COMPARISON OF GRADIENT BOOSTING DECISION TREE AND GRAPH NEURAL NETWORK FOR SHORT-TIME SPEED PREDICTION

DOI

Bibliographic Information

Other Title
  • 短時間速度予測におけるGradient Boosting Decision TreeとGraph Neural Networkの比較

Description

<p>By constructing a digital twin at key locations, real-time traffic flow forecasting and dynamic traffic control can be performed to avoid traffic congestion. In this paper, short-time speed prediction was conducted using open data from England in anticipation of the above applications. Gradient Boosting Decision Tree (GBDT) and Graph Neural Network (GNN) were used for the model, and a comparison was made between the two. The comparison results for the entire 170 target locations showed that the GNN was superior, but the evaluation of individual locations revealed that there were several locations where GBDT was superior. The results also confirmed the GNN was superior at the points where time contributed significantly, and confirmed that the addition of data from other points, which was judged to be valid based on the GNN adjacency matrix, contributed to improving the GBDT accuracy at these points. Finally, the use of GBDT and GNN is discussed.</p>

Journal

Keywords

Details 詳細情報について

  • CRID
    1390577674257870720
  • DOI
    10.11532/jsceiii.4.2_154
  • ISSN
    24359262
  • Text Lang
    ja
  • Data Source
    • JaLC
  • Abstract License Flag
    Disallowed

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