Spatio-temporal Parking Behaviour Forecasting and Analysis Before and During COVID-19

Shuhui Gong, Xiaopeng Mo, Rui Cao (Corresponding Author), Yu Liu, Wei Tu, Ruibin Bai

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review


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, ie 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.
Original languageEnglish
Title of host publicationDeepSpatial '21: 2nd ACM SIGKDD Workshop on Deep Learning for Spatiotemporal Data, Applications, and Systems
Publication statusPublished - Aug 2021


Dive into the research topics of 'Spatio-temporal Parking Behaviour Forecasting and Analysis Before and During COVID-19'. Together they form a unique fingerprint.

Cite this