User Trajectory Identification Based on Expandable Self-Attention Spatio-Temporal Graph Convolutional Neural Networks

LEI Tian-liang; JI Li-xin; WANG Geng-run; LIU Shu-xin; WU Lan

doi:10.12263/DZXB.20221225

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User Trajectory Identification Based on Expandable Self-Attention Spatio-Temporal Graph Convolutional Neural Networks

PAPERS | 更新时间：2025-12-11

- User Trajectory Identification Based on Expandable Self-Attention Spatio-Temporal Graph Convolutional Neural Networks
- ACTA ELECTRONICA SINICA Vol. 52, Issue 11, Pages: 3741-3750(2024)
- 作者机构：
  
  信息工程大学信息技术研究所，河南郑州 450002
- 作者简介：
- 基金信息：
  
  Major Science and Technology Project of Henan Province(221100210700)
- DOI：10.12263/DZXB.20221225
  CLC： TP391;
- Received：28 October 2022，
  
  Revised：2023-03-03，
  
  Published：25 November 2024
- 稿件说明：
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雷天亮, 吉立新, 王庚润, 等. 基于可拓展自注意力时空图卷积神经网络的用户轨迹识别模型[J]. 电子学报, 2024, 52(11): 3741-3750.

LEI Tian-liang, JI Li-xin, WANG Geng-run, et al. User Trajectory Identification Based on Expandable Self-Attention Spatio-Temporal Graph Convolutional Neural Networks[J]. Acta Electronica Sinica, 2024, 52(11): 3741-3750.
雷天亮, 吉立新, 王庚润, 等. 基于可拓展自注意力时空图卷积神经网络的用户轨迹识别模型[J]. 电子学报, 2024, 52(11): 3741-3750. DOI：10.12263/DZXB.20221225

LEI Tian-liang, JI Li-xin, WANG Geng-run, et al. User Trajectory Identification Based on Expandable Self-Attention Spatio-Temporal Graph Convolutional Neural Networks[J]. Acta Electronica Sinica, 2024, 52(11): 3741-3750. DOI：10.12263/DZXB.20221225

摘要

用户轨迹识别作为一项重要的时空数据挖掘任务，广泛应用于基于位置的个性化服务推荐、行程规划、犯罪行为检测和目标跟踪等领域，但依然面临预测精度不高的问题，主要原因是轨迹数据低采样且稀疏、轨迹类别数量巨大等.针对上述问题提出了基于可拓展自注意力时空图卷积神经网络的用户轨迹识别模型（Expandable Self-Attention Spatio-Temporal Graph Convolutional Neural Networks，ESAST-GCNN），该模型采用时空图卷积神经网络方式，深度挖掘时序特征与空间特征关系，并进行预测与拓展，结合自注意力机制获取用户轨迹特征向量内部相关性，最终根据该特征向量进行用户轨迹身份识别.在两个真实数据集上进行测试后发现，ESAST-GCNN相较于TULER-GRU（TUL via Embedding and RNN）在Geolife与Gowalla中准确率分别提高了13.95%、10.63%，实验结果表明ESAST-GCNN优于其他模型，识别效果更好，适用范围更广.

Abstract

As an important spatio-temporal data mining task

user trajectory identification is widely used in the fields of location-based personalized service recommendation

itinerary planning

crime behavior detection

and target tracking.However

it still has low prediction accuracy

mainly due to low sampling and sparse trajectory data

and a huge number of trajectory categories.To fill the research gaps

a user trajectory identification model based on an expandable self-attention spatio-temporal graph convolutional neural network (ESAST-GCNN) is proposed

which adopts the spatio-temporal graph convolutional neural network to deeply mine the relationship between time sequence features and spatial features to predict and expand the sequence.This model combines the self-attention mechanism to obtain the internal correlation of user trajectory feature vectors and identify user trajectories.After testing on two real datasets

the results show that the accuracy of ESAST-GCNN is improved by 13.95% and 10.63% in Geolife and Gowalla compared with TUL via Embedding and RNN (TULER-GRU)

respectively.The experimental results illustrate that ESAST-GCNN is superior to other comparative models

with better identification effect and wider applicability.

关键词

Keywords

references

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