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.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Forecasting vehicle crossing volumes by using Nonlinear Autoregressive Neural Networks sets


    Weitere Titelangaben:

    Communic.Comp.Inf.Science


    Beteiligte:
    Iliadis, Lazaros (Herausgeber:in) / Maglogiannis, Ilias (Herausgeber:in) / Kyriacou, Efthyvoulos (Herausgeber:in) / Jayne, Chrisina (Herausgeber:in) / Skopelitis, Ioannis (Autor:in) / Papaleonidas, Antonios (Autor:in) / Psathas, Anastasios Panagiotis (Autor:in) / Iliadis, Lazaros (Autor:in) / Botzoris, George N. (Autor:in)

    Kongress:

    International Conference on Engineering Applications of Neural Networks ; 2025 ; Limassol, Cyprus June 26, 2025 - June 29, 2025



    Erscheinungsdatum :

    22.06.2025


    Format / Umfang :

    16 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





    Development of 48-hour Precipitation Forecasting Model using Nonlinear Autoregressive Neural Network

    Le, Tien-Thinh / Pham, Binh Thai / Ly, Hai-Bang et al. | Springer Verlag | 2019



    Forecasting Travel Demand: A Nonlinear Approach Using Neural Networks

    de Carvalho, M. / Transportation Research Forum | British Library Conference Proceedings | 1999


    Prediction of Daily Traffic Volumes by Using Autoregressive Models

    Suzuki, Y. / IEEE / Institute of Electrical Engineers of Japan et al. | British Library Conference Proceedings | 1999