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Spatial-Temporal Interval Aware Individual Future Trajectory Prediction
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AI
DOI：10.1109/TKDE.2023.3332929.png)
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Abstract
En 中文
In this work, we present STiSAN+ + as an end-to-end mobility trajectory prediction framework. It is a Self-Attention Network (SAN)-based model augmented with two lightweight approaches: Rotary Time Aware Position Encoder (RoTAPE) and multi-head Interval Aware Attention Block (IAAB). These methods allow for the explicit, efficient and effective processing of spatial- temporal intervals among the user's historical trajectory, without the need for additional parameters or a substantial computational burden. On the one hand, RoTAPE simultaneously encodes day- and hour-level timestamps into the sequence representation via a sinusoidal encoding matrix. Notably, the multi-level temporal differences operate in a mutually independent manner to reflect the periodical pattern, and collectively measure the absolute time interval. On the other hand, IAAB, point-wise injecting the historical spatial-temporal intervals into the attention map, can promote SAN attaching importance to the spatial relations under the constraints of time conditions. Moreover, we equip STiSAN+ + with a novel MLP-based module, namely Spatial-Temporal Relation Memory (STR Memory). It endows the interactions inside historical intervals along different directions, and converts them into spatial-temporal relations in future trajectories for accurate predictions. The empirical study on six public LBSN shows that from Next Location Recommendation to Multi-location Future Trajectory Prediction, our STiSAN+ + gains average 15.05% and 18.35% improvements against several state-of-the-art sequential models, respectively. We demonstrate the effectiveness of proposed module with an ablation study, and validate the extensibility and interpretability of RoTAPE and IAAB through non-sampled metric evaluation and visualization.
Keywords:
Trajectory
Global Positioning System
Geography
Behavioral sciences
Matrix converters
Motion pictures
Indexes
Self-attention network
sequential location-based recommendation
spatial-temporal trajectory
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