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Modelling service quality of two-wheelers at signalized intersections using Artificial Intelligence techniques.

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Title: Modelling service quality of two-wheelers at signalized intersections using Artificial Intelligence techniques.
Authors: Biswal, M.1 518cel03@nitrkl.ac.in, Bhuyan, P. K.1
Source: Advances in Transportation Studies. Jul2026, Vol. 69, p407-426. 20p.
Subjects: Artificial intelligence, Genetic programming, Motorcycles, Traffic engineering, Signalized intersections, Fuzzy neural networks
Geographic Terms: India
Abstract: Service level assessment at signalized intersections is essential for effective traffic management, especially in urban areas with a high share of Motorized Two-Wheelers (MTWs). This study focuses on modelling the Motorized Two-Wheeler Level of Service (MLOS) using Artificial Intelligence (AT) techniques such as Multi-Gene Genetic Programming (MGGP) and Adaptive Neuro-Fuzzy Inference System (ANFIS). In this study Two independent Al-based models MGGP and ANFIS were constructed and evaluated, enabling a comparative assessment to identify the superior approach for accurately predicting MLOS scores. Dataset comprising intersection geometries, traffic flow and operational variables were collected from 21 signalized intersections located in six mid-sized cities in India. Important parameters considered for model developments are peak hour volume, average control delay, turning radius, road surface condition and few others. MGGP was employed to derive interpretable mathematical expressions for MLOS prediction, while ANFIS utilized fuzzy logic integrated with neural networks to adaptively generate inference rules. Model performance was evaluated using R² and RMSE metrics, with ANFIS achieving an R² of 0.91 and RMSE of 0.36, outperforming MGGP which attained an R² of 0.88 and RMSE of 0.42. The results confirmed the suitability of both methods for capturing the nonlinear dynamics of heterogeneous traffic, with ANFIS offering superior predictive accuracy and MGGP contributing interpretability for engineering analysis. Fuzzy C-Means (FCM) clustering was employed to categorize MLOS scores into six distinct service levels, to provide realistic thresholds for traffic service quality assessment in two-wheeler dominated traffic flow. [ABSTRACT FROM AUTHOR]
Copyright of Advances in Transportation Studies is the property of Advances in Transportation Studies and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Service level assessment at signalized intersections is essential for effective traffic management, especially in urban areas with a high share of Motorized Two-Wheelers (MTWs). This study focuses on modelling the Motorized Two-Wheeler Level of Service (MLOS) using Artificial Intelligence (AT) techniques such as Multi-Gene Genetic Programming (MGGP) and Adaptive Neuro-Fuzzy Inference System (ANFIS). In this study Two independent Al-based models MGGP and ANFIS were constructed and evaluated, enabling a comparative assessment to identify the superior approach for accurately predicting MLOS scores. Dataset comprising intersection geometries, traffic flow and operational variables were collected from 21 signalized intersections located in six mid-sized cities in India. Important parameters considered for model developments are peak hour volume, average control delay, turning radius, road surface condition and few others. MGGP was employed to derive interpretable mathematical expressions for MLOS prediction, while ANFIS utilized fuzzy logic integrated with neural networks to adaptively generate inference rules. Model performance was evaluated using R² and RMSE metrics, with ANFIS achieving an R² of 0.91 and RMSE of 0.36, outperforming MGGP which attained an R² of 0.88 and RMSE of 0.42. The results confirmed the suitability of both methods for capturing the nonlinear dynamics of heterogeneous traffic, with ANFIS offering superior predictive accuracy and MGGP contributing interpretability for engineering analysis. Fuzzy C-Means (FCM) clustering was employed to categorize MLOS scores into six distinct service levels, to provide realistic thresholds for traffic service quality assessment in two-wheeler dominated traffic flow. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Advances in Transportation Studies is the property of Advances in Transportation Studies and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.53136/979122182735424
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        Text: English
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        PageCount: 20
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      – SubjectFull: Artificial intelligence
        Type: general
      – SubjectFull: Genetic programming
        Type: general
      – SubjectFull: Motorcycles
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      – SubjectFull: Traffic engineering
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      – SubjectFull: Signalized intersections
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      – SubjectFull: Fuzzy neural networks
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      – SubjectFull: India
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            NameFull: Biswal, M.
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              M: 07
              Text: Jul2026
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              Y: 2026
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