Bike-sharing mobility patterns: a data-driven analysis for the city of Lisbon

Albuquerque, Vitória and Andrade, Francisco and Ferreira, João Carlos and Dias, Miguel Sales and Bacao, Fernando (2021) Bike-sharing mobility patterns: a data-driven analysis for the city of Lisbon. EAI Endorsed Transactions on Smart Cities. e7. ISSN 2518-3893

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Abstract

New technologies applied to transportation services in the city, enable the shift to sustainable transportation modes making bike-sharing systems (BSS) more popular in the urban mobility scenario. This study focuses on understanding the spatiotemporal station and trip activity patterns in the Lisbon BSS, based in 2018 data taken as the baseline, and understand trip rate changes in such system, that happened in the following years of 2019 and 2020. Furthermore, our paper aims to understand the COVID-19 pandemic impact in BSS mobility patterns. In this paper, we analyzed large datasets adopting a CRISP-DM data mining method. By studying and identifying spatiotemporal distribution of trips through stations, combined with weather factors, we looked at BSS improvements more suitable to accommodate users’ demand. Our major contribution was a new insight on how people move in the city using bikes, via a data science approach using BSS network usage data. Major findings show that most bike trips occur on weekdays, with no precipitation, and we observed a substantial growth of trip count, during the observed time frame, although cut short by the pandemic. We believe that our approach can be applied to any city with available urban mobility data.

Item Type: Article
Uncontrolled Keywords: bike-sharing system, urban mobility patterns, statistical analysis, cluster analysis
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
T Technology > T Technology (General)
Depositing User: EAI Editor IV
Date Deposited: 27 Jul 2021 13:49
Last Modified: 27 Jul 2021 13:49
URI: https://eprints.eudl.eu/id/eprint/5192

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