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TopAs mentioned in the previous section, there are two main types of traffic control management. There is a fixed time controller, with a predetermined and fixed cycle, and there is also an adaptive controller that modifies the cycle sequence and the duration of the phases as a function of the numbers of the vehicles present on each lane.
The authors in (Fayez et al., 2020) propose a tracking system for hidden objects for real-time monitoring. This hybrid system is composed of two techniques: a fast technique, circulating structure kernels with color names and an efficient real-time object tracking (ROT) technique aware of occultation.
In (Yousef et al., 2010), the authors propose an adaptive controller based on the theory of the queue and the number of vehicles in each direction as a decision criterion. However, the controller treated in (Helbing et al., 2005) is based on a dynamic fluid model. The system proposed in (Odeh, 2013) detects the level of congestion and abnormal situations in two main highways and for four intersections, and makes a real-time decision that determines the green-light interval for each traffic light at each intersection based on the genetic algorithm.
A self-organization of traffic lights based on historical traffic status data is presented in (Burguillo-Rial et al., 2012; Yousef et al., 2019). The controller in (Zhu et al., 2016) is cooperative between a network of intersections and semi adaptive. Hence, the decision was made once per cycle based on the number of vehicles in the intersections and on the roads connecting the intersection with its neighbors. All phases in the system in (Rithesh et al., 2018) have a constant duration of 30s, and the choice of the next phase is done according to a decision tree. In (Collotta et al., 2015; Salman et al., 2018; Zou et al., 2009), the authors propose adaptive controllers based on fuzzy logic.