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Blind classification of e-scooter trips according to their relationship with public transport
E-scooter services have multiplied worldwide as a form of urban transport. Their use has grown so quickly that policymakers and researchers still need to understand their interrelation with other transport modes. At present, e-scooter services are primarily seen as a first-and-last-mile solution for...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10018618/ https://www.ncbi.nlm.nih.gov/pubmed/37363372 http://dx.doi.org/10.1007/s11116-023-10382-4 |
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author | Vinagre Díaz, Juan José Fernández Pozo, Rubén Rodríguez González, Ana Belén Wilby, Mark Richard Anvari, Bani |
author_facet | Vinagre Díaz, Juan José Fernández Pozo, Rubén Rodríguez González, Ana Belén Wilby, Mark Richard Anvari, Bani |
author_sort | Vinagre Díaz, Juan José |
collection | PubMed |
description | E-scooter services have multiplied worldwide as a form of urban transport. Their use has grown so quickly that policymakers and researchers still need to understand their interrelation with other transport modes. At present, e-scooter services are primarily seen as a first-and-last-mile solution for public transport. However, we demonstrate that [Formula: see text] of e-scooter trips are either substituting it or covering areas with little public transportation infrastructure. To this end, we have developed a novel data-driven methodology that autonomously classifies e-scooter trips according to their relation to public transit. Instead of predefined design criteria, the blind nature of our approach extracts the city’s intrinsic parameters from real data. We applied this methodology to Rome (Italy), and our findings reveal that e-scooters provide specific mobility solutions in areas with particular needs. Thus, we believe that the proposed methodology will contribute to the understanding of e-scooter services as part of shared urban mobility. |
format | Online Article Text |
id | pubmed-10018618 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-100186182023-03-16 Blind classification of e-scooter trips according to their relationship with public transport Vinagre Díaz, Juan José Fernández Pozo, Rubén Rodríguez González, Ana Belén Wilby, Mark Richard Anvari, Bani Transportation (Amst) Article E-scooter services have multiplied worldwide as a form of urban transport. Their use has grown so quickly that policymakers and researchers still need to understand their interrelation with other transport modes. At present, e-scooter services are primarily seen as a first-and-last-mile solution for public transport. However, we demonstrate that [Formula: see text] of e-scooter trips are either substituting it or covering areas with little public transportation infrastructure. To this end, we have developed a novel data-driven methodology that autonomously classifies e-scooter trips according to their relation to public transit. Instead of predefined design criteria, the blind nature of our approach extracts the city’s intrinsic parameters from real data. We applied this methodology to Rome (Italy), and our findings reveal that e-scooters provide specific mobility solutions in areas with particular needs. Thus, we believe that the proposed methodology will contribute to the understanding of e-scooter services as part of shared urban mobility. Springer US 2023-03-16 /pmc/articles/PMC10018618/ /pubmed/37363372 http://dx.doi.org/10.1007/s11116-023-10382-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Vinagre Díaz, Juan José Fernández Pozo, Rubén Rodríguez González, Ana Belén Wilby, Mark Richard Anvari, Bani Blind classification of e-scooter trips according to their relationship with public transport |
title | Blind classification of e-scooter trips according to their relationship with public transport |
title_full | Blind classification of e-scooter trips according to their relationship with public transport |
title_fullStr | Blind classification of e-scooter trips according to their relationship with public transport |
title_full_unstemmed | Blind classification of e-scooter trips according to their relationship with public transport |
title_short | Blind classification of e-scooter trips according to their relationship with public transport |
title_sort | blind classification of e-scooter trips according to their relationship with public transport |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10018618/ https://www.ncbi.nlm.nih.gov/pubmed/37363372 http://dx.doi.org/10.1007/s11116-023-10382-4 |
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