A Comparative Study between Different Machine Learning Algorithms for Estimating the Vehicular Delay at Signalized Intersections
The delay at signalized intersections is a crucial parameter that determines the performance and level of service (LOS). The estimation models are commonly used to model delay; however, inaccurate predictions from these models can pose a significant limitation. Consequently, this study aimed to comp...
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Pouyan Press
2025-01-01
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| Series: | Journal of Soft Computing in Civil Engineering |
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| Online Access: | https://www.jsoftcivil.com/article_196451_5dc528466073e5ded39b18da885fda4c.pdf |
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| author | Yazan Alatoom Abdallah Al-Hamdan |
| author_facet | Yazan Alatoom Abdallah Al-Hamdan |
| author_sort | Yazan Alatoom |
| collection | DOAJ |
| description | The delay at signalized intersections is a crucial parameter that determines the performance and level of service (LOS). The estimation models are commonly used to model delay; however, inaccurate predictions from these models can pose a significant limitation. Consequently, this study aimed to compare a wide array of machine learning algorithms, including Artificial Neural Networks (ANN), Random Forest (RF), decision tree, Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), AdaBoost, Gradient Boost, XGBoost, and Partial Least Squares (PLS) regression. A comprehensive evaluation was undertaken across prediction accuracy, training-testing performance discrepancy, sensitivity to outliers, computational time cost, and model robustness. Additionally, the proposed methods were benchmarked against the Highway Capacity Manual (HCM), Webster, and Akçelik models. The results demonstrated that the RF model exhibited the most balanced performance across the specified criteria, with an average error below 4% and a rating of 35 out of 45 according to the proposed criteria. Moreover, the findings revealed that adopted analytical models should not be employed for vehicular delay estimation without calibration, as RMSE values were about 5 to 58 times higher than other models, varying by model. |
| format | Article |
| id | doaj-art-3847f5d2d29f4e669d1bf84970b97585 |
| institution | Kabale University |
| issn | 2588-2872 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Pouyan Press |
| record_format | Article |
| series | Journal of Soft Computing in Civil Engineering |
| spelling | doaj-art-3847f5d2d29f4e669d1bf84970b975852025-08-20T03:34:36ZengPouyan PressJournal of Soft Computing in Civil Engineering2588-28722025-01-019112215710.22115/scce.2024.418800.1725196451A Comparative Study between Different Machine Learning Algorithms for Estimating the Vehicular Delay at Signalized IntersectionsYazan Alatoom0Abdallah Al-Hamdan1Ph.D. Student, Department of Civil, Construction, and Environmental Engineering, Iowa State University, Ames, Iowa, 50011, United StatesPh.D. Student, Department of Civil, Construction, and Environmental Engineering, Iowa State University, Ames, Iowa, 50011, United StatesThe delay at signalized intersections is a crucial parameter that determines the performance and level of service (LOS). The estimation models are commonly used to model delay; however, inaccurate predictions from these models can pose a significant limitation. Consequently, this study aimed to compare a wide array of machine learning algorithms, including Artificial Neural Networks (ANN), Random Forest (RF), decision tree, Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), AdaBoost, Gradient Boost, XGBoost, and Partial Least Squares (PLS) regression. A comprehensive evaluation was undertaken across prediction accuracy, training-testing performance discrepancy, sensitivity to outliers, computational time cost, and model robustness. Additionally, the proposed methods were benchmarked against the Highway Capacity Manual (HCM), Webster, and Akçelik models. The results demonstrated that the RF model exhibited the most balanced performance across the specified criteria, with an average error below 4% and a rating of 35 out of 45 according to the proposed criteria. Moreover, the findings revealed that adopted analytical models should not be employed for vehicular delay estimation without calibration, as RMSE values were about 5 to 58 times higher than other models, varying by model.https://www.jsoftcivil.com/article_196451_5dc528466073e5ded39b18da885fda4c.pdfvehicle delay estimationtraffic signal delay modelingmachine learning for delay predictionsignalized intersection delaystop delay modelsdata-driven delay modelscomparative study of delay algorithmsrandom forest for delay estimation |
| spellingShingle | Yazan Alatoom Abdallah Al-Hamdan A Comparative Study between Different Machine Learning Algorithms for Estimating the Vehicular Delay at Signalized Intersections Journal of Soft Computing in Civil Engineering vehicle delay estimation traffic signal delay modeling machine learning for delay prediction signalized intersection delay stop delay models data-driven delay models comparative study of delay algorithms random forest for delay estimation |
| title | A Comparative Study between Different Machine Learning Algorithms for Estimating the Vehicular Delay at Signalized Intersections |
| title_full | A Comparative Study between Different Machine Learning Algorithms for Estimating the Vehicular Delay at Signalized Intersections |
| title_fullStr | A Comparative Study between Different Machine Learning Algorithms for Estimating the Vehicular Delay at Signalized Intersections |
| title_full_unstemmed | A Comparative Study between Different Machine Learning Algorithms for Estimating the Vehicular Delay at Signalized Intersections |
| title_short | A Comparative Study between Different Machine Learning Algorithms for Estimating the Vehicular Delay at Signalized Intersections |
| title_sort | comparative study between different machine learning algorithms for estimating the vehicular delay at signalized intersections |
| topic | vehicle delay estimation traffic signal delay modeling machine learning for delay prediction signalized intersection delay stop delay models data-driven delay models comparative study of delay algorithms random forest for delay estimation |
| url | https://www.jsoftcivil.com/article_196451_5dc528466073e5ded39b18da885fda4c.pdf |
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