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A Deep Learning Approach for Predicting Bus Passenger Demand Based on Weather Conditions Cover

A Deep Learning Approach for Predicting Bus Passenger Demand Based on Weather Conditions

Transport and Telecommunication Journal
Volume 21 (2020): Issue 4 (December 2020)
By: Tânia Fontes,  Ricardo Correia,  Joel Ribeiro and  José Luís Borges  
Open Access
|Nov 2020
Authors

Authors

Tânia Fontes

tania.d.fontes@inesctec.pt

INESC TEC - Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal

Ricardo Correia

ricardo.f.correia@inesctec.pt

INESC TEC and Faculty of Engineering, University of Porto, Porto, Portugal

Joel Ribeiro

joel.ribeiro@inesctec.pt

INESC TEC - Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal

José Luís Borges

jlborges@fe.up.pt

INESC TEC - Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal
DOI: https://doi.org/10.2478/ttj-2020-0020 | Journal eISSN: 1407-6179 | Journal ISSN: 1407-6160
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Language: English
Page range: 255 - 264
Published on: Nov 26, 2020
Published by: Transport and Telecommunication Institute
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
Predition,
Urban public transport,
Bus passenger demand,
Weather conditions,
Artificial neural networks
Related subjects:
Engineering,
Introductions and overviews,
Engineering, other

© 2020 Tânia Fontes, Ricardo Correia, Joel Ribeiro, José Luís Borges, published by Transport and Telecommunication Institute
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Volume 21 (2020): Issue 4 (December 2020)
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