# Sustainable Parking Space Management Using Machine Learning and Swarm Theory--The SPARK System
## 1 Introduction  
In recent years, with the acceleration of urbanization worldwide, cities have entered a new era of development. As cars have become a ubiquitous mode of transportation, they are now found in the majority of urban households. However, the increasing number of vehicles has led to significant traffic jams and parking difficulties, creating a bottleneck in urban development. For large cities, achieving a dynamic balance between road infrastructure and automobile resources, providing adequate parking spaces, and effectively solving traffic and parking issues are crucial. These measures not only facilitate the rapid development of urban areas but also enhance the convenience of travel for residents. By strengthening the planning and management of urban parking lots and allocating parking resources efficiently, cities can improve operational efficiency and quality of life for their inhabitants. Parking difficulties are multifaceted and extend beyond insufficient parking availability. Fuel consumption and emissions increase as drivers search for parking spaces, contributing to pollution and exacerbating environmental challenges. Illegal parking practices add to congestion, blocking essential roadways and impeding emergency services. Seasonal factors, such as snow or heavy rainfall, can further reduce access to parking areas, creating temporary but significant obstacles. The time of year also influences parking demand, with school terms, holidays, and peak vacation periods altering traffic patterns. Additionally, technological shifts, such as the rise of hybrid and electric vehicles, require the inclusion of charging infrastructure in parking facilities. Urban politics and real estatepolicies, often driven by economic priorities, can either limit or expand parking spaces in residential and commercial developments, complicating the planning process further. Implementing intelligent parking systems within the broader context of smart city frameworks is a promising solution to these challenges. Such systems leverage advanced technologies, including real-time data analytics, machine learning, and optimization algorithms, to maximize parking efficiency and reduce congestion. Optimizing parking systems presents complex computational challenges, primarily due to the need for real-time processing of large-scale, dynamic, and heterogeneous data. Parking optimization is classified as an NP-hard (non-deterministic polynomial-time hard) problem, which implies that identifying exact solutions within a feasible timeframe becomes impractical as urban systems grow in size and complexity. This computational difficulty stems from the combinatorial nature of aligning limited parking spaces with variable demand, shaped by spatial, temporal, and contextual factors [1]. Managing diverse data sources adds another layer of complexity, as datasets often come in various formats and update at different frequencies. These datasets require extensive preprocessing and standardization to facilitate effective optimization. For example, dynamically assigning parking spaces demands continual recalibration to reflect real-time changes in occupancy and user preferences [2]. The integration of real-time data from IoT (Internet of Things) devices, such as sensors and cameras, further amplifies these challenges, necessitating algorithms capable of efficiently processing large-scale information streams while ensuring scalability and precision [3]. Urban parking optimization models must dynamically respond to unpredictable factors, including traffic congestion and user behavior, making computational efficiency an essential aspect of their design [4]. The diverse nature of data sources further complicates this process, requiring robust preprocessing and harmonization techniques to enable effective integration and optimization [5].