In this paper, we present a comparative analysis of Statistical, Machine Learning and Deep Learning spatio-temporal models for parking occupancy prediction1. We evaluate such models on three public datasets, which are enriched by a set of hand-crafted features to take into account the temporal and spatial components when they are not natively handled by a model. Two approaches for this regression task are investigated: a univariate one and a multivariate one. In the former, we build a separate model for each parking lot. In the latter, a single model is used to predict the availability of all parking lots so as to learn the interactions and the co-movements among all time-series. All models exhibit similar performance. However, we highlight the higher effectiveness of gradient boosted methods when encompassing both temporal and spatial awareness in the feature space and of deep-learning models that take into account the spatial structure of the data.

A comparison of spatio-temporal prediction methods: a parking availability case study

Lucchese C.;Callegher G.;
2022

Abstract

In this paper, we present a comparative analysis of Statistical, Machine Learning and Deep Learning spatio-temporal models for parking occupancy prediction1. We evaluate such models on three public datasets, which are enriched by a set of hand-crafted features to take into account the temporal and spatial components when they are not natively handled by a model. Two approaches for this regression task are investigated: a univariate one and a multivariate one. In the former, we build a separate model for each parking lot. In the latter, a single model is used to predict the availability of all parking lots so as to learn the interactions and the co-movements among all time-series. All models exhibit similar performance. However, we highlight the higher effectiveness of gradient boosted methods when encompassing both temporal and spatial awareness in the feature space and of deep-learning models that take into account the spatial structure of the data.
Proceedings of the ACM Symposium on Applied Computing
File in questo prodotto:
File Dimensione Formato  
3477314.3507035.pdf

non disponibili

Tipologia: Versione dell'editore
Licenza: Accesso chiuso-personale
Dimensione 2.53 MB
Formato Adobe PDF
2.53 MB Adobe PDF   Visualizza/Apri

I documenti in ARCA sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/5004964
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact