Enhancing Predictive Models for On-Street Parking Occupancy: Integrating Adaptive GCN and GRU with Household Categories and POI Factors

Enhancing Predictive Models for On-Street Parking Occupancy: Integrating Adaptive GCN and GRU with Household Categories and POI Factors

Accurate predictions of parking occupancy are vital for navigation and autonomous transport systems. This research introduces a deep learning mode, AGCRU, which integrates Adaptive Graph Convolutional Networks (GCNs) with Gated Recurrent Units (GRUs) for predicting on-street parking occupancy. By le...

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Autori principali: Xiaohang Zhao, Mingyuan Zhang
Natura: Articolo
Lingua:English
Pubblicazione: MDPI AG 2024-09-01
Serie:Mathematics
Soggetti:
parking occupancy prediction
adaptive graph convolutional networks
gated recurrent units
spatiotemporal factors analysis
POI
household category
Accesso online:https://www.mdpi.com/2227-7390/12/18/2823

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https://www.mdpi.com/2227-7390/12/18/2823

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