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<p>In this paper, we present a novel approach for real-time detection of non-recurrent traffic patterns in urban roadway networks leveraging advanced machine learning techniques explained by traffic flow theory. The methodology comprises two key components. First, an LSTM-based Autoencoder is employed to extract typical expected traffic patterns from raw traffic data. Second, a clustering technique is utilized to identify non-recurrent congestion, applied on the deviation of the speed and volume measurements from the aforementioned typical patterns. The methodology is implemented using high-resolution multimodal traffic data from the urban road network of Athens, Greece. Findings reveal the presence of four distinct traffic states, three of which represent various types of non-recurrent traffic conditions, characterized by their deviations from typical traffic patterns. The methodology can promptly detect the shift between recurrent and non-recurrent conditions in real time and may have far reaching implications for efficient urban traffic management systems.</p>

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Revisiting the Non-recurrent Traffic Incident Identification Problem for Real-Time Applications Using Theory-Aware Unsupervised Learning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月16日

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  • Katerina Ν. Papadatou,
  • Panagiotis Fafoutellis,
  • Eleni I. Vlahogianni

摘要

In this paper, we present a novel approach for real-time detection of non-recurrent traffic patterns in urban roadway networks leveraging advanced machine learning techniques explained by traffic flow theory. The methodology comprises two key components. First, an LSTM-based Autoencoder is employed to extract typical expected traffic patterns from raw traffic data. Second, a clustering technique is utilized to identify non-recurrent congestion, applied on the deviation of the speed and volume measurements from the aforementioned typical patterns. The methodology is implemented using high-resolution multimodal traffic data from the urban road network of Athens, Greece. Findings reveal the presence of four distinct traffic states, three of which represent various types of non-recurrent traffic conditions, characterized by their deviations from typical traffic patterns. The methodology can promptly detect the shift between recurrent and non-recurrent conditions in real time and may have far reaching implications for efficient urban traffic management systems.