Evidence map›Paper›PMID 36407885›Full record

ArticleTransportation2022

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, Juan José Vinagre Díaz

Abstract read
In one paragraph

Article in Transportation, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Tulio Silveira-SantosTransport Research Center (TRANSyT), Universidad Politécnica de Madrid, 28040 Madrid, Spain.ORCID 0000-0002-1174-2662
Ana Belén Rodríguez GonzálezGroup of Biometrics, Biosignals, Security and Smart Mobility (GB2S), Universidad Politécnica de Madrid, 28040 Madrid, Spain.ORCID 0000-0002-0023-9607
Thais RangelTransport Research Center (TRANSyT), Universidad Politécnica de Madrid, 28040 Madrid, Spain.ORCID 0000-0002-0452-4501
Rubén Fernández PozoGroup of Biometrics, Biosignals, Security and Smart Mobility (GB2S), Universidad Politécnica de Madrid, 28040 Madrid, Spain.ORCID 0000-0001-7306-8450
Jose Manuel VassalloTransport Research Center (TRANSyT), Universidad Politécnica de Madrid, 28040 Madrid, Spain.ORCID 0000-0001-7151-4939
Juan José Vinagre DíazGroup of Biometrics, Biosignals, Security and Smart Mobility (GB2S), Universidad Politécnica de Madrid, 28040 Madrid, Spain.ORCID 0000-0003-1283-0311

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

COVID-19Dynamic PricingMachine LearningRide-HailingTime Series ForecastingTransport Policy

Identifiers

PMID36407885
PMCPMC9649021

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.