篇名 |
Real-time Allocation of Shared Parking Spaces Based on Deep Reinforcement Learning
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並列篇名 | Real-time Allocation of Shared Parking Spaces Based on Deep Reinforcement Learning |
作者 | Minghai Yuan、Chenxi Zhang、Kaiwen Zhou、Fengque Pei |
英文摘要 | Aiming at the parking space heterogeneity problem in shared parking space management, a multi-objective optimization model for parking space allocation is constructed with the optimization objectives of reducing the average walking distance of users and improving the utilization rate of parking spaces, a real-time allocation method for shared parking spaces based on deep reinforcement learning is proposed, which includes a state space for heterogeneous regions, an action space based on policy selection and a reward function with variable coefficients. To accurately evaluate the model performance, dynamic programming is used to derive the theoretical optimal values. Simulation results show that the improved algorithm not only improves the training success rate, but also increases the Agent performance by at least 12.63% and maintains the advantage for different sizes of parking demand, reducing the user walking distance by 53.58% and improving the parking utilization by 6.67% on average, and keeping the response time less than 0.2 seconds.
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起訖頁 | 035-043 |
關鍵詞 | Shared parking、Deep reinforcement learning、Space allocation model |
刊名 | 網際網路技術學刊 |
期數 | 202301 (24:1期) |
出版單位 | 台灣學術網路管理委員會 |
DOI |
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