Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorSanti Phithakkitnukooonen_US
dc.contributor.authorKarn Patanukhomen_US
dc.contributor.authorMerkebe Getachew Demissieen_US
dc.description.abstractDockless 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.en_US
dc.subjectEarth and Planetary Sciencesen_US
dc.subjectSocial Sciencesen_US
dc.titlePredicting spatiotemporal demand of dockless e-scooter sharing services with a masked fully convolutional networken_US
article.title.sourcetitleISPRS International Journal of Geo-Informationen_US
article.volume10en_US of Calgaryen_US Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

Files in This Item:
There are no files associated with this item.

Items in CMUIR are protected by copyright, with all rights reserved, unless otherwise indicated.