Abstract
Predicting traffic flow is of great importance to traffic management and public safety, and it has high requirements on accuracy and efficiency. However, the problem is very challenging because of high-dimensional features, spatial levels, and sequence dependencies. On the one hand, we propose an effective end-to-end model, called FedNet, to predict traffic flow of each region in a city. First, for the temporal trend, period, closeness properties, we obtain low-dimensional features by downsampling high-dimensional input features. Then we perform temporal fusion to get temporal aggregations of different spatial levels. Next, we generate traffic flow by upsampling the fused features which are obtained by combining the corresponding temporal aggregation and the output of the previous upsample block. Finally, the traffic flow is adjusted by external factors like weather and date. On the other hand, we transfer the original task into a sequence task and then use teacher forcing to train our model, which make it learn the sequence dependencies. We conduct extensive experiments on two types of traffic flow (new-flow/end-flow and inflow/outflow) in New York City and Beijing to demonstrate that the FedNet outperforms five well-known methods.
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References
Hoang, M.X., Zheng, Y., Singh, A.K.: FCCF: forecasting citywide crowd flows based on big data. In: Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, p. 6. ACM (2016)
Deep Learning Book Homepage. http://www.deeplearningbook.org/. Accessed 06 Mar 2019
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)
What is Teacher Forcing for Recurrent Neural Networks? https://machinelearningmastery.com/teacher-forcing-for-recurrent-neural-networks/. Accessed 06 Mar 2019
Fan, Z., Song, X., Shibasaki, R., Adachi, R.: Citymomentum: an online approach for crowd behavior prediction at a citywide level. In: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 559–569. ACM (2015)
Song, X., Zhang, Q., Sekimoto, Y., Shibasaki, R.: Prediction of human emergency behavior and their mobility following large-scale disaster. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 5–14. ACM (2014)
Abadi, A., Rajabioun, T., Ioannou, P.A.: Traffic flow prediction for road transportation networks with limited traffic data. IEEE Trans. Intell. Transp. Syst. 16(2), 653–662 (2015)
Silva, R., Kang, S.M., Airoldi, E.M.: Predicting traffic volumes and estimating the effects of shocks in massive transportation systems. Proc. Natl. Acad. Sci. 112(18), 5643–5648 (2015)
Xu, Y., Kong, Q.J., Klette, R., Liu, Y.: Accurate and interpretable bayesian mars for traffic flow prediction. IEEE Trans. Intell. Transp. Syst. 15(6), 2457–2469 (2014)
Zhang, J., Zheng, Y., Qi, D., Li, R., Yi, X.: DNN-based prediction model for spatial-temporal data. In: Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 92. ACM (2016)
Zhang, J.B., Zheng, Y., Qi, D.K.: Deep spatio-temporal residual networks for citywide crowd flows prediction. In: Thirty-First AAAI Conference on Artificial Intelligence, pp. 1655–1661 (2017)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, pp. 3104–3112 (2014)
Shi, X.J., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K., Woo, W.C.: Convolutional LSTM network: a machine learning approach for precipitation nowcasting. In: Advances in Neural Information Processing Systems, pp. 802–810 (2015)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017)
Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 694–711. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_43
Molchanov, P., Tyree, S., Karras, T, Aila, T., Kautz, J.: Pruning convolutional neural networks for resource efficient transfer learning. arXiv preprint arXiv:1611.06440 (2016)
One by One [1 x 1] Convolution – counter-intuitively useful https://iamaaditya.github.io/2016/03/one-by-one-convolution/. Accessed 06 Mar 2019
Lin, M., Chen, Q., Yan, S.: Network in network. arXiv preprint arXiv:1312.4400 (2013)
Deconvolution and checkerboard artifacts. https://distill.pub/2016/deconv-checkerboard/. Accessed 06 Mar 2019
Nearest-neighbor_interpolation. https://en.wikipedia.org/wiki/Nearest-neighbor_interpolation. Accessed 06 Mar 2019
Pytorch. https://pytorch.org/. Accessed 06 Mar 2019
Kingma, D., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Acknowledgement
The research is supported by National Natural Science Foundation of China (No. 61772560), and Natural Science Foundation of Hunan Province (No. 2019JJ40388).
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Zheng, X., Yang, Z., Liu, L., Kuang, L. (2019). Predicting Traffic Flow Based on Encoder-Decoder Framework. In: Wang, X., Gao, H., Iqbal, M., Min, G. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 292. Springer, Cham. https://doi.org/10.1007/978-3-030-30146-0_36
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