Data-Driven Method for the Prediction of Estimated Time of Arrival

2021 ◽  
pp. 036119812110332
Author(s):  
Xuhao Gui ◽  
Junfeng Zhang ◽  
Zihan Peng ◽  
Chunwei Yang
Keyword(s):  
Pattern Classification ◽  
Trajectory Optimization ◽  
Time Estimation ◽  
Flight Time ◽  
Classification Model ◽  
Time Of Arrival ◽  
Trajectory Clustering ◽  
Clustering Method ◽  
Arrival Pattern ◽  
Multiple Stages

Predicting the estimated time of arrival (ETA) plays an essential role in decision support (conflict detection, arrival sequencing, or trajectory optimization) for air traffic controllers. In this paper, a new multiple stages strategy for ETA prediction is proposed based on radar trajectories, including arrival pattern identification, arrival pattern classification, and flight time estimation. First, an intention-oriented trajectory clustering method is developed based on a new trajectory representation technique. Such a proposed trajectory clustering method can group trajectories into different arrival patterns in an efficient way. Second, an arrival pattern classification model is constructed based on random forest and XGBoost algorithms. Then, a flight time regression model is trained for each arrival pattern by using the XGBoost algorithm. Information on current states, historical states, and traffic situations is considered to build the feature set during these processes. Finally, the arrival operation toward Guangzhou International Airport is chosen as a case study. The results illustrate that the proposed method and feature engineering approach could improve the performance of ETA prediction. The proposed multiple stages strategy is superior to the single-model-based ETA prediction.

Classification method of marine target motion pattern based on spatial-temporal trajectories

2021 ◽  
pp. 1-15
Author(s):  
Baichen Jiang ◽  
Wei Zhou ◽  
Jian Guan ◽  
Jialong Jin
Keyword(s):  
Pattern Classification ◽  
Training Sample ◽  
Classification Algorithm ◽  
Classification Model ◽  
Motion Pattern ◽  
Motion Patterns ◽  
Lp Norm ◽  
Sparse Representation Classification ◽  
Route Safety

Classifying the motion pattern of marine targets is of important significance to promote target surveillance and management efficiency of marine area and to guarantee sea route safety. This paper proposes a moving target classification algorithm model based on channel extraction-segmentation-LCSCA-lp norm minimization. The algorithm firstly analyzes the entire distribution of channels in specific region, and defines the categories of potential ship motion patterns; on this basis, through secondary segmentation processing method, it obtains several line segment trajectories as training sample sets, to improve the accuracy of classification algorithm; then, it further uses the Leastsquares Cubic Spline Curves Approximation (LCSCA) technology to represent the training sample sets, and builds a motion pattern classification sample dictionary; finally, it uses lp norm minimized sparse representation classification model to realize the classification of motion patterns. The verification experiment based on real spatial-temporal trajectory dataset indicates that, this method can effectively realize the motion pattern classification of marine targets, and shows better time performance and classification accuracy than other representative classification methods.

scholarly journals Uncertainty Quantification of Trajectory Clustering Applied to Ocean Ensemble Forecasts

Fluids ◽  
2020 ◽  
Vol 5 (4) ◽  
pp. 184
Author(s):  
Guilherme S. Vieira ◽  
Irina I. Rypina ◽  
Michael R. Allshouse
Keyword(s):  
Spectral Clustering ◽  
Trajectory Clustering ◽  
Clustering Method ◽  
Material Transport ◽  
Ensemble Forecasts ◽  
Uncertainty Model ◽  
Ocean Flows ◽  
Ocean Forecasting ◽  
High Uncertainty ◽  
Spectral Clustering Method

Partitioning ocean flows into regions dynamically distinct from their surroundings based on material transport can assist search-and-rescue planning by reducing the search domain. The spectral clustering method partitions the domain by identifying fluid particle trajectories that are similar. The partitioning validity depends on the accuracy of the ocean forecasting, which is subject to several sources of uncertainty: model initialization, limited knowledge of the physical processes, boundary conditions, and forcing terms. Instead of a single model output, multiple realizations are produced spanning a range of potential outcomes, and trajectory clustering is used to identify robust features and quantify the uncertainty of the ensemble-averaged results. First, ensemble statistics are used to investigate the cluster sensitivity to the spectral clustering method free-parameters and the forecast parameters for the analytic Bickley jet, a geostrophic flow model. Then, we analyze an operational coastal ocean ensemble forecast and compare the clustering results to drifter trajectories south of Martha’s Vineyard. This approach identifies regions of low uncertainty where drifters released within a cluster predominantly remain there throughout the window of analysis. Drifters released in regions of high uncertainty tend to either enter neighboring clusters or deviate from all predicted outcomes.

scholarly journals A Novel Autonomous Perceptron Model for Pattern Classification Applications

Entropy ◽  
2019 ◽  
Vol 21 (8) ◽  
pp. 763 ◽  
Author(s):  
Alaa Sagheer ◽  
Mohammed Zidan ◽  
Mohammed M. Abdelsamea
Keyword(s):  
Pattern Classification ◽  
Data Science ◽  
Science Research ◽  
Efficient Solutions ◽  
Classification Model ◽  
Computational Time ◽  
Quantum Bit ◽  
Training Samples ◽  
Artificial Neural Network Ann ◽  
Definition Of

Pattern classification represents a challenging problem in machine learning and data science research domains, especially when there is a limited availability of training samples. In recent years, artificial neural network (ANN) algorithms have demonstrated astonishing performance when compared to traditional generative and discriminative classification algorithms. However, due to the complexity of classical ANN architectures, ANNs are sometimes incapable of providing efficient solutions when addressing complex distribution problems. Motivated by the mathematical definition of a quantum bit (qubit), we propose a novel autonomous perceptron model (APM) that can solve the problem of the architecture complexity of traditional ANNs. APM is a nonlinear classification model that has a simple and fixed architecture inspired by the computational superposition power of the qubit. The proposed perceptron is able to construct the activation operators autonomously after a limited number of iterations. Several experiments using various datasets are conducted, where all the empirical results show the superiority of the proposed model as a classifier in terms of accuracy and computational time when it is compared with baseline classification models.

Method to Design Pattern Classification Model with Block Missing Training Data

2019 ◽  
Vol 27 (06) ◽  
pp. 1003-1023
Author(s):  
Won-Chol Yang
Keyword(s):  
Missing Data ◽  
Pattern Classification ◽  
Design Pattern ◽  
Missing Values ◽  
Training Data ◽  
Classification Model ◽  
Accuracy Rate ◽  
Classifier Design ◽  
Usage Rate ◽  
Data Usage

Missing data is a usual drawback in many real-world applications of pattern classification. Methods of pattern classification with missing data are grouped into four types: (a) deletion of incomplete samples and classifier design using only the complete data portion, (b) imputation of missing data and learning of the classifier using the edited set, (c) use of model-based procedures and (d) use of machine learning procedures. These methods can be useful in case of small amount of missing values, but they may be unsuitable in case of relatively large amount of missing values. We proposed a method to design pattern classification model with block missing training data. First, we separated submatrices from the block missing training data. Second, we designed classification submodels using each submatrix. Third, we designed final classification model using a linear combination of these submodels. We tested the classifying accuracy rate and data usage rate of the classification model designed by means of the proposed method by simulation experiments on some datasets, and verified that the proposed method was effective from the viewpoint of classifying accuracy rate and data usage rate.

Pattern Classification for GIS Base on GK Clustering Algorithm

2013 ◽  
Vol 448-453 ◽  
pp. 1955-1958
Author(s):  
Hui Wang ◽  
Xiu Wei Li ◽  
Yu Xin Yun ◽  
Hai Yan Yuan
Keyword(s):  
Pattern Classification ◽  
Cross Correlation ◽  
Clustering Algorithm ◽  
Partial Discharge ◽  
Three Dimensional ◽  
Pulse Amplitude ◽  
Correlation Factor ◽  
Two Dimensional ◽  
Clustering Method

Four typical defects in GIS for PD detection are proposed, and the pulse, amplitude, phases, number of PD has been used to form the three-dimensional PQN matrix. Based on the PQN matrix, three two-dimensional distributions of Hqmax~Phi, Hqmean~Phi and Hn~Phi can be achieved. Then the new GK clustering method is introduced to separate the four different defects according to separate the four different partial discharge defects in gas in GIS, according to the parameters of Skewness (Sk), Kurtosis (Ku), number of peaks (Pe), cross-correlation factor (CC) and the discharge factor Q.

Correlation between Flight Time and Fuel Consumption in Airliner Flight Plan with Trajectory Optimization

2015 ◽  
Author(s):  
Navinda K. Wickramasinghe ◽  
Mark Brown ◽  
Sachiko Fukushima ◽  
Yutaka Fukuda ◽  
Akinori Harada ◽  
...  
Keyword(s):  
Fuel Consumption ◽  
Trajectory Optimization ◽  
Flight Time ◽  
Flight Plan

scholarly journals Parametric Estimations Based on Homomorphic Deconvolution for Time of Flight in Sound Source Localization System

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 925 ◽  
Author(s):  
Yeonseok Park ◽  
Anthony Choi ◽  
Keonwook Kim
Keyword(s):  
Sound Localization ◽  
Source Localization ◽  
Sound Source ◽  
Average Length ◽  
Time Estimation ◽  
Flight Time ◽  
Ensemble Average ◽  
Sound Source Localization ◽  
Localization System ◽  
Non Parametric

Vehicle-mounted sound source localization systems provide comprehensive information to improve driving conditions by monitoring the surroundings. The three-dimensional structure of vehicles hinders the omnidirectional sound localization system because of the long and uneven propagation. In the received signal, the flight times between microphones delivers the essential information to locate the sound source. This paper proposes a novel method to design a sound localization system based on the single analog microphone network. This article involves the flight time estimation for two microphones with non-parametric homomorphic deconvolution. The parametric methods are also suggested with Yule-walker, Prony, and Steiglitz-McBride algorithm to derive the coefficient values of the propagation model for flight time estimation. The non-parametric and Steiglitz-McBride method demonstrated significantly low bias and variance for 20 or higher ensemble average length. The Yule-walker and Prony algorithms showed gradually improved statistical performance for increased ensemble average length. Hence, the non-parametric and parametric homomorphic deconvolution well represent the flight time information. The derived non-parametric and parametric output with distinct length will serve as the featured information for a complete localization system based on machine learning or deep learning in future works.

Multi-representation Trajectory Clustering Method in Visualization of Road Traffic Trend

2019 ◽  
Vol 31 (7) ◽  
pp. 1194
Author(s):  
Qiang Lu ◽  
Guibing Yang ◽  
Juntao Tan ◽  
Ye Yu ◽  
Yuan Xiaohui
Keyword(s):  
Road Traffic ◽  
Trajectory Clustering ◽  
Clustering Method
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