Next generation smart carpark

Abstract

With the urbanized and ever-increasing demand for vehicle ownership, the need for innovative parking solutions in densely populated areas like Singapore has never been more critical. Automated Parking Systems (APS) have emerged as a promising solution, offering efficient space utilization and reduced carbon footprint. This study conducts a comprehensive simulation-based investigation of various allocation policies within a realistic model of the Changi APS. The main objective of this research is to enhance operational efficiency, reduce parking and retrieval waiting times, considering Singapore’s unique land constraints. The study analyzes the differences in performance of lifts, shuttles, and other automated hardware, with different allocation policies. These policies include Nearest Parking First, Balanced Parking, Randomized Parking, and Temporary Lot Parking. The key findings reveal that, in a lightly loaded system, the Temporary Lots Parking policy outperforms others in parking and service times, despite a slightly higher retrieval time compared to Nearest Parking First. Conversely, in a heavily loaded system, the Nearest Parking First policy excels in parking time but lags behind in service and retrieval times. In conclusion, this research underscores the potential of APS as an innovative solution for optimizing parking space in urban areas while reducing environmental impact. Future research may further explore the optimal configuration of parking resources, location-specific considerations, and hybrid allocation policies to address varied urban challenges. This study provides valuable insights for policymakers, urban planners, and engineers seeking to enhance parking efficiency in high-density urban environments.