Web: http://arxiv.org/abs/2209.06389

Sept. 15, 2022, 1:13 a.m. | Zhenyu Mao, Ziyue Li, Dedong Li, Lei Bai, Rui Zhao

cs.CV updates on arXiv.org arxiv.org

Road network and trajectory representation learning are essential for traffic
systems since the learned representation can be directly used in various
downstream tasks (e.g., traffic speed inference, and travel time estimation).
However, most existing methods only contrast within the same scale, i.e.,
treating road network and trajectory separately, which ignores valuable
inter-relations. In this paper, we aim to propose a unified framework that
jointly learns the road network and trajectory representations end-to-end. We
design domain-specific augmentations for road-road contrast and …

arxiv network representation representation learning

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