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<p>The paper addresses the task of improving the quality of traffic data by combining semantic analysis and statistical modeling. The study focuses on dynamic traffic variables, specifically speed and volume of traffic flow. The results are presented based on semantic quality control outputs for dynamic traffic variables. The semantic analysis aimed to find and label segments of time series waveforms that fell outside the expected value range at specific locations and times. This approach allowed for a more detailed analysis of specific cases, enhancing our understanding of traffic dynamics. We employed a Generalized Additive Model (GAM) to detect non-standard data segments successfully. Real traffic data from the motorway in the Czech Republic verified the effectiveness of the chosen statistical approach.</p>

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Detecting anomalies in traffic data using a flexible semi-parametric model

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月27日

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  • Zuzana Purkrábková,
  • Martin Langr,
  • Pavel Hrubeš,
  • Marek Brabec

摘要

The paper addresses the task of improving the quality of traffic data by combining semantic analysis and statistical modeling. The study focuses on dynamic traffic variables, specifically speed and volume of traffic flow. The results are presented based on semantic quality control outputs for dynamic traffic variables. The semantic analysis aimed to find and label segments of time series waveforms that fell outside the expected value range at specific locations and times. This approach allowed for a more detailed analysis of specific cases, enhancing our understanding of traffic dynamics. We employed a Generalized Additive Model (GAM) to detect non-standard data segments successfully. Real traffic data from the motorway in the Czech Republic verified the effectiveness of the chosen statistical approach.