Abstract
Reconstructing the fine-grained urban flow from the coarse-grained counterpart is an essential component in intelligent transportation systems, as it can provide accurate traffic flow information under a reduced number of sensors. However, current models based on Convolutional Neural Networks (CNNs) mainly focus on the local pixel correlations and ignore the long-range dependencies. To this end, we propose a TRansformer-guided Urban Flow Magnifier (TRUFM) that incorporates the transformer module in the traffic flow analysis system, which naturally enjoys the advantage of modeling the global-scale correlations. By utilizing this superiority, our framework facilitates the joint inference of the flow distribution across the entire map and hence estimates more precise fine-grained traffic flow. Experimental results demonstrate the effectiveness of our TRUFM, which exceeds the current state-of-the-art methods on various datasets.
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References
Zheng, Y., et al.: Urban computing: concepts, methodologies, and applications. ACM Trans. Intell. Syst. Technol. (TIST) 5(3), 1–55 (2014)
Srivastava, N., et al.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Kingma, D.P., Jimmy B.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Dong, C., et al.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2015)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, PMLR (2015)
Shi, W., et al.: Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)
Ba, J.L., Jamie R.K., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016)
He, K., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)
Kim, J., Jung K.L., Kyoung M.L.: Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)
Zhang, J., Zheng, Y., Qi, D.: Deep spatio-temporal residual networks for citywide crowd flows prediction. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)
Vandal, T., et al.: DeepSD: generating high resolution climate change projections through single image super-resolution. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2017)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems (2017)
Yu, B., Yin, H., Zhu, Z.: Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017)
Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)
Zong, Z., et al.: DeepDPM: dynamic population mapping via deep neural network. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, no. 01 (2019)
Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, vol. 32, pp. 8026–8037 (2019)
Liang, Y., et al.: UrbanFM: inferring fine-grained urban flows. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (2019)
Ouyang, K., et al.: Fine-grained urban flow inference. IEEE Trans. Knowl. Data Eng., 1 (2020). https://doi.org/10.1109/TKDE.2020.3017104
Dosovitskiy, A., et al.: An image is worth 16 \(\times \) 16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 213–229. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58452-8_13
Tedjopurnomo, D.A., Bao, Z., Zheng, B., Choudhury, F., Qin, A.K.: A survey on modern deep neural network for traffic prediction: trends, methods and challenges. IEEE Trans. Knowl. Data Eng., 1 (2020). https://doi.org/10.1109/TKDE.2020.3001195
Yin, X., et al.: A comprehensive survey on traffic prediction. arXiv preprint arXiv:2004.08555 (2020)
Han, K., et al.: A survey on visual transformer. arXiv preprint arXiv:2012.12556 (2020)
Touvron, H., et al.: Training data-efficient image transformers & distillation through attention. In: International Conference on Machine Learning, PMLR (2021)
Wu, B., et al.: Visual transformers: token-based image representation and processing for computer vision. arXiv preprint arXiv:2006.03677 (2020)
Liu, L., et al.: Road network guided fine-grained urban traffic flow inference. arXiv preprint arXiv:2109.14251 (2021)
Zhu, X., et al.: Deformable DETR: deformable transformers for end-to-end object detection. arXiv preprint arXiv:2010.04159 (2020)
Chen, H., et al.: Pre-trained image processing transformer. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2021)
Jiang, Y., Chang, S., Wang, Z.: TransGAN: two transformers can make one strong GAN. arXiv preprint arXiv:2102.07074 (2021)
Khan, S., et al.: Transformers in vision: a survey. arXiv preprint arXiv:2101.01169 (2021)
Acknowledgement
We would like to thank Didi Chuxing for providing the trajectory data of XiAn, China.
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Zhou, X., Zhou, D., Liu, L. (2021). TRUFM: a Transformer-Guided Framework for Fine-Grained Urban Flow Inference. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Lecture Notes in Computer Science(), vol 13111. Springer, Cham. https://doi.org/10.1007/978-3-030-92273-3_22
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