## 4 Conclusions  
In modern urban life, the efficient utilization of unmarked parking areas is as crucial as the solutions developed for marked parking spaces. This study has demonstrated optimization approaches that maximize vehicle capacity by effectively utilizing the available space in unmarked parking areas. The analyses conducted using different vehicle types and parking scenarios have clearly highlighted the performance differences among various optimization methods.  
The findings of this study lay the foundation for a system that could be integrated into future smart city applications. In such a system, users would be able to view available parking spaces suitable for their vehicle dimensions in real-time through a spatial application and be guided to the most optimal parking locations. For instance, large vehicles such as the Volkswagen Crafter, a long panel van, could be directed to wider spaces or areas where they can park at specific angles, while the Citroen AMI or standard-sized vehicles could be guided to narrower and more optimized spaces. By offering parking solutions tailored to different vehicle types, the available space could be utilized with maximum efficiency.  
This approach not only enhances individual convenience but also reduces traffic congestion caused by parking searches, lowers unnecessary fuel consumption, and significantly contributes to environmental sustainability. Moreover, it can prevent large vehicles from parking in inappropriate locations, making space utilization more efficient and enabling the development of smart parking management systems that save drivers time.  
Furthermore, unmarked parking areas in temporary market zones or fairgrounds could particularly benefit from the proposed block-based optimization framework. Future studies may explore the applicability of this method in dynamic and irregular settings such as open-air bazaars, where rapid and flexible space optimization is essential due to temporal and spatial constraints. This study makes a significant contribution to the literature on the optimization of unmarked parking areas while also opening new avenues for future research. Advanced smart systems could further accelerate optimization processes, providing real-time solutions and integrating them into urban transportation planning. As a result, beyond individual benefits, this approach could support the development of smarter and more sustainable parking management strategies at the city-wide level, contributing to urban planning and mobility solutions.  
Conceptualization, M.H., A.J. and A.E.K.; methodology, M.H., A.J. and A.E.K.; software, M.H., A.J. and A.E.K.; validation, M.H., A.J. and A.E.K.; formal analysis, M.H., A.J. and A.E.K.; investigation, M.H., A.J. and A.E.K.; resources, M.H., A.J. and A.E.K.; data curation, M.H., A.J. and A.E.K.; writing--original draft preparation, M.H., A.J. and A.E.K.; writing--review and editing, M.H., A.J. and A.E.K.; visualization, M.H., A.J. and A.E.K.; supervision, M.H., A.J. and A.E.K.; project administration, M.H., A.J. and A.E.K.; funding acquisition, M.H., A.J. and A.E.K. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. This data can be found here: [[https://github.com/Aekarkinli/Metaheuristic-Optimizing-Vehicle-Placement](https://github.com/Aekarkinli/Metaheuristic-Optimizing-Vehicle-Placement)]([https://github.com/Aekarkinli/Metaheuristic-Optimizing-Vehicle-Placement](https://github.com/Aekarkinli/Metaheuristic-Optimizing-Vehicle-Placement)) (accessed on 29 May 2025).