Detecting regional dominant movement patterns in trajectory data with a convolutional neural network

References

• Belongie, S., Malik, J., and Puzicha, J., 2002. Shape matching and object recognition using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24 (4), 509–522. doi:10.1109/34.993558
   Web of Science ®Google Scholar
• Bogorny, V., Kuijpers, B., and Alvares, L.O., 2009. ST-DMQL: a semantic trajectory data mining query language. International Journal of Geographical Information Science, 23 (10), 1245–1276. doi:10.1080/13658810802231449
   Web of Science ®Google Scholar
• Bresenham, J.E., 1965. Algorithm for computer control of a digital plotter. IBM Systems Journal, 4 (1), 25–30. doi:10.1147/sj.41.0025
   Google Scholar
• Cao, H., Mamoulis, N., and Cheung, D.W., 2005. Mining frequent spatio-temporal sequential patterns. In: Proceedings - IEEE international conference on data mining, ICDM, 82–89. doi:10.1016/j.chroma.2005.02.027
   Google Scholar
• Choi, D.W., Pei, J., and Heinis, T., 2017. Efficient mining of regional movement patterns in semantic trajectories. Proceedings of the VLDB Endowment, 10 (13), 2073–2084. doi:10.14778/3151106.3151111
   Web of Science ®Google Scholar
• Dalal, N. and Triggs, B., 2005. Histograms of oriented gradients for human detection. In: Proceedings of the 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05) - Volume 1 - Volume 01, CVPR ’05. Washington, DC: IEEE Computer Society, 886–893. doi:10.1213/01.ANE.0000159168.69934.CC
   Google Scholar
• De Lucca Siqueira, F. and Bogorny, V., 2011. Discovering chasing behavior in moving object trajectories. Transactions in GIS, 15 (5), 667–688. doi:10.1111/tgis.2011.15.issue-5
   Web of Science ®Google Scholar
• Demšar, U. and Virrantaus, K., 2010. Space–time density of trajectories: exploring spatio-temporal patterns in movement data. International Journal of Geographical Information Science, 24 (10), 1527–1542. doi:10.1080/13658816.2010.511223
   Web of Science ®Google Scholar
• Dodge, S., Weibel, R., and Lautenschütz, A.K., 2008. Towards a taxonomy of movement patterns. Information Visualization, 7 (April), 240–252. doi:10.1057/PALGRAVE.IVS.9500182
   Google Scholar
• Endo, Y., et al. 2016. Classifying spatial trajectories using representation learning. International Journal of Data Science and Analytics, 2 (3), 107–117. doi:10.1007/s41060-016-0014-1
   Google Scholar
• Ester, M., et al., 1996. A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the second international conference on knowledge discovery and data mining, KDD’96. Portland: Oregon AAAI Press, 226–231.
   Google Scholar
• Graves, A., Mohamed, A.R., and Hinton, G., 2013. Speech recognition with deep recurrent neural networks. In: 2013 IEEE international conference on acoustics, speech and signal processing, May Vancouver, BC. Canada: IEEE, 6645–6649.
   Google Scholar
• Guo, D., et al., 2012. Discovering spatial patterns in origin-destination mobility data. Transactions in GIS, 16 (3), 411–429. doi:10.1111/tgis.2012.16.issue-3
   Web of Science ®Google Scholar
• He, K., et al., 2015. Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37 (9), 1904–1916. doi:10.1109/TPAMI.2015.2389824
   PubMed Web of Science ®Google Scholar
• Hinton, G.E., Osindero, S., and Teh, Y.W., 2006. A fast learning algorithm for deep belief nets. Neural Computation, 18 (7), 1527–1554. doi:10.1162/neco.2006.18.7.1527
   PubMed Web of Science ®Google Scholar
• Karpathy, A., et al., 2014. Large-scale video classification with convolutional neural networks. In: Proceedings of the 2014 IEEE conference on computer vision and pattern recognition, CVPR ’14. Washington, DC: IEEE Computer Society, 1725–1732. doi:10.1007/s00330-014-3197-7
   Google Scholar
• Kim, Y., 2014. Convolutional neural networks for sentence classification. CoRR, computing research repository, abs/1408.5882.
   Google Scholar
• Krizhevsky, A., Sutskever, I., and Hinton, G.E., 2012. ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th international conference on neural information processing systems - Volume 1, NIPS’12. Lake Tahoe, Nevada: Curran Associates Inc, 1097–1105.
   Google Scholar
• Laube, P., Imfeld, S., and Weibel, R., 2005. Discovering relative motion patterns in groups of moving point objects. International Journal of Geographical Information Science, 19 (6), 639–668. doi:10.1080/13658810500105572
   Web of Science ®Google Scholar
• Lecun, Y., et al., 1998. Gradient-based learning applied to document recognition. In: Proceedings of the IEEE, 2278–2324.
   Google Scholar
• Lee, J.G., Han, J., and Li, X., 2015. A unifying framework of mining trajectory patterns of various temporal tightness. IEEE Transactions on Knowledge and Data Engineering, 27 (6), 1478–1490. doi:10.1109/TKDE.2014.2377742
   Web of Science ®Google Scholar
• Li, B., et al., 2011. Hunting or waiting? Discovering passenger-finding strategies from a large-scale real-world taxi dataset. In: 2011 IEEE international conference on pervasive computing and communications workshops (PERCOM Workshops), March, 63–68. doi:10.1016/j.tvjl.2010.05.029
   Google Scholar
• Li, Z., Wu, F., and Crofoot, M.C., 2013. Mining following relationships in movement data. In: 2013 IEEE 13th international conference on data mining (ICDM), 458–467.
   Google Scholar
• Mikolov, T., et al., 2010. Recurrent neural network based language model. In: Eleventh annual conference of the international speech communication association, Chiba, Japan.
   Google Scholar
• Prince, S.J.D., 2012. Computer vision: models, learning, and inference. 1st ed. New York, NY: Cambridge University Press.
   Google Scholar
• Ren, S., et al., 2015. Faster r-cnn: towards real-time object detection with region proposal networks. In: Advances in neural information processing systems, Montreal, Canada, 91–99.
   Google Scholar
• Shu, X. and Wu, X.J., 2011. A novel contour descriptor for 2D shape matching and its application to image retrieval. Image and Vision Computing, 29 (4), 286–294. doi:10.1016/j.imavis.2010.11.001
   Web of Science ®Google Scholar
• Wachowicz, M., et al. 2011. Finding moving flock patterns among pedestrians through collective coherence. International Journal of Geographical Information Science, 25 (11), 1849–1864. doi:10.1080/13658816.2011.561209
   Web of Science ®Google Scholar
• Wang, D., et al., 2017a. DeepSD: supply-demand prediction for online car-hailing services using deep neural networks. In: 2017 IEEE 33rd international conference on data engineering (ICDE), April, San Diego, California, 243–254.
   Google Scholar
• Wang, J., et al., 2017b. Automatic intersection and traffic rule detection by mining motor-vehicle GPS trajectories. Computers, Environment and Urban Systems, 64, 19–29. doi:10.1016/j.compenvurbsys.2016.12.006
   Web of Science ®Google Scholar
• Wang, X., et al. 2014. Bag of contour fragments for robust shape classification. Pattern Recognition, 47 (6), 2116–2125. doi:10.1016/j.patcog.2013.12.008
   Web of Science ®Google Scholar
• Xu, C., Liu, J., and Tang, X., 2009. 2D shape matching by contour flexibility. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31 (1), 180–186. doi:10.1109/TPAMI.2008.199
   PubMed Web of Science ®Google Scholar
• Yang, C. and Gidfalvi, G., 2018. Mining and visual exploration of closed contiguous sequential patterns in trajectories. International Journal of Geographical Information Science, 32 (7), 1282–1304. doi:10.1080/13658816.2017.1393542
   Web of Science ®Google Scholar
• Yao, D., et al., 2017. Trajectory clustering via deep representation learning. In: 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, 3880–3887.
   Google Scholar
• Yao, H., et al., 2018. Deep multi-view spatial-temporal network for taxi demand prediction. In: AAAI conference on artificial intelligence, New Orleans, Louisiana.
   Google Scholar
• Ye, Y., et al., 2009. Mining individual life pattern based on location history. In: 2009 Tenth international conference on mobile data management: systems, services and middleware IEEE, Taipei, Taiwan, 1–10.
   Google Scholar
• Zeiler, M.D., 2012. ADADELTA: an adaptive learning rate method. CoRR, computing research repository, abs/1212.5701. doi:10.1094/PDIS-11-11-0999-PDN
   Google Scholar
• Zhang, J., Zheng, Y., and Qi, D., 2017. Deep spatio-temporal residual networks for citywide crowd flows prediction. In: AAAI conference on artificial intelligence, San Francisco, California, 1655–1661.
   Google Scholar