MULTI-CHOICE ANALYSIS USE OF SPACE-TIME INFORMATION OF TRANSPORT SYSTEMS

2021 ◽  
Vol 2021 (7) ◽  
pp. 28-37
Author(s):  
Igor' L'vovich ◽  
Yakov L'vovich ◽  
Andrey Preobrazhenskiy ◽  
Yuriy Preobrazhenskiy ◽  
Oleg Choporov
Keyword(s):  
Space Time ◽  
Transport Systems ◽  
System Control ◽  
Information Control ◽  
Control Center ◽  
Choice Analysis ◽  
Investigation Methods ◽  
Integral Estimation ◽  
And Control ◽  
Time Information

The work purpose consists in the development of models and control algorithms of distributed transport systems. In the work there is carried out a formalization of control processes in transport systems. The analysis of connection intensities between transport components is analyzed. There is developed a circuit of the multi-choice analysis of space-time information for tie intensities on the efficient interaction of objects. It is shown, in what way a significance of objects of the transport system control center is taken into account. There is developed a circuit for a space-time information analysis according to object significance of the control center during the interaction with subordinate components. The investigation methods: multi-criterion optimization, expert approach, integral estimation, time series. The investigation results and novelty: there are analyzed different tie intensities between constituents in transport systems. A circuit of the multi-choice analysis of space-time information for tie intensities on object efficient interaction is shown, a also a circuit of the multi-choice analysis of space-time information on object significance of the control center during interaction with subordinate components. Conclusions: there are shown peculiarities of space-time information control in transport systems. Three versions of the analysis of control center objects are considered in order to have interaction support with the components of subordinate systems.

The All-Optical Fiber Perimeter Alarm System

2012 ◽  
Vol 241-244 ◽  
pp. 160-163
Author(s):  
Dong Sheng Wang ◽  
Yu Tian Wang ◽  
Wei Wei Pan
Keyword(s):  
Optical Fiber ◽  
Real Time ◽  
Optical Fiber Sensor ◽  
System Control ◽  
Alarm System ◽  
Information Control ◽  
Control Center ◽  
Optical Cable ◽  
Sensor Module ◽  
Optical Fiber Cable

A all-fiber perimeter alarm system is designed which is composed of the optical Fiber Sensor Module(FSM) laid along the perimeter and Security Information Control Center(SICC) (including display terminals-the large screen display, the terminal of personnel operation, the system control host, etc.) and optical fiber fence (or sensor board). It uses optical fiber (cable) as the sensing and transmission device, through directly contacting with optical fiber (cable) or through the carrying material, passing various disturbances to the fiber (cable), carrying continuous real-time monitoring and collecting disturbance data. Through the back-end analysis, processing and intelligent identification, it can determine the different types of external disturbances. The system can automatically obtain the real-time alarm information that the optical cable generates and process these warning messages according to predefined business logic, then generate sound and light warning prompt and provide several functions of statistics, query on the warning message.

Modeling human-like longitudinal driver model for intelligent vehicles based on reinforcement learning

2021 ◽  
pp. 095440702098357
Author(s):  
Ju Xie ◽  
Xing Xu ◽  
Feng Wang ◽  
Haobin Jiang
Keyword(s):  
Reinforcement Learning ◽  
Comprehensive Evaluation ◽  
Path Following ◽  
Intelligent Vehicles ◽  
Driver Model ◽  
Control Center ◽  
Training Performance ◽  
Learning Agents ◽  
System A ◽  
And Control

The driver model is the decision-making and control center of intelligent vehicle. In order to improve the adaptability of intelligent vehicles under complex driving conditions, and simulate the manipulation characteristics of the skilled driver under the driver-vehicle-road closed-loop system, a kind of human-like longitudinal driver model for intelligent vehicles based on reinforcement learning is proposed. This paper builds the lateral driver model for intelligent vehicles based on optimal preview control theory. Then, the control correction link of longitudinal driver model is established to calculate the throttle opening or brake pedal travel for the desired longitudinal acceleration. Moreover, the reinforcement learning agents for longitudinal driver model is parallel trained by comprehensive evaluation index and skilled driver data. Lastly, training performance and scenarios verification between the simulation experiment and the real car test are performed to verify the effectiveness of the reinforcement learning based longitudinal driver model. The results show that the proposed human-like longitudinal driver model based on reinforcement learning can help intelligent vehicles effectively imitate the speed control behavior of the skilled driver in various path-following scenarios.

scholarly journals Information Source Quality in Intelligent Transport Systems

2012 ◽  
Vol 22 (2) ◽  
pp. 125-131 ◽  
Author(s):  
Niko Jelušić ◽  
Mario Anžek ◽  
Božidar Ivanković
Keyword(s):  
Real Time ◽  
Traffic Control ◽  
Information Sources ◽  
Information Source ◽  
Transport Systems ◽  
Intelligent Transport Systems ◽  
Traffic System ◽  
Intelligent Transport ◽  
Real Time Information ◽  
Time Information

Advanced automatic traffic control systems and various other ITS (Intelligent Transport Systems) applications and services rely on real-time information from the traffic system. This paper presents the overview and general functions of different information sources which provide real-time information that are used or could be used in ITS. The objective is to formally define the quality of information sources suitable for ITS based on formal models of the traffic system and information sources. The definition of quality encompasses these essential factors: traffic system information that exists or may be requested, user requirements and attributes that describe the information sources. This provides the framework and guidelines for the evaluation of information sources that accounts for relevant factors that influence their selection for specific ITS applications. KEY WORDS: information source, information source quality, Intelligent Transport Systems (ITS), automatic traffic control

scholarly journals STATISTICAL PROBABILITY EVALUATION OF OPERATING ITS

Transport ◽  
2010 ◽  
Vol 25 (2) ◽  
pp. 163-170 ◽  
Author(s):  
Adolfas Baublys ◽  
Aldona Jarašūnienė
Keyword(s):  
Analytical Description ◽  
Statistical Information ◽  
Transport Systems ◽  
Statistical Probability ◽  
Intelligent Transport ◽  
Loop Detectors ◽  
Probability Evaluation ◽  
Control Technologies ◽  
And Control

Intelligent Transport Systems (ITS) work with information and control technologies providing the core of ITS functions. Some of these technologies like loop detectors are well known to transportation professionals. However, there are a number of less familiar technologies and system concepts that are keys to ITS functions. Although information and control technologies act as a technical core of ITS, human factors also remain vitally important and potentially very complex issues. The process of operating ITS is influenced by a number of random factors. Along with an assessment of dependence upon separate random factors, the classification of those in the whole hierarchical structure of operating ITS is presented. Statistical information on operating ITS is renewed and replenished in the course of time. With the growth of information amounts, the costs of storing them also increase. Therefore, the article presents relevant algorithms for obtaining required statistical assessments with the least statistical information. It is deduced that while modelling the process of operating ITS, an analytical description of random factors applying non‐parametric assessment is suitable.

Sign in / Sign up

Export Citation Format

Share Document