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

Vitória Albuquerque, Francisco Andrade, João Carlos Ferreira, Miguel Sales Dias, Fernando Bacao

Research output: Contribution to journalArticlepeer-review

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.
Original languageEnglish
Article number169580
Pages (from-to)1-20
Number of pages20
JournalEAI Endorsed Transactions on Smart Cities
DOIs
Publication statusE-pub ahead of print - 4 May 2021

Keywords

  • Bike-sharing system
  • Urban mobility patterns
  • Statistical analysis
  • Cluster analysis

UN Sustainable Development Goals (SDGs)

  • SDG 11 - Sustainable Cities and Communities

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