Abstract

The article deals with the implementation of automatic traffic control of automobile flows in urban areas of regulation based on the use of fuzzy models. The relevance of the topic of the article is due to the problem of traffic management in the Smart City ecosystem. Traffic flow control is a complex dynamic task, for the solution of which it is proposed to use artificial intelligence methods for processing fuzzy knowledge. The article proposes a model of a traffic flow control system at an intersection based on the use of fuzzy knowledge. Knowledge processing in the system is carried out by the module “Fuzzy Controller”. The input data for the fuzzy controller is information about the number of cars that have passed and information about the current duration of the traffic light phases. The fuzzy controller has a number of output variables corresponding to the number of phases of the traffic light. The fuzzy controller is implemented by means of the fuzzy sets apparatus. The system solves the following tasks: tracking the increase in traffic in the regulation zone; tracking the approach of the flow density on all streets of the regulation zone to the critical one; collecting information about the filling of the road departing from the intersection; implementing indirect unloading of the road section after the intersection, implementing management in transit sections of the city. To increase the efficiency of the model, an improved traffic management process is proposed within the framework of a single intersection, which takes into account traffic situations after the intersection. This approach has a positive impact on traffic in the regulated area due to the decentralized structure of the system consisting of such controlled intersections. The authors also implement the priorities of the directions of movement within the framework of the proposed model. Priorities are set when setting up the system at each of the traffic lights and allow you to speed up the circulation of traffic within the control zone.

Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.