# Sustainable Parking Space Management Using Machine Learning and Swarm Theory--The SPARK System
## 1 Introduction
Traditional heuristic methods, such as genetic algorithms and simulated annealing, often fall short when addressing the performance demands of dynamic urban systems due to their computational intensity and iterative characteristics [6]. Emerging methodologies, such as swarm intelligence and deep learning, present promising solutions to the limitations inherent in traditional heuristic approaches within dynamic urban systems. The implementation of deep neural networks and parallel computing architectures has notably enhanced the scalability and adaptability of intelligent urban parking systems. In the study titled \"Deep Reinforcement Learning Approach Towards a Smart Parking Architecture\" [7], the authors propose a real-time parking management system leveraging deep reinforcement learning. This system integrates cameras, fog nodes, and cloud servers to facilitate intelligent allocation of parking spaces and vehicle classification. The findings indicate that this approach improves detection accuracy and reduces processing time compared to traditional methods. Another investigation, 'Future of Smart Parking: Automated Valet Parking Using Deep Q-Learning' [8], introduces an automated parking system based on the Deep Q-Learning algorithm. This system optimizes parking space utilization while minimizing fuel consumption and emissions. The application of deep reinforcement learning enables dynamic adaptation to evolving urban conditions, rendering it more efficient than conventional heuristic methods. Furthermore, the review 'Swarm Intelligence and IoT-Based Smart Cities: A Review' [9] examines the application of swarm intelligence algorithms within the context of IoT-enabled smart cities. The authors analyze how decentralized, self-organizing systems can effectively manage complex and dynamic urban environments, offering flexible and scalable solutions to challenges such as traffic management and resource allocation. The integration of swarm intelligence and deep learning into urban systems facilitates the development of more adaptive and efficient solutions, surpassing traditional heuristic methods in terms of scalability and precision within dynamic environments. Parking optimization is further complicated in areas without clearly defined parking lines, where vehicles can park at varying angles or in parallel arrangements. Unlike traditional parking areas with marked slots dictating specific parking positions, these irregular spaces require more advanced methods to identify empty spaces and optimize vehicle arrangements. This study addresses this unique challenge by utilizing YOLO-based (You Only Look Once) image segmentation and the Artificial Bee Colony (ABC) optimization algorithm to determine the most efficient use of such spaces. This approach is critical for managing parking in urban areas where predefined slots are unavailable, highlighting the necessity for innovative computational solutions tailored to irregular environments. This article will explore the challenges and potential of intelligent parking systems, drawing on examples from various cities to illustrate best practices and innovative solutions. By addressing these issues, the authors aim to provide a comprehensive understanding of how intelligent parking systems can transform urban mobility and contribute to the smart city paradigm. The main objective of this article is to present a proof-of-concept of a web application, SPARK (Smart Parking Assistance and Resource Knowledge), that supports finding available parking spaces using a crowdsourcing approach, based on deep learning image object segmentation and an optimization algorithm utilizing swarm theory. The system is designed to address the challenge of locating parking spaces in urban areas, a problem exacerbated by the growing number of vehicles and urban congestion.