Next-generation smart car park

Abstract

This project presents the design, implementation, and evaluation of a next-generation smart car park system. Focusing on automation and efficiency, the system optimizes parking space utilization and vehicle movement through algorithms and data structures. Using a rectangular multiple-deep configuration with a centralized lift and shuttle mechanism, the system achieves high space utilization while maintaining efficient retrieval times.

The implementation uses a random storage policy with recursive shuffling algorithms to balance space efficiency with operational performance. The facility-wide reservation approach demonstrates superior performance compared to traditional per-storey strategies, achieving theoretical utilization rates of up to 98.7%. Extensive simulation testing across varying occupancy levels and time periods demonstrates the system's ability to handle dynamic traffic patterns, peak hour demands, and priority-based vehicle management.

Results show that the smart car park system reduces parking footprint requirements by 40 to 50% compared to conventional car parks, while maintaining average retrieval times below one minute in optimal configurations. Comparative analysis between shallow-depth and deeper storage configurations reveals important trade-offs between equipment costs and operational efficiency, with deeper configurations resulting in 49.46% longer retrieval times despite potential equipment savings. The implementation provides valuable insights into automated parking system design, scheduling algorithms, and space management strategies that can inform future development of smart urban infrastructure.