桥梁主动防撞预警系统在常泰大桥上的应用

近年来,长江航运量增幅显著提升,导致桥梁遭遇船舶撞击的风险越来越高。为了避免船舶撞击桥梁事故的发生,本文提出了一套跨江长大桥梁主动防撞预警系统。该系统首先基于AIS、雷达、激光、视频等监测设备,融合海事数据,实时精确识别船舶特征、航速、航迹等信息。然后利用AIS、VHF、手机App、短信、声光报警等多种手段,提前告知船员超高、偏航等异常状况,对存在碰撞风险的船舶进行提前预警并提供避险策略,降低船桥碰撞事件的发生风险。

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.

Efficient and Effective 4D Trajectory Data Cleansing

As the rapid development of aviation industry and newly emerging crowd-sourcing projects such as Flightradar24 and FlightAware,large amount of air traffic data,particularly four-dimension(4D)trajectory data,have become available for the public.In order to guarantee the accuracy and reliability of results,data cleansing is the first step in analyzing 4D trajectory data,including error identification and mitigation.Data cleansing techniques for the 4D trajectory data are investigated.Back propagation(BP)neural network algorithm is applied to repair errors.Newton interpolation method is used to obtain even-spaced trajectory samples over a uniform distribution of each flight’s 4D trajectory data.Furthermore,a new method is proposed to compress data while maintaining the intrinsic characteristics of the trajectories.Density-based spatial clustering of applications with noise(DBSCAN)is applied to identify remaining outliers of sample points.Experiments are performed on a data set of one-day 4D trajectory data over Europe.The results show that the proposed method can achieve more efficient and effective results than the existing approaches.The work contributes to the first step of data preprocessing and lays foundation for further downstream 4D trajectory analysis.

An Aircraft Trajectory Anomaly Detection Method Based on Deep Mixture Density Network

The timely and accurately detection of abnormal aircraft trajectory is critical to improving flight safety.However,the existing anomaly detection methods based on machine learning cannot well characterize the features of aircraft trajectories.Low anomaly detection accuracy still exists due to the high-dimensionality,heterogeneity and temporality of flight trajectory data.To this end,this paper proposes an abnormal trajectory detection method based on the deep mixture density network(DMDN)to detect flights with unusual data patterns and evaluate flight trajectory safety.The technique consists of two components:Utilization of the deep long short-term memory(LSTM)network to encode features of flight trajectories effectively,and parameterization of the statistical properties of flight trajectory using the Gaussian mixture model(GMM).Experiment results on Guangzhou Baiyun International Airport terminal airspace show that the proposed method can effectively capture the statistical patterns of aircraft trajectories.The model can detect abnormal flights with elevated risks and its performance is superior to two mainstream methods.The proposed model can be used as an assistant decision-making tool for air traffic controllers.