Deep spatio-temporal residual neural networks for road-network-based data modeling

References

• Abadi, M., et al., 2016. Tensorflow: A system for large-scale machine learning. (ed.), Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation, Savannah, GA, USA, 265–283.
   Google Scholar
• Boeing, G., 2017. OSMnx: new methods for acquiring, constructing, analyzing, and visualizing complex street networks. Computers, Environment and Urban Systems, 65, 126–139.
   Web of Science ®Google Scholar
• Box, G.E. and Pierce, D.A., 1970. Distribution of residual autocorrelations in autoregressive-integrated moving average time series models. Journal of the American Statistical Association, 65 (332), 1509–1526.
   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, 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
• 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.
   Web of Science ®Google Scholar
• Cheng, T., Haworth, J., and Wang, J., 2011. Spatio-temporal autocorrelation of road network data. Journal of Geographical Systems, 14 (4), 389–413.
   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.
   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.
   Web of Science ®Google Scholar
• Chollet, F. 2015. Keras: Deep learning library for theano and tensorflow. io/k, 7, 8. Available from: https://keras
   Google Scholar
• Chuxing, D., Didi Chuxing [online]. Available from: https://outreach.didichuxing.com.
   Google Scholar
• Haworth, J., et al. 2014. Local online kernel ridge regression for forecasting of urban travel times. Transportation Research Part C: Emerging Technologies, 46, 151–178.
   Web of Science ®Google Scholar
• He, K., et al., 2016a. Deep residual learning for image recognition. ed. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 27–30 June 2016, Seattle, USA, 770–778.
   Google Scholar
• He, K., et al., 2016b. Identity mappings in deep residual networks. ed. European Conference on Computer Vision, Amsterdam, Netherlands, 630–645.
   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. ed. The ACM Sigspatial International Conference, San Francisco Bay Area, California, USA ,1–10.
   Google Scholar
• Huang, W., et al., 2015. Predicting human mobility with activity changes. International Journal of Geographical Information Science, 29 (9), 1569–1587.
   Web of Science ®Google Scholar
• Jiang, B., 2009. Ranking spaces for predicting human movement in an urban environment. International Journal of Geographical Information Science, 23 (7), 823–837.
   Web of Science ®Google Scholar
• Jiang, B. and Liu, C., 2009. Street-based topological representations and analyses for predicting traffic flow in GIS. International Journal of Geographical Information Science, 23 (9), 1119–1137.
   Web of Science ®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.
   Web of Science ®Google Scholar
• Kingma, D. and Ba, J., 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
   Google Scholar
• Krizhevsky, A., Sutskever, I., and Hinton, G.E., 2012. ImageNet classification with deep convolutional neural networks. ed. Advances in neural information processing systems, Stateline, Nevada, USA, 1097-1105.
   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
• Li, X., et al., 2016. T-DesP: Destination prediction based on big trajectory data. IEEE Transactions on Intelligent Transportation Systems, 17 (8), 2344–2354.
   Web of Science ®Google Scholar
• Lv, Y., et al. 2015. Traffic flow prediction with big data: a deep learning approach. IEEE Transactions on Intelligent Transportation Systems, 16 (2), 865–873.
   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.
   Web of Science ®Google Scholar
• Newson, P. and Krumm, J., 2009. Hidden Markov map matching through noise and sparseness. ed. Proceedings of the 17th ACM SIGSPATIAL international conference on advances in geographic information systems, Seattle, WA, USA, 336–343. DOI:10.1177/1753193408101464
   Google Scholar
• Rosser, G., et al., 2016. Predictive crime mapping: Arbitrary grids or street networks? Journal of Quantitative Criminology, 33 (3), 569–594.
   Web of Science ®Google Scholar
• Schmidhuber, J., 2015. Deep learning in neural networks: An overview. Neural networks, 61, 85–117.
   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.
   Web of Science ®Google Scholar
• Stockwell, D., 1999. The GARP modelling system: problems and solutions to automated spatial prediction. International Journal of Geographical Information Science, 13 (2), 143–158.
   Web of Science ®Google Scholar
• Wang, J., et al., 2010. STARIMA for journey time prediction in London. ed. 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. ed. 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.
   Google Scholar
• Xingjian, S., et al. 2015. Convolutional LSTM network: A machine learning approach for precipitation nowcasting. Advances in Neural Information Processing Systems, Montréal, Quebec, Canada, 802–810.
   Google Scholar
• Yu, H., et al., 2017. Spatiotemporal recurrent convolutional networks for traffic prediction in transportation networks. Sensors (Basel, Switzerland), 17, 7. doi:10.3390/s17050968
   Web of Science ®Google Scholar
• Zhang, J., et al., 2016. DNN-based prediction model for spatio-temporal data. ed. Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, California, USA, 92.
   Google Scholar
• Zhang, J., et al., 2017b. Predicting citywide crowd flows using deep spatio-temporal residual networks. arXiv preprint arXiv:1701.02543.
   Google Scholar
• Zhang, J., et al. 2018. Predicting citywide crowd flows using deep spatio-temporal residual networks. Artificial Intelligence, 259, 147–166.
   Google Scholar
• Zhang, J., Zheng, Y., and Qi, D., 2017a. Deep spatio-temporal residual networks for citywide crowd flows prediction. ed. Aaai, 1655–1661.
   Google Scholar
• Zhang, Y., and Cheng, T., 2019. A deep learning approach to infer employment status of passengers by using smart card data. IEEE Transactions on Intelligent Transportation Systems. doi:10.1109/TITS.2019.2896460
   Google Scholar
• Zhu, X. and Guo, D., 2014. Mapping large spatial flow data with hierarchical clustering. Transactions in GIS, 18 (3), 421–435.
   Web of Science ®Google Scholar