Who changed and who maintained their urban bike-sharing mobility after the COVID-19 outbreak? A within-subjects study

Seifert, Rudi; Pellicer-Chenoll, Maite; Antón-González, Laura; Pans, Miquel; Devís‐Devís, José; Moreno, Luís Millán González

doi:10.1016/j.cities.2023.104343

Cited by 4 publications

(1 citation statement)

References 59 publications

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“…The relative change was estimated by comparing a mobility level with a pre-pandemic baseline value. Some uses of the GCMR's PT index were the characterisation of the use of PT, the clustering of cities with similar PT demand change levels, and the assessment of the effectiveness of mobility restrictions [4,12,21,[36][37][38]. On the other hand, AMTR reported indices estimated based on navigation data from the Apple Maps app service to describe its users' mobility trends [32].…”

Section: Aggregated Mobility Indicesmentioning

confidence: 99%

Investigating the potential of aggregated mobility indices for inferring public transport ridership changes

Lizana,

Choudhury,

Watling

2024

PLoS ONE

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Aggregated mobility indices (AMIs) derived from information and communications technologies have recently emerged as a new data source for transport planners, with particular value during periods of major disturbances or when other sources of mobility data are scarce. Particularly, indices estimated on the aggregate user concentration in public transport (PT) hubs based on GPS of smartphones, or the number of PT navigation queries in smartphone applications have been used as proxies for the temporal changes in PT aggregate demand levels. Despite the popularity of these indices, it remains largely untested whether they can provide a reasonable characterisation of actual PT ridership changes. This study aims to address this research gap by investigating the reliability of using AMIs for inferring PT ridership changes by offering the first rigorous benchmarking between them and ridership data derived from smart card validations and tickets. For the comparison, we use monthly and daily ridership data from 12 cities worldwide and two AMIs shared globally by Google and Apple during periods of major change in 2020–22. We also explore the complementary role of AMIs on traditional ridership data. The comparative analysis revealed that the index based on human mobility (Google) exhibited a notable alignment with the trends reported by ridership data and performed better than the one based on PT queries (Apple). Our results differ from previous studies by showing that AMIs performed considerably better for similar periods. This finding highlights the huge relevance of dealing with methodological differences in datasets before comparing. Moreover, we demonstrated that AMIs can also complement data from smart card records when ticketing is missing or of doubtful quality. The outcomes of this study are particularly relevant for cities of developing countries, which usually have limited data to analyse their PT ridership, and AMIs may offer an attractive alternative.

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