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Traffic congestion remains one of the most significant issues affecting highways worldwide. This problem is directly associated with productivity loss (due to time wasted in traffic jams), environmental pollution, increased fuel consumption, and adverse effects on human health. Toll booths constitute a major contributor to congestion, especially during peak hours and periods of high travel demand, such as holidays and vacation seasons. In this research, the authors examine the case of the Egnatia Odos Motorway, focusing on the Mesti-Komotini toll station over a three-year period. Using a large set of Nonlinear Autoregressive Neural Networks (NARNNs), the study forecasts vehicle crossing volumes based on past traffic values. The models were trained using the Hold-Out Validation Method, and their performance was evaluated using the R-squared (R2) index, achieving values above 0.94. The results are highly promising, indicating that the more historical data is available, the higher the prediction accuracy becomes. The proposed models can serve as valuable tools for traffic management authorities to alleviate motorway congestion when necessary.

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Forecasting vehicle crossing volumes by using Nonlinear Autoregressive Neural Networks sets

  • 会议论文

  • 发表日期：

    2025年

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  • Ioannis Skopelitis,
  • Antonios Papaleonidas,
  • Anastasios Panagiotis Psathas,
  • Lazaros Iliadis,
  • George N. Botzoris

摘要

Traffic congestion remains one of the most significant issues affecting highways worldwide. This problem is directly associated with productivity loss (due to time wasted in traffic jams), environmental pollution, increased fuel consumption, and adverse effects on human health. Toll booths constitute a major contributor to congestion, especially during peak hours and periods of high travel demand, such as holidays and vacation seasons. In this research, the authors examine the case of the Egnatia Odos Motorway, focusing on the Mesti-Komotini toll station over a three-year period. Using a large set of Nonlinear Autoregressive Neural Networks (NARNNs), the study forecasts vehicle crossing volumes based on past traffic values. The models were trained using the Hold-Out Validation Method, and their performance was evaluated using the R-squared (R2) index, achieving values above 0.94. The results are highly promising, indicating that the more historical data is available, the higher the prediction accuracy becomes. The proposed models can serve as valuable tools for traffic management authorities to alleviate motorway congestion when necessary.