Predicting vacant parking space availability zone-wisely: a graph based spatio-temporal prediction approach

• URL: http://arxiv.org/abs/2205.02113v1
• Date: Tue, 3 May 2022 12:24:39 GMT
• Title: Predicting vacant parking space availability zone-wisely: a graph based spatio-temporal prediction approach
• Authors: Yajing Feng, Qian Hu, Zhenzhou Tang
• Abstract summary: Accurately predicting vacant parking space (VPS) information plays a crucial role in intelligent parking guidance systems. This paper proposes a graph data-based model ST-GBGRU to predict the number of VPSs in short-term and long-term. The results show that in the short-term and long-term prediction tasks, ST-GBGRU model can achieve high accuracy and have good application prospects.
• Score: 0.25782420501870296
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vacant parking space (VPS) prediction is one of the key issues of intelligent parking guidance systems. Accurately predicting VPS information plays a crucial role in intelligent parking guidance systems, which can help drivers find parking space quickly, reducing unnecessary waste of time and excessive environmental pollution. Through the simple analysis of historical data, we found that there not only exists a obvious temporal correlation in each parking lot, but also a clear spatial correlation between different parking lots. In view of this, this paper proposed a graph data-based model ST-GBGRU (Spatial-Temporal Graph Based Gated Recurrent Unit), the number of VPSs can be predicted both in short-term (i.e., within 30 min) and in long-term (i.e., over 30min). On the one hand, the temporal correlation of historical VPS data is extracted by GRU, on the other hand, the spatial correlation of historical VPS data is extracted by GCN inside GRU. Two prediction methods, namely direct prediction and iterative prediction, are combined with the proposed model. Finally, the prediction model is applied to predict the number VPSs of 8 public parking lots in Santa Monica. The results show that in the short-term and long-term prediction tasks, ST-GBGRU model can achieve high accuracy and have good application prospects.

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