Como inferir o meio de transporte das pessoas em cenários urbanos a partir de smartphones
Resumo
Este trabalho apresenta um mecanismo para classificar o meio de transporte utilizado pelas pessoas em cenários urbanos por meio de traces (registros) de movimento coletados por smartphone da própria pessoa (usuário do mecanismo). Além disso, uma análise do desempenho dos classificadores do modo de transporte é realizada com o objetivo de fornecer informações valiosas para sua utilização por sistemas cientes de contexto, em especial através de middleware com processamento na nuvem.
Referências
Apple Inc., Location Manager Class Reference, disponível em: [link], acessado em 20/04/2016.
Cram.org, Spherical trigonometry for geographic applications, página 16, disponível em: [link], acessado em 01/06/2016.
Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., “The KDD process for extracting useful knowledge from volumes of data”, Communications of the ACM, v. 39, n. 11, 1996.
Idrissov, A., Nascimento, M., Trajectory Cleaning Framework, Mobile Data Challenge (by Nokia) Workshop, 2012.
Noulas, A., Scellato, S., Lambiotte, R., et. al., “A tale of many cities: universal patterns in human urban mobility”, PloS one, Public Library of Science, v. 7, n. 5, 2012.
Reddy, S., Burke, J. Estrin D., Et al. “Determining Transportation Mode On Mobile Phones”, IEEE ISWC, page 25-28, 2008.
Reddy, S., Mun, M., Burke, J., et al., “Using mobile phones to determine transportation modes,” ACM Transactions on Sensor Networks, v. 6, n. 2, p. 13, 2010.
Russel, S., Norvig, P., Artificial Intelligence: A Modern Approach, 3rd edition, New Jersey - EUA, Prentice Hall, 2010.
Shafique, M. A., Hato E., “Travel Mode Detection with Varying Smartphone Data Collection Frequencies”, Sensors, 16(5), MDPI, p. 1-24, 2016.
Shin, D., Aliaga, D., Tunçer, B., et al., “Urban sensing: Using smartphones for transportation mode classification”, Computers, Environment and Urban Systems, v. 53, p. 76–86, 2015.
Sokolova, M., Japkowicz, N., Szpakowicz, S., “Beyond Accuracy, F-score and ROC: A Family of Discriminant Measures for Performance Evaluation”, AAI 2006, p. 1015-1021, Springer.
Stenneth, L., Wolfson, O., Yu, P., et al., “Transportation mode detection using mobile phones and GIS information”, 19th ACM SIGSPATIAL, p. 54-63, 2011.
Tan, P., Steinbach, M., Kumar, V., Introdução ao Data Mining, Mineração de Dados, Rio de janeiro - Brasil, Editora Ciência Moderna Ltda., 2009.
Thiagaranjan, A. “Probabilistic Models for Mobile Phone Trajectory Estimation”, Tese de doutorado, Massachusets Institute of Technology, Setembro de 2011.
Wu, X., Kumar, V., Quinlan, J. R., et al., “Top 10 algorithms in data mining”, Knowledge and Information Systems, 14(1), 1-37, 2008.
Zheng, Y., Liu, L., Wang, L., et al., “Learning transportation mode from raw GPS data for geographic applications on the web”, 17th ACM WWW, p. 247, 2008.
Zheng, Y., Li, Q., Chen, Y., Xie, X. and Ma, W., “Understanding mobility based on GPS data”, Proceedings of the 10th ACM Ubicomp, p. 312-321, 2008.
Zheng, Y., Zhang, L., Xie, X., and Ma, W., “Mining interesting locations and travel sequences from GPS trajectories”, Proc. of the 18th ACM WWW, p. 791-800, 2009.
Zheng, Y., Chen, Q., Xie, X., et al., “Understanding transportation modes based on GPS data for web applications,” ACM Transactions on the Web, v. 4, no. 1, p. 1-36, 2010.
Zhoua, X., Yub, W., Sullivan W. “Making pervasive sensing possible: Effective travel mode sensing based on smartphones”, Computers, Environment and Urban Systems, v. 58, 2016.
Cram.org, Spherical trigonometry for geographic applications, página 16, disponível em: [link], acessado em 01/06/2016.
Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., “The KDD process for extracting useful knowledge from volumes of data”, Communications of the ACM, v. 39, n. 11, 1996.
Idrissov, A., Nascimento, M., Trajectory Cleaning Framework, Mobile Data Challenge (by Nokia) Workshop, 2012.
Noulas, A., Scellato, S., Lambiotte, R., et. al., “A tale of many cities: universal patterns in human urban mobility”, PloS one, Public Library of Science, v. 7, n. 5, 2012.
Reddy, S., Burke, J. Estrin D., Et al. “Determining Transportation Mode On Mobile Phones”, IEEE ISWC, page 25-28, 2008.
Reddy, S., Mun, M., Burke, J., et al., “Using mobile phones to determine transportation modes,” ACM Transactions on Sensor Networks, v. 6, n. 2, p. 13, 2010.
Russel, S., Norvig, P., Artificial Intelligence: A Modern Approach, 3rd edition, New Jersey - EUA, Prentice Hall, 2010.
Shafique, M. A., Hato E., “Travel Mode Detection with Varying Smartphone Data Collection Frequencies”, Sensors, 16(5), MDPI, p. 1-24, 2016.
Shin, D., Aliaga, D., Tunçer, B., et al., “Urban sensing: Using smartphones for transportation mode classification”, Computers, Environment and Urban Systems, v. 53, p. 76–86, 2015.
Sokolova, M., Japkowicz, N., Szpakowicz, S., “Beyond Accuracy, F-score and ROC: A Family of Discriminant Measures for Performance Evaluation”, AAI 2006, p. 1015-1021, Springer.
Stenneth, L., Wolfson, O., Yu, P., et al., “Transportation mode detection using mobile phones and GIS information”, 19th ACM SIGSPATIAL, p. 54-63, 2011.
Tan, P., Steinbach, M., Kumar, V., Introdução ao Data Mining, Mineração de Dados, Rio de janeiro - Brasil, Editora Ciência Moderna Ltda., 2009.
Thiagaranjan, A. “Probabilistic Models for Mobile Phone Trajectory Estimation”, Tese de doutorado, Massachusets Institute of Technology, Setembro de 2011.
Wu, X., Kumar, V., Quinlan, J. R., et al., “Top 10 algorithms in data mining”, Knowledge and Information Systems, 14(1), 1-37, 2008.
Zheng, Y., Liu, L., Wang, L., et al., “Learning transportation mode from raw GPS data for geographic applications on the web”, 17th ACM WWW, p. 247, 2008.
Zheng, Y., Li, Q., Chen, Y., Xie, X. and Ma, W., “Understanding mobility based on GPS data”, Proceedings of the 10th ACM Ubicomp, p. 312-321, 2008.
Zheng, Y., Zhang, L., Xie, X., and Ma, W., “Mining interesting locations and travel sequences from GPS trajectories”, Proc. of the 18th ACM WWW, p. 791-800, 2009.
Zheng, Y., Chen, Q., Xie, X., et al., “Understanding transportation modes based on GPS data for web applications,” ACM Transactions on the Web, v. 4, no. 1, p. 1-36, 2010.
Zhoua, X., Yub, W., Sullivan W. “Making pervasive sensing possible: Effective travel mode sensing based on smartphones”, Computers, Environment and Urban Systems, v. 58, 2016.
Publicado
04/07/2016
Como Citar
QUINTELLA, Carlos Alvaro M. S.; CAMPOS, Carlos Alberto V.; ANDRADE, Leila C. V..
Como inferir o meio de transporte das pessoas em cenários urbanos a partir de smartphones. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO UBÍQUA E PERVASIVA (SBCUP), 8. , 2016, Porto Alegre.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2016
.
p. 986-995.
ISSN 2595-6183.
DOI: https://doi.org/10.5753/sbcup.2016.9447.
