论文标题
预测空置的停车空间可用性区域：基于图形的时空预测方法
Predicting vacant parking space availability zone-wisely: a graph based spatio-temporal prediction approach
论文作者
论文摘要
空置停车位（VPS）预测是智能停车指导系统的关键问题之一。准确地预测VPS信息在智能停车指导系统中起着至关重要的作用，这可以帮助驾驶员快速找到停车位，减少不必要的浪费时间和过度的环境污染。通过对历史数据的简单分析，我们发现，不仅存在每个停车场中明显的时间相关性，而且还存在不同停车场之间的明显空间相关性。鉴于这一点，本文提出了基于图的基于图的模型ST-GBGRU（基于时空的基于封闭式复发单元），可以在短期（即30分钟内）和长期（即超过30min）中预测VPS的数量。一方面，GRU提取了历史VPS数据的时间相关性，另一方面，GCN在GRU内部提取了历史VPS数据的空间相关性。两种预测方法，即直接预测和迭代预测，与所提出的模型结合使用。最后，预测模型用于预测圣莫尼卡8个公共停车场的VPS数量。结果表明，在短期和长期预测任务中，ST-GBGRU模型可以实现高精度并具有良好的应用前景。
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.
