Deep Representation Learning for Trajectory Similarity Computation

By Xiucheng Li, Gao Cong, Christian S. Jensen, Wei Wei. IEEE International Conference on Data Engineering, 2018

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Deep Learning for Spatio-Temporal Data Mining: A Survey (2019, background): “Application domains Related works Transportation [177], [187], [116], [31], [29], [6], [32], [127], [53], [112], [26], [105], [166], [82], [23], [66], [108], [126], [81], [139], [78], [128], [146], [148], [72], [98], [189], [22], [24], [99], [96], [16], [97], [85], [15], [164], [165], [95], [140], [”
A Survey on Trajectory Data Management, Analytics, and Learning (2020, methodology): “[100] proposed a seq2seq-based model to learn trajectory representations, where the spatial proximity is taken into account in the design of the loss function.”
TrajPrism: A Multi-Task Benchmark for Language-Grounded Urban Trajectory Understanding (2026, unknown): “Spatiotemporal trajectory modeling has matured into a rich research area [17], from early sequence-to-sequence similarity models [13, 30] to recent self-supervised representation learning [4, 12, 11, 36, 18].”