The Design and Development of a Ship Trajectory Data Management and Analysis System Based on AIS

To address the data storage, management, analysis, and mining of ship targets, the object-oriented method was employed to design the overall structure and functional modules of a ship trajectory data management and analysis system (STDMAS). This paper elaborates the detailed design and technical information of the system’s logical structure, module composition, physical deployment, and main functional modules such as database management, trajectory analysis, trajectory mining, and situation analysis. A ship identification method based on the motion features was put forward. With the method, ship trajectory was first partitioned into sub-trajectories in various behavioral patterns, and effective motion features were then extracted. Machine learning algorithms were utilized for training and testing to identify many types of ships. STDMAS implements such functions as database management, trajectory analysis, historical situation review, and ship identification and outlier detection based on trajectory classification. STDMAS can satisfy the practical needs for the data management, analysis, and mining of maritime targets because it is easy to apply, maintain, and expand.

A Deep Reinforcement Learning for Medical Trajectory Data Clustering Type 2 Diabetes

Deep Reinforcement learning is incorporated in trajectory data clustering to investigate the trajectories gathered from medical information’s. Generally Trajectory mining determines the patterns in data, detects anomalies, and does informative clustering, location prediction, and classification. The main intent of Medical trajectory data clustering is identifying the trajectories with identical patterns for better patient treatment outcomes. Medical trajectory data stored in a multidimensional format which is further processed using the machine learning and deep learning architectures. Machine learning approaches employed to mine trajectory data and identifying the future treatment is a complicated task. To deal with this, the deep learning approaches in trajectory mining concentrate to eliminate the computational complexity on type 2 diabetic’s data. To overcome this problem, deep reinforcement learning for medical trajectory data clustering approach is proposed that is a combination of various strategies to flexible adapt to changes of the trajectory data. After the proposed pre-processing and feature transformation, features are clustered on basis of the weights of the model with lesser efforts and the proposed clustering plays a key role in the process of multi-attribute trajectory data investigation. The proposed deep learning methodology is more suitable for clustering the multi-attribute trajectory with fewer complexity computations than existing machine learning based methods. The experimental results also states that the results of deep reinforcement learning are promising than the other approaches with respect to precision, Recall and F Measure respectively.

Applicability Evaluation of Several Spatial Clustering Methods in Spatiotemporal Data Mining of Floating Car Trajectory

Spatial analysis is an important means of mining floating car trajectory information, and clustering method and density analysis are common methods among them. The choice of the clustering method affects the accuracy and time efficiency of the analysis results. Therefore, clarifying the principles and characteristics of each method is the primary prerequisite for problem solving. Taking four representative spatial analysis methods—KMeans, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Clustering by Fast Search and Find of Density Peaks (CFSFDP), and Kernel Density Estimation (KDE)—as examples, combined with the hotspot spatiotemporal mining problem of taxi trajectory, through quantitative analysis and experimental verification, it is found that DBSCAN and KDE algorithms have strong hotspot discovery capabilities, but the heat regions’ shape of DBSCAN is found to be relatively more robust. DBSCAN and CFSFDP can achieve high spatial accuracy in calculating the entrance and exit position of a Point of Interest (POI). KDE and DBSCAN are more suitable for the classification of heat index. When the dataset scale is similar, KMeans has the highest operating efficiency, while CFSFDP and KDE are inferior. This paper resolves to a certain extent the lack of scientific basis for selecting spatial analysis methods in current research. The conclusions drawn in this paper can provide technical support and act as a reference for the selection of methods to solve the taxi trajectory mining problem.

Sunsetting skim matrices: A trajectory-mining approach to derive travel time skim matrix in dynamic traffic assignment for activity-base model integration

The travel impedance skim matrix is one of the most essential intermediate products within transportation forecasting models and is a fundamental input for activity-based transportation forecasting models. It reflects interzonal travel time, travel time reliability, travel costs, etc. by time of day. The traditional method to obtain skim matrices is to execute multiple times of time-dependent, shortest-path calculations. However, the computational and memory use burden can easily increase to an intractable level when dealing with mega-scale networks, such as those with thousands of traffic-analysis zones. This research proposes two new approaches to extract the interzonal travel impedance information from the already existing vehicle trajectory data. Vehicle trajectories store travel impedance information in a more compact format when compared to time-dependent link performance profiles. The numerical experiments highlight huge potential advantages of the proposed approaches in terms of saving both memory and CPU time.

DESIGN AND IMPLEMENTATION OF TRAJECTORY DATA MANAGEMENT AND ANALYSIS TECHNOLOGY FRAMEWORK BASED ON SPATIOTEMPORAL GRID MODEL

The trajectory data generated by various position-aware devices is widely used in various fields of society, but its conventional vector representation and various analysis algorithms based on it have high computational complexity. This makes it difficult to meet the application requirements of real-time or near real-time management and analysis of large-scale trajectory data. In view of the above challenges, this paper proposes a trajectory data management and analysis technology framework based on the Spatiotemporal Grid Model (STGM). First, the trajectory data is represented by spatiotemporal grid encoding instead of vector coordinates, and it can achieve dimensionality reduction and integrated management of high-dimensional heterogeneous trajectory data. Second, the trajectory computing and analysis methods based on STGM are introduced, which reduce the computing complexity of algorithms. Furthermore, various types of trajectory mining and applications are realized on the basis of high-performance computing technologies. Finally, a trajectory data management and analysis prototype system based on the STGM is developed, and experimental results verify the reliability and effectiveness of the proposed technology framework.