Journal of Computer Science and Technology ›› 2020, Vol. 35 ›› Issue (2): 338-352.doi: 10.1007/s11390-020-9970-y
• Special Section on Learning and Mining in Dynamic Environments • Previous Articles Next Articles
Qiang Zhou, Jing-Jing Gu*, Member, CCF, Chao Ling, Wen-Bo Li, Yi Zhuang, Jian Wang, Senior Member, CCF, Member, ACM, IEEE
|  Zheng Y, Capra L, Wolfson O, Yang H. Urban computing:Concepts, methodologies, and applications. ACM Transactions on Intelligent Systems and Technology, 2014, 5(3):Article No. 38.
 Zhang J B, Zheng Y, Qi D K. Deep spatio-temporal residual networks for citywide crowd flows prediction. In Proc. the 31st AAAI Conference on Artificial Intelligence, February 2017, pp.1655-1661.
 Zheng Z, Yang Y, Liu J et al. Deep and embedded learning approach for traffic flow prediction in urban informatics. IEEE Transactions on Intelligent Transportation Systems, 2019, 20(10):3927-3939.
 Sun J, Zhang J, Li Q et al. Predicting citywide crowd flows in irregular regions using multi-view graph convolutional networks. arXiv:1903.07789, 2019. https://arxiv.org/abs/1903.07789,August 2019.
 Du B, Peng H, Wang S et al. Deep irregular convolutional residual LSTM for urban traffic passenger flows prediction. IEEE Transactions on Intelligent Transportation Systems. doi:10.1109/TITS.2019.2900481.
 Chai D, Wang L, Yang Q. Bike flow prediction with multigraph convolutional networks. In Proc. the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, November 2018, pp.397-400.
 Geng X, Li Y, Wang L et al. Spatiotemporal multi-graph convolution network for ride-hailing demand forecasting. In Proc. the 33rd AAAI Conference on Artificial Intelligence, January 2019, pp.3656-3663.
 Ramaswami A, Russell A G, Culligan P J et al. Metaprinciples for developing smart, sustainable, and healthy cities. Science, 2016, 352(6288):940-943.
 Ai Y, Li Z, Gan M et al. A deep learning approach on short-term spatiotemporal distribution forecasting of dockless bike-sharing system. Neural Computing and Applications, 2019, 31(5):1665-1677.
 Shuman D I, Narang S K, Frossard P, Ortega A, Vandergheynst P. The emerging field of signal processing on graphs:Extending high-dimensional data analysis to networks and other irregular domains. IEEE Signal Processing Magazine, 2013, 30(3):83-98.
 Li Y X, Zheng Y, Zhang H C, Chen L. Traffic prediction in a bike-sharing system. In Proc. the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems, November 2015, Article No. 33.
 Yao H, Wu F, Ke J, Tang X, Jia Y, Lu S, Gong P, Ye J, Li Z. Deep multi-view spatial-temporal network for taxi demand prediction. In Proc. the 32nd AAAI Conference on Artificial Intelligence, February 2018, pp.2588-2595.
 Holmgren J, Aspegren S, Dahlstroma J. Prediction of bicycle counter data using regression. Procedia Computer Science, 2017, 113:502-507.
 Kumar S V, Vanajakshi L. Short-term traffic flow prediction using seasonal ARIMA model with limited input data. European Transport Research Review, 2015, 7(3):Article No. 21.
 Abadi A, Rajabioun T, Ioannou P A. Traffic flow prediction for road transportation networks with limited traffic data. IEEE Transactions on Intelligent Transportation Systems, 2015, 16(2):653-662.
 Li Y, Yu R, Shahabi C, Liu Y. Diffusion convolutional recurrent neural network:Data-driven traffic forecasting. In Proc. the 6th International Conference on Learning Representations, April 2018.
 Cheng A Y, Jiang X, Li Y F, Zhang C, Zhu H. Multiple sources and multiple measures based traffic flow prediction using the chaos theory and support vector regression method. Physica A:Statistical Mechanics and its Applications, 2017, 466:422-434.
 Achar A, Bharathi D, Kumar B A et al. Bus arrival time prediction:A spatial Kalman filter approach. IEEE Transactions on Intelligent Transportation Systems. doi:10.1109/TITS.2019.2909314.
 Liu J M, Sun L L, Li Q, Ming J C, Liu Y C, Xiong H. Functional zone based hierarchical demand prediction for bike system expansion. In Proc. the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2017, pp.957-966.
 Liu J M, Sun L L, Chen W W, Xiong H. Rebalancing bike sharing systems:A multi-source data smart optimization. In Proc. the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2016, pp.1005-1014.
 Srivastava N, Mansimov E, Salakhutdinov R. Unsupervised learning of video representations using LSTMs. arXiv:1502.04681, 2015. https://arxiv.org/abs/1502.04681,August 2019.
 Bahdanau D, Cho K, Bengio Y. Neural machine translation by jointly learning to align and translate. arXiv:1409.0473, 2014. https://arxiv.org/abs/1409.0473,August 2019.
 Cho K, van Merrienboer B, Gulcehre C et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv:1406.1078, 2014. https://arxiv.org/abs/1406.1078,August 2019.
 Thirumalai C, Koppuravuri R. Bike sharing prediction using deep neural networks. JOIV:International Journal on Informatics Visualization, 2017, 1(3):83-87.
 Shi X J, Chen Z R, Wang H, Yeung D Y, Wong W K, WOO W C. Convolutional LSTM network:A machine learning approach for precipitation nowcasting. In Proc. the 2015 Annual Conference on Neural Information Processing Systems, December 2015, pp.802-810.
 Bruna J, Zaremba W, Szlam A et al. Spectral networks and locally connected networks on graphs. arXiv:1312.6203, 2013. https://arxiv.org/abs/1312.6203,August 2019.
 Defferrard M, Bresson X, Vandergheynst P. Convolutional neural networks on graphs with fast localized spectral filtering. In Proc. the 2016 Annual Conference on Neural Information Processing Systems, December 2016, pp.3844-3852.
 Kipf T N, Welling M. Semi-supervised classification with graph convolutional networks. arXiv:1609.02907, 2016. https://arxiv.org/abs/1609.02907,August 2019.
 Zhang X, He L, Chen K, Luo Y, Zhou J, Wang F. Multi-view graph convolutional network and its applications on neuroimage analysis for Parkinson's disease. arXiv:1805.08801, 2018. https://arxiv.org/abs/1805.08801,August 2019.
 Yao H, Tang X, Wei H, Zheng G, Yu Y, Li Z. Modeling spatial-temporal dynamics for traffic prediction. arXiv:1803.01254, 2018. https://arxiv.org/abs/1803.01254,August 2019.
 Yuan N J, Zheng Y, Xie X et al. Discovering urban functional zones using latent activity trajectories. IEEE Transactions on Knowledge and Data Engineering, 2015, 27(3):712-725.
 Erman J, Arlitt M F, Mahanti A. Traffic classification using clustering algorithms. In Proc. the 2nd Annual ACM Workshop on Mining Network Data, September 2006, pp.281-286.
 Cho K, van Merrienboer B, Bahdanau D et al. On the properties of neural machine translation:Encoder-decoder approaches. arXiv:1409.1259, 2014. https://arxiv.org/abs/1409.1259,August 2019.
 Friedman J H. Greedy function approximation:A gradient boosting machine. The Annals of Statistics, 2001, 29(5):1189-1232.
 Sutskever I, Vinyals O, Le Q V. Sequence to sequence learning with neural networks. In Proc. the 2014 Annual Conference on Neural Information Processing Systems, December 2014, pp.3104-3112.
|No related articles found!|