ST2Vec: Spatio-Temporal Trajectory Similarity Learning in Road Networks
- URL: http://arxiv.org/abs/2112.09339v1
- Date: Fri, 17 Dec 2021 06:18:04 GMT
- Title: ST2Vec: Spatio-Temporal Trajectory Similarity Learning in Road Networks
- Authors: Ziquan Fang, Yuntao Du, Xinjun Zhu, Lu Chen, Yunjun Gao, Christian S.
Jensen
- Abstract summary: We propose ST2Vec, a trajectory-learning based architecture that considers fine-grained spatial and temporal between pairs of trajectories.
Inspired by curriculum concept, ST2Vec employs curriculum learning for model optimization to improve both convergence and effectiveness.
An experimental study offers evidence that ST2Vec outperforms all state-of-the-art competitors substantially in terms of effectiveness, efficiency, and robustness.
- Score: 27.452831603278565
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: People and vehicle trajectories embody important information of
transportation infrastructures, and trajectory similarity computation is
functionality in many real-world applications involving trajectory data
analysis. Recently, deep-learning based trajectory similarity techniques hold
the potential to offer improved efficiency and adaptability over traditional
similarity techniques. Nevertheless, the existing trajectory similarity
learning proposals emphasize spatial similarity over temporal similarity,
making them suboptimal for time-aware analyses. To this end, we propose ST2Vec,
a trajectory-representation-learning based architecture that considers
fine-grained spatial and temporal correlations between pairs of trajectories
for spatio-temporal similarity learning in road networks. To the best of our
knowledge, this is the first deep-learning proposal for spatio-temporal
trajectory similarity analytics. Specifically, ST2Vec encompasses three phases:
(i) training data preparation that selects representative training samples;
(ii) spatial and temporal modeling that encode spatial and temporal
characteristics of trajectories, where a generic temporal modeling module (TMM)
is designed; and (iii) spatio-temporal co-attention fusion (STCF), where a
unified fusion (UF) approach is developed to help generating unified
spatio-temporal trajectory embeddings that capture the spatio-temporal
similarity relations between trajectories. Further, inspired by curriculum
concept, ST2Vec employs the curriculum learning for model optimization to
improve both convergence and effectiveness. An experimental study offers
evidence that ST2Vec outperforms all state-of-the-art competitors substantially
in terms of effectiveness, efficiency, and scalability, while showing low
parameter sensitivity and good model robustness.
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