Traffic Information Interpolation System Based on Swarm Intelligence Applied for Real World Traffic Scenario

Traffic congestion has become one of the most prevalent social problems in today's society, and research on appropriate traffic signal control is active. At present, most traffic signal control methods define traffic signal parameters based on traffic information such as the number of passing vehicles. Installing sensors at a vast number of intersections is necessary for more precise and real-time adaptive control, but this is unrealistic from the viewpoint of cost. In this paper, we propose a control approach based on swarm intelligence for dynamically interpolating traffic information. Our simulation results show that the proposed methodology has an excellent interpolation performance for heavy traffic flows and can adapt and interpolate to situations where traffic flow changes suddenly. The effectiveness of the proposed method was verified by applying it to real-world traffic volume data obtained by installing millimeter-wave radar in a real environment. The real-world traffic data showed cyclical changes with the time of day, and our experimental findings demonstrated that the proposed method can adapt to cyclical changes and interpolate traffic volumes.