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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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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