Traffic Flow Prediction Based on Recurrent Independent Mechanisms

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

doi:10.12052/gdutxb.230005

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Traffic Flow Prediction Based on Recurrent Independent Mechanisms

更新时间：2026-05-27

- Traffic Flow Prediction Based on Recurrent Independent Mechanisms
- Vol. 41, Issue 1, Pages: 86-92(2024)
- 作者机构：
  
  广东工业大学计算机学院,广东,广州,510006
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.230005
  CLC： TP183
- Online First：25 January 2024，
  
  Published：2024
- 稿件说明：
移动端阅览
温雯, 江建强, 蔡瑞初, 郝志峰. 基于循环独立机制的交通流量预测[J]. 广东工业大学学报, 2024, 41(1): 86-92.

Wen Wen, Jiang Jian-qiang, Cai Rui-chu, et al. Traffic Flow Prediction Based on Recurrent Independent Mechanisms[J]. 2024, 41(1): 86-92.
温雯, 江建强, 蔡瑞初, 郝志峰. 基于循环独立机制的交通流量预测[J]. 广东工业大学学报, 2024, 41(1): 86-92. DOI： 10.12052/gdutxb.230005.

Wen Wen, Jiang Jian-qiang, Cai Rui-chu, et al. Traffic Flow Prediction Based on Recurrent Independent Mechanisms[J]. 2024, 41(1): 86-92. DOI： 10.12052/gdutxb.230005.

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。

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.

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Keywords

references

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