CityTracks-RT: An application for real-time travel mode detection in urban centers
Abstract
Context-aware applications in intelligent transport systems have a growing need for travel mode detection systems. However, few applications allow real-time travel mode detection through the use of smartphones. In this paper, we propose a real-time travel mode detection application based on GPS traces using a data mining technique through which these traces are preprocessed, grouped in motion segments and classified by supervised machine learning algorithms. An application prototype was implemented on the Android platform, used by smartphones, for movement data collection and user travel mode detection using the WEKA API in Java. Finally, to evaluate the performance of the application in a real environment, field tests were carried out with dozens of volunteers in the metropolitan area of Rio de Janeiro through which 1338 travel mode inferences were obtained by four machine learning techniques and the results were evaluated and compared through the indicators of the confusion matrix. Thus, through the performance evaluation carried out, it was possible to verify that the proposed application is useful for real-time travel mode detection in urban centers.
References
A. d. P. Braga, A. C. P. d. L. Ferreira, and T. B. Ludermir. Redes Neurais Artificiais: Teoria e Prática. LTC, São Paulo, 2007.
CERT.br. Centro de Estudos, Resposta e Tratamento de Incidentes de Segurança no Brasil - Estatísticas dos Incidentes Reportados ao CERT.br. Disponível em:
R. Fonseca, P. Silva, and R. Silva. Acordo inter-juízes: o caso do coeficiente Kappa. Laboratório de Psicologia, 5(1):81–90, 2013.
I. V. M. d. Lima et al. Uma abordagem simplificada de detecção de intrusão baseada em redes neurais artificiais. Florianópolis, SC, 2005.
M. Salem, S. Reissmann, and U. Buehler. Persistent dataset generation using real-time operative framework. In Conference on Computing, Networking and Communications International, pages 1023–1027. IEEE, 2014.
H. N. Scalco. Reconhecimento de intrusão em redes de computadores utilizando pybrain. In: 12 Congresso Brasileiro de Inteligência Computacional, 2015, Paraná. Anais..., pages 1–6.
H. Scalco Neto. Sistema de detecção de intrusão em redes de computadores com técnicas de inteligência computacional. Master’s thesis, Universidade Federal de Lavras, 2016. Disponivel em:
P. M. K. Scarfone. Guide to Intrusion Detection and Prevention Systems (IDPS). National Institute of Standards and Technology Gaithersburg, Gaithersburg, 2007.
T. Schaul, J. Bayer, D. Wierstra, Y. Sun, M. Felder, F. Sehnke, T. Rückstieß, and J. Schmidhuber. Pybrain. The Journal of Machine Learning Research, 11:743–746, 2010.
T. Schaul and J. Schmidhuber. Scalable neural networks for board games. In: ALIPPI, C. et al. Artificial neural networks–ICANN 2009, pages 1005–1014, 2009.
A. Shiravi, H. Shiravi, M. Tavallaee, and A. A. Ghorbani. Toward developing a systematic approach to generate benchmark datasets for intrusion detection. Computers & Security, 31(3):357–374, 2012.
H. Simon. Redes neurais. Bookman, Porto Alegre, 2001.
E. P. d. Souza and J. A. S. Monteiro. Estudo sobre sistema de detecção de intrusão por anomalias: Uma abordagem utilizando redes neurais. In: Workshop de gerência e operação de redes e serviços, 14, 2008, Vitória. Anais..., 2008.
J. Stolfo, W. Fan, W. Lee, A. Prodromidis, and P. K. Chan. Cost-based modeling and evaluation for data mining with application to fraud and intrusion detection. [s.n.], New York, 2000.
J. Q. Uchoa. Algoritmos imunoinspirados aplicados em segurança computacional: utilizaçao de algoritmos inspirados no sistema imune para detecçao de intrusos em redes de computadores, 2009. Tese (Doutorado em Bioinformática) – Universidade Federal de Minas Gerais, Belo Horizonte, 2009.
J. Wang. Computer network security. Springer, Massachusetts, 2009.
