PREDICTING AND MANAGING LANE CAPACITY AT A SIGNAL-CONTROLLED INTERSECTION

Abstract

Accurate forecasting of traffic flow parameters in real-time is the basis for making decisions about dynamic lane management, which plays an important role in reducing congestion. The existing methods are not able to remember long-term dependencies to obtain an accurate result of predicting the sequence. In our study, we focused on the development of an algorithm for adaptive adjustment of signal control cycles, which ensures the passage of all group vehicles based on the use of R-CNN and YOLOv4 neural networks. In this study, we used surveillance cameras with a large viewing angle to predict the traffic flow. In the process of learning, long-run short-term memory and a recurrent neural network were adapted. The training algorithms of neural networks take into account the dynamic dimensions of vehicles; discrete parameters of the queue before the intersection, and the duration of the cycle. The result of the study was the development of an algorithm for adaptive adjustment of the duration of the resolving cycle of a traffic light object, taking into account the parameters of the traffic flow in the tasks of eliminating or minimizing the possibility of a traffic jam