Abstract
This paper addresses the problem of feature extraction for estimating users’ transportation modes from their movement trajectories. Previous studies have adopted supervised learning approaches and used engineers’ skills to find effective features for accurate estimation. However, such hand-crafted features cannot always work well because human behaviors are diverse and trajectories include noise due to measurement error. To compensate for the shortcomings of hand-crafted features, we propose a method that automatically extracts additional features using a deep neural network (DNN). In order that a DNN can easily handle input trajectories, our method converts a raw trajectory data structure into an image data structure while maintaining effective spatio-temporal information. A classification model is constructed in a supervised manner using both of the deep features and hand-crafted features. We demonstrate the effectiveness of the proposed method through several experiments using two real datasets, such as accuracy comparisons with previous methods and feature visualization.
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References
Arel, I., Rose, D.C., Karnowski, T.P.: Deep machine learning - a new Frontier in artificial intelligence research. IEEE Comput. Int. Mag. 5(4), 13–18 (2010)
Bengio, Y., Lamblin, P., Popovici, D., Larochelle, H.: Greedy layer-wise training of deep networks. In: NIPS, pp. 153–160 (2006)
Bengio, Y.: Learning deep architectures for AI. FTML 2(1), 1–127 (2009)
Dahl, G.E., Yu, D., Deng, L., Acero, A.: Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition. TASLP 20(1), 30–42 (2012)
Ermes, M., Parkka, J., Mantyjarvi, J., Korhonen, I.: Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions. IEEE Trans. Inform. Tech. Biomed. 12(1), 20–26 (2006)
Hinton, G.E., Salakhutdinov, R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)
Hung, C.-C., Peng, W.C., Lee, W.C.: Clustering and aggregating clues of trajectories for mining trajectory patterns and routes. VLDB J. 24(2), 169–192 (2015)
Krizhevsky, A., Sutskever, I., Hinton, G.: Imagenet classification with deep convolutional neural networks. In: NIPS. pp. 1106–1114 (2012)
Le, Q.V., Ngiam, J., Coates, A., Lahiri, A., Prochnow, B., Ng, A.Y.: On optimization methods for deep learning. In: ICML, pp. 265–272 (2011)
Liao, L., Fox, D., Kautz, H.: Learning and inferring transportation routines. In: AAAI 2004, pp. 348–353 (2004)
Parker, J.A., Kenyon, R.V., Troxel, D.: Comparison of interpolating methods for image resampling. IEEE Trans. Med. Imaging 2(1), 31–39 (1983)
Parkka, J., Ermes, M., Korpippa, P., Mantyjarvi, J., Peltola, J.: Activity classification using realistic data from wearable sensors. IEEE Trans. Inform. Technol. Biomed. 10(1), 119–128 (2006)
Patterson, D., Liao, L., Fox, D., Kautz, H.: Inferring high-level behavior from low-level sensors. In: UbiComp, pp. 73–89 (2003)
Shah, R.C., Wan, C.-Y., Lu, H., Nachman, L.: Classifying the mode of transportation on mobile phones using GIS information. In: UbiComp, pp. 225–229 (2014)
Shaw, B., Shea, J., Sinha, S., Hogue, A.: Learning to rank for spatiotemporal search. In: WSDM, pp. 717–726 (2013)
Song, X., Zhang, Q., Sekimoto, Y., Shibasaki, R.: Prediction of human emergency behavior and their mobility following large-scale disaster. In: KDD, pp. 5–14 (2014)
Toda, H., Yasuda, N., Matsuura, Y., Kataoka, R.: Geographic information retrieval to suit immediate surroundings. In: GIS, pp. 452–455 (2009)
Vedaldi, A., Fulkerson, B.: VLFeat: an open and portable library of computer vision algorithms. In: MM. pp. 1469–1472 (2010)
Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.-A.: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. JMLR 11, 3371–3408 (2010)
Zheng, Y.: Trajectory data mining: an overview. ACM TIST 6(3), 29 (2015)
Zheng, Y., Liu, L., Wang, L., Xie, X.: Learning transportation mode from raw GPS data for geographic applications on the web. In: WWW, pp. 247–256 (2008)
Zheng, Y., Li, Q., Chen, Y., Xie, X., Ma, W.-Y.: Understanding mobility based on GPS data. In: Ubicomp, pp. 312–321 (2008)
Zheng, Y., Chen, Y., Li, Q., Xie, X., Ma, W.-Y.: Understanding transportation modes based on GPS data for web applications. TWEB. 4(1), 1 (2010)
Zheng, Y., Zhou, X. (eds.): Computing with Spatial Trajectories. Springer, New York (2011)
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Endo, Y., Toda, H., Nishida, K., Kawanobe, A. (2016). Deep Feature Extraction from Trajectories for Transportation Mode Estimation. In: Bailey, J., Khan, L., Washio, T., Dobbie, G., Huang, J., Wang, R. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2016. Lecture Notes in Computer Science(), vol 9652. Springer, Cham. https://doi.org/10.1007/978-3-319-31750-2_5
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DOI: https://doi.org/10.1007/978-3-319-31750-2_5
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