izvorni znanstveni rad
Urban traffic congestion is a significant problem for almost every city, affecting various aspects of life. Besides increasing travel time, congestion also affects air and life quality causing economic losses. The construction of infrastructure to solve congestion problems is not always feasible, and, at the end, attracts only additional traffic demand. Thus, a better approach for solving the problem of city congestion is by optimal management of the existing infrastructure. Timely detection of traffic congestion on the road level can prevent congestion formation and even improve road network capacity when used for appropriate traffic control actions. Detecting congestion is a complex process that depends on available traffic data. In this paper, for traffic state estimation, including congestion level, at the intersection level, a new method based on Speed Transition Matrix and Fuzzy-Based System is presented. The proposed method utilizes the Connected Vehicle environment. It is tested on a model of an isolated intersection made in SUMO simulation software based on real-world traffic data. The validation results confirm the successful detection of traffic state (congestion level) at intersections.
Intersection State Estimation, Bottleneck Detection, Connected Vehicles, Fuzzy-based System, Speed Transition Matrix.
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