Density-based trajectory outlier detection algorithm

With the development of global position system（GPS）,wireless technology and location aware services,it is possible to collect a large quantity of trajectory data.In the field of data mining for moving objects,the problem of anomaly detection is a hot topic.Based on the development of anomalous trajectory detection of moving objects,this paper introduces the classical trajectory outlier detection（TRAOD） algorithm,and then proposes a density-based trajectory outlier detection（DBTOD） algorithm,which compensates the disadvantages of the TRAOD algorithm that it is unable to detect anomalous defects when the trajectory is local and dense.The results of employing the proposed algorithm to Elk1993 and Deer1995 datasets are also presented,which show the effectiveness of the algorithm.

An Improved Underwater Confrontation Simulation Method of Naval Amphibious Operational Training System

This paper described an improved underwater confrontation simulation method of naval amphibious operational training system. The initial position of submarine forces on the enemy is generated automatically, and the attacking distance model of torpedoes is established based on the kinematics theory, which is more flexible and reasonable to judge the launch condition compared to traditional method. The two kinds of confrontation behavior models on the enemy submarine are created to depict its tactical action from the defensive to the offensive as well as the contrary, ensuring that operational style is simulated more comprehensively and properly. The existing motion trajectory estimation and collision detection algorithms on operational platforms are also improved to reduce the iteration error and further enhance the detection accuracy of target hit.

Road traffic anomaly monitoring and warning based on DeepWalk algorithm

In the complex urban road traffic network,a sudden accident leads to rapid congestion in the nearby traffic region,which even makes the local traffic network capacity quickly reduce.Therefore,an efficient monitoring system for abnormal conditions of the urban road network plays a crucial role in the tolerance of the urban road network.The traditional traffic monitoring system not only costs a lot in construction and maintenance,but also may not cover the road network comprehensively,which could not meet the basic needs of traffic management.Only a more comprehensive and intelligent monitoring method is able to identify traffic anomalies more effectively and quickly,so that it can provide more effective support for traffic management decisions.The extensive use of positioning equipment made us able to obtain accurate trajectory data.This paper presents a traffic anomaly monitoring and prediction method based on vehicle trajectory data.This model uses deep learning to detect abnormal trajectory on the traffic road network.The method effectively analyses the abnormal source and potential anomaly to judge the abnormal region,which provides an important reference for the traffic department to take effective traffic control measures.Finally,the paper uses Internet vehicle trajectory data from Chengdu(China)to test and obtains an accurate result.