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

Tulio Silveira-Santos (), Ana Belén Rodríguez González (), Thais Rangel (), Rubén Fernández Pozo (), Jose Manuel Vassallo () and Juan José Vinagre Díaz ()
Additional contact information
Tulio Silveira-Santos: Universidad Politécnica de Madrid
Ana Belén Rodríguez González: Universidad Politécnica de Madrid
Thais Rangel: Universidad Politécnica de Madrid
Rubén Fernández Pozo: Universidad Politécnica de Madrid
Jose Manuel Vassallo: Universidad Politécnica de Madrid
Juan José Vinagre Díaz: Universidad Politécnica de Madrid

Transportation, 2024, vol. 51, issue 3, No 2, 822 pages

Abstract: Abstract 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.

Keywords: Ride-Hailing; Dynamic Pricing; Time Series Forecasting; Machine Learning; COVID-19; Transport Policy (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11116-022-10349-x Abstract (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:kap:transp:v:51:y:2024:i:3:d:10.1007_s11116-022-10349-x

Ordering information: This journal article can be ordered from
http://www.springer. ... ce/journal/11116/PS2

DOI: 10.1007/s11116-022-10349-x

Access Statistics for this article

Transportation is currently edited by Kay W. Axhausen

More articles in Transportation from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().