首页|Studies from Yarmouk University in the Area of Machine Learning Published (Modelling Driver Behaviour at Urban Signalised Intersections Using Logistic Regression and Machine Learning)
Studies from Yarmouk University in the Area of Machine Learning Published (Modelling Driver Behaviour at Urban Signalised Intersections Using Logistic Regression and Machine Learning)
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Data detailed on artificial intelligence have been presented. According to news reporting from Yarmouk University by NewsRx journalists, research stated, “This study investigated several factors that may influence driver actions throughout the yellow interval at urban signalised intersections. The selected samples include 2,168 observations.” The news correspondents obtained a quote from the research from Yarmouk University: “Almost 33% of drivers stopped ahead of the stop line, 60% passed the intersection through the yellow interval, and 7% passed after the yellow interval was complete (red light running, RLR violations). Binary logistic regression models showed that the chance of passing went up as vehicle speed went up and down as the gap between the vehicle and the traffic light and green interval went up. The movement type and vehicle position influenced the passing probability, but the vehicle type did not. Moreover, multinomial logistic regression models showed that the legal passing probability declined with the growth in the green time and vehicle distance to the traffic signal. It also increased with the growth in the speed of approaching vehicles. Also, movement type directly affected the chance of legally passing, but vehicle position and type did not.” According to the news reporters, the research concluded: “Furthermore, the driver’s performance during the yellow phase was studied using the k-nearest neighbours algorithm (KNN), support vector machines (SVM), random forest (RF) and AdaBoost machine learning techniques. The driver’s action run prediction was the most accurate, and the run-on-red camera was the least accurate.”
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