DSpace Repository

Leveraging IoT and Weather Conditions to Estimate the Riders Waiting for the Bus Transit on Campus

Show simple item record

dc.contributor.author Arai, Ismail en
dc.contributor.author Elnoshokaty, Ahmed en
dc.contributor.author El-Tawab, Samy en
dc.date.accessioned 2022-04-26T04:29:11Z en
dc.date.available 2022-04-26T04:29:11Z en
dc.date.issued 2021-05-25 en
dc.identifier.isbn 978-1-6654-0424-2 en
dc.identifier.uri http://hdl.handle.net/10061/14723 en
dc.description 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops),22-26 March 2021,Kassel, Germany en
dc.description.abstract The communication technology revolution in this era has increased the use of smartphones in the world of transportation. In this paper, we propose to leverage IoT device data, capturing passengers' smartphones' Wi-Fi data in conjunction with weather conditions to predict the expected number of passengers waiting at a bus stop at a specific time using deep learning models. Our study collected data from the transit bus system at James Madison University (JMU) in Virginia, USA. This paper studies the correlation between the number of passengers waiting at bus stops and weather conditions. Empirically, an experiment with several bus stops in JMU, was utilized to confirm a high precision level. We compared our Deep Neural Network (DNN) model against two baseline models: Linear Regression (LR) and a Wide Neural Network (WNN). The gap between the baseline models and DNN was 35% and 14% better Mean Squared Error (MSE) scores for predictions in favor of the DNN compared to LR and WNN, respectively. en
dc.language.iso en en
dc.publisher IEEE en
dc.relation.isversionof https://ieeexplore.ieee.org/document/9431016 en
dc.rights © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. 出版社許諾条件により、本文は2023年5月26日以降に公開 ja
dc.subject Deep learning en
dc.subject Pandemics en
dc.subject Conferences en
dc.subject Urban areas en
dc.subject Neural networks en
dc.subject Transportation en
dc.subject Predictive models en
dc.title Leveraging IoT and Weather Conditions to Estimate the Riders Waiting for the Bus Transit on Campus en
dc.type.nii Conference Paper en
dc.contributor.transcription アライ, イスマイル ja
dc.contributor.alternative 新井, イスマイル ja
dc.textversion author en
dc.identifier.spage 552 en
dc.identifier.epage 557 en
dc.relation.doi 10.1109/PerComWorkshops51409.2021.9431016 en
dc.identifier.NAIST-ID 74652785 en

Files in this item

This item appears in the following Collection(s)

Show simple item record

Search DSpace

Advanced Search


My Account