Traffic Flow Prediction of Digital Twin Based on Two-Graph Convolution Mechanism

HU Chun-hua; ZENG E-lan; RONG Hui-gui

doi:10.12263/DZXB.20221308

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Traffic Flow Prediction of Digital Twin Based on Two-Graph Convolution Mechanism

PAPERS | 更新时间：2025-12-08

- Traffic Flow Prediction of Digital Twin Based on Two-Graph Convolution Mechanism
- ACTA ELECTRONICA SINICA Vol. 53, Issue 1, Pages: 141-150(2025)
- 作者机构：
  
  1.湖南工商大学人工智能与先进计算学院，湖南长沙 410205
  2.湘江实验室，湖南长沙 410205
  3.湖南工商大学长沙人工智能社会实验室，湖南长沙 410205
  4.湖南大学信息科学与工程学院，湖南长沙 410082
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(72072053;91846301);National Key Research and Development Program of China(2021YFC3340403)
- DOI：10.12263/DZXB.20221308
  CLC： TP181;
- Received：16 November 2022，
  
  Revised：2023-09-13，
  
  Published：25 January 2025
- 稿件说明：
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胡春华, 曾萼岚, 荣辉桂. 基于双图卷积机制的数字孪生交通流预测[J]. 电子学报, 2025, 53(01): 141-150.

HU Chun-hua, ZENG E-lan, RONG Hui-gui. Traffic Flow Prediction of Digital Twin Based on Two-Graph Convolution Mechanism[J]. Acta Electronica Sinica, 2025, 53(01): 141-150.
胡春华, 曾萼岚, 荣辉桂. 基于双图卷积机制的数字孪生交通流预测[J]. 电子学报, 2025, 53(01): 141-150. DOI：10.12263/DZXB.20221308

HU Chun-hua, ZENG E-lan, RONG Hui-gui. Traffic Flow Prediction of Digital Twin Based on Two-Graph Convolution Mechanism[J]. Acta Electronica Sinica, 2025, 53(01): 141-150. DOI：10.12263/DZXB.20221308

摘要

城市数字化程度提升产生了大量数据，通过对交通流数据和天气数据的整合分析，能有效缓解各种天气状况下产生的城市交通拥堵.而现有交通流预测算法，未能充分考虑交通流中潜在的空间关系，且忽略了天气等外部因素造成的预测误差，极大地影响了预测的准确性.针对上述问题，本文提出了基于双图卷积机制的数字孪生交通流预测方法（Two-graph Convolution Mechanism-based Digital Twin Flow Prediction，TCM-DTFP）.该算法将交通流数据与天气特征相结合，构建了融合交通流特征与天气特征的增广矩阵，提出基于TCN（Temporal Convolutional Networks）的双图卷积机制，算法综合考虑了交通中时间相关性、空间相关性与区域流量间的动态相互作用对交通流的影响，同时避免了复杂天气状况对交通流预测的影响，提高了算法的鲁棒性.最后基于TaxiBJ和PeMSD4真实数据集进行的大量实验表明了本文方法的有效性.

Abstract

The improvement of urban digitalization has generated a large amount of data. Through the integrated analysis of traffic flow data and weather data

urban traffic congestion caused by various weather conditions can be effectively alleviated. However

in the existing traffic flow prediction algorithms

the potential spatial relationship in the traffic flow has not been fully considered

and the prediction errors caused by external factors such as weather are ignored

which greatly affects the accuracy of the prediction. In response to the above problems

this paper proposes a digital twin traffic flow prediction method TCM-DTFP (Two-graph Convolution Mechanism-based Digital twin flow Prediction) based on the double-graph convolution mechanism. The algorithm builds an augmented matrix that integrates traffic flow features and weather features

adds weather features to traffic flow data

avoids the impact of complex weather conditions on traffic flow prediction

and improves the robustness of the algorithm; at the same time in order to improve the algorithm’s ability to capture the spatial correlation of traffic flow

a two-graph convolution mechanism based on TCN (Temporal Convolutional Networks) is proposed to comprehensively consider the dynamic interaction between temporal correlation

spatial correlation and regional flow in traffic influence of flow. Finally

extensive experiments on two real datasets

TaxiBJ and PeMSD4

demonstrate the effectiveness of our method.

关键词

Keywords

references

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