According to the World Health Organization, the total number of road traffic deaths worldwide is approximately 1.3 million per year.
Anomaly 2 speed driver#
Therefore, outlier detection is an important analysis task.Įvery day, thousands of people are victims of traffic accidents, which are generally directly related to driver behavior. Kingan and Westhuis presented a regression model approach for average daily traffic. adopted a Dirichlet process mixture model (DPMM) for detecting outliers in large-scale urban traffic data. In the surveillance application, vehicle trajectories can be used in automatic visual surveillance, traffic management, suspicious activity detection, sports video analysis, video summarization, synopsis generation, and video-to-text descriptors, among others. The detection results can help identify suspicious activities of vehicles and be used in many applications such as security surveillance, scheduling, and city planning.
An obvious rare pattern may indicate an abnormal event. An abnormal trajectory differs clearly from most trajectories scrutinized under a similarity evaluation mechanism. Īn abnormality generally implies that a data object is extremely deviant from the remaining set of retrieved data due to some of its unusual features. 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.
Big trajectory data feature analysis for mobile networks is a popular big data analysis task.
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