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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.
Afiliação
  • Silveira-Santos T; Transport Research Center (TRANSyT), Universidad Politécnica de Madrid, 28040 Madrid, Spain.
  • González ABR; Group of Biometrics, Biosignals, Security and Smart Mobility (GB2S), Universidad Politécnica de Madrid, 28040 Madrid, Spain.
  • Rangel T; Transport Research Center (TRANSyT), Universidad Politécnica de Madrid, 28040 Madrid, Spain.
  • Pozo RF; Department of Organizational Engineering, Business Administration and Statistics, Universidad Politécnica de Madrid, 28012 Madrid, Spain.
  • Vassallo JM; Group of Biometrics, Biosignals, Security and Smart Mobility (GB2S), Universidad Politécnica de Madrid, 28040 Madrid, Spain.
  • Díaz JJV; Transport Research Center (TRANSyT), Universidad Politécnica de Madrid, 28040 Madrid, Spain.
Transportation (Amst) ; : 1-32, 2022 Nov 10.
Article em En | MEDLINE | ID: mdl-36407885
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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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Transportation (Amst) Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Transportation (Amst) Ano de publicação: 2022 Tipo de documento: Article