Analytical Modeling of Computer Attacks on Intelligent Transport Systems Based on the Transformation of Stochastic Networks

2021 ◽  
pp. 489-498
Author(s):  
Sergei Sokolov ◽  
Igor Saenko ◽  
Mikhail Mitrofanov ◽  
Oleg Lepeshkin ◽  
Oleg Lauta
Keyword(s):  
Analytical Modeling ◽  
Stochastic Networks ◽  
Transport Systems ◽  
Intelligent Transport Systems ◽  
Intelligent Transport ◽  
Computer Attacks

scholarly journals Model for Determining the Optimal Trajectory of Movement of Consignments

2019 ◽  
Vol 17 (2) ◽  
pp. 56-62
Author(s):  
V. A. Demin ◽  
S. Eurich ◽  
D. B. Efimenko
Keyword(s):  
Dynamic Systems ◽  
Time Management ◽  
Analytical Modeling ◽  
Optimization Methods ◽  
Performance Criteria ◽  
Transport Systems ◽  
Intelligent Transport Systems ◽  
Intelligent Transport ◽  
Logistics Systems ◽  
And Storage

The suggested model for determining the optimal trajectories of moving consignments that form cargo flows in transport and logistics systems (TLS) is based on a combination of dynamic systems and multi-criteria optimization methods. This approach develops a methodology for solving applied control problems in TLS. Its main result is the principle of finding the maximum, subject to the criterion preferences, based on methods for determining the set of effective plans (Pareto set). At the same time, management in TLS should form models of cargo traffic taking into account the location of transport and storage complexes within the boundaries of the system being studied or designed, as well as should provide for movement of consignments according to specified performance criteria and the most rational trajectories using analytical modeling. Analytics together with digital technologies help to consider the core sense of TLS as of a subsystem of intelligent transport systems.

Multiple vehicles detection and tracking for intelligent transport systems using machine learning approaches

2019 ◽  
Vol 70 (3) ◽  
pp. 214-224
Author(s):  
Bui Ngoc Dung ◽  
Manh Dzung Lai ◽  
Tran Vu Hieu ◽  
Nguyen Binh T. H.
Keyword(s):  
Machine Learning ◽  
Gaussian Mixture ◽  
Research Field ◽  
Transport Systems ◽  
Learning Approaches ◽  
Subtraction Method ◽  
Intelligent Transport Systems ◽  
Intelligent Transport ◽  
Detection And Tracking ◽  
Multiple Vehicles

Video surveillance is emerging research field of intelligent transport systems. This paper presents some techniques which use machine learning and computer vision in vehicles detection and tracking. Firstly the machine learning approaches using Haar-like features and Ada-Boost algorithm for vehicle detection are presented. Secondly approaches to detect vehicles using the background subtraction method based on Gaussian Mixture Model and to track vehicles using optical flow and multiple Kalman filters were given. The method takes advantages of distinguish and tracking multiple vehicles individually. The experimental results demonstrate high accurately of the method.

ASSESSMENT OF THE READINESS OF THE OREL CITY AGGLOMERATION TO IMPLEMENT INTELLIGENT TRANSPORT SYSTEMS

2020 ◽  
Vol 70 (3) ◽  
pp. 64-71
Author(s):  
A.S. BODROV ◽  
◽  
M.V. KULEV ◽  
D.S. DEVYATINA ◽  
O.A. LOBYNTSEVA ◽  
...  
Keyword(s):  
Transport Systems ◽  
Intelligent Transport Systems ◽  
Intelligent Transport
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