A PROCESS-ORIENTED SPATIOTEMPORAL CLUSTERING METHOD FOR COMPLEX TRAJECTORIES

Improved partitioning technique for density cube-based spatio-temporal clustering method

Trajectory clustering has played an essential role in trajectory mining tasks. It serves in a wide range of real-life applications, including transportation, location-based services, behavioral study, and so on. To support trajectory clustering analytics, a plethora of trajectory clustering methods have been proposed, which mainly extend traditional clustering algorithms by using spatio-temporal characteristics of trajectories. However, existing traditional trajectory clustering approaches based on raw trajectory representation highly rely on hand-craft similarity metrics, and can not capture hidden spatial dependencies in trajectory data, which is inefficient and inflexible for clustering analysis. To this end, we propose an end-to-end deep trajectory clustering framework via self-training, termed as E2DTC, inspired by the data-driven capabilities of deep neural networks. E2DTC does not require any additional manual feature extraction operations, and can be easily adapted for trajectory clustering analytics on any trajectory dataset. Extensive experimental evaluations on three real-life datasets show that our framework E2DTC achieves superior accuracy and efficiency, compared with classical clustering methods (i.e., K-Medoids) and state-of-the-art neural-network based approaches (i.e., t2vec).

This paper proposes a method for clustering asynchronous events generated upon scene activities by a dynamic 3D vision system. The inherent detection of moving objects offered by the dynamic stereo vision system comprising a pair of dynamic vision sensors allows event-based stereo vision in real-time and a 3D representation of moving objects. The clustering method exploits the sparse spatio-temporal representation of sensor's events for real-time detection and separation between moving objects. The method makes use of density and distance metrics for clustering asynchronous events generated by scene dynamics (changes in the scene). It has been evaluated on clustering the events of moving persons across the sensor field of view. Tests on real scenarios with more than 100 persons show that the resulting asynchronous events can be successfully clustered and the persons can be detected.

With the development of wireless network technology and the increasingly widespread application of mobile intelligent terminals, a large amount of wireless network data has been generated in smart campuses. By analyzing the trajectory data through clustering algorithms, more valuable user data information can be obtained. However, current clustering algorithms have high computational difficulty, long running time, and poor clustering accuracy for complex trajectories. Based on this background, the study proposes a spatial clustering of Application with Noise by Density-Based Spatial Clustering of Application with Noise combining Hausdorff and Frechet. The method is denoted as (Hausdorff and Frechet-Density Based Spatial Clustering of Application with Noise, HF-DBSCAN). DBSCAN denotes the Density Based Spatial Clustering of Application with Noise. In the experimental results, compared to the AH-DBSCAN, DBSCAN, and FD-DBSCAN algorithms, the running time of HF-DBSCAN algorithm in complex trajectory cluster analysis is reduced by 59.33%, 39.82%, and 35.12%, respectively, and the contour coefficient is closer to 1; the Davies-Boldin Index (DBI) values decreased by 59.24%, 64.42%, and 68.24%, respectively. The experiment shows that the optimized HF-DBSCAN algorithm has lower computational difficulty, better clustering performance, and higher clustering accuracy, which verifies the effectiveness of this study.

Trajectory association and clustering are two key problems in motion analysis. While association links the points of interest to form trajectories, clustering discovers motion patterns of these trajectories and group them into clusters. Despite mutually related, the two problems have been typically studied separately in the literature. In this letter, we formulate them as a unified optimization problem and take the advantage of high-order information to capture the interrelations for Motion Segmentation by Trajectory Association and Clustering (MSTAC). To solve this unified problem, we propose an alternating optimization strategy to improve the association and clustering in each iteration. Specifically, a tensor-based multidimensional assignment method with high-order motion context information is proposed for trajectory association; and a minimum cost multicut-based trajectory clustering method is introduced for trajectory clustering. While the association process provides incomplete trajectories to clustering, the clustering method presents high-order context information to improve the performance of association; and thus they benefit from each other. Experiments on the Hopkins 155 dataset and a realistic airport sequence demonstrate that the proposed MSTAC framework obtains high accuracy on both trajectory association and clustering.

In a cloud environment, resources should be acquired quickly and automatically released at runtime. Traditional trajectory data partitions, indexing and query processing techniques are extended, so that they can take advantage of the cloud of large clusters of highly parallel processing capabilities. There are ways with trajectory data in the cloud database query processing. The advanced sensing techniques available today caused the existence of many types of trajectory datasets whose study can form a strong basis for decision making regarding a data context. Trajectory data generally includes trajectory classification, trajectory clustering, and trajectory associations and so on. All these tools need trajectory similarity measures to get comparisons in trajectory data. Some of the popular existing methods of trajectory similarity include Euclidean distance, semantic distance and there near variants. Each variant of these techniques exhibits notable differences in measurement of similarity and computational difficulty. This paper targeted to deal with this situation with the goal of lower computational efforts in trajectory similarity computations. A two‐phase trajectory similarity measure is defined. The first phase does point level analysis of trajectory points representing all trajectories and the following phase use the analysis to find the similarities. Using a hybrid similarity framework, new means for trajectory clustering and trajectory query processing are developed. From trajectory analysis followed by trajectory clustering, evaluation of cluster quality and processing of trajectory query are undertaken in this paper in different ways from the existing methods. All the proposed developments are tested with trajectory datasets with multiple attributes and the results are more favour to proposed framework than the existing ones.

the traditional anomaly detecting methods for power equipment do not consider the spatial information of the state data. This paper proposed a method for anomaly detection of state data of power equipment based on spatiotemporal clustering method, which considers historical big data and visualizes the structures revealed within data. Using a sliding window, the time series are divided into a number of subsequences. The available spatiotemporal structure within each time window is discovered using the FCM method. In the sequel, an anomaly score is assigned to each cluster. By using a fuzzy relation formed between revealed structures, a propagation of anomalies occurring in consecutive time intervals is visualized. At last, the effectiveness of the method is verified by an icing example.

Exploring urban travel patterns can analyze the mobility regularity of residents to provide guidance for urban traffic planning and emergency decision. Clustering methods have been widely applied to explore the hidden information from large-scale trajectory data on travel patterns exploring. How to implement soft constraints in the clustering method and evaluate the effectiveness quantitatively is still a challenge. In this study, we propose an improved trajectory clustering method based on fuzzy density-based spatial clustering of applications with noise (TC-FDBSCAN) to conduct classification on trajectory data. Firstly, we define the trajectory distance which considers the influence of different attributes and determines the corresponding weight coefficients to measure the similarity among trajectories. Secondly, membership degrees and membership functions are designed in the fuzzy clustering method as the extension of the classical DBSCAN method. Finally, trajectory data collected in Shenzhen city, China, are divided into two types (workdays and weekends) and then implemented in the experiment to explore different travel patterns. Moreover, three indices including Silhouette Coefficient, Davies–Bouldin index, and Calinski–Harabasz index are used to evaluate the effectiveness among the proposed method and other traditional clustering methods. The results also demonstrate the advantage of the proposed method.

Applying Spatio-temporal Scan Statistics and Spatial Autocorrelation Statistics to identify Covid-19 clusters in the world - A Vaccination Strategy?

Oceanic trajectories frequently exhibit multiple periodic patterns across various time intervals, e.g., tidal variations, mesoscale eddies, and El Niño events correspond to diurnal, seasonal, and interannual fluctuations in environmental factors. To explore hidden spatiotemporal multiple periodic behaviors in noisy ocean data, we propose a novel trajectory clustering method, namely DTID-STFC. It first identifies dense time intervals (DTIs) in which trajectories occur frequently. Subsequently, within each DTI, it utilizes spectral embedding to project trajectories onto a latent subspace and proposes three-way fuzzy clustering to obtain results. We evaluate the proposed method on simulated datasets and compare it with traditional and state-of-the-art trajectory clustering approaches. Experimental results indicate that it outperforms other methods across all five metrics. Moreover, when applying the DTID-STFC method to the analysis of mesoscale cyclonic eddies in the South China Sea and vessel data, it demonstrates more discernible results than traditional methods, and it aligns well with physical oceanographic processes. This proposed method offers valuable insights into identifying periodic behaviors from complex and noisy spatiotemporal oceanic trajectory data.

Analyzing trajectory data plays an important role in practical applications, and clustering is one of the most widely used techniques for this task. The clustering approach based on information bottleneck (IB) principle has shown its effectiveness for trajectory data, in which a predefined number of the clusters and an explicit distance measure between trajectories are not required. However, presenting directly the final results of IB clustering gives no clear idea of both trajectory data and clustering process. Visual analytics actually provides a powerful methodology to address this issue. In this paper, we present an interactive visual analytics prototype called IBVis to supply an expressive investigation of IB-based trajectory clustering. IBVis provides various views to graphically present the key components of IB and the current clustering results. Rich user interactions drive different views work together, so as to monitor and steer the clustering procedure and to refine the results. In this way, insights on how to make better use of IB for different featured trajectory data can be gained for users, leading to better analyzing and understanding trajectory data. The applicability of IBVis has been evidenced in usage scenarios. In addition, the conducted user study shows IBVis is well designed and helpful for users.

Traffic signal performance measures based on connected vehicle (CV) trajectory data can provide practitioners actionable information on the operational state of their systems. A visualization framework that can enable agencies to quickly assess arterial-level performance by time-of-day (TOD) to identify challenges and opportunities has been recently developed. The framework provides information on four relevant signal performance measures over a corridor, including traditional Highway Capacity Manual (HCM) level of service (LOS), arrivals on green (AOG), split failures (SF), and downstream blockage (DSB). This document uses the proposed framework to provide 58 arterial-level performance reports analyzing 571 unique signalized intersections on 42 corridors in 14 different states. Results are estimated from over 18,000,000 vehicle trajectories and 328,000,000 GPS points. Since the reporting approach can provide up to 3,072 performance data points per intersection, this manuscript provides almost 2 million measures for all the analyzed locations.

Tremendous growth of Location-based technologies resulted in the generation of a huge volume of spatial data, which needs to be analyzed to get potentially important patterns. The spatial patterns extracted can be used to design a better infrastructure ensuring reliable service coverage. Trajectory data is one variant of spatial data that are generated by moving objects travelling across. It is represented as a sequence of spatial coordinates (latitude, longitude) of a location. Trajectory clustering tries to group similar spatial data points to extract the most common movement behaviors. Trajectory data poses major challenges including uncertainty, sampling rate, representation, relationships, spatial autocorrelation, serialization, redundancy, and triviality, which makes it hard to apply traditional clustering algorithms over trajectory data. In this paper, K-Means and DBSCAN (Density-based spatial clustering of applications with noise) clustering algorithms are analyzed using different similarity measures like Euclidean, Hausdorff and Haversine distances with the help of index measures say Adjusted Rand Index (ARI) and Fowlkes-Mallows scores (FMS). Experiment is carried out over two different trajectory datasets and it is proved that usage of Haversine distance for clustering is efficient than Euclidean and Hausdorff distances in terms of spatial trajectory data.

Trajectory Data have been considered as a treasure for various hidden patterns which provide deeper understanding of the underlying moving objects. Several studies are focused to extract repetitive, frequent and group patterns. Conventional algorithms defined for Sequential Patterns Mining problems are not directly applicable for trajectory data. Space Partitioning strategies were proposed to capture space proximity first and then time proximity to discover the knowledge in the data. Our proposal addresses time proximity first by identifying trajectories which meet at a minimum of [Formula: see text] time stamps in sequence. A novel tree structure is proposed to ease the process. Our method investigates space proximity using Mahalanobis distance (MD). We have used the Manhattan distance to form prior knowledge that helps the supervised learning-based MD to derive the clusters of trajectories along the true spreads of the objects. With the help of minsup threshold, clusters of frequent trajectories are found and then in sequence they form [Formula: see text] length Sequential Patterns. Illustrative examples are provided to compare the MD metric with Euclidean distance metric, Synthetic dataset is generated and results are presented considering the various parameters such as number of objects, minsup, [Formula: see text] value, number of hops in any trajectory and computational time. Experiments are done on available real-time dataset, taxi dataset, too. Sequential Patterns are proved to be worthy of knowledge to understand dynamics of the moving objects and to recommend the movements in constrained networks.

Capturing variability within flow is an important task for traffic flow models. The linearity of the congested part of the fundamental diagram induces a linear speed-spacing relationship at an individual level, characterized by two parameters. This study assumes that most intervehicle variability can be accounted for by estimating these two parameters for each vehicle. Two methods are presented to quantify individual linear speed-spacing relationships. The first method is based on data: it estimates the speed-spacing relationship by fitting the experimental speed-spacing scatter plot with a straight line. The second method is based on simulation: it computes the optimum parameters so that the simulated trajectories obtained by Newell's car-following algorithm reproduce as closely as possible the experimental vehicle's trajectories. Both proposed methods are implemented on the Next Generation Simulation trajectory data set recorded on I-80. The individual parameters for the speed-spacing relationship are quantified, and their distributions are specified. The need to distinguish driver behavior on a lane-by-lane basis is discussed. The results tend to prove that taking into account individual variability between drivers can improve the accuracy of simulated trajectories.