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<p>The quality of public transport is essential when considering urban mobility in large cities. Several factors, such as the increase in urban population, rain, and traffic events, can impact mobility, causing congestion. Addressing this issue is essential for the population and is part of the UN’s 2030 Agenda for Sustainable Development goals. Integrating data from different sources is crucial to understanding and planning urban traffic. This work aims to provide a dataset with spatiotemporal information on the mobility of municipal buses, including the estimated emission of polluting gases and the rainfall volume in Rio de Janeiro from 2014 to 2023. Its format facilitates integration with other Rio de Janeiro City Hall datasets, enabling the increase and deepening of the analyses. This work is the first to combine data from bus observation with positional information on neighborhoods and rainfall regions, rainfall volumes, and pollutant gas emissions. Thus, its availability opens opportunities for research topics involving public transport associated with environmental indicators and data science with time series studies and positional data.</p>

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Dataset on bus mobility and environmental indicators from Rio de Janeiro

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月26日

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  • Diego Carvalho,
  • Vinícius Vancellote,
  • Pablo Moreira Casais,
  • João Luiz Carabetta,
  • Bruno Almeida,
  • Fabio Porto,
  • Eduardo Mendes,
  • Renato Rocha Souza,
  • Douglas de O. Cardoso,
  • Peter Fernandes Wanke,
  • Rafael Barbastefano,
  • Rafaelli Coutinho,
  • Diego Brandão,
  • Miguel Diaz-Cacho,
  • André Mendes,
  • Eduardo Ogasawara

摘要

The quality of public transport is essential when considering urban mobility in large cities. Several factors, such as the increase in urban population, rain, and traffic events, can impact mobility, causing congestion. Addressing this issue is essential for the population and is part of the UN’s 2030 Agenda for Sustainable Development goals. Integrating data from different sources is crucial to understanding and planning urban traffic. This work aims to provide a dataset with spatiotemporal information on the mobility of municipal buses, including the estimated emission of polluting gases and the rainfall volume in Rio de Janeiro from 2014 to 2023. Its format facilitates integration with other Rio de Janeiro City Hall datasets, enabling the increase and deepening of the analyses. This work is the first to combine data from bus observation with positional information on neighborhoods and rainfall regions, rainfall volumes, and pollutant gas emissions. Thus, its availability opens opportunities for research topics involving public transport associated with environmental indicators and data science with time series studies and positional data.