Modeling Crashes Severity Using Ensemble Techniques
Copyright policy )Modeling Crashes Severity Using Ensemble Techniques
Traffic crashes are modelled using different techniques and contributing factors. In this work, several ensemble machine learning algorithms were used to model crash severity at urban roundabouts using data from 15 roundabouts in Jordan. The original dataset covers four years, from 2017 to 2021. A total of 15 variables were collected and used in this work. Results indicated that ten variables are important. The various models show their ability to classify traffic crash severity with a high overall accuracy range from 96% to 98%. Results indicated that driver fault and age are the most significant contributing factors for crash severity.
- University of Science and Technologies Uzbekistan
- Queensland University of Technology Australia
- Al-Ahliyya Amman University Jordan
Driver age, Support vector machine, K-nearest neighborhood, Çevresel ve Sürdürülebilir Süreçler, Machine learning, Environmental and Sustainable Processes, Machine learning;K-nearest neighborhood;Support vector machine;Safety;Driver age;driver fault, Safety, driver fault, 620
Driver age, Support vector machine, K-nearest neighborhood, Çevresel ve Sürdürülebilir Süreçler, Machine learning, Environmental and Sustainable Processes, Machine learning;K-nearest neighborhood;Support vector machine;Safety;Driver age;driver fault, Safety, driver fault, 620
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