Predicting vacant parking space availability zone-wisely: a graph based spatio-temporal prediction approach
This work addresses the problem of efficient parking guidance for drivers, potentially reducing time waste and environmental pollution, but it appears incremental as it builds on existing graph and recurrent neural network methods.
The paper tackles the problem of predicting vacant parking space availability zone-wisely by proposing a graph-based spatio-temporal model called ST-GBGRU, which combines GRU and GCN to capture temporal and spatial correlations, achieving high accuracy in both short-term (within 30 min) and long-term (over 30 min) predictions on data from 8 public parking lots in Santa Monica.
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.