Spatio-temporal parking occupancy forecasting integrating parking sensing records and street-level images

The prediction of parking occupancy is of great importance to urban planning. As the number of cars increases and parking resources become limited, the lack of parking supply has become a challenge for urban design. Previous works ignore the correlation between car parks when predicting parking occupancy, which limits the accuracy of the prediction. To address this issue, this study proposes a Temporal-GCN-based correlated parking prediction model (CPPM) to forecast the temporal occupancy of car parks. In particular, the model utilises Convolutional Neural Networks (CNN) and Bayesian probabilities to extract street view similarities in car parks, as well as their spatial correlations, cosine similarity is used to calculate the activity type similarity, and Graph Convolutional Networks (GCN) and Gate Recurrent Units (GRU) are integrated to predict spatio-temporal car park occupancy, taking into account both temporal parking records, similarities in car parks, and their spatial correlations. We conducted two case studies in Ningbo and Beijing, China, integrating over 10 million parking sensing records and corresponding street view images of parking lots to predict parking occupancy. The results show that our model has outstanding performance over the baselines and can be extended for various types of car parks in cities of different sizes and different levels of development. The results also reveal the parking preferences of the citizens of Ningbo and Beijing, which is valuable for a quantitative understanding of commuters' parking patterns and behaviour and can be used as a guide for urban planning and management.