Analysis on such data to serve fields including intelligent transportation and smart cities has attracted the interest of a large number of researchers. The “big trajectory data” under the mobile networks have contributed to the emergence of many data-driven trajectory-based applications such as route recommendation, transit time estimation, traffic dynamic analysis, fraud detection, and city planning. A massive amount of vehicle trajectory data is collected by GPS-embedded vehicles. They have the characteristics of time and space, spatially static but temporally dynamic. The trajectory data for the mobile networks, which is a branch of big data, comprise a rich sequence of geospatial locations with timestamps and carry the information of the moving object’s actual movement. Introductionīig data analysis is the detection of massive data and a type of thinking process, technology, and resource. Furthermore, experiments show that the proposed algorithms perform better than the classical algorithm in terms of high accuracy and recall rate thus, the proposed methods can accurately detect drivers’ abnormal behavior. Using a real-life dataset, we demonstrate the effectiveness of our methods in detecting outliers. Anomaly detection, including sports behaviors, are (i) detour behavior detection using an algorithm for global router anomaly detection of trajectories having a pair of same starting and ending points this method is based on the isolation forest algorithm (ii) local speed anomaly detection based on the DBSCAN algorithm and (iii) local shape anomaly detection based on the local outlier factor algorithm. Then, we explore sports behaviors from the three types of features and build a taxi trajectory model for anomaly detection. Therefore, this study determines the peripheral features required for anomaly detection, including spatial location, sequence, and behavioral features. Our framework takes into account the fact that anomalous behavior manifests the overall shape of unusual locations and trajectories in the spatial domain as well as the way these locations appear. In this study, we propose a framework for urban trajectory modeling and anomaly detection. Some existing methods are not suitable for the detection of abnormal urban vehicle trajectories because they use the limited single detection techniques, such as determining the common patterns. Due to the large coverage and complexity of the mobile networks, it is difficult to define and detect anomalies in urban motion behavior. Finally, the intelligent operation and maintenance data service platform is designed.Big trajectory data feature analysis for mobile networks is a popular big data analysis task. The results show that the model has the strongest generalization ability with the accuracy of 0.987. And different algorithms are taken to find the optimal parameters. The support vector regression method is used to fit the complex functional relationship between phase point and predicted point.
#Anomaly 2 speed series#
Then, with historical data, a failure prediction model based on time series is established, where one-dimensional time series of failure rate are reconstructed to high-dimensional space.
Experimental results show that the classification accuracy is 0.981, which is better than the existing methods.
It is trained with sequence matrices and its learning performance under different parameters is tested to find the optimal model. First, with real-time operating data, an anomaly perception model based on long short-term memory network is established, where unstructured data are parsed into structured log keys and parameter vectors.
In order to improve the protection capability of trains, this paper proposes an online anomaly perception and failure prediction method. However, the existing operation and maintenance mode for ATP systems cannot diagnose fault in time. Automatic train protection (ATP) system is the key to ensure the safe operation of high-speed trains.
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