper, we propose a model named VesNet, based on the attentional seq2seq framework, to predict vessel future movement sequence by observing the current trajectory. Firstly, we extract the route patterns from the raw AIS data during preprocessing. Then, we design a multi-task learning structure to learn how to implement route pattern classification and vessel trajectory prediction simultaneously. By comparing with representative baseline models, we find that our VesNet has the best performance in terms of long-term prediction precision. Additionally, VesNet can recognize the route pattern by capturing the implicit moving characteristics. The experimental results prove that the proposed multi-task learning assists the vessel trajectory prediction mission. [ABSTRACT FROM AUTHOR]

The object of this study is a clustering algorithm using various technologies. This paper compares clustering algorithms that are more commonly used to analyze urban road network trajectories, the growth curve model, with the elbow method and the x-means algorithm. Experiments were conducted with various volumes of big data to determine calculation time, accuracy, and ways to increase calculation time. These results can be used to manage traffic jams in congested areas and city streets. Considering the widespread use of clustering algorithms for solving various problems, this study proposes to introduce GCM, SPGK methods for monitoring and analyzing the state of congestion on city roads. The work was carried out in the following steps: research and selection of methods based on efficiency and time, implementation of parallel computing technologies to improve computation speed, demonstration of the selected method based on collected data from a real city with visualization of the results. The growth curve model algorithm has been proven to be almost 5 times more effective than the elbow method and the x-means algorithm. The time allocated for data processing has been calculated. An increase in the volume of processed data showed an almost stable execution time t=3 s for the GCM algorithm for data with a volume of up to almost 2,000 units. The effectiveness of SPGK-means was shown for different values of the number of points. Models of the Chengdu transport network obtained using a clustering algorithm with maximum grid density of neighborhoods are presented. There are some deviations between the grid and the road network due to the large grid size. This error is explained by an error of up to one between the points and the real grid. The results obtained clearly show how optimization of congested roads can be influenced. They provide information to obtain data on available routes, which allows you to analyze the road network individually and as a whole. [ABSTRACT FROM AUTHOR]

The terminal airspace is considered the most complex area within the air traffic system, as it encompasses multiple nearby airports whose operations are interdependent, thereby increasing the complexity of management. A thorough understanding of airspace traffic patterns and operational characteristics is crucial for ensuring the safety and stability of air traffic. The paper proposes a data-driven analysis framework for traffic patterns in multi-airport terminal airspace. This framework utilizes machine learning methods applied to airport arrival and departure trajectory data to explore the operational characteristics within the terminal airspace. The framework comprises (i) a trajectory pattern identification module, which identifies trajectory patterns from a large volume of trajectories and analyzes the characteristics of these patterns, and (ii) a traffic flow pattern identification module, which utilizes the trajectory pattern to identify traffic flow patterns within the terminal airspace, thereby characterizing the operational structure of airspace traffic flows and the spatiotemporal dependency between trajectory patterns. This framework can analyze the operational characteristics of trajectory patterns, route intersections, and traffic flow patterns within the airport terminal area. It helps managers better understand aircraft behavior in the terminal area, identify risk locations at intersections in the airspace, and reveal typical traffic flow structures. This supports the optimization of terminal area airspace structure and provides decision-making tools. By analyzing the terminal airspace operations of two airports in Shanghai (ZSPD and ZSSS), the framework’s outcomes and capabilities are demonstrated. The study found that the airspace design of ZSPD is relatively complex, identifying multiple prevalent trajectory patterns. It also revealed that the airspace traffic flow structure exhibits certain temporal regularities, which aids in predicting the airspace’s operational structure and capacity, thereby facilitating informed decision-making.

Accurate vehicle counting at intersections is crucial for assessing traffic flow and gaining insights into vehicle trajectories captured by traffic cameras. This paper introduces an innovative framework that leverages a fisheye camera system to count vehicle movements at intersections with two significant contributions: First, the proposed algorithm employs a novel zone-based counting methodology to categorize and collect trajectory data and autonomously learn movement patterns with the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm at intersections. Second, as the algorithm becomes proficient in recognizing the paths traversed by vehicles, it seamlessly transitions into a hybrid mode, integrating both zone-based and path-based counting techniques. It enables accurate vehicle counting even in challenging scenarios involving broken tracks or partial trajectories. The self-learning capability of the proposed method enhances its flexibility and scalability, enabling it to adapt to diverse traffic patterns at different intersections without manual intervention. The performance of the proposed method is accessed by conducting experiments on three real-world fisheye camera footage datasets. The counting results underscore the efficacy of our innovative approach, showcasing an outstanding F1 score surpassing 98% across all evaluated intersections. This performance highlights its potential for real-world applications, including intelligent traffic signal control, urban planning, and emission estimations within traffic management frameworks.

The task of accurately forecasting the trajectory of a vessel, and in general a moving object operating in free space until its destination remains an open challenge. This paper addresses this problem by describing an unsupervised data-driven framework for short and extended horizon forecasts, from the perspectives of data mining and machine learning. We propose a data-driven algorithmic approach named “EnvClus*’ that models efficiently historical vessel trajectories at a global scale, forming a mobility graph that depicts the most likely movements among two ports. EnvClus* is able to make tailored route forecasts considering the characteristics of the vessels (i.e. length, draught) along with information regarding the executed trip. The proposed method is able to forecast the most likely realistic and smooth trajectory from a given query position of a vessel (entire route or underway forecasting) towards its destination port. We illustrate the accuracy and effectiveness of our method through a series of scenarios for long and short term forecasting using real world data from around the globe. These experiments indicate an overall improvement of 33% over state-of-the-art and baseline methods; with the benefits of our approach being more apparent when dealing with longer trips from container vessels.

This study addresses the challenges of maritime traffic management in the western waters of Taiwan, a region characterized by substantial commercial shipping activity and ongoing environmental development. Using 2023 Automatic Identification System (AIS) data, this study develops a robust feature extraction framework involving data cleaning, anomaly trajectory point detection, trajectory compression, and advanced processing techniques. Dynamic Time Warping (DTW) and the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithms are applied to cluster the trajectory data, revealing 16 distinct maritime traffic patterns, key navigation routes, and intersections. The findings provide fresh perspectives on analyzing maritime traffic, identifying high-risk areas, and informing safety and spatial planning. In practical applications, the results help navigators optimize route planning, improve resource allocation for maritime authorities, and inform the development of infrastructure and navigational aids. Furthermore, these outcomes are essential for detecting abnormal ship behavior, and they highlight the potential of route extraction in maritime surveillance.

Maternal depression and anxiety through pregnancy have lasting societal impacts. It is thus crucial to understand the trajectories of its progression from preconception to postnatal period, and the risk factors associated with it. Within the Bayesian framework, we propose to jointly model seven outcomes, of which two are physiological and five non-physiological indicators of maternal depression and anxiety over time. We model the former two by a Gaussian process and the latter by an autoregressive model, while imposing a multidimensional Dirichlet process prior on the subject-specific random effects to account for subject heterogeneity and induce clustering. The model allows for the inclusion of covariates through a regression term. Our findings reveal four distinct clusters of trajectories of the seven health outcomes, characterising women's mental health progression from before to after pregnancy. Importantly, our results caution against the loose use of hair corticosteroids as a biomarker, or even a causal factor, for pregnancy mental health progression. Additionally, the regression analysis reveals a range of preconception determinants and risk factors for depressive and anxiety symptoms during pregnancy. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The extraction of ship behavior patterns from Automatic Identification System (AIS) data and the subsequent prediction of travel routes play crucial roles in mitigating the risk of ship accidents. This study focuses on the Wuhan section of the dendritic river system in the middle reaches of the Yangtze River and the partial reticulated river system in the northern part of the Zhejiang Province as its primary investigation areas. Considering the structure and attributes of AIS data, we introduce a novel algorithm known as the Combination of DBSCAN and DTW (CDDTW) to identify regional navigation characteristics of ships. Subsequently, we develop a real-time ship trajectory prediction model (RSTPM) to facilitate real-time ship trajectory predictions. Experimental tests on two distinct types of river sections are conducted to assess the model's reliability. The results indicate that the RSTPM exhibits superior prediction accuracy when compared to conventional trajectory prediction models, achieving an approximate 20 m prediction accuracy for ship trajectories on inland waterways. This showcases the advancements made by this model. [ABSTRACT FROM AUTHOR]

The benchmarking of CO2 emissions serves as the foundation for the accurate assessment of the environmental impact of air traffic. To calculate the environmental benchmarks of arrival traffic flows with Point Merge System (PMS) patterns, this study proposes a 4D trajectory optimization method that combines data-driven and optimal control models. First, the predominant arrival routes of traffic flows are identified using the trajectory spectral clustering method, which provides the horizontal reference for 4D trajectory optimization. Second, an optimal control model for vertical profiles with point merging topology is established, with the objective of minimizing the fuel–time cost. Finally, considering the complex structure of the PMS, a flexible and adaptable genetic algorithm-based vertical profile nonlinear optimization model is created. The experimental results demonstrate that the proposed method is adaptable to variations in aircraft type and cost index parameters, enabling the generation of different 4D trajectories. The results also indicate an environmental efficiency gap of approximately 10% between the actual CO2 emissions of the arrival traffic flow example and the obtained benchmark. With this benchmark trajectory generation methodology, the environmental performance of PMSs and associated arrival aircraft scheduling designs can be assessed on the basis of reliable data.

This paper proposes a flight data-driven model to identify air traffic flow patterns based on multi-layer cluster analysis for identifying and characterizing traffic flow patterns in the airspace of the Beijing-Tianjin-Hebei multi-airport system. The spatial and temporal trends of flight data are mined to capture the air traffic flow characteristics, and the busyness of waypoints help air traffic managers to understand the reuse status of runways and airspace for capacity planning decision support in the complex airspace of Beijing-Tianjin-Hebei.

Background: The corona virus disease 2019 (COVID-19) pandemic has spread to more than 210 countries and regions around the world, with different characteristics recorded depending on the location. A systematic summarization of COVID-19 outbreaks that occurred during the “dynamic zero-COVID” policy period in Chinese mainland had not been previously conducted. In-depth mining of the big data from the past two years of the COVID-19 pandemics must be performed to clarify their epidemiological characteristics and dynamic transmissions. Methods: Trajectory clustering was used to group epidemic and time-varying reproduction number (Rt) curves of mass outbreaks into different models and reveal the epidemiological characteristics and dynamic transmissions of COVID-19. For the selected single-peak epidemic curves, we constructed a peak-point judgment model based on the dynamic slope and adopted a single-peak fitting model to identify the key time points and peak parameters. Finally, we developed an extreme gradient boosting-based prediction model for peak infection cases based on the total number of infections on the first 3, 5, and 7 days of the initial average incubation period. Results: (1) A total of 7 52298 cases, including 587 outbreaks in 251 cities in Chinese mainland between June 11, 2020, and June 29, 2022, were collected, and the first wave of COVID-19 outbreaks was excluded. Excluding the Shanghai outbreak in 2022, the 586 remaining outbreaks resulted in 1 25425 infections, with an infection rate of 4.21 per 1 00000 individuals. The number of outbreaks varied based on location, season, and temperature.(2) Trajectory clustering analysis showed that 77 epidemic curves were divided into four patterns, which were dominated by two single-peak clustering patterns (63.3%). A total of 77 Rt curves were grouped into seven patterns, with the leading patterns including four downward dynamic transmission patterns (74.03%). These curves revealed that the interval from peak to the point where the Rt value dropped below 1 was approximately 5 days.(3) The peak-point judgment model achieved a better result in the area under the curve (0.96, 95% confidence interval = 0.90–1.00). The single-peak fitting results on the epidemic curves indicated that the interval from the slow-growth point to the sharp-decline point was approximately 4–6 days in more than 50% of mass outbreaks.(4) The peak-infection-case prediction model exhibited the superior clustering results of epidemic and Rt curves compared with the findings without grouping. Conclusion: Overall, our findings suggest the variation in the infection rates during the “dynamic zero-COVID” policy period based on the geographic division, level of economic development, seasonal division, and temperature. Trajectory clustering can be a useful tool for discovering epidemiological characteristics and dynamic transmissions, judging peak points, and predicting peak infection cases using different patterns.

The analysis of maritime traffic patterns for safety and security purposes is increasing in importance and, hence, Vessel Traffic Service operators need efficient and contextualized tools for the detection of abnormal maritime behavior. Current models lack interpretability and contextualization of their predictions and are generally not quantitatively evaluated on a large annotated dataset comprising all expected traffic in a Region of Interest. We propose a model for the detection of abnormal maritime behaviors that provides the closest behaviors as context to the predictions. The normalcy model relies on two-step clustering, which is first computed based on the positions of the vessels and then refined based on their kinematics. We design for each step a similarity measure, which combined are able to distinguish boats cruising shipping lanes in different directions, but also vessels with more freedom, such as pilot boats. Our proposed abnormality detection model achieved, on a large annotated dataset extracted from AIS logs that we publish, an ROC-AUC of 0.79, which is on a par with State-of-the-Art deep neural networks, while being more computationally efficient and more interpretable, thanks to the contextualization offered by our two-step clustering. [ABSTRACT FROM AUTHOR]

Automatic identification system (AIS) data record a ship's position, speed over ground (SOG), course over ground (COG), and other behavioral attributes at specific time intervals during a ship's voyage. At present, there are few studies in the literature on ship trajectory classification, especially the clustering of trajectory segments, to measure the multi-dimensional information of trajectories. Therefore, it is necessary to fully utilize the multi-dimensional information from AIS data when utilizing ship trajectory classification methods. Here, we propose a ship trajectory classification method based on multi-attribute trajectory similarity metrics which utilizes the following steps: (1) Improve the Douglas–Peucker (DP) algorithm by considering the SOG and COG; (2) use a multi-attribute symmetric segmentation path distance (MSSPD) for the similarity metric between trajectories; (3) cluster the segmented sub-trajectories based on the density-based spatial clustering of applications with noise (DBSCAN) algorithm; (4) adaptively determinate the optimal input parameters based on the proposed comprehensive clustering performance metrics. The proposed method was tested on real AIS data from Bohai Sea waters, and the experimental results show that the algorithm can accurately cluster the ship trajectory groups and extract traffic distributions in key waters. [ABSTRACT FROM AUTHOR]

Location privacy preserving for location-based social networks (LBSNs) has been attracting a great deal of attention. Existing location privacy protection methods are disadvantaged by issues such as information leakage and low data availability, which are no longer suitable for the current diverse and personalized location-based services. To address these issues, we propose a differential privacy-based spatial-temporal trajectory clustering (DP-STTC) scheme, which mainly transforms the existing location privacy protection mechanism into a spatial-temporal trajectory protection mechanism by adjusting the privacy parameters. Then, the trajectories were clustered to uncover users with similar trajectory characteristics. Finally, experiments were conducted on two real datasets. The experimental results show that our DP-STTC scheme can not only achieve better accuracy in trajectory clustering, but also protect user privacy. [ABSTRACT FROM AUTHOR]

As a coastal area, Bohai Bay plays an important role in the economic development and urbanization of its neighboring cities. However, its water exchange capacity has been affected by the reclamation. This study uses Delft3D to contrast the water exchange processes before and after reclamation in Bohai Bay. To find out the change of water exchange in Bohai Bay after reclamation, TRACLUS, a trajectory clustering method, is used to obtain the representative trajectories for comparison in the water exchange processes. The influence from hydrodynamics is described by comparing the residual current distribution in the water exchange processes. The comparison of the representative trajectory distribution shows that the local water exchange around the new shoreline has been reduced. The change of residual current distribution shows a similar feature to the change of trajectory distribution after reclamation. This phenomenon means that water transport has been hindered by the inward shorelines built during the reclamation period, so the water exchange has been weakened in the coastal areas. [ABSTRACT FROM AUTHOR]

Aiming at the problem that traditional clustering algorithms cannot capture the implicit relationship of high-dimen-sional trajectory data in low-dimensional space,and it is difficult to define appropriate similarity measures to consider both local and global features of trajectories,a multivariate trajectory deep clustering(MTDC) framework based on deep neural network(DNN) is proposed and used for air traffic flow recognition and anomaly detection.The framework mainly includes an asymmetric autoencoder and a custom trajectory clustering layer.The autoencoder is mainly composed of 1D convolutional neural network and bi-directional long short-term memory to learn the feature representation of the original input in the low-dimensional latent space.The trajectory clustering layer realizes clustering by calculating the Q distribution of samples in the hidden space.Combined with reconstruction loss of autoencoder and trajectory clustering Q distribution,a new anomaly score is defined for anomaly trajectory detection.The results of experiments using real trajectory data based on automatic dependent surveillance-broadcast(ADS-B) show that the proposed framework is effective for air traffic flow recognition and can detect anomaly trajectories that are mea-ningful and interpretable.

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. [ABSTRACT FROM AUTHOR]

Maritime transportation is one of the major contributors to the development of the global economy. To ensure its safety and reduce the occurrence of a maritime accident, intelligent maritime monitoring and ship behavior identification have been drawing much attention from industry and academia, among which, the accurate prediction of ship trajectory is one of the key questions. This paper proposed a trajectory prediction model integrating the Convolutional LSTM (ConvLSTM) and Sequence to Sequence (Seq2Seq) models to facilitate simultaneous extraction of temporal and spatial features of ship trajectories, thereby enhancing the accuracy of prediction. Firstly, the trajectories are preprocessed using kinematic-based anomaly removal and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) to improve the data quality for the training process of trajectory prediction. Secondly, the ConvLSTM-based Seq2seq model is designed to extract temporal and spatial features of the ship trajectory and improve the performance of long-time prediction. Finally, by using real AIS data, the proposed model is compared with the Seq2Seq and Bidirectional LSTM based on attention mechanism (Bi-Attention-LSTM) models to verify its effectiveness. The experimental results demonstrate that the proposed model achieves excellent performance in predicting turning trajectories, good predictive accuracy on straight line motions, and greater improvement in prediction accuracy compared to the other two benchmark models. Overall, the proposed model represents a promising contribution to improving ship trajectory prediction accuracy and may enhance the safety and quality of ship navigation in complex and volatile marine environments. [ABSTRACT FROM AUTHOR]

In the context of the surveillance of the maritime traffic, a major challenge is the automatic identification of traffic flows from a set of observed trajectories, in order to derive good management measures or to detect abnormal or illegal behaviours for example. In this paper, we propose a new modelling framework to cluster sequences of a large amount of trajectories recorded at potentially irregular frequencies. The model is specified within a continuous time framework, being robust to irregular sampling in records and accounting for possible heterogeneous movement patterns within a single trajectory. It partitions a trajectory into sub-trajectories, or movement modes, allowing a clustering of both individuals' movement patterns and trajectories. The clustering is performed using non parametric Bayesian methods, namely the hierarchical Dirichlet process, and considers a stochastic variational inference to estimate the model's parameters, hence providing a scalable method in an easy-to-distribute framework. Performance is assessed on both simulated data and on our motivational large trajectory dataset from the automatic identification system, used to monitor the world maritime traffic: the clusters represent significant, atomic motion-patterns, making the model informative for stakeholders. [ABSTRACT FROM AUTHOR]

In view of the complicated information of wargaming system, which is not conducive for users to understand the combat situation, a trajectory clustering algorithm CTUW (clustering trajectories of units in wargame) based on space-time and combat grouping is proposed. The algorithm is divided into four parts: trajectory compression, similarity measurement, trajectory segments’ clustering and visualization. The main content can be extracted from the complicated track information and summarized, so as to achieve the purpose of concise overview of the overall situation changes of the military chess deduction without losing the important details in the process of chess maneuver.The experiment shows that the trajectory clustering effect of the CTUW algorithm is more refined than that of the TRACLUS algorithm and the CTECW algorithm, and the computational complexity is lower. It can maintain a good clustering effect even when dealing with special trajectory data with abnormal maneuvering speed, special trajectory shape and trajectories overlap.

Automatic Identification System (AIS) can obtain a large amount of data on ship trajectories and movement characteristics, which provides data support for ship route planning, navigation safety, and maritime traffic control. Therefore, it is of great importance to analyze and cluster the AIS data to obtain an understanding of the movement behavior of ships. The Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is capable of discovering arbitrarily shaped clusters and is suitable for clustering ship trajectories. However, the traditional DBSCAN algorithm suffers from the following shortcomings. First, the DBSCAN algorithm requires the input of a similarity matrix between the trajectories, and the similarity matrix obtained from the traditional Hausdorff distances does not identify the trajectory directions. Second, the DBSCAN algorithm is sensitive to the input parameters, and the clustering results obtained from different datasets with the same parameters vary widely. Finally, the DBSCAN algorithm is poor at clustering multi-density distribution datasets. To address these shortcomings, this study proposes a multi-density adaptive trajectory clustering (MDA-Traclus) algorithm, which considers the multi-density distribution trajectory dataset, adaptively determines the input parameters, and adds a trajectory direction identification mechanism to realize the clustering of trajectory clusters with different directions and different densities. Here, the classical DBSCAN algorithm, KANN-DBSCAN algorithm, and MDA-Traclus algorithm are compared and analyzed using four manually labeled trajectory datasets and a real trajectory dataset. The results show that the MDA-Traclus algorithm and KANN-DBSCAN algorithm are able to automatically determine the input parameters when clustering trajectory datasets with uniformly distributed densities and achieve the same clustering effect as the DBSCAN algorithm. When the trajectory density distribution is not uniform and the directions of the trajectory clusters are not consistent, the MDA-Traclus algorithm can effectively identify the low-density regions, differentiate the different directions of trajectories, and improve the clustering of the trajectories.

Abstract In the military and civilian surveillance domain, it is of great significance to mine regular behaviours of targets for situation awareness and command decision support. Most of the existing trajectory clustering algorithms only consider the similarity of spatial position of the trajectory, without sufficient multi‐dimensional information such as time, course and velocity. Some approaches based on information fusion take these multi‐dimensional information into account, but the features with different dimensions fused by weight coefficients are not robust and universal for different scenarios. In this paper, a regular behaviour mining method based on spatiotemporal trajectory multi‐dimensional features and density clustering is proposed. Firstly, multi‐dimensional Hausdorff similarity is defined to measure spatiotemporal trajectory from different feature dimensionalities. Different from methods based on information fusion, the proposed method defines trajectory density in feature similarity of different dimensions and adaptively determines parameters according to feature distribution in different dimensions. Experimental results in simulated and radar measured trajectory data show that the proposed method can be accurate and robust in clustering evaluation indexes such as Purity, Precision, Recall and Rand Index from different scenarios, which has a good application prospect in intelligent surveillance tasks.

Coherent motions depict the individuals' collective movements in widely existing moving crowds in physical, biological, and other systems. In recent years, similarity-based clustering algorithms, particularly the Coherent Filtering (CF) clustering approach, have accomplished wide-scale popularity and acceptance in the field of coherent motion detection. In this work, a tracklet-before-clustering initialization strategy is introduced to enhance coherent motion detection. Moreover, a Hierarchical Tracklet Association (HTA) algorithm is proposed to address the disconnected KLT tracklets problem of the input motion feature, thereby making proper trajectories repair to optimize the CF performance of the moving crowd clustering. The experimental results showed that the proposed method is effective and capable of extracting significant motion patterns taken from crowd scenes. Quantitative evaluation methods, such as Purity, Normalized Mutual Information Index (NMI), Rand Index (RI), and F-measure (Fm), were conducted on real-world data using a huge number of video clips. This work has established a key, initial step toward achieving rich pattern recognition. [ABSTRACT FROM AUTHOR]

The extraction of ship behavior patterns from Automatic Identification System (AIS) data and the subsequent prediction of travel routes play crucial roles in mitigating the risk of ship accidents. This study focuses on the Wuhan section of the dendritic river system in the middle reaches of the Yangtze River and the partial reticulated river system in the northern part of the Zhejiang Province as its primary investigation areas. Considering the structure and attributes of AIS data, we introduce a novel algorithm known as the Combination of DBSCAN and DTW (CDDTW) to identify regional navigation characteristics of ships. Subsequently, we develop a real-time ship trajectory prediction model (RSTPM) to facilitate real-time ship trajectory predictions. Experimental tests on two distinct types of river sections are conducted to assess the model’s reliability. The results indicate that the RSTPM exhibits superior prediction accuracy when compared to conventional trajectory prediction models, achieving an approximate 20 m prediction accuracy for ship trajectories on inland waterways. This showcases the advancements made by this model.

Aircraft departures often follow standardized and restrictive routes intended to guarantee a safe transition to the en-route network. Since the procedures must take the flight performance of many aircraft types into account, they represent a compromise between numerous optima and must be consistent with noise abatement strategies. This paper investigates the concept of departure funnels, in which flights can adopt their optimal profile within a procedural space based on actual flight performance to replace standard routes. For this, an algorithm based on DBSCAN identifies typical traffic flow funnels for a set of radar tracks as reference and individually optimized flight trajectories as preferred funnels. For the latter, an innovative 3D pathfinding grid is developed, which expands dynamically using the specific flight performance of the aircraft type and enables evaluation of operating costs due to wind and fuel consumption. From the clustered traffic flows, a funnel starting at the runway is determined based on the variance of the flight profiles along their mean trajectory. This funnel provides a restricted space for individual trajectory optimization for the day of operation. The procedure is applied using the example of Munich Airport, where the funnel size and the associated fuel-saving potential are determined. The results indicate an average fuel-saving potential of 0.4% with respect to the trip fuel.

Automatic identification systems (AIS) provides massive ship trajectory data for maritime traffic management, route planning, and other research. In order to explore the valuable ship traffic characteristics contained implicitly in massive AIS data, a ship trajectory clustering method based on ship trajectory resampling and enhanced BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) algorithm is proposed. The method has been tested using 764,393 AIS trajectory points of 13,845 ships in the waters of the Taiwan Strait of China, and 832 ship trajectories have been generated and clustered to obtain 172 classes of ship trajectory line clusters among 40 port pairs. The experimental results show that the proposed method has exhibited a good clustering effect on ship trajectories. Compared with the existing ship trajectory clustering methods, the proposed method can more efficiently detect and identify differences between trajectories with largely similar spatial distribution characteristics, so as to obtain legitimate clustering results. In addition, this study has constructed the main ship navigation routes between ports based on the extracted ship trajectory line clusters, and the constructed main routes are directional, refined, and rich in content compared with the existing ship routes. This research provides theoretical and technical support for ship route planning and maritime traffic management. [ABSTRACT FROM AUTHOR]

In the military and civilian surveillance domain, it is of great significance to mine regular behaviours of targets for situation awareness and command decision support. Most of the existing trajectory clustering algorithms only consider the similarity of spatial position of the trajectory, without sufficient multi‐dimensional information such as time, course and velocity. Some approaches based on information fusion take these multi‐dimensional information into account, but the features with different dimensions fused by weight coefficients are not robust and universal for different scenarios. In this paper, a regular behaviour mining method based on spatiotemporal trajectory multi‐dimensional features and density clustering is proposed. Firstly, multi‐dimensional Hausdorff similarity is defined to measure spatiotemporal trajectory from different feature dimensionalities. Different from methods based on information fusion, the proposed method defines trajectory density in feature similarity of different dimensions and adaptively determines parameters according to feature distribution in different dimensions. Experimental results in simulated and radar measured trajectory data show that the proposed method can be accurate and robust in clustering evaluation indexes such as Purity, Precision, Recall and Rand Index from different scenarios, which has a good application prospect in intelligent surveillance tasks. [ABSTRACT FROM AUTHOR]

With the widespread adoption of data intensive applications such as navigation systems for mobile devices and unmanned vehicles, analyzing trajectory data has become a key research area. One of the main tasks is trajectory clustering, which consists of automatically grouping similar trajectories into clusters. To perform this task, Density Peak Clustering (DPC) is widely used due to its speed and small number of artificial parameters. However, a major problem is that its performance does not scale well for large datasets. To address this issue, this paper proposes an efficient parallel trajectory clustering algorithm, named Tra-PDPC (Trajectory-Parallel DPC). It is applied in three steps, namely trajectory division and partition, trajectory similarity calculation, and clustering. Those steps are all designed to run in a distributed fashion using the Spark programming model. For the first step, a scheme is proposed to divide sub-trajectories based on local grid area density. Then, a combined similarity measurement method based on Euclidean space and grid space is defined for sub-trajectories similarity calculation. Finally, a version of DPC is applied, which dramatically improves clustering speed. Experiments on multiple large realistic trajectory datasets have demonstrated that the proposed Tra-PDPC algorithm can considerably decrease runtime while providing a high accuracy. [ABSTRACT FROM AUTHOR]

Semi-enclosed bays often have weak water exchange capacities, leading to frequent environmental pollution, particularly localized pollution. This study examines the local effects of changes in local factors on water circulation within Bohai Bay after land reclamation. To address the limitations of previous methods in measuring sub-regional water exchange, we introduce the concept of Local Average Influence Time (LAIT) to qualitatively and quantitatively analyze the impact of land reclamation on water exchange between sub-regions in semi-enclosed bays. Results indicate that land reclamation can enhance the self-purification capacity of sub-regions with significant shoreline changes in Bohai Bay, but this improvement is closely linked to dynamic factors such as wind, tide, and runoff. The degree of water exchange between sub-regions shows significant spatial heterogeneity, with land reclamation influencing the primary direction of water transport. This is largely due to the obstruction caused by newly constructed artificial headlands, making the neighbor area new high-risk zones for pollution. Wind can promote water circulation within the bay, but its effects are spatially heterogeneous and sensitive to shoreline topography changes. River discharge can enhance local water exchange but is weakened by obstruction from artificial headlands. Tide promotes water exchange between the bay mouth and inner bay areas, while their impact on sub-regional water exchange is also spatially heterogeneous and sensitive to changes in shoreline and topography. This study provides a quantitative method for assessing water exchange between regions and offers insights into the impact of land reclamation on water circulation within semi-enclosed bays. • Proposing LAIT to measure the water exchange between sub-regions in semi-enclosed bays • Using TRACLUS to describe the water exchange path effectively • Offering insights into the impact of reclamation on water circulation within semi-enclosed bays [ABSTRACT FROM AUTHOR]

Predicting vessel trajectories is crucial for enhancing situational awareness and preventing collisions at sea. However, achieving accurate and efficient predictions is challenging due to the heterogeneity in vessel movement patterns and changes in vessel mobility modes during voyages. To address this, we propose a new approach that uses historical AIS data to cluster route patterns for each vessel type, thereby improving prediction accuracy. By training machine learning algorithms to focus only on similar vessel types, this approach can better predict individual vessel mobility patterns. This approach offers computational advantages by using a relatively small set of trajectories from the nearest cluster of a selected vessel. Both spatial and course attributes are considered to determine the nearest cluster, while engineered features capture changes in vessel mobility modes. Using an AIS dataset from UTM Zone 10N (US West Coast), we achieved distance errors of 370 m , 742 m , and 1. 2 k m for horizons 10, 20, and 30 min, respectively, using the Random Forest algorithm for short-term trajectory prediction (≤ 30 min) with the last 1-hour trajectory of selected vessels as input. • Proposed framework for predicting the short-term future trajectory of vessels. • Enhanced prediction accuracy by addressing heterogeneity in mobility patterns. • Engineered features are incorporated to consider changes in mobility modes. • Spatial and course attributes are used for trajectory classification. • Evaluation shows high prediction accuracy with low computational cost. [ABSTRACT FROM AUTHOR]

The analysis of maritime traffic patterns for safety and security purposes is increasing in importance and, hence, Vessel Traffic Service operators need efficient and contextualized tools for the detection of abnormal maritime behavior. Current models lack interpretability and contextualization of their predictions and are generally not quantitatively evaluated on a large annotated dataset comprising all expected traffic in a Region of Interest. We propose a model for the detection of abnormal maritime behaviors that provides the closest behaviors as context to the predictions. The normalcy model relies on two-step clustering, which is first computed based on the positions of the vessels and then refined based on their kinematics. We design for each step a similarity measure, which combined are able to distinguish boats cruising shipping lanes in different directions, but also vessels with more freedom, such as pilot boats. Our proposed abnormality detection model achieved, on a large annotated dataset extracted from AIS logs that we publish, an ROC-AUC of 0.79, which is on a par with State-of-the-Art deep neural networks, while being more computationally efficient and more interpretable, thanks to the contextualization offered by our two-step clustering.

Automatic identification system (AIS) data record a ship’s position, speed over ground (SOG), course over ground (COG), and other behavioral attributes at specific time intervals during a ship’s voyage. At present, there are few studies in the literature on ship trajectory classification, especially the clustering of trajectory segments, to measure the multi-dimensional information of trajectories. Therefore, it is necessary to fully utilize the multi-dimensional information from AIS data when utilizing ship trajectory classification methods. Here, we propose a ship trajectory classification method based on multi-attribute trajectory similarity metrics which utilizes the following steps: (1) Improve the Douglas–Peucker (DP) algorithm by considering the SOG and COG; (2) use a multi-attribute symmetric segmentation path distance (MSSPD) for the similarity metric between trajectories; (3) cluster the segmented sub-trajectories based on the density-based spatial clustering of applications with noise (DBSCAN) algorithm; (4) adaptively determinate the optimal input parameters based on the proposed comprehensive clustering performance metrics. The proposed method was tested on real AIS data from Bohai Sea waters, and the experimental results show that the algorithm can accurately cluster the ship trajectory groups and extract traffic distributions in key waters.