Leverage Multi-source Traffic Demand Data Fusion with Transformer Model for Urban Parking Prediction

arXiv:2405.01055v1 Announce Type: new
Abstract: The escalation in urban private car ownership has worsened the urban parking predicament, necessitating effective parking availability prediction for urban planning and management. However, the existing prediction methods suffer from low prediction accuracy with the lack of spatial-temporal correlation features related to parking volume, and neglect of flow patterns and correlations between similar parking lots within certain areas. To address these challenges, this study proposes a parking availability prediction framework integrating spatial-temporal deep learning with …

abstract accuracy arxiv availability car correlation cs.ai cs.et cs.lg data demand features fusion however low management ownership parking planning prediction spatial temporal traffic transformer transformer model type urban urban planning