sc 18: e7

Research Article

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

Download66 downloads
  • @ARTICLE{10.4108/eai.4-5-2021.169580,
        author={Vit\^{o}ria Albuquerque and Francisco Andrade and Jo\"{a}o Carlos Ferreira and Miguel Sales Dias and Fernando Bacao},
        title={Bike-sharing mobility patterns: a data-driven analysis for the city of Lisbon},
        journal={EAI Endorsed Transactions on Smart Cities: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={SC},
        year={2021},
        month={5},
        keywords={bike-sharing system, urban mobility patterns, statistical analysis, cluster analysis},
        doi={10.4108/eai.4-5-2021.169580}
    }
    
  • Vitória Albuquerque
    Francisco Andrade
    João Carlos Ferreira
    Miguel Sales Dias
    Fernando Bacao
    Year: 2021
    Bike-sharing mobility patterns: a data-driven analysis for the city of Lisbon
    SC
    EAI
    DOI: 10.4108/eai.4-5-2021.169580
Vitória Albuquerque1, Francisco Andrade2, João Carlos Ferreira2,3,*, Miguel Sales Dias1,2, Fernando Bacao1
  • 1: NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312 Lisboa, Portugal
  • 2: Instituto Universitário de Lisboa (ISCTE-IUL), ISTAR, 1649-026 Lisboa, Portugal
  • 3: Inov Inesc Inovação—Instituto de Novas Tecnologias, 1000-029 Lisbon, Portugal
*Contact email: joao.carlos.ferreira@iscte-iul.pt

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.