A hybrid integrated deep learning model for the prediction of citywide spatio-temporal flow volumes

References

• Abadi, M., et al. 2016. Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467.
   Google Scholar
• Bishop, C.M., 2006. Pattern recognition and machine learning (information science and statistics). Springer-Verlag Berlin, Heidelberg.
   Google Scholar
• Caruana, R., Lawrence, S., and Giles, C.L., 2001. Overfitting in neural nets: Backpropagation, conjugate gradient, and early stopping. Advances in Neural Information Processing Systems, Vancouver, British Columbia, Canada, 402–408.
   Google Scholar
• Chen, J., et al., 2018. Fine-grained prediction of urban population using mobile phone location data. International Journal of Geographical Information Science,  32 (9), 1770–1786.
   Web of Science ®Google Scholar
• Chen, P.T., Chen, F., and Qian, Z., 2016. Road traffic congestion monitoring in social media with Hinge-loss Markov random fields. In: IEEE International conference on data mining, Shenzhen, China. 80–89.
   Google Scholar
• Cheng, T., et al., 2014. A dynamic spatial weight matrix and localized space–time autoregressive integrated moving average for network modeling. Geographical Analysis, 46 (1), 75–97. doi:10.1111/gean.12026
   Web of Science ®Google Scholar
• Cheng, T. and Wang, J., 2008. Integrated spatio-temporal data mining for forest fire prediction. Transactions in GIS, 12 (5), 591–611. doi:10.1111/tgis.2008.12.issue-5
   Google Scholar
• Cheng, T. and Wang, J., 2009. Accommodating spatial associations in DRNN for space–time analysis. Computers, Environment and Urban Systems, 33 (6), 409–418. doi:10.1016/j.compenvurbsys.2009.08.004
   Web of Science ®Google Scholar
• Cheng, T., Wang, J., and Li, X., 2008. Space-time series forecasting by artificial neural networks. In: International conference on earth observation data processing and analysis (ICEODPA), Wuhan, China. 72853I.
   Google Scholar
• Chollet, F. 2015. Keras: Deep learning library for theano and tensorflow. URL: https://keras.io/k, 7, 8.
   Google Scholar
• Gao, S., et al., 2013. Understanding urban traffic-flow characteristics: a rethinking of betweenness centrality. Environment and Planning B: Planning and Design, 40 (1), 135–153. doi:10.1068/b38141
   Web of Science ®Google Scholar
• Gong, L., et al., 2016. Inferring trip purposes and uncovering travel patterns from taxi trajectory data. Cartography and Geographic Information Science, 43 (2), 103–114. doi:10.1080/15230406.2015.1014424
   Web of Science ®Google Scholar
• Graves, A., Mohamed, A.R., and Hinton, G., 2013. Speech recognition with deep recurrent neural networks. In: IEEE International conference on acoustics, speech and signal processing, Vancouver, British Columbia, Canada. 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., 2016a. Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR). 27–30 June, Seattle, USA, 770–778.
   Google Scholar
• He, K., et al., 2016b. Identity mappings in deep residual networks. European Conference on Computer Vision, Amsterdam, Netherlands, 630–645.
   Google Scholar
• Heeren, T. and D’agostino, R., 1987. Robustness of the two independent samples t‐test when applied to ordinal scaled data. Statistics in Medicine, 6 (1), 79–90.
   PubMed Web of Science ®Google Scholar
• Hinton, G.E. and Salakhutdinov, R.R., 2006. Reducing the dimensionality of data with neural networks. Science, 313 (5786), 504–507. doi:10.1126/science.1127647
   PubMed Web of Science ®Google Scholar
• Hoang, M.X., Zheng, Y., and Singh, A.K., 2016. FCCF: forecasting citywide crowd flows based on big data. In: The ACM sigspatial international conference. Burlingame, California, 1–10.
   Google Scholar
• Hochreiter, S. and Schmidhuber, J., 1997. Long short-term memory. Neural Computation, 9 (8), 1735–1780.
   PubMed Web of Science ®Google Scholar
• Ioffe, S. and Szegedy, C. 2015. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167.
   Google Scholar
• Ke, J., et al., 2017. Short-term forecasting of passenger demand under on-demand ride services: A spatio-temporal deep learning approach. Transportation Research Part C: Emerging Technologies, 85, 591–608. doi:10.1016/j.trc.2017.10.016
   Web of Science ®Google Scholar
• Kingma, D. and Ba, J., 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
   Google Scholar
• Lecun, Y., Bengio, Y., and Hinton, G., 2015. Deep learning. Nature, 521 (7553), 436–444. doi:10.1038/nature14539
   PubMed Web of Science ®Google Scholar
• Lei, T., Zhang, Y., and Artzi, Y. 2017. Training rnns as fast as cnns. arXiv preprint arXiv:1709.02755.
   Google Scholar
• Ma, X., et al., 2015. Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transportation Research Part C: Emerging Technologies, 54, 187–197. doi:10.1016/j.trc.2015.03.014
   Web of Science ®Google Scholar
• Ma, X., et al., 2017. Learning traffic as images: A deep convolutional neural network for large-scale transportation network speed prediction. Sensors, 17 (4). doi:10.3390/s17050968
   Web of Science ®Google Scholar
• Ma, Z., et al., 2014. Predicting short-term bus passenger demand using a pattern hybrid approach. Transportation Research Part C: Emerging Technologies, 39, 148–163. doi:10.1016/j.trc.2013.12.008
   Web of Science ®Google Scholar
• Nair, V. and Hinton, G.E., 2010. Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th international conference on machine learning (ICML-10), Haifa, Israel, 807–814.
   Google Scholar
• Shaw, S.-L., Tsou, M.-H., and Ye, X., 2016. Editorial: human dynamics in the mobile and big data era. International Journal of Geographical Information Science, 30 (9), 1687–1693. doi:10.1080/13658816.2016.1164317
   Web of Science ®Google Scholar
• Shen, J. and Cheng, T., 2016. A framework for identifying activity groups from individual space-time profiles. International Journal of Geographical Information Science, 30 (9), 1785–1805. doi:10.1080/13658816.2016.1139119
   Web of Science ®Google Scholar
• Shi, X., et al., 2015. Convolutional LSTM network: A machine learning approach for precipitation nowcasting. In: Advances in neural information processing systems, Montreal, Canada, 802–810.
   Google Scholar
• Silva, R., Kang, S.M., and Airoldi, E.M., 2015. Predicting traffic volumes and estimating the effects of shocks in massive transportation systems. Proceedings of the National Academy of Sciences of the United States of America, 112 (18), 5643–5648. doi:10.1073/pnas.1412908112
   PubMed Web of Science ®Google Scholar
• Simonyan, K. and Zisserman, A. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
   Google Scholar
• Wang, J., et al., 2010. STARIMA for journey time prediction in London. In: Proceedings of the 5th IMA conference on mathematics in transport, London, UK.
   Google Scholar
• Wang, J., Cheng, T., and Li, X., 2007. Nonlinear integration of spatial and temporal forecasting by support vector machines. In: International conference on fuzzy systems and knowledge discovery. Xi’an, China, 61–66.
   Google Scholar
• Wang, J., Tsapakis, I., and Zhong, C., 2016. A space–time delay neural network model for travel time prediction. Engineering Applications of Artificial Intelligence, 52 (C), 145–160. doi:10.1016/j.engappai.2016.02.012
   Web of Science ®Google Scholar
• Wu, Y. and Tan, H. 2016. Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework. arXiv preprint arXiv:1612.01022.
   Google Scholar
• Zhang, J., et al., 2016. DNN-based prediction model for spatio-temporal data. In: ACM Sigspatial international conference on advances in geographic information systems, California, USA, 92.
   Google Scholar
• Zhang, J., et al., 2018a. Predicting citywide crowd flows using deep spatio-temporal residual networks. Artificial Intelligence, 259, 147–166. doi:10.1016/j.artint.2018.03.002
   Google Scholar
• Zhang, L., et al., 2018b. Attribute trajectory analysis: a framework to analyse attribute changes using trajectory analysis techniques. International Journal of Geographical Information Science, (2), 1–17. doi:10.1080/13658816.2018.1554812
   Google Scholar
• Zheng, Y., et al., 2014. Urban computing: Concepts, methodologies, and applications. Acm Transactions on Intelligent Systems & Technology, 5 (3), 1–55.
   Web of Science ®Google Scholar
• Zhu, D., et al., 2018. Inferring spatial interaction patterns from sequential snapshots of spatial distributions. International Journal of Geographical Information Science, 32 (4), 783–805. doi:10.1080/13658816.2017.1413192
   Web of Science ®Google Scholar
• Zhu, X. and Guo, D., 2014. Mapping large spatial flow data with hierarchical clustering. Transactions in GIS, 18 (3), 421–435. doi:10.1111/tgis.2014.18.issue-3
   Web of Science ®Google Scholar