Volume 25 Issue 6
Dec.  2025
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YANG Jia-yi, MEI Zhen-yu, TANG Wei, GONG Jin-rui, FENG Chi. A proactive control-based dynamic allocation model for high-density autonomous parking lots[J]. Journal of Traffic and Transportation Engineering, 2025, 25(6): 219-228. doi: 10.19818/j.cnki.1671-1637.2025.06.018
Citation: YANG Jia-yi, MEI Zhen-yu, TANG Wei, GONG Jin-rui, FENG Chi. A proactive control-based dynamic allocation model for high-density autonomous parking lots[J]. Journal of Traffic and Transportation Engineering, 2025, 25(6): 219-228. doi: 10.19818/j.cnki.1671-1637.2025.06.018
shu

A proactive control-based dynamic allocation model for high-density autonomous parking lots

doi: 10.19818/j.cnki.1671-1637.2025.06.018
  • 1.

    Institute of Intelligent Transportation Systems, Zhejiang University, Hangzhou 310058, Zhejiang, China

  • 2.

    College of Mechanical Engineering, Quzhou College, Quzhou 324000, Zhejiang, China

Funds:

Guangxi Key Technologies R&D Program AB24010305

More Information
  • Corresponding author: MEI Zhen-yu (1979-), male, associate professor, PhD, meizhenyu@zju.edu.cn
  • Received Date: 2025-02-15
  • Accepted Date: 2025-06-05
  • Rev Recd Date: 2025-06-03
  • Publish Date: 2025-12-28
    Abstract
  • A proactive predictive dynamic parking space allocation model (P3DD) was proposed to address the dynamic parking space allocation optimization problem in high-density automated parking lots amid the development of intelligent and intensive parking facilities. Based on the model predictive control (MPC) framework, dynamic optimization decisions for parking space allocation were made by considering the vehicle arrival, departure, and potential conflicts within the parking lot. Meanwhile, a convolutional neural network-long short-term memory (CNN-LSTM) framework is constructed for the rolling prediction of future vehicle arrival and departure time. An optimization objective function that comprehensively considers driving distance, vehicle movement times, waiting time for vehicle retrieval, and response failure rates was developed to minimize the comprehensive parking cost. A tabu search-based optimization algorithm was employed in the predictive control environment to optimize real-time allocation decisions. Based on real-world data from multiple parking lots in Hangzhou, the P3DD model's optimization performance was tested under various parking layouts and parking demand-to-capacity ratios. The results indicate that compared to the baseline model adopting reactive heuristic rules, the P3DD model can directly optimize the parking system performance indicators via proactive prediction and real-time optimization decisions, with improvement rates ranging from 44.8% to 56.5% across nine test cases, and the average comprehensive parking cost decreasing by 48.0%. The CNN-LSTM model achieves an average prediction accuracy of 0.81 for vehicle arrival/departure within the next two hours. Tests under different parking layouts and demand-to-capacity ratios demonstrate that as the stack depth of parking layouts increases, the optimization effect of the P3DD model improves, with optimal performance under balanced parking demand and supply. Furthermore, the P3DD model demonstrates sound adaptability and scalability, providing an efficient and flexible solution for dynamic resource allocation in high-density parking lots.

     

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