# Efficient Intersection Management Based on an Adaptive Fuzzy-Logic Traffic Signal
## 2 Related Work
### Adaptive Controllers  
Adaptive traffic signal controllers are characterized by continuously sensing the traffic conditions and adjusting the signal timing accordingly. Most of these controllers implement self-adjusting mechanisms with the ability to address unpredictable traffic conditions by modifying their parameters and internal logic. Artificial intelligence has been widely applied to develop adaptive controllers. In the literature, a considerable variety of solutions mainly based on reinforcement learning (RL) [13; 14; 15; 59; 60], neural networks (NN) [16; 17; 18], deep reinforcement learning (DRL) [19; 20; 21; 22], and fuzzy logic (FL) can be found. However, as has been surveyed by [PERSON] et al. [35], RL has the worst performance in terms of accuracy, speed, and capacity to manage a huge amount of data compared to the other approaches. The main drawback of RL-based controllers is that they require the extraction of features from input data to create models to later identify useful information for the output. This procedure can lead to an overload of states, producing a performance decay as the amount of input data increases. Unlike traditional RL techniques, DRL has the capacity to learn features and tasks directly from input data, allowing them to increase their performance as data increases. Most DRL-based controllers are based on decision-making models integrated by scalar representations for _states_ (queue length, light timing, phases, vehicles' speed/position), _actions_ (selected phase, phase splitting), and _rewards_ (waiting time, queue length, phase transition) [37]. Due to these representations, a DRL approach requires that input data be expressed in a matrix or tensor formats [20; 21]. Nevertheless, the effective computation of those formats requires nonlinear processing units, such as high-performance GPUs, and even the implementation of parallel computing techniques [19]. In addition, as was surveyed by [PERSON] et al. [36], since DRL controllers need to learn from multiple levels of representation and abstraction, they require a significant amount of traffic data extracted and fused from multiple heterogeneous sources, which must be arranged into large sets of image-like representations. Many of the traffic agencies would have difficulties meeting the latter condition in the short and mid-term. In practice, since FL systems are based on simple mappings between the inputs and the output, the result is more understandable and transparent for operators in comparison to NN, RL, and DRL approaches. Furthermore, FL systems are based on if-then rules with the capability to include experts' knowledge and experience in their design, without requiring the tuning of extra attributes for training, feedback, or reward functions. This advantage also facilitates the development of solutions with lesser computing requirements, allowing implementations over straightforward processing units, for instance, microcontrollers. An FL-based controller relies on the definition of fuzzy sets and an inference system. Fuzzy sets are collections whose elements have degrees of membership that are used to mathematically describe the vagueness or uncertainty about a scalar magnitude. Such a description is done by using linguistics concepts such as _very low_, _low_, _average_, _high_, _veryhigh_, which are named fuzzy values. Hence, the inference system consists of methods to translate scalar values to fuzzy values, and vice versa, which are supported by a base of rules (conditional statements) in the form: _if x is A then y is B_, where A and B are linguistic values determined by fuzzy sets. Based on FL, the design of a basic traffic signal controller is conducted by the reasoning: _the higher the traffic demand, the longer the green time_[23; 27; 30; 61]. For this purpose, arrival flow rates, queue lengths, and green interval lengths are commonly converted to fuzzy values to be used as the system's inputs and output, respectively. [PERSON] et al.