MobilitApp: a deep learning-based tool for transport mode detection to support sustainable urban mobility
The visible effects of climate change in urban areas are driving a paradigm shift in societal and political priorities. Urban planners, public transport providers, and traffic managers are increasingly focused on redesigning cities to promote sustainable mobility and create green spaces for pedestri...
| Autores: | , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/428448 |
| Acceso en línea: | https://hdl.handle.net/2117/428448 https://dx.doi.org/10.1109/ACCESS.2025.3561238 |
| Access Level: | acceso abierto |
| Palabra clave: | Transport mode detection Activity recognition Deep learning Mobility sensors Sustainable urban mobility MobilitApp Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors |
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| dc.title.none.fl_str_mv |
MobilitApp: a deep learning-based tool for transport mode detection to support sustainable urban mobility |
| title |
MobilitApp: a deep learning-based tool for transport mode detection to support sustainable urban mobility |
| spellingShingle |
MobilitApp: a deep learning-based tool for transport mode detection to support sustainable urban mobility Caravaca Ibáñez, Gerard Transport mode detection Activity recognition Deep learning Mobility sensors Sustainable urban mobility MobilitApp Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors |
| title_short |
MobilitApp: a deep learning-based tool for transport mode detection to support sustainable urban mobility |
| title_full |
MobilitApp: a deep learning-based tool for transport mode detection to support sustainable urban mobility |
| title_fullStr |
MobilitApp: a deep learning-based tool for transport mode detection to support sustainable urban mobility |
| title_full_unstemmed |
MobilitApp: a deep learning-based tool for transport mode detection to support sustainable urban mobility |
| title_sort |
MobilitApp: a deep learning-based tool for transport mode detection to support sustainable urban mobility |
| dc.creator.none.fl_str_mv |
Caravaca Ibáñez, Gerard Cruz Llopis, Luis Javier de la|||0000-0003-4171-8310 Catalin Diaconeasa, Adrian Bazán Guillén, Alberto|||0000-0001-8634-6907 Aguilar Igartua, Mónica|||0000-0002-6518-888X |
| author |
Caravaca Ibáñez, Gerard |
| author_facet |
Caravaca Ibáñez, Gerard Cruz Llopis, Luis Javier de la|||0000-0003-4171-8310 Catalin Diaconeasa, Adrian Bazán Guillén, Alberto|||0000-0001-8634-6907 Aguilar Igartua, Mónica|||0000-0002-6518-888X |
| author_role |
author |
| author2 |
Cruz Llopis, Luis Javier de la|||0000-0003-4171-8310 Catalin Diaconeasa, Adrian Bazán Guillén, Alberto|||0000-0001-8634-6907 Aguilar Igartua, Mónica|||0000-0002-6518-888X |
| author2_role |
author author author author |
| dc.subject.none.fl_str_mv |
Transport mode detection Activity recognition Deep learning Mobility sensors Sustainable urban mobility MobilitApp Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors |
| topic |
Transport mode detection Activity recognition Deep learning Mobility sensors Sustainable urban mobility MobilitApp Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors |
| description |
The visible effects of climate change in urban areas are driving a paradigm shift in societal and political priorities. Urban planners, public transport providers, and traffic managers are increasingly focused on redesigning cities to promote sustainable mobility and create green spaces for pedestrians, cyclists, and scooter users. In alignment with these objectives, the European Climate Law mandates a minimum 55% reduction in greenhouse gas emissions by 2030 and climate neutrality by 2050. Achieving these targets requires robust tools to collect and analyze mobility data, enabling the evaluation of citizens’ travel habits and the planning of sustainable urban infrastructure. This study presents MobilitApp, a tool developed based on a deep learning (DL) model for real-time detection of transportation modes using smartphone sensor data. Our approach leverages a hierarchical model combining convolutional neural networks (CNNs) for feature extraction and long short-term memory (LSTM) layers for temporal processing, enhanced by skip connections. To ensure computational efficiency on mobile devices, the system integrates statistical techniques for early motion detection, minimizing reliance on DL models. The model was trained on a dataset of multimodal trips in Barcelona, achieving over 80% accuracy for most transport modes and a weighted average accuracy of 88%. These results highlight the effectiveness of our approach for accurately predicting users’ transport modes during their trips. The MobilitApp tool provides an intuitive platform for collecting and analyzing urban mobility data. By analyzing travel patterns, transport modes, and mode-switching behaviors, it delivers actionable insights to city planners, aiding in the enhancement of urban mobility, promotion of sustainable development, and transition to greener cities. |
| publishDate |
2025 |
| dc.date.none.fl_str_mv |
2025 2025-04-15 2025 2025-04-25 |
| 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/2117/428448 https://dx.doi.org/10.1109/ACCESS.2025.3561238 |
| url |
https://hdl.handle.net/2117/428448 https://dx.doi.org/10.1109/ACCESS.2025.3561238 |
| dc.language.none.fl_str_mv |
Inglés eng |
| language_invalid_str_mv |
Inglés |
| language |
eng |
| dc.relation.none.fl_str_mv |
Agencia Estatal de Investigación http://doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023 PID2023-148716OB-C32 DISCOVERY: PROTOCOLOS EN REDES DE COMUNICACIONES Y PRIVACIDAD DE DATOS |
| dc.rights.none.fl_str_mv |
open access http://purl.org/coar/access_right/c_abf2 Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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info:eu-repo/semantics/openAccess |
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open access http://purl.org/coar/access_right/c_abf2 Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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openAccess |
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application/pdf |
| dc.publisher.none.fl_str_mv |
Institute of Electrical and Electronics Engineers (IEEE) |
| publisher.none.fl_str_mv |
Institute of Electrical and Electronics Engineers (IEEE) |
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reponame:UPCommons. Portal del coneixement obert de la UPC instname:Universitat Politècnica de Catalunya (UPC) |
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Universitat Politècnica de Catalunya (UPC) |
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UPCommons. Portal del coneixement obert de la UPC |
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UPCommons. Portal del coneixement obert de la UPC |
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1869419488334577664 |
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MobilitApp: a deep learning-based tool for transport mode detection to support sustainable urban mobilityCaravaca Ibáñez, GerardCruz Llopis, Luis Javier de la|||0000-0003-4171-8310Catalin Diaconeasa, AdrianBazán Guillén, Alberto|||0000-0001-8634-6907Aguilar Igartua, Mónica|||0000-0002-6518-888XTransport mode detectionActivity recognitionDeep learningMobility sensorsSustainable urban mobilityMobilitAppÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadorsThe visible effects of climate change in urban areas are driving a paradigm shift in societal and political priorities. Urban planners, public transport providers, and traffic managers are increasingly focused on redesigning cities to promote sustainable mobility and create green spaces for pedestrians, cyclists, and scooter users. In alignment with these objectives, the European Climate Law mandates a minimum 55% reduction in greenhouse gas emissions by 2030 and climate neutrality by 2050. Achieving these targets requires robust tools to collect and analyze mobility data, enabling the evaluation of citizens’ travel habits and the planning of sustainable urban infrastructure. This study presents MobilitApp, a tool developed based on a deep learning (DL) model for real-time detection of transportation modes using smartphone sensor data. Our approach leverages a hierarchical model combining convolutional neural networks (CNNs) for feature extraction and long short-term memory (LSTM) layers for temporal processing, enhanced by skip connections. To ensure computational efficiency on mobile devices, the system integrates statistical techniques for early motion detection, minimizing reliance on DL models. The model was trained on a dataset of multimodal trips in Barcelona, achieving over 80% accuracy for most transport modes and a weighted average accuracy of 88%. These results highlight the effectiveness of our approach for accurately predicting users’ transport modes during their trips. The MobilitApp tool provides an intuitive platform for collecting and analyzing urban mobility data. By analyzing travel patterns, transport modes, and mode-switching behaviors, it delivers actionable insights to city planners, aiding in the enhancement of urban mobility, promotion of sustainable development, and transition to greener cities.This work was supported in part by Spanish Government funded by MCIN/AEI/10.13039/501100011033 under Research Projects ’’DIstributed Smart Communications with Verifiable EneRgy-optimal Yields (DISCOVERY)’’ under Grant PID2023-148716OB-C32 and ’’Enhancing Communication Protocols with Machine Learning while Protecting Sensitive Data (COMPROMISE)’’ under Grant PID2020-113795RB-C31; partially funded by MCIN/AEI/10.13039/501100011033 and by the European Union (EU) NextGenerationEU/PRTR (Plan de Recuperación, Transformación y Resiliencia) under Research Project ’’Anonymization Technology for AI-Based Analytics of Mobility Data (MOBILYTICS)’’ under Grant TED2021-129782B-I00; in part by the Predoctoral Scholarship ‘‘Generación de Conocimiento- Projects Call 2022’’ under Grant PRE2021-099830; and in part by the Generalitat de Catalunya under AGAUR(Agència de Gestió d’Ajuts Universitaris i de Recerca) Grant 2021-SGR-01413.Peer ReviewedObjectius de Desenvolupament Sostenible::11 - Ciutats i Comunitats SosteniblesObjectius de Desenvolupament Sostenible::11 - Ciutats i Comunitats Sostenibles::11.2 - Per a 2030, proporcionar accés a sistemes de transport segurs, assequibles, accessibles i sostenibles per a totes les persones, i millorar la seguretat viària, en particular mitjançant l’ampliació del transport públic, amb especial atenció a les necessitats de les persones en situació vulnerable, dones, nenes, nens, persones amb discapacitat i persones gransInstitute of Electrical and Electronics Engineers (IEEE)20252025-04-1520252025-04-25journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/428448https://dx.doi.org/10.1109/ACCESS.2025.3561238reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)InglésengAgencia Estatal de Investigación http://doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023 PID2023-148716OB-C32 DISCOVERY: PROTOCOLOS EN REDES DE COMUNICACIONES Y PRIVACIDAD DE DATOSopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/4284482026-05-27T15:37:01Z |
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15,812455 |