Machine Learning-Based Modeling of Celeration for Predicting Red-Light Violations
This research examines the intricate correlation between speed variation (celeration), a metric of driver behavior associated with vehicle control, and occurrences of running red lights. The study is based on a thorough analysis of a large dataset that includes a variety of parameters, such as excee...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Open Journal of Intelligent Transportation Systems |
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| Online Access: | https://ieeexplore.ieee.org/document/10693532/ |
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| _version_ | 1832590369273937920 |
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| author | Mahmoud Masoud |
| author_facet | Mahmoud Masoud |
| author_sort | Mahmoud Masoud |
| collection | DOAJ |
| description | This research examines the intricate correlation between speed variation (celeration), a metric of driver behavior associated with vehicle control, and occurrences of running red lights. The study is based on a thorough analysis of a large dataset that includes a variety of parameters, such as exceeding speed limits, driver age, passenger count, weather, road condition, and temporal factors. Using cutting-edge machine learning methods like AdaBoost and Bagging, predictive models for red-light violations are painstakingly built, achieving remarkable validation accuracies of 90.4% and 90.1%, respectively. The study acknowledges the dataset’s limitations in capturing real-world traffic complexities while focusing on the effectiveness and trade-offs inherent in these methodologies. This emphasizes how important it is to have synchronized and thorough data sources to guarantee accurate representation. The research field is enhancing predictive modeling techniques and improving transportation safety by connecting celebration, speed variation patterns over time, with instances of red-light violations. |
| format | Article |
| id | doaj-art-f9286dc5e9224e14baee67236b8737ce |
| institution | Kabale University |
| issn | 2687-7813 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Intelligent Transportation Systems |
| spelling | doaj-art-f9286dc5e9224e14baee67236b8737ce2025-01-24T00:02:43ZengIEEEIEEE Open Journal of Intelligent Transportation Systems2687-78132024-01-01560861610.1109/OJITS.2024.346722210693532Machine Learning-Based Modeling of Celeration for Predicting Red-Light ViolationsMahmoud Masoud0https://orcid.org/0000-0002-0130-4327Department of Information Systems and Operations Management, King Fahd University of Petroleum and Minerals, Dhahran, Saudi ArabiaThis research examines the intricate correlation between speed variation (celeration), a metric of driver behavior associated with vehicle control, and occurrences of running red lights. The study is based on a thorough analysis of a large dataset that includes a variety of parameters, such as exceeding speed limits, driver age, passenger count, weather, road condition, and temporal factors. Using cutting-edge machine learning methods like AdaBoost and Bagging, predictive models for red-light violations are painstakingly built, achieving remarkable validation accuracies of 90.4% and 90.1%, respectively. The study acknowledges the dataset’s limitations in capturing real-world traffic complexities while focusing on the effectiveness and trade-offs inherent in these methodologies. This emphasizes how important it is to have synchronized and thorough data sources to guarantee accurate representation. The research field is enhancing predictive modeling techniques and improving transportation safety by connecting celebration, speed variation patterns over time, with instances of red-light violations.https://ieeexplore.ieee.org/document/10693532/Machine learningmodelingcelebrationred-light |
| spellingShingle | Mahmoud Masoud Machine Learning-Based Modeling of Celeration for Predicting Red-Light Violations IEEE Open Journal of Intelligent Transportation Systems Machine learning modeling celebration red-light |
| title | Machine Learning-Based Modeling of Celeration for Predicting Red-Light Violations |
| title_full | Machine Learning-Based Modeling of Celeration for Predicting Red-Light Violations |
| title_fullStr | Machine Learning-Based Modeling of Celeration for Predicting Red-Light Violations |
| title_full_unstemmed | Machine Learning-Based Modeling of Celeration for Predicting Red-Light Violations |
| title_short | Machine Learning-Based Modeling of Celeration for Predicting Red-Light Violations |
| title_sort | machine learning based modeling of celeration for predicting red light violations |
| topic | Machine learning modeling celebration red-light |
| url | https://ieeexplore.ieee.org/document/10693532/ |
| work_keys_str_mv | AT mahmoudmasoud machinelearningbasedmodelingofcelerationforpredictingredlightviolations |