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A learning-based method for optimal dynamic privileged parking permit policy
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DOI：10.1111/mice.13228.png)
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The privileged permit service can be provided as an alternative to the conventional meter and reserved services in the off-street parking lots. In view of the unbalanced demand and the simplistic off-street parking lot management, this paper proposes a novel parking management problem for setting up and withdrawing the temporary permit-only policy. To optimize the access rule regarding uncertainty demand on the time of day and the utilization of the parking lot, a deep Q-learning (DQL) method is proposed to address the uncertainty and dimensionality in the framework of deep reinforcement learning (DRL). To replicate real-world demand pattern for training deep Q network, a short-term parking demand model is presented by integrating the long-short term memory neural network and multivariant Gaussian process. A case study is performed on urban parking lots on university campus. The numerical experiments of a rule-based strategy, a tabular Q-learning (TQL) method, and the proposed DQL method are conducted to justify the effectiveness of the proposed method. The proposed method outperforms the static (s, S) inventory policy by 65% and TQL with linear Q-value estimation by 15% in the total revenue. The sensitivity analyses show the DQL method is capable to handle capacity-reduced, demand-increased, and special-event scenarios while the comparable strategy underperforms the proposed method
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