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Characterization of Public Transit Mobility Patterns of Different Economic Classes

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  • Priscila Santin

    (Programa de Pós-Graduação em Engenharia Elétrica e Informática Industrial, Universidade Tecnológica Federal do Paraná, Curitiba PR 80230-901, Brazil)

  • Fernanda R. Gubert

    (Programa de Pós-Graduação em Engenharia Elétrica e Informática Industrial, Universidade Tecnológica Federal do Paraná, Curitiba PR 80230-901, Brazil)

  • Mauro Fonseca

    (Programa de Pós-Graduação em Engenharia Elétrica e Informática Industrial, Universidade Tecnológica Federal do Paraná, Curitiba PR 80230-901, Brazil)

  • Anelise Munaretto

    (Programa de Pós-Graduação em Engenharia Elétrica e Informática Industrial, Universidade Tecnológica Federal do Paraná, Curitiba PR 80230-901, Brazil)

  • Thiago Henrique Silva

    (Programa de Pós-Graduação em Engenharia Elétrica e Informática Industrial, Universidade Tecnológica Federal do Paraná, Curitiba PR 80230-901, Brazil
    School of Cities, University of Toronto, Toronto, ON M5S 0C9, Canada)

Abstract

This paper analyzes public transit mobility of different economic classes of Curitiba, Brazil, exploring an official smart card dataset provided by the city. With the population divided into subsets corresponding to economic strata, we characterized vital spatial-temporal transit usage patterns, such as departure times and destinations reached by different economic classes. We also constructed a network representing the common origin and destination of public transit users, enabling discovering distinct patterns. Among the results, we observe that with the increase in wealth, the morning activity is postponed (on average for 2 h), and the spatial distribution of the trips becomes more localized compared with lower classes. We also show that our model captures fairly well realistic mobility patterns exploring a cheaper and larger-scale data source by comparing our results with a household travel survey from Curitiba. Understand how people in different economic classes appropriate urban spaces help to provide subsidies for, e.g., more sustainable economic development propositions.

Suggested Citation

  • Priscila Santin & Fernanda R. Gubert & Mauro Fonseca & Anelise Munaretto & Thiago Henrique Silva, 2020. "Characterization of Public Transit Mobility Patterns of Different Economic Classes," Sustainability, MDPI, vol. 12(22), pages 1-24, November.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:22:p:9603-:d:446931
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    References listed on IDEAS

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    1. Anastasios Noulas & Salvatore Scellato & Renaud Lambiotte & Massimiliano Pontil & Cecilia Mascolo, 2012. "A Tale of Many Cities: Universal Patterns in Human Urban Mobility," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-10, May.
    2. Daniel Oviedo & Lynn Scholl & Marco Innao & Lauramaria Pedraza, 2019. "Do Bus Rapid Transit Systems Improve Accessibility to Job Opportunities for the Poor? The Case of Lima, Peru," Sustainability, MDPI, vol. 11(10), pages 1-24, May.
    3. Miranda, Hellem de Freitas & Rodrigues da Silva, Antônio Nélson, 2012. "Benchmarking sustainable urban mobility: The case of Curitiba, Brazil," Transport Policy, Elsevier, vol. 21(C), pages 141-151.
    4. Scholl, Lynn & Oviedo, Daniel & Innao, Marco & Pedraza, Lauramaría, 2018. "Do Bus Rapid Transit Systems Improve Accessibility to Jobs?: The Case of Lima, Peru," IDB Publications (Working Papers) 9451, Inter-American Development Bank.
    5. Zhang, Xiaohu & Xu, Yang & Tu, Wei & Ratti, Carlo, 2018. "Do different datasets tell the same story about urban mobility — A comparative study of public transit and taxi usage," Journal of Transport Geography, Elsevier, vol. 70(C), pages 78-90.
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