Exploratory Analysis of Public Transportation Data in Curitiba
Resumo
Smart transportation systems have been providing more data over time (such as bus routes, users, smartphones, etc.). Such data provides a number of opportunities to identify various facets of user behavior and traffic trends. In this paper we address some of the urban mobility challenges (already discussed by the Brazilian Computer Society), from a number of different perspectives, including (i) pattern discovery, (ii) statistical analysis, (iii) data integration, and (iv) open and connected data. In particular, we present an exploratory data analysis with GIS for public transportation toward a case study in Curitiba, Brazil.
Referências
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Barczyszyn, G. L. (2015). Integração de dados geográficos para planejamento urbano da cidade de Curitiba. Universidade Tecnológica Federal do Paraná.
Butler, J. A. (2008). Designing Geodatabases for Transportation. Esri Press.
Calixto, A., Diniz, F. B., and Zannin, P. (2003). The statistical modeling of road traffic noise in an urban setting. Cities, 20:1–74.
Chourabi, H., Nam, T., Walker, S., Gil-Garcia, J. R., Mellouli, S., Nahon, K., Pardo, T. A., and Scholl, H. J. (2012). Understanding smart cities: An integrative framework. In System Science (HICSS), 2012 45th Hawaii International Conference on, pages 2289–2297.
da F. Costa, L., Rodrigues, F. A., Travieso, G., and Boas, P. R. V. (2007). Characterization of complex networks: A survey of measurements. Advances in Physics, 56(1):167–242.
de Oliveira, T. H. M., Painho, M., and Henriques, R. (2012). A spatial decision support system for the portuguese public transportation sector. In IWGS ’12, pages 84–90, New York, NY, USA. ACM.
Duarte, F., Gadda, T., Luna, C. A. M., and Souza, F. T. (2016). What to expect from the future leaders of bogot´a and curitiba in terms of public transport: Opinions and practices among university students. Transportation Research Part F: Traffic Psychology and Behaviour, 38:7 – 21.
Goodchild, M. F. (2000). Gis and transportation: Status and challenges. Geoinformatica, 4(2):127–139.
Hartwig, F. and Dearing, B. (1979). Exploratory Data Analysis. 07. SAGE Publications.
Martinez, W., Martinez, A., and Solka, J. (2010). Exploratory Data Analysis with MATLAB, Second Edition. Chapman & Hall/CRC Computer Science & Data Analysis. Taylor & Francis.
Mennis, J. and Guo, D. (2009). Spatial data mining and geographic knowledge discoveryan introduction. Computers, Environment and Urban Systems, 33(6):403 – 408. Spatial Data Mining-Methods and Applications.
NIST/SEMATECH (2012). E-Handbook of Statistical Methods, available at http://www.itl.nist.gov/div898/handbook/.
Park, H.-S. and Kim, J.-D. (2011). Modeling and Analysis of DTN in Metropolitan Bus Network. In ICUIMC ’11, pages 20:1–20:10, New York, NY, USA. ACM.
Sebastiani, M. T., Luders, R., and Fonseca, K. V. O. (2016). Evaluating electric bus operation for a real-world brt public transportation using simulation optimization. IEEE Transactions on Intelligent Transportation Systems, PP(99):1–10.
Souza, R., Oliveira, I. P., Junior, F., Sales, L., and Ferraz, F. (2015). Beyond efficiency: How to use geolocation applications to improve citizens well-being. In The Fourth International Conference on Smart Systems, Devices and Technologies, pages 37 – 40.
Stenneth, L., Wolfson, O., Yu, P. S., and Xu, B. (2011). Transportation mode detection using mobile phones and gis information. In GIS ’11, pages 54–63, New York, NY, USA. ACM.
Suresh, D. C., Agrawal, B., Yang, J., and Najjar, W. (2009). Energy-efficient encoding techniques for off-chip data buses. ACM Trans. Embed. Comput. Syst., 8(2):9:1–9:23.
Vilajosana, I., Llosa, J., Martinez, B., Domingo-Prieto, M., Angles, A., and Vilajosana, X. (2013). Bootstrapping smart cities through a self-sustainable model based on big data flows. IEEE Communications Magazine, 51(6):128–134.
Zannin, P. H. T., Calixto, A., Diniz, F. B., and C., J. A. (2003). A survey of urban noise annoyance in a large brazilian city: the importance of a subjective analysis in conjunction with an objective Environmental Impact Assessment Review, 23:245–255.
Zannin, P. H. T., Diniz, F. B., and Barbosa, W. A. (2002). Environmental noise pollution in the city of Curitiba, Brazil . Applied Acoustics, 63(4):351 – 358.
Barczyszyn, G. L. (2015). Integração de dados geográficos para planejamento urbano da cidade de Curitiba. Universidade Tecnológica Federal do Paraná.
Butler, J. A. (2008). Designing Geodatabases for Transportation. Esri Press.
Calixto, A., Diniz, F. B., and Zannin, P. (2003). The statistical modeling of road traffic noise in an urban setting. Cities, 20:1–74.
Chourabi, H., Nam, T., Walker, S., Gil-Garcia, J. R., Mellouli, S., Nahon, K., Pardo, T. A., and Scholl, H. J. (2012). Understanding smart cities: An integrative framework. In System Science (HICSS), 2012 45th Hawaii International Conference on, pages 2289–2297.
da F. Costa, L., Rodrigues, F. A., Travieso, G., and Boas, P. R. V. (2007). Characterization of complex networks: A survey of measurements. Advances in Physics, 56(1):167–242.
de Oliveira, T. H. M., Painho, M., and Henriques, R. (2012). A spatial decision support system for the portuguese public transportation sector. In IWGS ’12, pages 84–90, New York, NY, USA. ACM.
Duarte, F., Gadda, T., Luna, C. A. M., and Souza, F. T. (2016). What to expect from the future leaders of bogot´a and curitiba in terms of public transport: Opinions and practices among university students. Transportation Research Part F: Traffic Psychology and Behaviour, 38:7 – 21.
Goodchild, M. F. (2000). Gis and transportation: Status and challenges. Geoinformatica, 4(2):127–139.
Hartwig, F. and Dearing, B. (1979). Exploratory Data Analysis. 07. SAGE Publications.
Martinez, W., Martinez, A., and Solka, J. (2010). Exploratory Data Analysis with MATLAB, Second Edition. Chapman & Hall/CRC Computer Science & Data Analysis. Taylor & Francis.
Mennis, J. and Guo, D. (2009). Spatial data mining and geographic knowledge discoveryan introduction. Computers, Environment and Urban Systems, 33(6):403 – 408. Spatial Data Mining-Methods and Applications.
NIST/SEMATECH (2012). E-Handbook of Statistical Methods, available at http://www.itl.nist.gov/div898/handbook/.
Park, H.-S. and Kim, J.-D. (2011). Modeling and Analysis of DTN in Metropolitan Bus Network. In ICUIMC ’11, pages 20:1–20:10, New York, NY, USA. ACM.
Sebastiani, M. T., Luders, R., and Fonseca, K. V. O. (2016). Evaluating electric bus operation for a real-world brt public transportation using simulation optimization. IEEE Transactions on Intelligent Transportation Systems, PP(99):1–10.
Souza, R., Oliveira, I. P., Junior, F., Sales, L., and Ferraz, F. (2015). Beyond efficiency: How to use geolocation applications to improve citizens well-being. In The Fourth International Conference on Smart Systems, Devices and Technologies, pages 37 – 40.
Stenneth, L., Wolfson, O., Yu, P. S., and Xu, B. (2011). Transportation mode detection using mobile phones and gis information. In GIS ’11, pages 54–63, New York, NY, USA. ACM.
Suresh, D. C., Agrawal, B., Yang, J., and Najjar, W. (2009). Energy-efficient encoding techniques for off-chip data buses. ACM Trans. Embed. Comput. Syst., 8(2):9:1–9:23.
Vilajosana, I., Llosa, J., Martinez, B., Domingo-Prieto, M., Angles, A., and Vilajosana, X. (2013). Bootstrapping smart cities through a self-sustainable model based on big data flows. IEEE Communications Magazine, 51(6):128–134.
Zannin, P. H. T., Calixto, A., Diniz, F. B., and C., J. A. (2003). A survey of urban noise annoyance in a large brazilian city: the importance of a subjective analysis in conjunction with an objective Environmental Impact Assessment Review, 23:245–255.
Zannin, P. H. T., Diniz, F. B., and Barbosa, W. A. (2002). Environmental noise pollution in the city of Curitiba, Brazil . Applied Acoustics, 63(4):351 – 358.
Publicado
04/07/2016
Como Citar
KOZIEVITCH, Nádia P.; GADDA, Tatiana M. C.; FONSECA, Keiko V. O.; ROSA, Marcelo O.; GOMES JR., Luiz C.; ABKAR, Monika.
Exploratory Analysis of Public Transportation Data in Curitiba. In: SEMINÁRIO INTEGRADO DE SOFTWARE E HARDWARE (SEMISH), 43. , 2016, Porto Alegre.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2016
.
p. 1656-1667.
ISSN 2595-6205.
DOI: https://doi.org/10.5753/semish.2016.9516.
