LAUSR

With the development of smart cities and smart transportation, cities can gradually provide people with more information to facilitate their life and travel, and parking is also inseparable from both of them. Accurate on-street parking demand prediction can improve parking resource utilization and parking management efficiency, as well as potentially improve urban traffic conditions. Previous parking demand prediction methods seldom consider the correlation between the parking demand of a road section and its surroundings. Therefore, in order to capture the correlation of parking demand in the temporal and spatial dimensions as carefully as possible and enrich the relevant features in the prediction model so as to achieve more accurate prediction results, we designed a parking demand prediction structure that considers different features from two perspectives: overall and internal. We used gated recurrent units (GRU) to extract demand influences in the temporal dimension. The GRU is used in combination with a graph convolutional neural network (GCN) to extract demand influencing factors in the spatial dimension. Additionally, a more detailed representation is designed to express spatial dimensional features. Then, based on the historical parking demand features extracted using encoder–decoder, we fuse the extracted spatio-temporal features with them to finally obtain an on-street parking demand prediction model combining the overall and the internal information. By combining them, we can integrate more correlation factors to achieve a more accurate parking demand prediction. The performance of the model is evaluated by real parking data in Xiufeng District of Guilin. The results show that the proposed model achieves good prediction performance compared with other baselines. In addition, we also design feature ablation experiments. Through the comparison of the results, we find that each feature considered in the proposed model is important in parking demand prediction.