<ul><li>Imputation of random or non-random missing data is a long-standing research topic and a crucial application for Intelligent Transportation Systems (ITS).</li><li>A novel spatiotemporal traffic imputation method, Multimode Nonlinear Transformed Tensor Nuclear Norm (MNT-TNN), is proposed to address the challenges in random missing value imputation and spatiotemporal dependency modeling.</li><li>MNT-TNN utilizes the Transform-based Tensor Nuclear Norm (TTNN) optimization framework, extending it to a multimode transform with nonlinear activation to capture spatiotemporal correlations and low-rankness of the traffic tensor.</li><li>Experimental results show that MNT-TNN and its enhancement framework, ATTNNs, outperform existing imputation methods for random missing traffic value imputation.</li></ul>

MNT-TNN: Spatiotemporal Traffic Data Imputation via Compact Multimode Nonlinear Transform-based Tensor Nuclear Norm

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