  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.
