Uma Abordagem para Geração de Séries Temporais de Mobilidade Urbana Baseada em Aprendizado Profundo
Resumo
Um dos grandes desafios na coleta e divulgação de dados de mobilidade urbana está no fato de que esses dados possuem informações que podem comprometer a privacidade dos usuários. Uma alternativa a esse problema é a geração de dados sintéticos que possam preservar as características dos dados reais. Este trabalho analisa a eficácia da utilização de um modelo estatístico clássico e propõe o uso de algoritmos de aprendizado profundo, como as Redes Generativas Adversarias (GANs, em inglês) para geração de séries temporais baseadas em dados de mobilidade urbana. As séries geradas foram comparadas com os dados reais por meio de uma análise visual e uma análise quantitativa. Os resultados mostraram que os modelos baseados em aprendizado profundo são capazes de gerar dados com as mesmas características dos dados reais.
Referências
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Qu, Y., Yu, S., Zhou, W., and Tian, Y. (2020). Gan-driven personalized spatial-temporal private data sharing in cyber-physical social systems. IEEE Transactions on Network Science and Engineering.
Ribeiro, I., Castanheira, L., Schaeffer-Filho, A., Cordeiro, W., and Mota, V. (2020). Caracterização de mobilidade e detecção de comunidades baseadas em tópicos de interesse. In Anais do XXXVIII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 603–616, Porto Alegre, RS, Brasil. SBC.
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Uppoor, S., Trullols-Cruces, O., Fiore, M., and Barcelo-Ordinas, J. M. (2013). Generation and analysis of a large-scale urban vehicular mobility dataset. IEEE Transactions on Mobile Computing, 13(5):1061–1075.
Yoon, J., Jarrett, D., and van der Schaar, M. (2019). Time-series generative adversarial networks.
Zhang, G. P. (2003). Time series forecasting using a hybrid arima and neural network model. Neurocomputing, 50:159–175.
Zhang, H., Wu, Y., Tan, H., Dong, H., Ding, F., and Ran, B. (2020). Understanding and IEEE modeling urban mobility dynamics via disentangled representation learning. Transactions on Intelligent Transportation Systems.
Zheng, Y., Zhang, L., Xie, X., and Ma, W. (2009). Mining interesting locations and travel sequences from gps trajectories. In World wide web, pages 791–800. ACM.
Zhu, J.-Y., Park, T., Isola, P., and Efros, A. A. (2017). Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proceedings of the IEEE international conference on computer vision, pages 2223–2232.
Brock, A., Donahue, J., and Simonyan, K. (2018). Large scale gan training for high fidelity natural image synthesis. arXiv preprint arXiv:1809.11096.
Brockwell, P. J., Brockwell, P. J., Davis, R. A., and Davis, R. A. (2016). Introduction to time series and forecasting. Springer.
Esteban, C., Hyland, S. L., and Rätsch, G. (2017). Real-valued (medical) time series generation with recurrent conditional gans. arXiv preprint arXiv:1706.02633.
Fanaee-T, H. and Gama, J. (2013). Event labeling combining ensemble detectors and background knowledge. Progress in Artificial Intelligence, pages 1–15.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014). Generative adversarial nets. In Advances in neural information processing systems, pages 2672–2680.
Gupta, A., Johnson, J., Fei-Fei, L., Savarese, S., and Alahi, A. (2018). Social gan: Socially acceptable trajectories with generative adversarial networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2255–2264.
Isola, P., Zhu, J.-Y., Zhou, T., and Efros, A. A. (2017). Image-to-image translation with conditional adversarial networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1125–1134.
Jauhri, A., Stocks, B., Li, J. H., Yamada, K., and Shen, J. P. (2020). Generating realistic ride-hailing datasets using gans. ACM Transactions on Spatial Algorithms and Systems (TSAS), 6(3):1–14.
Keskar, N. S., Mudigere, D., Nocedal, J., Smelyanskiy, M., and Tang, P. T. P. (2016). On large-batch training for deep learning: Generalization gap and sharp minima. arXiv preprint arXiv:1609.04836.
Ledig, C., Theis, L., Huszár, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al. (2017). Photo-realistic single image superresolution using a generative adversarial network. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4681–4690.
Lei, K., Qin, M., Bai, B., Zhang, G., and Yang, M. (2019). Gcn-gan: A non-linear temporal link prediction model for weighted dynamic networks. In IEEE INFOCOM 2019-IEEE Conference on Computer Communications, pages 388–396. IEEE.
Malandrino, F., Chiasserini, C., and Kirkpatrick, S. (2018). Cellular network traces towards 5g: Usage, analysis and generation. IEEE Transactions on Mobile Computing, 17(3):529–542.
Mogren, O. (2016). C-rnn-gan: Continuous recurrent neural networks with adversarial training. arXiv preprint arXiv:1611.09904.
Montgomery, D. C. and Hines, W. W. (1980). Probability and statistics in engineering and management science. John Wiley & Sons.
Mota, V. F., Cunha, F. D., Macedo, D. F., Nogueira, J. M., and Loureiro, A. A. (2014). Protocols, mobility models and tools in opportunistic networks: A survey. Computer Communications, 48:5 – 19. Opportunistic networks.
Qu, Y., Yu, S., Zhou, W., and Tian, Y. (2020). Gan-driven personalized spatial-temporal private data sharing in cyber-physical social systems. IEEE Transactions on Network Science and Engineering.
Ribeiro, I., Castanheira, L., Schaeffer-Filho, A., Cordeiro, W., and Mota, V. (2020). Caracterização de mobilidade e detecção de comunidades baseadas em tópicos de interesse. In Anais do XXXVIII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 603–616, Porto Alegre, RS, Brasil. SBC.
Song, H. Y., Baek, M. S., and Sung, M. (2019). Generating human mobility route based on generative adversarial network. In 2019 Federated Conference on Computer Science and Information Systems (FedCSIS), pages 91–99. IEEE.
Thomé, M., Prestes, A., Gomes, R., and Mota, V. (2020). Um arcabouço para detecção In Anais do XXXVIII e alerta de anomalias de mobilidade urbana em tempo real. Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 784– 797. SBC.
Uppoor, S., Trullols-Cruces, O., Fiore, M., and Barcelo-Ordinas, J. M. (2013). Generation and analysis of a large-scale urban vehicular mobility dataset. IEEE Transactions on Mobile Computing, 13(5):1061–1075.
Yoon, J., Jarrett, D., and van der Schaar, M. (2019). Time-series generative adversarial networks.
Zhang, G. P. (2003). Time series forecasting using a hybrid arima and neural network model. Neurocomputing, 50:159–175.
Zhang, H., Wu, Y., Tan, H., Dong, H., Ding, F., and Ran, B. (2020). Understanding and IEEE modeling urban mobility dynamics via disentangled representation learning. Transactions on Intelligent Transportation Systems.
Zheng, Y., Zhang, L., Xie, X., and Ma, W. (2009). Mining interesting locations and travel sequences from gps trajectories. In World wide web, pages 791–800. ACM.
Zhu, J.-Y., Park, T., Isola, P., and Efros, A. A. (2017). Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proceedings of the IEEE international conference on computer vision, pages 2223–2232.
Publicado
16/08/2021
Como Citar
RIBEIRO, Iran F.; SIMOURA, Gabriel; RAMOS, Heitor S.; COMARELA, Giovanni; MOTA, Vinícius F. S..
Uma Abordagem para Geração de Séries Temporais de Mobilidade Urbana Baseada em Aprendizado Profundo. In: WORKSHOP DE COMPUTAÇÃO URBANA (COURB), 5. , 2021, Uberlândia.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 251-264.
ISSN 2595-2706.
DOI: https://doi.org/10.5753/courb.2021.17118.
