Developing a unified centralized transport flow control system

2021 ◽  
Vol 2 (1) ◽  
pp. 71-77
Author(s):  
R. A. Bagutdinov ◽  
D. V. Bezhuashvili
Keyword(s):  
Control System ◽  
Traffic Control ◽  
Road Traffic ◽  
Dissimilar Materials ◽  
Heterogeneous Data ◽  
Transport Systems ◽  
Cloud Infrastructure ◽  
Heterogeneous Information ◽  
Toxic Emissions ◽  
Unstructured Information

Currently, there is an increase in information for data mining in transport systems, the main reason is the increase in the number of heterogeneous sources. The relevance of the topic lies in the need to collect, process, aggregate, and model large volumes of unstructured information that cannot be effectively processed by traditional methods. With the increasing flow of vehicles, its diversity, there is a need to optimize the processes of transportation and logistics, increase the system safety of road traffic. The creation of an information knowledge base will help to solve a number of important problems, including: the efficiency of road use, reduction of toxic emissions, control and unloading of traffic flows, reduction in the number of accidents, and prompt notification of services.The idea of developing a unified centralized traffic control system is described. To collect, store and process heterogeneous information, it is proposed to use a cloud infrastructure with split computation. For the purpose of high-quality processing and aggregation of heterogeneous information, it is recommended to investigate hidden dependencies in the data, build and analyze various aggregation options and interpret them in relation to specific tasks.The system should connect all participants in ground traffic, collect dissimilar materials that can be obtained from their devices and a variety of sensors, and also automate the management and decision-making in transport systems. Unstructured information must be correctly interpreted, categorized, and consistently labeled to identify implicit relationships between data.The scientific novelty of the research consists in the formation of the functions of the system being developed, the description of the main aspects, requirements, interfaces, models and methods for aggregating heterogeneous data.The results of the work can be used not only for analyzing big data in the field of transport, but also in other directions when solving problems of processing heterogeneous information.

scholarly journals A Back-of-Queue Model of a Signal-Controlled Intersection Approach Developed Based on Analysis of Vehicle Driver Behavior

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1204
Author(s):  
Elżbieta Macioszek ◽  
Damian Iwanowicz
Keyword(s):  
Traffic Control ◽  
Traffic Management ◽  
Energy Savings ◽  
New Technologies ◽  
Road Traffic ◽  
Smart Cities ◽  
Data Gathering ◽  
Transport Systems ◽  
Practical Applications ◽  
On The Road

In smart cities, it is expected that transport, communication as well as the movement of people and goods will take place in the shortest possible time while maintaining a high level of safety. In recent years, due to the significant increase in the number of passengers and vehicles on the road and the capacity limitations of transport networks, it has become necessary to use new technologies for intelligent control and traffic management. Intelligent transport systems use advanced technologies in the field of data gathering, information processing, and traffic control to meet current transport needs. To be able to effectively control and manage road traffic, it is necessary to have reliable mathematical models that allow for a faithful representation of the real traffic conditions. Models of this type are usually the basis of complex algorithms used in practice in road traffic control. The application of appropriate models reflecting the behavior of road users contributes to the reduction of congestion, the vehicles travel time on the transport network, fuel consumption and the emissions, which in turn support broadly understood energy savings. The article proposes a model that allows for the estimation of the maximum queue size at the signal-controlled intersection approach (so-called: maximum back-of-queue). This model takes into account the most important traffic characteristics of the vehicles forming this queue. The verification allowed for the conclusion that the proposed model is characterized by high compliance with the actual traffic and road conditions at the intersections with signal controllers located in built-up areas in Poland. The obtained compliance confirms the possibility of using the model for practical applications in calculating the maximum back-of-queue at signal-controlled intersections located in built-up areas in Poland.

Intelligent traffic control system based on open IoT and machine learning

2020 ◽  
pp. 1-12
Author(s):  
Jiaona Chen ◽  
Hailong Liu
Keyword(s):  
Machine Learning ◽  
Control System ◽  
Traffic Control ◽  
Road Traffic ◽  
Control Process ◽  
Vehicle Speed ◽  
Smart Transportation ◽  
The Road ◽  
Traffic Control System ◽  
On The Road

Smart transportation relies on data collection, transmission, processing, and release, involving various terminal devices, control systems, central platforms, and communication links, so its control process is more complicated. In order to improve the operation efficiency of the intelligent traffic control system, based on the open Internet of Things and machine learning, this paper builds an intelligent three-way intelligent traffic control system, sets various parameters, and builds a simulation model using cellular automata as a platform. Moreover, in order to study the performance of the model, the model constructed in this paper is compared with the model of the traditional road traffic control system. In addition, this paper analyzes the model constructed in this paper through the statistics of the highest vehicle flow on the road and the relationship between road occupancy and vehicle speed. The research results show that the model constructed in this paper has good performance and can be applied to intelligent traffic control.

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