scholarly journals Hybrid Graph Embedding Techniques in Estimated Time of Arrival Task

2022 ◽  
pp. 575-586
Author(s):  
Vadim Porvatov ◽  
Natalia Semenova ◽  
Andrey Chertok
Keyword(s):  
Graph Embedding ◽  
Time Of Arrival

A Transition-Based Knowledge Graph Embedding with Adapting New Entities

2016 ◽  
Author(s):  
A-Yeong Kim ◽  
◽  
Hee-Guen Yoon ◽  
Seong-Bae Park ◽  
Se-Young Park ◽  
...  
Keyword(s):  
Graph Embedding ◽  
Knowledge Graph

Analytical support to organization system of children transportation in long-distance trains

2018 ◽  
Vol 77 (6) ◽  
pp. 232-330
Author(s):  
A. V. Komissarov ◽  
E. A. Makarova ◽  
S. V. Muktepavel ◽  
I. A. Nestrakhov ◽  
I. N. Spesivtseva
Keyword(s):  
Time Of Arrival ◽  
Control Analysis ◽  
Social Significance ◽  
Passenger Cars ◽  
Long Distance ◽  
Number Of Children ◽  
Passenger Traffic ◽  
Destination Station ◽  
Increasing Demand ◽  
Management Functions

Abstract. In modern conditions for passenger complex of Russian Railways, important tasks include improvement of transportation quality, maintenance of stable positions in a competitive environment and increasing demand. To address these issues, a customer-oriented approach is applied based on the segmentation of transport market in relation to certain groups of passengers. Performance of children's transportation is of particular relevance and social significance. Railways are charged with a huge range of work, including sale of travel documents, preparation and equipping of passenger cars, provision of food during the trip, instructing workers, ensuring security during the embarkation/disembarkation of passengers, etc. Children can travel as individually with accompanying persons and as part of organized groups. Processes of planning, organizing, monitoring the transportation of this age category of passengers are associated with the analysis of a large amount of reference and regulatory and reporting documentation. On the basis of the ACS “Express-3”, a program-analytical complex “Children's transportation” was developed and implemented, which allows to receive data at the regional and network levels in the operational (train number, day) and statistical (period of dates, month) modes. This information technology provides analytical support for key transportation management functions — planning, control, analysis. Planning of transportation of organized children's groups is carried out on the basis of a study of the dynamics of data on the number of applications received and travel documents issued, determining the routes of trains, periods of the highest intensity of passenger traffic, obtaining information about the stations of embarkation and disembarkation. To perform the functions of monitoring the embarkation and disembarkation at the destination station of groups of children, the employees involved receive information on the train number, car number, date and time of arrival, number of children in the group using the Children's Transportation software. For the analysis of transportation of children's age categories, a functional has been developed that ensures the construction of aggregated reporting based on trains data that completed the trip. Users receive reporting information in table form, including “strict” (designed according to the approved layout) and “flexible” forms (construction is performed according to specified parameters). Software and analytical complex is designed for managers and specialists of the passenger unit of the JSC “Russian Railways”, has a modular principle of increasing functionality and provides a solution to current problems in the system of organizing children's transport service.

scholarly journals Graph Learning for Combinatorial Optimization: A Survey of State-of-the-Art

2021 ◽  
Author(s):  
Yun Peng ◽  
Byron Choi ◽  
Jianliang Xu
Keyword(s):  
Machine Learning ◽  
Combinatorial Optimization ◽  
Graph Embedding ◽  
Partial Solution ◽  
Complex Data ◽  
Learning Methods ◽  
Graph Learning ◽  
Second Stage ◽  
End To End ◽  
Embedding Methods

AbstractGraphs have been widely used to represent complex data in many applications, such as e-commerce, social networks, and bioinformatics. Efficient and effective analysis of graph data is important for graph-based applications. However, most graph analysis tasks are combinatorial optimization (CO) problems, which are NP-hard. Recent studies have focused a lot on the potential of using machine learning (ML) to solve graph-based CO problems. Most recent methods follow the two-stage framework. The first stage is graph representation learning, which embeds the graphs into low-dimension vectors. The second stage uses machine learning to solve the CO problems using the embeddings of the graphs learned in the first stage. The works for the first stage can be classified into two categories, graph embedding methods and end-to-end learning methods. For graph embedding methods, the learning of the the embeddings of the graphs has its own objective, which may not rely on the CO problems to be solved. The CO problems are solved by independent downstream tasks. For end-to-end learning methods, the learning of the embeddings of the graphs does not have its own objective and is an intermediate step of the learning procedure of solving the CO problems. The works for the second stage can also be classified into two categories, non-autoregressive methods and autoregressive methods. Non-autoregressive methods predict a solution for a CO problem in one shot. A non-autoregressive method predicts a matrix that denotes the probability of each node/edge being a part of a solution of the CO problem. The solution can be computed from the matrix using search heuristics such as beam search. Autoregressive methods iteratively extend a partial solution step by step. At each step, an autoregressive method predicts a node/edge conditioned to current partial solution, which is used to its extension. In this survey, we provide a thorough overview of recent studies of the graph learning-based CO methods. The survey ends with several remarks on future research directions.

Prediction of aircraft estimated time of arrival using machine learning methods

2021 ◽  
pp. 1-15
Author(s):  
O. Basturk ◽  
C. Cetek
Keyword(s):  
Machine Learning ◽  
Web Application ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Weather Data ◽  
Time Of Arrival ◽  
Learning Models ◽  
Trajectory Data ◽  
Different Sources ◽  
Machine Learning Models

ABSTRACT In this study, prediction of aircraft Estimated Time of Arrival (ETA) is proposed using machine learning algorithms. Accurate prediction of ETA is important for management of delay and air traffic flow, runway assignment, gate assignment, collaborative decision making (CDM), coordination of ground personnel and equipment, and optimisation of arrival sequence etc. Machine learning is able to learn from experience and make predictions with weak assumptions or no assumptions at all. In the proposed approach, general flight information, trajectory data and weather data were obtained from different sources in various formats. Raw data were converted to tidy data and inserted into a relational database. To obtain the features for training the machine learning models, the data were explored, cleaned and transformed into convenient features. New features were also derived from the available data. Random forests and deep neural networks were used to train the machine learning models. Both models can predict the ETA with a mean absolute error (MAE) less than 6min after departure, and less than 3min after terminal manoeuvring area (TMA) entrance. Additionally, a web application was developed to dynamically predict the ETA using proposed models.

scholarly journals TDMA Datalink Cooperative Navigation Algorithm Based on INS/JTIDS/BA

Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 782
Author(s):  
Shuo Cao ◽  
Honglei Qin ◽  
Li Cong ◽  
Yingtao Huang
Keyword(s):  
Distribution System ◽  
Large Scale ◽  
Satellite System ◽  
Time Of Arrival ◽  
Military Operations ◽  
Position Information ◽  
Cooperative Navigation ◽  
Navigation Algorithm ◽  
Geometric Dilution Of Precision ◽  
Global Navigation Satellite

Position information is very important tactical information in large-scale joint military operations. Positioning with datalink time of arrival (TOA) measurements is a primary choice when a global navigation satellite system (GNSS) is not available, datalink members are randomly distributed, only estimates with measurements between navigation sources and positioning users may lead to a unsatisfactory accuracy, and positioning geometry of altitude is poor. A time division multiple address (TDMA) datalink cooperative navigation algorithm based on INS/JTIDS/BA is presented in this paper. The proposed algorithm is used to revise the errors of the inertial navigation system (INS), clock bias is calibrated via round-trip timing (RTT), and altitude is located with height filter. The TDMA datalink cooperative navigation algorithm estimate errors are stated with general navigation measurements, cooperative navigation measurements, and predicted states. Weighted horizontal geometric dilution of precision (WHDOP) of the proposed algorithm and the effect of the cooperative measurements on positioning accuracy is analyzed in theory. We simulate a joint tactical information distribution system (JTIDS) network with multiple members to evaluate the performance of the proposed algorithm. The simulation results show that compared to an extended Kalman filter (EKF) that processes TOA measurements sequentially and a TDMA datalink navigation algorithm without cooperative measurements, the TDMA datalink cooperative navigation algorithm performs better.

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