A learning‐based method for optimal dynamic privileged parking permit policy

Abstract

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