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

TL;DR

This paper introduces a graph-based spatio-temporal model, ST-GBGRU, that accurately predicts vacant parking spaces in both short-term and long-term scenarios by capturing temporal and spatial correlations.

Contribution

The paper proposes a novel graph neural network model, ST-GBGRU, integrating GCN and GRU for improved parking space prediction considering spatial-temporal correlations.

Findings

High accuracy in short-term VPS prediction

Effective long-term VPS forecasting over 30 minutes

Model demonstrates good application prospects

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.