BiciMAD: Bike-sharing system dataset creation and preliminary hourly demand prediction using machine and deep learning approaches

This project consists on the construction of a database for the BiciMAD bike sharing system putting together several relevant data sources containing information such as weather, festivities, and bike station demand among others, as well as realizing a preliminary prediction study with 2 state of th...

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Bibliographic Details
Author: Roset Cardona, Javier
Format: master thesis
Publication Date:2024
Country:España
Institution:Universitat Politècnica de Catalunya (UPC)
Repository:UPCommons. Portal del coneixement obert de la UPC
Language:English
OAI Identifier:oai:upcommons.upc.edu:2117/419563
Online Access:https://hdl.handle.net/2117/419563
Access Level:Open access
Keyword:Machine learning
Databases
Bicycle sharing programs
Supply and demand
Bike sharing system
Graph Convolutional Neural networks (GCNN)
XGBOOST
Data science
Data processing
BiciMAD
Aprenentatge automàtic
Bases de dades
Serveis de bicicletes públiques
Oferta i demanda
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
id ES_f9f6b00f82f4399bf92b11f7bb87a032
oai_identifier_str oai:upcommons.upc.edu:2117/419563
network_acronym_str ES
network_name_str España
repository_id_str
spelling BiciMAD: Bike-sharing system dataset creation and preliminary hourly demand prediction using machine and deep learning approachesRoset Cardona, JavierMachine learningDatabasesBicycle sharing programsSupply and demandBike sharing systemGraph Convolutional Neural networks (GCNN)XGBOOSTMachine learningData scienceData processingBiciMADAprenentatge automàticBases de dadesServeis de bicicletes públiquesOferta i demandaÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàticThis project consists on the construction of a database for the BiciMAD bike sharing system putting together several relevant data sources containing information such as weather, festivities, and bike station demand among others, as well as realizing a preliminary prediction study with 2 state of the art models. This database is tailored so that it is easy to explore different predictive techniques in order to optimize the prediction of future bikes demand in all the network. To build the database, the following sources have been used; BiciMAD database, containing the status of each bike docking station for every day and hour as well as the recorded bike trip transactions from January 2019 to December 2022 and DatosAbiertos Madrid, specifically Sistema Integral de Calidad del Aire del Ayuntamiento de Madrid, containing several weather variables such as temperature, precipitations, or wind speed in different locations of Madrid (weather stations) at each hour. Also, from DatosAbiertos we obtained information about the working days and holidays. The final dataset consists on a table where each row represents the state of a specific station in a specific hour, being the number of rows of the table stations × hours even though the number of stations was dynamic over time. For the preliminary prediction study over the dataset we have used XGBOOST optimized with Optuna and Graphic Convolutional Neural Networks in a static graph configuration, achieving higher performance for XGBOOST even though in the literature usually GCNN has better performance. As it was a preliminary study, we have not exploited the complete power of each solution. We have analyzed the model performance in several environments such as areas with different characteristics (office areas, residential areas and parks), or time spans with different weather (winter, spring, summer, and rainy days) and also special events such as holidays and work days, observing how the models react to those environments; in most cases capturing the effects present in each test scenario.Universitat Politècnica de CatalunyaGarcía-Almiñana, Daniel20242024-07-1120242024-11-29master thesishttp://purl.org/coar/resource_type/c_bdccNAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/masterThesisapplication/pdfapplication/pdfapplication/pdfhttps://hdl.handle.net/2117/419563reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/4195632026-05-27T15:37:01Z
dc.title.none.fl_str_mv BiciMAD: Bike-sharing system dataset creation and preliminary hourly demand prediction using machine and deep learning approaches
title BiciMAD: Bike-sharing system dataset creation and preliminary hourly demand prediction using machine and deep learning approaches
spellingShingle BiciMAD: Bike-sharing system dataset creation and preliminary hourly demand prediction using machine and deep learning approaches
Roset Cardona, Javier
Machine learning
Databases
Bicycle sharing programs
Supply and demand
Bike sharing system
Graph Convolutional Neural networks (GCNN)
XGBOOST
Machine learning
Data science
Data processing
BiciMAD
Aprenentatge automàtic
Bases de dades
Serveis de bicicletes públiques
Oferta i demanda
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
title_short BiciMAD: Bike-sharing system dataset creation and preliminary hourly demand prediction using machine and deep learning approaches
title_full BiciMAD: Bike-sharing system dataset creation and preliminary hourly demand prediction using machine and deep learning approaches
title_fullStr BiciMAD: Bike-sharing system dataset creation and preliminary hourly demand prediction using machine and deep learning approaches
title_full_unstemmed BiciMAD: Bike-sharing system dataset creation and preliminary hourly demand prediction using machine and deep learning approaches
title_sort BiciMAD: Bike-sharing system dataset creation and preliminary hourly demand prediction using machine and deep learning approaches
dc.creator.none.fl_str_mv Roset Cardona, Javier
author Roset Cardona, Javier
author_facet Roset Cardona, Javier
author_role author
dc.contributor.none.fl_str_mv García-Almiñana, Daniel
dc.subject.none.fl_str_mv Machine learning
Databases
Bicycle sharing programs
Supply and demand
Bike sharing system
Graph Convolutional Neural networks (GCNN)
XGBOOST
Machine learning
Data science
Data processing
BiciMAD
Aprenentatge automàtic
Bases de dades
Serveis de bicicletes públiques
Oferta i demanda
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
topic Machine learning
Databases
Bicycle sharing programs
Supply and demand
Bike sharing system
Graph Convolutional Neural networks (GCNN)
XGBOOST
Machine learning
Data science
Data processing
BiciMAD
Aprenentatge automàtic
Bases de dades
Serveis de bicicletes públiques
Oferta i demanda
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
description This project consists on the construction of a database for the BiciMAD bike sharing system putting together several relevant data sources containing information such as weather, festivities, and bike station demand among others, as well as realizing a preliminary prediction study with 2 state of the art models. This database is tailored so that it is easy to explore different predictive techniques in order to optimize the prediction of future bikes demand in all the network. To build the database, the following sources have been used; BiciMAD database, containing the status of each bike docking station for every day and hour as well as the recorded bike trip transactions from January 2019 to December 2022 and DatosAbiertos Madrid, specifically Sistema Integral de Calidad del Aire del Ayuntamiento de Madrid, containing several weather variables such as temperature, precipitations, or wind speed in different locations of Madrid (weather stations) at each hour. Also, from DatosAbiertos we obtained information about the working days and holidays. The final dataset consists on a table where each row represents the state of a specific station in a specific hour, being the number of rows of the table stations × hours even though the number of stations was dynamic over time. For the preliminary prediction study over the dataset we have used XGBOOST optimized with Optuna and Graphic Convolutional Neural Networks in a static graph configuration, achieving higher performance for XGBOOST even though in the literature usually GCNN has better performance. As it was a preliminary study, we have not exploited the complete power of each solution. We have analyzed the model performance in several environments such as areas with different characteristics (office areas, residential areas and parks), or time spans with different weather (winter, spring, summer, and rainy days) and also special events such as holidays and work days, observing how the models react to those environments; in most cases capturing the effects present in each test scenario.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-07-11
2024
2024-11-29
dc.type.none.fl_str_mv master thesis
http://purl.org/coar/resource_type/c_bdcc
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/419563
url https://hdl.handle.net/2117/419563
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Universitat Politècnica de Catalunya
publisher.none.fl_str_mv Universitat Politècnica de Catalunya
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.811543

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