Abstract:
© 2020 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved The article deals with the problem of intelligent traffic control at intersections of road networks of large cities. Due to the advances in cyber-physical systems (CPS), autonomous driving, as part of Intelligent Transport Systems (ITS), will obviously be in the centre of future urban transport. However, the existing ITSs do not fully take into account the size, structure, and parameters of the queue of vehicles waiting at inter-sections, which in turn affects the traffic capacity of the intersection. In the study, we used computer vision to interpret a queue of vehicles and record the parameters at the intersection on a real time basis. We studied the mutual impact of two generalized categories of transport standing in the queue before the stop line at the intersection. We developed a general conceptual research model, which includes both the task of forming the original sample and statistical analysis of the time needed to cross an intersection by the vehicles located in different initial positions. The main research results showed a statistically significant reduction in the vehicle speed to 58% in case there are various categories of vehicles standing in the queue at the intersection.
