  Parking demand forecasting and behaviour analysis have received increasing
attention in recent years because of their critical role in mitigating traffic
congestion and understanding travel behaviours. However, previous studies
usually only consider temporal dependence but ignore the spatial correlations
among parking lots for parking prediction. This is mainly due to the lack of
direct physical connections or observable interactions between them. Thus, how
to quantify the spatial correlation remains a significant challenge. To bridge
the gap, in this study, we propose a spatial-aware parking prediction
framework, which includes two steps, i.e. spatial connection graph construction
and spatio-temporal forecasting. A case study in Ningbo, China is conducted
using parking data of over one million records before and during COVID-19. The
results show that the approach is superior on parking occupancy forecasting
than baseline methods, especially for the cases with high temporal irregularity
such as during COVID-19. Our work has revealed the impact of the pandemic on
parking behaviour and also accentuated the importance of modelling spatial
dependence in parking behaviour forecasting, which can benefit future studies
on epidemiology and human travel behaviours.
