BRBus - building a dataset for geospatial monitoring of buses in Brazilian cities
Abstract
In Brazil, according to ANTP data, in cities with more than 60,000 inhabitants, 85% of trips made by public transport take place using buses. Considering the problem of Urban Mobility, geospatial data produced by different devices (e.g. buses, personal cars, traffic lights, radars) offer great analytical potential, being relevant for making decisions that impact the quality of life in smart cities. In this work, we describe the process of collecting, standardizing and enriching BRBus – a dataset with information from the geospatial monitoring of bus traffic in four Brazilian cities. BRBus is available in an open format and covers data collected between 12/06/2023 and 20/06/2023.
Keywords:
Geospatial data, Dataset, Public transportation, Buses, Gps data monitoring
References
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Braz, T., Maciel, M., Mestre, D. G., Andrade, N., Pires, C. E., Queiroz, A. R., and Santos, V. B. (2018). Estimating inefficiency in bus trip choices from a user perspective with schedule, positioning, and ticketing data. IEEE Transactions on Intelligent Transportation Systems, 19(11):3630–3641
Cruz Junior, J. I. S. d. (2020). Métodos de análise de dados no transporte público urbano. Monografia (Trabalho de Conclusão de Curso) - Ciência da Computação - Universidade Federal de Campina Grande, Campina Grande, 2020.
CSC (2022). Ranking connected smart cities 2022. Disponível em: [link]. [Acessado 20-Jun-2023]
da Cruz, S. M. S., Andrade, L. S., and Sampaio, J. O. (2016). Explorando dados abertos governamentais sobre a mobilidade urbana na cidade do rio de janeiro. In Anais do XLIII Seminario Integrado de Software e Hardware, pages 1645–1655. SBC.
de Transporte Público, A. N. (2018). Sistema de informações da mobilidade urbana da associação nacional de transportes público-simob/antp
Jose, R. and Mitra, S. (2018). Identifying and classifying highway bottlenecks based on spatial and temporal variation of speed. Journal of Transportation Engineering, PartA: Systems, 144(12):04018075.
Larsen, G. H., Yoshioka, L. R., and Marte, C. L. (2020). Bus travel times prediction based on real-time traffic data forecast using artificial neural networks. In 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), pages 1–6.
Queiroz, A. R. M., Santos, V. B., Nascimento, D. C., and Pires, C. E. S. (2019). Conformity analysis of GTFS routes and bus trajectories. In Anais do XXXIV Simpósio Brasileiro de Banco de Dados, pages 199–204. SBC
Statista (2016). IoT devices installed base worldwide 2015-2025 — Statista — statista.com. [link]. [Acessado 02-Jan2023]
TAO, W. (2013). Interdisciplinary urban GIS for smart cities: advancements and opportunities. Geo-spatial Information Science, 16(1):25–34
Published
2023-09-25
How to Cite
MELO, Ruan T.; VASCONCELOS, Felipe F.; SILVA, Rafael Luciano L.; SANTOS, Pedro Victor; RAMOS, Vinicius T.; COUTINHO, Fabio J..
BRBus - building a dataset for geospatial monitoring of buses in Brazilian cities. In: DATASET SHOWCASE WORKSHOP (DSW), 5. , 2023, Belo Horizonte/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 25-35.
DOI: https://doi.org/10.5753/dsw.2023.234242.
