Research On Trajectory Anomaly Detection In Urban Traffic Scene

With the advancement of modern technology and the vigorous development of location acquisition technologies,positioning equipment is becoming more and more popular.As a result,various industries have accumulated massive trajectory data,which contains timestamp,latitude and longitude coordinates,speed,and direction,etc.In real scenes,trajectory data is often collected in real time and increases continually in the form of large-scale and high-speed trajectory stream.Timely and effective anomaly detection of trajectory stream data can perceive the abnormal movement of objects,and then reveal the special behavior patterns hidden behind the movement.The main task of trajectory anomaly detection is to identify "minority" trajectories that have obvious differences from most other trajectories.It has great significance to the fields of security and road facility optimization that designing an efficient real-time anomaly detection method over trajectory streams.At present,there is less research work on trajectory anomaly detection based on urban traffic scenes,and further more on dynamic real-time anomaly detection over trajectory streams.Considering the different sampling frequency of urban vehicle’s locations and the impacts come from the road network,this article is dedicated to the dynamic real-time detection method of trajectory anomalies.The main research contents and contributions are summarized as follows:(1)Trajectory Anomaly Detection Method Based On Distance MeasurementIn the anomaly detection task of urban traffic scenes,the sampling time interval of the vehicle positioning equipment may be large,and the geometric characteristics of the trajectory are easily affected by the road network.This paper proposes a novel judgment criterion for the adjacent trajectory points and trajectory segments.The criterion defines the adjacent relations based on whether the trajectory points are "passed" by the trajectory segment,rather than simply based on the geometric distance between the point to the line segment.On this basis,this paper proposes a new definition of trajectory anomaly based on distance measurement.Then according to the inverted index mechanism,a new secondary index structure N-IID is constructed,which can reduce the search range from the entire map to some regions around the trajectory to be detected.Finally,this paper gives a specific algorithm for trajectory anomaly detection combined with the N-IID index,and conducts experiments on the real trajectory data sets.(2)Anomaly Detection Method Based On Light GBMThe trajectory data often contains a wealth of information,such as location,time,speed,direction,etc.,and sometimes also includes some environmental factors,such as weather,holidays,etc.This information usually reflects a certain behavior pattern of moving objects.Considering this situation,this paper proposes a novel trajectory feature construction method,and innovatively applies the Light GBM-based algorithm to trajectory anomaly detection,and finally proves its advantages through experiments on real trajectory data sets.(3)MOASM: The Framework For Real-time Monitoring Objects’ Outliers Over Trajectory StreamsWe usually have to deal with dynamic trajectory streams in most scenes of the real-world,rather than static trajectories lying in the database.Nowadays,with the popular of car-hailing and shared cars,passenger’s security is highly valued.For these reasons,this article combines the aforementioned anomaly detection technology and proposes an anomaly detection framework over trajectory streams,called MOASM.This framework can detect the trajectory anomalies of moving objects in real time,and has certain significance for application scenarios such as safety and security,road optimization,etc.