Identifying Anomaly Aircraft Trajectories in Terminal Areas Based on Deep Autoencoder and Its Application in Trajectory Clustering

Anomalous trajectory detection and traffic flow classification for complicated airspace are of vital importance to safety and efficiency analysis.Some researchers employed density-based unsupervised machine learning method to exploit these trajectories related to air traffic control(ATC)actions.However,the quality of position data and the tiny density difference between traffic flows in the terminal area make it particularly challenging.To alleviate these two challenges,this paper proposes a novel framework which combines robust deep auto-encoder(RDAE)model and density peak(DP)clustering algorithm.Specifically,the RDAE model is utilized to reconstruct denoising trajectory and identify anomaly trajectories in the terminal area by two different regularizations.Then,the nonlinear components captured by the encoder of RDAE are input in the DP algorithm to classify the global traffic flows.An experiment on a terminal airspace at Guangzhou Baiyun Airport(ZGGG)with anomaly label shows that the proposed combination can automatically capture non-conventional spatiotemporal traffic patterns in the aircraft movement.The superiority of RDAE and combination are also demonstrated by visualizing and quantitatively evaluating the experimental results.

Clustered trajectories anonymity in wireless sensor networks

This paper proposes a clustered trajectories anonymity scheme （CTA） that enhances the kano nymity scheme to provide the intended level of source location privacy in mobile event monitoring when a global attacker is assumed. CTA applies isomorphic property of rotation to create traces of the fake sources distributions which are similar to those of the real sources. Thus anonymity of each trajectory and that of the clustered is achieved. In addition, location kdiversity is achieved by dis tributing fake sources around the base station. To reduce the time delay, tree rooted at the base sta tion is constructed to overlap part of the beacon interval of the nodes in the hierarchy. Both the ana lytical analysis and the simulation results prove that proved energy overhead and time delay. our scheme provides perfect anonymity with improved energy overhead and time delay.

Optimal midcourse trajectory cluster generation and trajectory modification for hypersonic interceptions

The hypersonic interception in near space is a great challenge because of the target’s unpredictable trajectory, which demands the interceptors of trajectory cluster coverage of the predicted area and optimal trajectory modification capability aiming at the consistently updating predicted impact point(PIP) in the midcourse phase. A novel midcourse optimal trajectory cluster generation and trajectory modification algorithm is proposed based on the neighboring optimal control theory. Firstly, the midcourse trajectory optimization problem is introduced; the necessary conditions for the optimal control and the transversality constraints are given.Secondly, with the description of the neighboring optimal trajectory existence theory(NOTET), the neighboring optimal control(NOC)algorithm is derived by taking the second order partial derivations with the necessary conditions and transversality conditions. The revised terminal constraints are reversely integrated to the initial time and the perturbations of the co-states are further expressed with the states deviations and terminal constraints modifications.Thirdly, the simulations of two different scenarios are carried out and the results prove the effectiveness and optimality of the proposed method.

Trajectory clustering for arrival aircraft via new trajectory representation

Trajectory clustering can identify the flight patterns of the air traffic,which in turn contributes to the airspace planning,air traffic flow management,and flight time estimation.This paper presents a semantic-based trajectory clustering method for arrival aircraft via new proposed trajectory representation.The proposed method consists of four significant steps:representing the trajectories,grouping the trajectories based on the new representation,measuring the similarities between different trajectories through dynamic time warping(DTW)in each group,and clustering the trajectories based on k-means and densitybased spatial clustering of applications with noise(DBSCAN).We take the inbound trajectories toward Shanghai Pudong International Airport(ZSPD)to carry out the case studies.The corresponding results indicate that the proposed method could not only distinguish the particular flight patterns,but also improve the performance of flight time estimation.

Outlier Behavior Detection for Indoor Environment Based on t-SNE Clustering

In this study,we propose a low-cost system that can detect the space outlier utilization of residents in an indoor environment.We focus on the users’app usage to analyze unusual behavior,especially in indoor spaces.This is reflected in the behavioral analysis in that the frequency of using smartphones in personal spaces has recently increased.Our system facilitates autonomous data collection from mobile app logs and Google app servers and generates a high-dimensional dataset that can detect outlier behaviors.The density-based spatial clustering of applications with noise(DBSCAN)algorithm was applied for effective singular movement analysis.To analyze high-level mobile phone usage,the t-distributed stochastic neighbor embedding(t-SNE)algorithm was employed.These two clustering algorithms can effectively detect outlier behaviors in terms of movement and app usage in indoor spaces.The experimental results showed that our system enables effective spatial behavioral analysis at a low cost when applied to logs collected in actual living spaces.Moreover,large volumes of data required for outlier detection can be easily acquired.The system can automatically detect the unusual behavior of a user in an indoor space.In particular,this study aims to reflect the recent trend of the increasing use of smartphones in indoor spaces to the behavioral analysis.

Continuous Clustering Trajectory Stream of Moving Objects

The clustering of trajectories over huge volumes of streaming data has been rec- ognized as critical for many modem applica- tions. In this work, we propose a continuous clustering of trajectories of moving objects over high speed data streams, which updates online trajectory clusters on basis of incremental line- segment clustering. The proposed clustering algorithm obtains trajectory clusters efficiently and stores all closed trajectory clusters in a bi- tree index with efficient search capability. Next, we present two query processing methods by utilising three proposed pruning strategies to fast handle two continuous spatio-temporal queries, threshold-based trajectory clustering queries and threshold-based trajectory outlier detections. Finally, the comprehensive experi- mental studies demonstrate that our algorithm achieves excellent effectiveness and high effi- ciency for continuous clustering on both syn- thetic and real streaming data, and the propo- sed query processing methods utilise average 90% less time than the naive query methods.

A privacy-preserving vehicle trajectory clustering framework

As one of the essential tools for spatio‒temporal traffic data mining,vehicle trajectory clustering is widely used to mine the behavior patterns of vehicles.However,uploading original vehicle trajectory data to the server and clustering carry the risk of privacy leakage.Therefore,one of the current challenges is determining how to perform vehicle trajectory clustering while protecting user privacy.We propose a privacy-preserving vehicle trajectory clustering framework and construct a vehicle trajectory clustering model(IKV)based on the variational autoencoder(VAE)and an improved K-means algorithm.In the framework,the client calculates the hidden variables of the vehicle trajectory and uploads the variables to the server;the server uses the hidden variables for clustering analysis and delivers the analysis results to the client.The IKV’workflow is as follows:first,we train the VAE with historical vehicle trajectory data(when VAE’s decoder can approximate the original data,the encoder is deployed to the edge computing device);second,the edge device transmits the hidden variables to the server;finally,clustering is performed using improved K-means,which prevents the leakage of the vehicle trajectory.IKV is compared to numerous clustering methods on three datasets.In the nine performance comparison experiments,IKV achieves optimal or sub-optimal performance in six of the experiments.Furthermore,in the nine sensitivity analysis experiments,IKV not only demonstrates significant stability in seven experiments but also shows good robustness to hyperparameter variations.These results validate that the framework proposed in this paper is not only suitable for privacy-conscious production environments,such as carpooling tasks,but also adapts to clustering tasks of different magnitudes due to the low sensitivity to the number of cluster centers.

Online Real-Time Trajectory Analysis Based on Adaptive Time Interval Clustering Algorithm

With the development of Chinese international trade,real-time processing systems based on ship trajectory have been used to cluster trajectory in real-time,so that the hot zone information of a sea ship can be discovered in real-time.This technology has great research value for the future planning of maritime traffic.However,ship navigation characteristics cannot be found in real-time with a ship Automatic Identification System(AIS)positioning system,and the clustering effect based on the density grid fixed-time-interval algorithm cannot resolve the shortcomings of real-time clustering.This study proposes an adaptive time interval clustering algorithm based on density grid(called DAC-Stream).This algorithm can perform adaptive time-interval clustering according to the size of the real-time ship trajectory data stream,so that a ship’s hot zone information can be found efficiently and in real-time.Experimental results show that the DAC-Stream algorithm improves the clustering effect and accelerates data processing compared with the fixed-time-interval clustering algorithm based on density grid(called DC-Stream).

An Approach for User’s Trajectory Prediction Based on the Trajectory Cluster

The existing user’s trajectory prediction methods considered little about the interrelation among users and would fail if the user historical trajectory data were lack. This paper presents a user’s trajectory prediction model and corresponding algorithms by the historical trajectories of users based on the trajectory cluster. The experimental results on MDC dataset show that the proposed method has great improvement in efficiency, accuracy, and scalability comparing with the traditional methods, and it also be applied to the situation where user’s historical trajectory data are lacked.

Controlled islanding schemes for interconnected power systems based on coherent generator group identification and wide-area measurements

A reasonable islanding strategy of a power system is the final resort for preventing a cascading failure and/or a large-area blackout from occurrence. In recent years, the applications of wide area measurement systems(WAMS) in emergency control of power systems are increasing. Therefore, a new WAMS-based controlled islanding scheme for interconnected power systems is proposed. First, four similarity indexes associated with the trajectories of generators are defined, and the weights of these four indexes are determined by using the well-developed entropy theory. Then, a coherency identification algorithm based on hierarchical clustering is presented to determine the coherent groups of generators.Secondly, an optimization model for determining controlled islanding schemes based on the coherent groups of generators is developed to seek the optimal cutset. Finally, a 16-generator68-bus power system and a reduced WECC 29-unit 179-bus power system are employed to demonstrate the proposed WAMS-based controlled islanding schemes, and comparisons with existing slow coherency based controlled islanding strategies are also carried out.

Lane-changing trajectory prediction based on multi-task learning

As a complex driving behaviour,lane-changing(LC)behaviour has a great influence on traffic flow.Improper lane-changing behaviour often leads to traffic accidents.Numerous studies are currently being conducted to predict lane-change trajectories to minimize dangers.However,most of their models focus on how to optimize input variables without considering the interaction between output variables.This study proposes an LC trajectory prediction model based on a multi-task deep learning framework to improve driving safety.Concretely,in this work,the coupling effect of lateral and longitudinal movement is considered in the L.C process.Trajectory changes in two directions will be modelled separately,and the information interaction is completed under the multi-task learing framework.In addition,the trajectory fragents are clustered by the driving features,and trajectory type recognition is added to the trajectory prediction framework as an auxiliary task.Finally,the prediction process of lateral and longitudinal trajectory and LC style is completed by long short-term memory(LSTM).The model training and testing are conducted with the data collected by the driving simulator,and the proposed method expresses better performance in LC trjectory prediction compared with several traditional models.The results of this study can enhance the trajectory prediction accuracy of advanced driving assistance systems(ADASs)and reduce the traffic accidents caused by lane changes.

Behavior pattern mining based on spatiotemporal trajectory multidimensional information fusion

Trajectory data mining is widely used in military and civil applications,such as early warning and surveillance system,intelligent traffic system and so on.Through trajectory similarity measurement and clustering,target behavior patterns can be found from massive spatiotemporal trajectory data.In order to mine frequent behaviors of targets from complex historical trajectory data,a behavior pattern mining algorithm based on spatiotemporal trajectory multidimensional information fusion is proposed in this paper.Firstly,spatial–temporal Hausdorff distance is pro-posed to measure multidimensional information differences of spatiotemporal trajectories,which can distinguish the behaviors with similar location but different course and velocity.On this basis,by combining the idea of k-nearest neighbor and density peak clustering,a new trajectory clustering algorithm is proposed to mine behavior patterns from trajectory data with uneven density distribu-tion.Finally,we implement the proposed algorithm in simulated and radar measured trajectory data respectively.The experimental results show that the proposed algorithm can mine target behavior patterns from different complex application scenarios more quickly and accurately com-pared to the existing methods,which has a good application prospect in intelligent monitoring tasks.

Behavior pattern extraction by trajectory analysis

Trajectory clustering and behavior pattern extraction are the foundations of research into activity perception of objects in motion. In this paper, a new framework is proposed to extract behavior patterns through trajectory analysis. Firstly, we introduce directional trimmed mean distance （DTMD）, a novel method used to measure similarity between trajectories. DTMD has the attributes of anti-noise, self-adaptation and the capability to determine the direction for each trajectory. Secondly, we use a hierarchical clustering algorithm to cluster trajectories. We design a length-weighted linkage rule to enhance the accuracy of trajectory clustering and reduce problems associated with incomplete trajectories. Thirdly, the motion model parameters are estimated for each trajectory＇s classification, and behavior patterns for trajectories are extracted. Finally, the difference between normal and abnormal behaviors can be distinguished.