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Were ride-hailing fares affected by the COVID-19 pandemic? Empirical analyses in Atlanta and Boston

Ride-hailing services such as Lyft, Uber, and Cabify operate through smartphone apps and are a popular and growing mobility option in cities around the world. These companies can adjust their fares in real time using dynamic algorithms to balance the needs of drivers and riders, but it is still scar...

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Autores principales: Silveira-Santos, Tulio, González, Ana Belén Rodríguez, Rangel, Thais, Pozo, Rubén Fernández, Vassallo, Jose Manuel, Díaz, Juan José Vinagre
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9649021/
https://www.ncbi.nlm.nih.gov/pubmed/36407885
http://dx.doi.org/10.1007/s11116-022-10349-x
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author Silveira-Santos, Tulio
González, Ana Belén Rodríguez
Rangel, Thais
Pozo, Rubén Fernández
Vassallo, Jose Manuel
Díaz, Juan José Vinagre
author_facet Silveira-Santos, Tulio
González, Ana Belén Rodríguez
Rangel, Thais
Pozo, Rubén Fernández
Vassallo, Jose Manuel
Díaz, Juan José Vinagre
author_sort Silveira-Santos, Tulio
collection PubMed
description Ride-hailing services such as Lyft, Uber, and Cabify operate through smartphone apps and are a popular and growing mobility option in cities around the world. These companies can adjust their fares in real time using dynamic algorithms to balance the needs of drivers and riders, but it is still scarcely known how prices evolve at any given time. This research analyzes ride-hailing fares before and during the COVID-19 pandemic, focusing on applications of time series forecasting and machine learning models that may be useful for transport policy purposes. The Lyft Application Programming Interface was used to collect data on Lyft ride supply in Atlanta and Boston over 2 years (2019 and 2020). The Facebook Prophet model was used for long-term prediction to analyze the trends and global evolution of Lyft fares, while the Random Forest model was used for short-term prediction of ride-hailing fares. The results indicate that ride-hailing fares are affected during the COVID-19 pandemic, with values in the year 2020 being lower than those predicted by the models. The effects of fare peaks, uncontrollable events, and the impact of COVID-19 cases are also investigated. This study comes up with crucial policy recommendations for the ride-hailing market to better understand, regulate and integrate these services.
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spelling pubmed-96490212022-11-14 Were ride-hailing fares affected by the COVID-19 pandemic? Empirical analyses in Atlanta and Boston Silveira-Santos, Tulio González, Ana Belén Rodríguez Rangel, Thais Pozo, Rubén Fernández Vassallo, Jose Manuel Díaz, Juan José Vinagre Transportation (Amst) Article Ride-hailing services such as Lyft, Uber, and Cabify operate through smartphone apps and are a popular and growing mobility option in cities around the world. These companies can adjust their fares in real time using dynamic algorithms to balance the needs of drivers and riders, but it is still scarcely known how prices evolve at any given time. This research analyzes ride-hailing fares before and during the COVID-19 pandemic, focusing on applications of time series forecasting and machine learning models that may be useful for transport policy purposes. The Lyft Application Programming Interface was used to collect data on Lyft ride supply in Atlanta and Boston over 2 years (2019 and 2020). The Facebook Prophet model was used for long-term prediction to analyze the trends and global evolution of Lyft fares, while the Random Forest model was used for short-term prediction of ride-hailing fares. The results indicate that ride-hailing fares are affected during the COVID-19 pandemic, with values in the year 2020 being lower than those predicted by the models. The effects of fare peaks, uncontrollable events, and the impact of COVID-19 cases are also investigated. This study comes up with crucial policy recommendations for the ride-hailing market to better understand, regulate and integrate these services. Springer US 2022-11-10 /pmc/articles/PMC9649021/ /pubmed/36407885 http://dx.doi.org/10.1007/s11116-022-10349-x Text en © The Author(s) 2022 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
Silveira-Santos, Tulio
González, Ana Belén Rodríguez
Rangel, Thais
Pozo, Rubén Fernández
Vassallo, Jose Manuel
Díaz, Juan José Vinagre
Were ride-hailing fares affected by the COVID-19 pandemic? Empirical analyses in Atlanta and Boston
title Were ride-hailing fares affected by the COVID-19 pandemic? Empirical analyses in Atlanta and Boston
title_full Were ride-hailing fares affected by the COVID-19 pandemic? Empirical analyses in Atlanta and Boston
title_fullStr Were ride-hailing fares affected by the COVID-19 pandemic? Empirical analyses in Atlanta and Boston
title_full_unstemmed Were ride-hailing fares affected by the COVID-19 pandemic? Empirical analyses in Atlanta and Boston
title_short Were ride-hailing fares affected by the COVID-19 pandemic? Empirical analyses in Atlanta and Boston
title_sort were ride-hailing fares affected by the covid-19 pandemic? empirical analyses in atlanta and boston
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9649021/
https://www.ncbi.nlm.nih.gov/pubmed/36407885
http://dx.doi.org/10.1007/s11116-022-10349-x
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