• Title/Summary/Keyword: Intelligent Transport Systems (ITS)

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Development of Highway Traffic Information Prediction Models Using the Stacking Ensemble Technique Based on Cross-validation (스태킹 앙상블 기법을 활용한 고속도로 교통정보 예측모델 개발 및 교차검증에 따른 성능 비교)

  • Yoseph Lee;Seok Jin Oh;Yejin Kim;Sung-ho Park;Ilsoo Yun
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.22 no.6
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    • pp.1-16
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    • 2023

  • Accurate traffic information prediction is considered to be one of the most important aspects of intelligent transport systems(ITS), as it can be used to guide users of transportation facilities to avoid congested routes. Various deep learning models have been developed for accurate traffic prediction. Recently, ensemble techniques have been utilized to combine the strengths and weaknesses of various models in various ways to improve prediction accuracy and stability. Therefore, in this study, we developed and evaluated a traffic information prediction model using various deep learning models, and evaluated the performance of the developed deep learning models as a stacking ensemble. The individual models showed error rates within 10% for traffic volume prediction and 3% for speed prediction. The ensemble model showed higher accuracy compared to other models when no cross-validation was performed, and when cross-validation was performed, it showed a uniform error rate in long-term forecasting.

  • https://doi.org/10.12815/kits.2023.22.6.1 인용 PDF