Predicting Spatiotemporal Demand of Dockless E-Scooter Sharing Services with a Masked Fully Convolutional Network

Dockless electric scooters (e-scooter) have emerged as a green alternative to automobiles and a solution to the first- and last-mile problems. Demand anticipation, or being able to accurately predict spatiotemporal demand of e-scooter usage, is one supply–demand balancing strategy. In this paper, we...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Santi Phithakkitnukooon, Karn Patanukhom, Merkebe Getachew Demissie
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	e-scooter micromobility free-floating systems urban mobility mobility-as-a-service spatiotemporal demand Geography (General) G1-922
Acceso en línea:	https://doaj.org/article/25383b616feb4c89bd22128ffb8532f0
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:25383b616feb4c89bd22128ffb8532f0
record_format	dspace
spelling	oai:doaj.org-article:25383b616feb4c89bd22128ffb8532f02021-11-25T17:53:09ZPredicting Spatiotemporal Demand of Dockless E-Scooter Sharing Services with a Masked Fully Convolutional Network10.3390/ijgi101107732220-9964https://doaj.org/article/25383b616feb4c89bd22128ffb8532f02021-11-01T00:00:00Zhttps://www.mdpi.com/2220-9964/10/11/773https://doaj.org/toc/2220-9964Dockless electric scooters (e-scooter) have emerged as a green alternative to automobiles and a solution to the first- and last-mile problems. Demand anticipation, or being able to accurately predict spatiotemporal demand of e-scooter usage, is one supply–demand balancing strategy. In this paper, we present a dockless e-scooter demand prediction model based on a fully convolutional network (FCN) coupled with a masking process and a weighted loss function, namely, masked FCN (or MFCN). The MFCN model handles the sparse e-scooter usage data with its masking process and weighted loss function. The model is trained with highly correlated features through our feature selection process. Next-hour and next 24-h prediction schemes have been tested for both pick-up and drop-off demands. Overall, the proposed MFCN outperforms other baseline models including a naïve forecasting, linear regression, and convolutional long short-term memory networks with mean absolute errors of 0.0434 and 0.0464 for the next-hour pick-up and drop-off demand prediction, respectively, and the errors of 0.0491 and 0.0501 for the next 24-h pick-up and drop-off demand prediction, respectively. The developed MFCN expands the collection of deep learning techniques that can be applied in the transportation domain, especially spatiotemporal demand prediction.Santi PhithakkitnukooonKarn PatanukhomMerkebe Getachew DemissieMDPI AGarticlee-scootermicromobilityfree-floating systemsurban mobilitymobility-as-a-servicespatiotemporal demandGeography (General)G1-922ENISPRS International Journal of Geo-Information, Vol 10, Iss 773, p 773 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	e-scooter micromobility free-floating systems urban mobility mobility-as-a-service spatiotemporal demand Geography (General) G1-922
spellingShingle	e-scooter micromobility free-floating systems urban mobility mobility-as-a-service spatiotemporal demand Geography (General) G1-922 Santi Phithakkitnukooon Karn Patanukhom Merkebe Getachew Demissie Predicting Spatiotemporal Demand of Dockless E-Scooter Sharing Services with a Masked Fully Convolutional Network
description	Dockless electric scooters (e-scooter) have emerged as a green alternative to automobiles and a solution to the first- and last-mile problems. Demand anticipation, or being able to accurately predict spatiotemporal demand of e-scooter usage, is one supply–demand balancing strategy. In this paper, we present a dockless e-scooter demand prediction model based on a fully convolutional network (FCN) coupled with a masking process and a weighted loss function, namely, masked FCN (or MFCN). The MFCN model handles the sparse e-scooter usage data with its masking process and weighted loss function. The model is trained with highly correlated features through our feature selection process. Next-hour and next 24-h prediction schemes have been tested for both pick-up and drop-off demands. Overall, the proposed MFCN outperforms other baseline models including a naïve forecasting, linear regression, and convolutional long short-term memory networks with mean absolute errors of 0.0434 and 0.0464 for the next-hour pick-up and drop-off demand prediction, respectively, and the errors of 0.0491 and 0.0501 for the next 24-h pick-up and drop-off demand prediction, respectively. The developed MFCN expands the collection of deep learning techniques that can be applied in the transportation domain, especially spatiotemporal demand prediction.
format	article
author	Santi Phithakkitnukooon Karn Patanukhom Merkebe Getachew Demissie
author_facet	Santi Phithakkitnukooon Karn Patanukhom Merkebe Getachew Demissie
author_sort	Santi Phithakkitnukooon
title	Predicting Spatiotemporal Demand of Dockless E-Scooter Sharing Services with a Masked Fully Convolutional Network
title_short	Predicting Spatiotemporal Demand of Dockless E-Scooter Sharing Services with a Masked Fully Convolutional Network
title_full	Predicting Spatiotemporal Demand of Dockless E-Scooter Sharing Services with a Masked Fully Convolutional Network
title_fullStr	Predicting Spatiotemporal Demand of Dockless E-Scooter Sharing Services with a Masked Fully Convolutional Network
title_full_unstemmed	Predicting Spatiotemporal Demand of Dockless E-Scooter Sharing Services with a Masked Fully Convolutional Network
title_sort	predicting spatiotemporal demand of dockless e-scooter sharing services with a masked fully convolutional network
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/25383b616feb4c89bd22128ffb8532f0
work_keys_str_mv	AT santiphithakkitnukooon predictingspatiotemporaldemandofdocklessescootersharingserviceswithamaskedfullyconvolutionalnetwork AT karnpatanukhom predictingspatiotemporaldemandofdocklessescootersharingserviceswithamaskedfullyconvolutionalnetwork AT merkebegetachewdemissie predictingspatiotemporaldemandofdocklessescootersharingserviceswithamaskedfullyconvolutionalnetwork
_version_	1718411852102238208

Predicting Spatiotemporal Demand of Dockless E-Scooter Sharing Services with a Masked Fully Convolutional Network

Ejemplares similares