Short-term Prediction of Local Traffic Conditions Reflecting the Relationship between Traffic Variables by Deep Learning

• BABA Shizuha

  Graduate School of Information Sciences, Tohoku University

• INOUE Ryo

  Graduate School of Information Sciences, Tohoku University

Bibliographic Information

Other Title

• 交通変数間の関係を反映した深層学習による地域の交通状態の短期的予測

Abstract

Deep learning has been attracting attention as a prediction method of traffic condition. Deep learning, which can automatically extract the relationships inherent in the data, has shown high performance for many types of prediction problems, and its usefulness for prediction of traffic condition has also been confirmed. However, most previous prediction methods using deep learning do not consider the relationships between multiple traffic variables and only consider location-based predictions. However, when considering traffic control for mitigating traffic congestion as an example of application of the prediction results, the prediction of traffic condition for a region, which is the unit of control implementation, are highly useful, rather than a point-based one. In this study, the LSTM, a deep learning model, was used to examine short-term predictions aimed at representing the relationship between multiple macroscopic traffic condition indices aggregated on a regional basis, and its performance was confirmed through application to the observed data of urban roads.

Journal

• JSTE Journal of Traffic Engineering

  JSTE Journal of Traffic Engineering 7 (2), A_110-A_118, 2021-02-01

  Japan Society of Traffic Engineers

Keywords

• Deep Learning
• Prediction of Traffic Condition
• Macroscopic Fundamental Diagram
• LSTM

Details

• CRID: 1390005667263964288
• NII Article ID: 130007989072
• DOI: 10.14954/jste.7.2_a_110
• ISSN: 21872929
• Text Lang: ja
• Data Source:
  • JaLC
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed