Optimizing Vehicle Placement in the Residual Spaces of Unmarked Parking Areas: A Comparative Study of Heuristic Methods
Source abstract
Abstract
Optimizing vehicle placement in unmarked parking areas is essential for maximizing space efficiency, particularly in irregular and high-demand urban environments. This study investigates the optimal allocation of additional vehicles in spaces left unoccupied around parked cars by comparing seven heuristic optimization algorithms: Particle Swarm Optimization, Artificial Bee Colony, Gray Wolf Optimizer, Harris Hawks Optimizer, Phasor Particle Swarm Optimization, Multi-Population Based Differential Evolution, and the Colony-Based Search Algorithm. The experiments were conducted in two different parking areas, one designed for parallel parking and the other for perpendicular parking, under three scenarios allowing different levels of cars’ rotational flexibility. The results indicate that MDE consistently outperforms other methods in both speed and robustness, achieving the highest vehicle capacity. These findings provide a foundation for smart parking systems, enabling real-time optimization, reduced congestion, and improved urban mobility.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Smart Parking Systems Research · Advanced Manufacturing and Logistics Optimization · Traffic control and management
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Geomatics Engineering, Faculty of Engineering, Niğde Ömer Halisdemir University, Niğde 51240, Türkiye
Institute of Geodesy and Construction, University of Warmia and Mazury in Olsztyn, 10-720 Olsztyn, Poland
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