# Smart Pedestrian Crossing Management at Traffic Light Junctions through a Fuzzy-Based Approach
## 3 The Proposed Fuzzy Logic Controller  
A generalized representation of the fuzzy-based solution proposed in this work is depicted in Figure 5, which shows the inputs and the output of the FLC. It is helpful to note that the proposed FLC is applied to manage dynamically the phases of traffic lights located in pedestrian crossings, allowing the best disposal of pedestrians according to the time of day. The structure of the proposed FLC (Figure 5) is considerably simple because it allows not only a straightforward implementation on real devices, but notably, low computing needs. The smart system proposed in this paper requires small data transfers, as the data to be controlled by the FLC are the number of pedestrians waiting to cross the road and the time slot. Consequently, there is a lower computing need [29]. On the contrary, other techniques based on composite fuzzy controllers (such as Petri net or Type-2 fuzzy), other systems that use multiple controllers in parallel or other methods that are based on numerous parameters as  
Figure 4: Pedestrian flows during the peak hours. input to the controller require higher computing needs. These advantages of the proposed FLC are presumed to be useful regarding lower data processing times and lower control actuation times. As shown in Figure 5, an input parameter is represented by the number of pedestrians, standing in front of the traffic lights, who are preparing to cross the road. Several solutions can be utilized to identify and count groups of people, whether stationary or on the move. In the scenario analyzed in this paper, the group of people is represented by standing pedestrians waiting for the green of the traffic light. For this reason, to provide an input value to the FLC, in this paper, the pedestrians are counted through the technique proposed in [33], which is based on the use of cameras. It is based on a novel projection method to evaluate the area that represents each blob in real-world coordinates. This approach has been chosen because it decreases issues due to objects that are not similar to human height. Moreover, the contrasts between the projected area of people at various distances from the camera are also taken into account. Another original feature of the solution presented in [33], compared to other standard techniques, is the management of groups as single objects rather than treating individuals separately. However, this system has been extended in this work, with the aim of making it able to deal with stationary people in a scene, by including a different type of background segmentation, which is not based on motion. From an implementation point of view, the approach proposed in [33] requires the use of a single camera to count groups of people in a reference area. At the intersection of Enna taken into account in this paper, eight cameras would be needed to calculate the groups of people in all pedestrian crossings. Regarding the computational power, the approach proposed in [33] does not require many hardware resources. The authors only state that it is appropriate to use a calibrated camera, i.e., able to adapt its characteristics to the conditions of the recording area. The cost of a video camera of this type ranges between about 100 E and 3000 E according to the features. As a result, it is possible to estimate the installation cost of the entire infrastructure proposed in this paper. The price could range between about 2000 E and 25,000 E.  
Following the description of the proposed FLC scheme, it is relevant to concentrate on the fuzzy inference system that determines the phases of the traffic lights and, primarily, concisely specifying the membership functions applied in this paper. The proposed fuzzy logic controller employs three membership functions (_Low, Medium, High_)for the pedestrians number (input) and phases of the traffic light (output). These functions fuzzify the crisp inputs, while their range for pedestrians number (input) fluctuates from 0-200.