广东工业大学学报 ›› 2024, Vol. 41 ›› Issue (01): 86-92.doi: 10.12052/gdutxb.230005

• 计算机科学与技术 • 上一篇    下一篇

基于循环独立机制的交通流量预测

温雯, 江建强, 蔡瑞初, 郝志峰   

  1. 广东工业大学 计算机学院, 广东 广州 510006
  • 收稿日期:2023-01-09 出版日期:2024-01-25 发布日期:2024-02-01
  • 作者简介:温雯(1981–)，女，教授，博士，主要研究方向为机器学习、数据挖掘，E-mail：wwen@gdut.edu.cn
  • 基金资助:
    广东省自然科学基金资助项目(2021A1515011965)

Traffic Flow Prediction Based on Recurrent Independent Mechanisms

Wen Wen, Jiang Jian-qiang, Cai Rui-chu, Hao Zhi-feng   

  1. School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2023-01-09 Online:2024-01-25 Published:2024-02-01

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摘要： 交通流量预测是智能交通控制和管理系统的一个重要环节，但交通流量数据具有时间和空间上的非线性和复杂性等特征，为对其进行精准预测，本文提出了Graph Temopral Recurrent Independent Mechanisms (G-tRIM)模型。该模型使用图注意力网络(Graph Attention Networks, GAT)来有效捕获交通流量数据的空间依赖关系，使用循环独立机制(Recurrent Independent Mechanisms, RIM)来精准刻画交通流量数据的潜在状态。最后在北京和贵州数据集上，以均方误差(Mean Square Error, MSE)和平均绝对误差(Mean Absolute Error, MAE)为指标进行实验，结果表明，G-tRIM在各个数据集上的表现均优于基准模型。

关键词: 交通流量预测, 图注意力网络, 循环独立机制

Abstract: Traffic flow prediction is an important issue of the intelligent traffic control and management systems. However, traffic flow data has nonlinear and complex characteristics in both time and space, making it challenging to accurately predict it. In this regard, this paper proposes a Graph temopral recurrent independent mechanisms (G-tRIM) model, which uses Graph attention networks (GAT) to effectively capture the spatial dependencies of traffic flow data, and uses Recurrent independent mechanisms (RIM) to accurately characterize the latent state of traffic flow data. We conduct experiments on the Beijing and Guizhou datasets, and the experimental results show that our proposed G-tRIM outperforms the baseline models on both datasets in terms of MSE and MAE.

Key words: traffic flow prediction, graph attention networks, recurrent independent mechanisms

中图分类号: 

  • TP183
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