Analyzing Patterns of a Bicycle Sharing System for Generating Rental Flow Predictive Models
Resumo
Urban mobility has been highlighted as one of the most relevant themes in Smart Cities. Alongside this, following a principle of resource optimization and seeking greater sustainability, bicycle sharing systems have stood out as a resource that can be used to assess urban mobility. The correct analysis of these data and the understanding of the dynamics in these systems can aid in decision making, in addition to optimize the complex urban mobility system. Thus, this work analyzes a Bicycle-Sharing System dataset, which is enriched for us with meteorological and seasonal information. In order to achieve our results, we recognize cyclist activity patterns related to date and climate information, as well as we identify a set of parameters that influences bicycle rental flow. Finally, we explore the relationship between these parameters and patterns, in order to present predictive regression models for rental flow prediction. In our results, Random Forest algorithm was the best approach for the creation of an effective regression model, explaining 95% of the explanatory variables.
Palavras-chave:
Cidades Inteligentes, Serviços Compartilhados, Avaliação de Performance
Referências
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Correa, R., da Cunha, K. B., and Boareto, R. (2010). A bicicleta e as cidades: como inserir a bicicleta na política de mobilidade urbana. Instituto de energia e meio ambi-ente, 2 edition. [In Portuguese].
Düsing, R. (2000). Knowledge discovery in databases. Wirtschaftsinformatik, 42(1):74-75.
Echeverry-Galvis, M., Peterson, J., and Caceres, R. (2014). The social nestwork: Tree structure determines nest placement in kenyan weaverbird colonies. PloS one, 9:e88761.
Fanaee-T, H. and Gama, J. (2013). Event labeling combining ensemble detectors and background knowledge. Progress in Artificial Intelligence, pages 1-15.
Fishman, E., Washington, S., and Haworth, N. (2013). Bike share: A synthesis of the literature. Transport Reviews, 33(2):148-165.
Georgescu, M., Pavaloaia, V., Popescul, D., and Tugui, A. (2015). The race for making up the list of emergent smart cities. an eastern european country's approach. Transfor-mations in Business and Economics, 14:529-549.
Hamilton, T. L. and Wichman, C. J. (2018). Bicycle infrastructure and traffic conges-tion: Evidence from dc's capital bikeshare. Journal of Environmental Economics and Management, 87:72 -93.
J.Ham and Kamber, M. (2011). Data Mining: Concepts and Techniques. Morgan Kauf-mann Publishers is an imprint of Elsevier, 3 edition.
Jäppinen, S., Toivonen, T., and Salonen, M. (2013). Modelling the potential effect of shared bicycles on public transport travel times in greater helsinki: An open data ap-proach. Applied Geography, 43:13 -24.
Kaltenbrunner, A., Meza, R., Grivolla, J., Codina, J., and Banchs, R. (2010). Urban cycles and mobility patterns: Exploring and predicting trends in a bicycle-based public transport system. Pervasive and Mobile Computing, 6(4):455 -466. Human Behavior in Ubiquitous Environments: Modeling of Human Mobility Patterns.
Kassambara, A. (2018). Machine Learning Essentials: Practical Guide in R. STHDA.
Long, W. J., Griffith, J. L., Selker, H. P., and D'agostino, R. B. (1993). A comparison of logistic regression to decision-tree induction in a medical domain. Computers and Biomedical Research, 26(1):74-97.
Moncayo-Martínez, L. A. and Ramirez-Nafarrate, A. (2016). Visualization of the mobility patterns in the bike-sharing transport systems in mexico city. In 2016 IEEE Interna-tional Conference on Industrial Engineering and Engineering Management (IEEM), pages 1851-1855.
Mátrai, T. and Tóth, J. (2016). Comparative assessment of public bike sharing sys-tems. Transportation Research Procedia, 14:2344 -2351. Transport Research Arena TRA2016.
Randhawa, A. and Kumar, A. (2017). Exploring sustainability of smart development initiatives in india. International Journal of Sustainable Built Environment, 6(2):701 -710.
Razzaque, M. A. and Clarke, S. (2015). Smart management of next generation bike sharing systems using internet of things. In 2015 IEEE First International Smart Cities Conference (ISC2), pages 1-8.
Souza, L. C. and Gomes, E. T. A. (2014). O uso da bicicleta como meio de transporte: Mo-bilidade urbana na cidade do recife. In Anais do I Congresso Brasileiro de Geografia Política, Geopolítica e Gestão do Território, pages 384-395. Letra1. [In Portuguese].
System, C. B. S. (2018). Capital bike sharing trip history data.
Vogel, P., Greiser, T., and Mattfeld, D. C. (2011). Understanding bike-sharing systems using data mining: Exploring activity patterns. Procedia -Social and Behavioral Sci-ences, 20:514 -523.
Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York.
Wickham, H. and Grolemund, G. (2017). R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. O'Reilly Media, 1 edition.
Zheng, Y., Capra, L., Wolfson, O., and Yang, H. (2014). Urban computing: Concepts, methodologies, and applications. ACM Trans. Intell. Syst. Technol., 5(3):38:1-38:55.
Chen, L. and Jakubowicz, J. (2015). Inferring bike trip patterns from bike sharing system open data. In 2015 IEEE International Conference on Big Data (Big Data), pages 2898-2900.
Correa, R., da Cunha, K. B., and Boareto, R. (2010). A bicicleta e as cidades: como inserir a bicicleta na política de mobilidade urbana. Instituto de energia e meio ambi-ente, 2 edition. [In Portuguese].
Düsing, R. (2000). Knowledge discovery in databases. Wirtschaftsinformatik, 42(1):74-75.
Echeverry-Galvis, M., Peterson, J., and Caceres, R. (2014). The social nestwork: Tree structure determines nest placement in kenyan weaverbird colonies. PloS one, 9:e88761.
Fanaee-T, H. and Gama, J. (2013). Event labeling combining ensemble detectors and background knowledge. Progress in Artificial Intelligence, pages 1-15.
Fishman, E., Washington, S., and Haworth, N. (2013). Bike share: A synthesis of the literature. Transport Reviews, 33(2):148-165.
Georgescu, M., Pavaloaia, V., Popescul, D., and Tugui, A. (2015). The race for making up the list of emergent smart cities. an eastern european country's approach. Transfor-mations in Business and Economics, 14:529-549.
Hamilton, T. L. and Wichman, C. J. (2018). Bicycle infrastructure and traffic conges-tion: Evidence from dc's capital bikeshare. Journal of Environmental Economics and Management, 87:72 -93.
J.Ham and Kamber, M. (2011). Data Mining: Concepts and Techniques. Morgan Kauf-mann Publishers is an imprint of Elsevier, 3 edition.
Jäppinen, S., Toivonen, T., and Salonen, M. (2013). Modelling the potential effect of shared bicycles on public transport travel times in greater helsinki: An open data ap-proach. Applied Geography, 43:13 -24.
Kaltenbrunner, A., Meza, R., Grivolla, J., Codina, J., and Banchs, R. (2010). Urban cycles and mobility patterns: Exploring and predicting trends in a bicycle-based public transport system. Pervasive and Mobile Computing, 6(4):455 -466. Human Behavior in Ubiquitous Environments: Modeling of Human Mobility Patterns.
Kassambara, A. (2018). Machine Learning Essentials: Practical Guide in R. STHDA.
Long, W. J., Griffith, J. L., Selker, H. P., and D'agostino, R. B. (1993). A comparison of logistic regression to decision-tree induction in a medical domain. Computers and Biomedical Research, 26(1):74-97.
Moncayo-Martínez, L. A. and Ramirez-Nafarrate, A. (2016). Visualization of the mobility patterns in the bike-sharing transport systems in mexico city. In 2016 IEEE Interna-tional Conference on Industrial Engineering and Engineering Management (IEEM), pages 1851-1855.
Mátrai, T. and Tóth, J. (2016). Comparative assessment of public bike sharing sys-tems. Transportation Research Procedia, 14:2344 -2351. Transport Research Arena TRA2016.
Randhawa, A. and Kumar, A. (2017). Exploring sustainability of smart development initiatives in india. International Journal of Sustainable Built Environment, 6(2):701 -710.
Razzaque, M. A. and Clarke, S. (2015). Smart management of next generation bike sharing systems using internet of things. In 2015 IEEE First International Smart Cities Conference (ISC2), pages 1-8.
Souza, L. C. and Gomes, E. T. A. (2014). O uso da bicicleta como meio de transporte: Mo-bilidade urbana na cidade do recife. In Anais do I Congresso Brasileiro de Geografia Política, Geopolítica e Gestão do Território, pages 384-395. Letra1. [In Portuguese].
System, C. B. S. (2018). Capital bike sharing trip history data.
Vogel, P., Greiser, T., and Mattfeld, D. C. (2011). Understanding bike-sharing systems using data mining: Exploring activity patterns. Procedia -Social and Behavioral Sci-ences, 20:514 -523.
Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York.
Wickham, H. and Grolemund, G. (2017). R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. O'Reilly Media, 1 edition.
Zheng, Y., Capra, L., Wolfson, O., and Yang, H. (2014). Urban computing: Concepts, methodologies, and applications. ACM Trans. Intell. Syst. Technol., 5(3):38:1-38:55.
Publicado
10/09/2019
Como Citar
FERREIRA VIANA, Johnattan Douglas ; BRAGA, Oton Crispim; SILVA, Lenardo Chaves e; MENDES NETO, Francisco Milton.
Analyzing Patterns of a Bicycle Sharing System for Generating Rental Flow Predictive Models. In: WORKSHOP DE COMPUTAÇÃO URBANA (COURB), 3. , 2019, Gramado.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 57-70.
ISSN 2595-2706.
DOI: https://doi.org/10.5753/courb.2019.7468.
