# Sustainable Parking Space Management Using Machine Learning and Swarm Theory--The SPARK System
## 2 Literature Review
### Innovative Parking Solutions for Optimizing Parking Placement  
The effective management of urban traffic increasingly demands innovative strategies to address the growing demand for parking in densely populated areas. Ad hoc parking zones, designed to serve both private and shared vehicles, present a promising solution to alleviate congestion and streamline traffic flow. These dynamic parking areas offer flexibility in adapting to varying urban needs, particularly during peak hours or special events. Optimally locating such zones is critical not only to improve accessibility but also to minimize the environmental and social impacts of prolonged parking searches and traffic bottlenecks. Several studies highlight the importance of optimizing parking placement to balance the spatial demands of urban mobility systems. For instance, the use of mathematical and heuristic models to strategically allocate parking slots for shared vehicles has demonstrated significant potential in enhancing urban mobility efficiency [21]. The study focuses on a Binary Linear Programming model integrated with a genetic-based metaheuristic to optimize parking slot allocation for car-sharing services. The research highlights the crucial role of parking policies in promoting shared mobility and addresses the need for territorial equity in parking slot distribution, particularly using realistic data from Rome. Similarly, parking zones designed for electric vehicles, when aligned with smart grid systems, can improve grid stability while providing convenient charging options, showcasing the broader infrastructural benefits of optimized parking solutions [22]. The authors comprehensively examine the integration of electric vehicle parking lots into smart grids, focusing on two key problems: the optimal placement of parking lots and the impact on distribution networks. Using a combination of the Cuckoo Optimization Algorithm and Bayesian neural networks, the authors optimize parking placement while accounting for probabilistic load models and energy distribution. Their results emphasize the importance of precise parking placement to minimize power losses and enhance voltage stability within the grid, making the study highly relevant to ad hoc parking zones and their interplay with urban transport systems. Additionally, [PERSON], et al. [23] investigate parking management strategies for shared mobility using simulation and mixed-integer programming techniques. Their research focuses on optimizing the spatial distribution of parking slots to reduce relocation costs and improve service accessibility, demonstrating how strategically placed parking zones can alleviate congestion and support the efficient operation of shared vehicles. Building on this, [PERSON] [24] explores parking choices for shared autonomous vehicles (SAVs), emphasizing the importance of integrating parking management systems with public transit networks. Their study employs optimization models to determine the ideal placement of parking zones, highlighting their role in enhancing multimodal connectivity and improving traffic flow within urban environments. Furthermore, advanced algorithmic approaches, such as the Firefly Algorithm and neural networks, have shown the capability to reduce parking search times and improve traffic flow, underscoring the technological dimension of this challenge [25]. This study addresses the persistent problem of parking space shortages and the inefficiency of traditional parking mechanisms. The authors propose a novel metaheuristic-based approach combining the Firefly Algorithm and a Feed-Forward Back Propagation Neural Network to optimize parking space allocation and reduce search times. Their results demonstrate significant improvements in parking efficiency and reduced congestion through the proposed system, particularly in densely packed urban areas or during peak traffic scenarios. Illegal and wild parking pose significant challenges to urban mobility and governance, disrupting traffic flow and reducing the effectiveness of urban infrastructure.