# Sustainable Parking Space Management Using Machine Learning and Swarm Theory--The SPARK System
## 3 Methodology Concept
### Applied Algorithms and Technologies in This Study
#### 3.3.1 Study Area  
The study area is situated in Kayseri, Turkiye, specifically within the Alpaslan neighbourhood along Bahar Street. This location experiences significant parking congestion, particularly during school hours, as it includes several educational institutions (elementary, middle, and high schools), contributing to a high population density. The selected area presents realistic challenges for parking space management, as residents often struggle to find available parking, especially during peak traffic times. The defined area accommodates approximately 70 families, owning around 90 vehicles in total. This results in a daily parking demand that far exceeds the available capacity. The total parking capacity within the study area ranges from 70 to 75 spaces, including 23 spaces designated within the private parking areas of two buildings and unmarked roadside parking spaces that can accommodate 45 to 55 vehicles. These roadside spaces allow parking at various angles, such as parallel, diagonal, or perpendicular, adding complexity to managing parking efficiently. This capacity is frequently inadequate due to the high demand generated by local residents, school-related traffic, and visitors to a popular cafe located within the study area. The congestion intensifies during school pick-up and drop-off times, as families park up to six times per day to escort their children to and from the schools. This creates an additional demand for at least 100 parking spaces during peak hours. Consequently, the study area is well-known for its intense parking activity, making it an ideal location to test and demonstrate the effectiveness of parking management systems that utilize advanced segmentation and optimization techniques. A unique feature of the study area is the absence of defined parking lines along the street, allowing vehicles to park in parallel or at various angles. In contrast to areas with marked parking spaces, where the simple distinction of \"occupied\" or \"vacant\" is sufficient, this irregularity complicates the detection of available spaces. To optimize the number of vehicles that can be accommodated, both the parking angle and position must be considered for optimization. This complexity necessitates advanced segmentation and optimization techniques to manage parking more effectively in such unique locations. Therefore, the study area was selected as an appropriate and distinctive setting to demonstrate how a framework integrating advanced segmentation and swarm optimization methods can be utilized to solve this particular problem. Figure 2 shows a satellite image captured in 2022 and obtained from Google Earth (c) 2024 Maxar Technologies), that illustrates the parking challenges and high traffic density in the region. To monitor the area, two volunteers residing in adjacent buildings on the 11 th floor provided continuous camera footage. These cameras were strategically positioned to capture the most congested parking spots, offering a comprehensive view that covered approximately 3200 m\({}^{2}\), of which about 1710 m\({}^{2}\) comprised parking spaces. The parking areas included both roadside parking spaces and private parking areas belonging to the two buildings. The cameras captured live video at a resolution of 1920 \(\times\) 1080 pixels and 30 frames per second, with the feed continuously streamed to a web server for real-time processing by the YOLO algorithm. The Imou Bullet 2E cameras, equipped with a 2 MP (Mega Pixel) sensor and a fixed 2.8 mm lens providing a 102\({}^{\circ}\) horizontal and 54\({}^{\circ}\) vertical field of view, were installed at a 12-degree downward angle. Their night vision capability of up to 30 m and IP67 weatherproof rating made them suitable for outdoor use. Figure 3 illustrates the volunteers' buildings where the cameras were installed and provides an example image captured by the cameras. Figure 4 presents the map of the study area, showing the extent of parking coverage and the reference points used for the transformations.