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

EAI Endorsed Transactions on Smart Cities

Francisco Andrade¹  João Ferreira²  Fernando Bacao³  Vitória Albuquerque³  Miguel Dias⁴ 

[1] Instituto Universitário de Lisboa (ISCTE-IUL), ISTAR, 1649-026 Lisboa, Portugal;Instituto Universitário de Lisboa (ISCTE-IUL), ISTAR, 1649-026 Lisboa, PortugalInov Inesc Inovação—Instituto de Novas Tecnologias, 1000-029 Lisbon, Portugal;NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312 Lisboa, Portugal;NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312 Lisboa, PortugalInstituto Universitário de Lisboa (ISCTE-IUL), ISTAR, 1649-026 Lisboa, Portugal;

关键词: bike-sharing system;    urban mobility patterns;    statistical analysis;    cluster analysis;   

DOI  :  10.4108/eai.4-5-2021.169580

来源: DOAJ

【 摘 要 】

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 spatiotemporalstationandtripactivitypatternsin the Lisbon BSS,based in2018 datatakenasthebaseline, and understandtripratechanges in such system,thathappenedinthefollowingyearsof 2019and2020.Furthermore, ourpaperaimsto understandtheCOVID-19pandemicimpactin BSS mobilitypatterns.Inthispaper,we analyzed large datasets adopting a CRISP-DM data mining method. By studying and identifying spatiotemporal distributionoftripsthroughstations,combinedwithweatherfactors, welookedat BSS improvementsmoresuitableto accommodate users’demand. Ourmajorcontributionwas anewinsighton howpeoplemoveinthecityusingbikes,via a data science approach using BSS network usage data. Major findings show that most bike trips occur on weekdays, withnoprecipitation,andweobserved asubstantial growthof trip count,duringtheobservedtimeframe,althoughcutshort by the pandemic. We believe that our approach can be applied to any city with available urban mobility data.

【 授权许可】

Unknown