Developing a unified centralized transport flow control system

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.

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