Enhancing carsharing experiences for Barcelona citizens with data analytics and intelligent algorithms

Carsharing practices are spreading across many cities in the world. This paper analyzes real-life data obtained from a private carsharing company operating in the city of Barcelona, Spain. After describing the main trends in the data, machine learning and time-series analysis methods are employed to...

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Authors: Herrera Machado, Erika Magdalena, Calvet Liñán, Laura, Ghorbani, Elnaz, Panadero Martínez, Javier, Juan, Angel A.
Format: article
Publication Date:2023
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:2117/384883
Online Access:https://hdl.handle.net/2117/384883
https://dx.doi.org/10.3390/computers12020033
Access Level:Open access
Keyword:Artificial intelligence
Data structures (Computer science)
Smart cities
Carsharing
Data analytics
Machine learning
Intelligent algorithms
Intel·ligència artificial
Estructures de dades (Informàtica)
Ciutats intel·ligents
Àrees temàtiques de la UPC::Economia i organització d'empreses
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oai_identifier_str oai:upcommons.upc.edu:2117/384883
network_acronym_str ES
network_name_str España
repository_id_str
spelling Enhancing carsharing experiences for Barcelona citizens with data analytics and intelligent algorithmsHerrera Machado, Erika MagdalenaCalvet Liñán, LauraGhorbani, ElnazPanadero Martínez, JavierJuan, Angel A.Artificial intelligenceData structures (Computer science)Smart citiesCarsharingData analyticsMachine learningIntelligent algorithmsSmart citiesIntel·ligència artificialEstructures de dades (Informàtica)Ciutats intel·ligentsÀrees temàtiques de la UPC::Economia i organització d'empresesCarsharing practices are spreading across many cities in the world. This paper analyzes real-life data obtained from a private carsharing company operating in the city of Barcelona, Spain. After describing the main trends in the data, machine learning and time-series analysis methods are employed to better understand citizens’ needs and behavior, as well as to make predictions about the evolution of their demand for this service. In addition, an original proposal is made regarding the location of the pick-up points. This proposal is based on a capacitated dispersion algorithm, and aims at balancing two relevant factors, including scattering of pick-up points (so that most users can benefit from the service) and efficiency (so that areas with higher demand are well covered). Our aim is to gain a deeper understanding of citizens’ needs and behavior in relation to carsharing services. The analysis includes three main components: descriptive, predictive, and prescriptive, resulting in customer segmentation and forecast of service demand, as well as original concepts for optimizing parking station location.Peer ReviewedMultidisciplinary Digital Publishing Institute (MDPI)20232023-02-0120232023-03-13journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/384883https://dx.doi.org/10.3390/computers12020033reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/3848832026-05-27T15:37:01Z
dc.title.none.fl_str_mv Enhancing carsharing experiences for Barcelona citizens with data analytics and intelligent algorithms
title Enhancing carsharing experiences for Barcelona citizens with data analytics and intelligent algorithms
spellingShingle Enhancing carsharing experiences for Barcelona citizens with data analytics and intelligent algorithms
Herrera Machado, Erika Magdalena
Artificial intelligence
Data structures (Computer science)
Smart cities
Carsharing
Data analytics
Machine learning
Intelligent algorithms
Smart cities
Intel·ligència artificial
Estructures de dades (Informàtica)
Ciutats intel·ligents
Àrees temàtiques de la UPC::Economia i organització d'empreses
title_short Enhancing carsharing experiences for Barcelona citizens with data analytics and intelligent algorithms
title_full Enhancing carsharing experiences for Barcelona citizens with data analytics and intelligent algorithms
title_fullStr Enhancing carsharing experiences for Barcelona citizens with data analytics and intelligent algorithms
title_full_unstemmed Enhancing carsharing experiences for Barcelona citizens with data analytics and intelligent algorithms
title_sort Enhancing carsharing experiences for Barcelona citizens with data analytics and intelligent algorithms
dc.creator.none.fl_str_mv Herrera Machado, Erika Magdalena
Calvet Liñán, Laura
Ghorbani, Elnaz
Panadero Martínez, Javier
Juan, Angel A.
author Herrera Machado, Erika Magdalena
author_facet Herrera Machado, Erika Magdalena
Calvet Liñán, Laura
Ghorbani, Elnaz
Panadero Martínez, Javier
Juan, Angel A.
author_role author
author2 Calvet Liñán, Laura
Ghorbani, Elnaz
Panadero Martínez, Javier
Juan, Angel A.
author2_role author
author
author
author
dc.subject.none.fl_str_mv Artificial intelligence
Data structures (Computer science)
Smart cities
Carsharing
Data analytics
Machine learning
Intelligent algorithms
Smart cities
Intel·ligència artificial
Estructures de dades (Informàtica)
Ciutats intel·ligents
Àrees temàtiques de la UPC::Economia i organització d'empreses
topic Artificial intelligence
Data structures (Computer science)
Smart cities
Carsharing
Data analytics
Machine learning
Intelligent algorithms
Smart cities
Intel·ligència artificial
Estructures de dades (Informàtica)
Ciutats intel·ligents
Àrees temàtiques de la UPC::Economia i organització d'empreses
description Carsharing practices are spreading across many cities in the world. This paper analyzes real-life data obtained from a private carsharing company operating in the city of Barcelona, Spain. After describing the main trends in the data, machine learning and time-series analysis methods are employed to better understand citizens’ needs and behavior, as well as to make predictions about the evolution of their demand for this service. In addition, an original proposal is made regarding the location of the pick-up points. This proposal is based on a capacitated dispersion algorithm, and aims at balancing two relevant factors, including scattering of pick-up points (so that most users can benefit from the service) and efficiency (so that areas with higher demand are well covered). Our aim is to gain a deeper understanding of citizens’ needs and behavior in relation to carsharing services. The analysis includes three main components: descriptive, predictive, and prescriptive, resulting in customer segmentation and forecast of service demand, as well as original concepts for optimizing parking station location.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023-02-01
2023
2023-03-13
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/384883
https://dx.doi.org/10.3390/computers12020033
url https://hdl.handle.net/2117/384883
https://dx.doi.org/10.3390/computers12020033
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
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
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
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute (MDPI)
publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute (MDPI)
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
_version_ 1869415539370098688
score 15,301603

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