With the rapid advancement of drone technology, its application scope and potential are expected to grow substantially in the future. Currently, urban parking enforcement predominantly relies on traditional manual operations, where personnel patrol on motorcycles to issue parking tickets and collect fees. However, this approach faces significant challenges in efficiency and resource allocation, especially amidst growing labor shortages. To address these issues, this study proposes an innovative and efficient solution: utilizing drone technology to replace traditional manual parking enforcement.
The research focuses on optimizing routes, determining charging station placement and analyzing operational costs. Using real parking data from New Taipei City, this study conducts simulations to evaluate three routing strategies: road-based, non-road-based and automated clustering. The goal is to identify the most efficient and feasible drone routing strategy for urban parking enforcement. Additionally, the study examines the effects of single versus multiple charging station layouts on operational outcomes, offering valuable insights for practical implementation.
The experimental results demonstrate that drones significantly reduce total route distances, energy consumption and operational costs. In high-, medium- and low-density parking areas, automated routing strategies that combine K-means clustering with nearest-neighbor algorithms effectively balance workload distribution and minimize flight time. Moreover, the findings reveal that positioning charging stations in overlapping gray zones enhances route efficiency while reducing infrastructure costs.
This research provides an innovative framework for drone-based parking management, laying a solid foundation for integrating emerging technologies into smart city management. Furthermore, the proposed drone-based enforcement approach achieves substantial benefits in energy efficiency and operational costs, with potential cost reductions of up to 90% compared to traditional manual enforcement strategies.
