Original scientific paper
https://doi.org/10.7307/ptt.v35i6.287
Modelling Driver Behaviour at Urban Signalised Intersections Using Logistic Regression and Machine Learning
Ahmad H. Alomari
; Department of Civil Engineering, Yarmouk University
Bara’ W. Al-Mistarehi
; Department of Civil Engineering, Jordan University of Science & Technology
Al-Jammal A. Al-Jammal
; Department of Civil Engineering, Jordan University of Science & Technology
Taqwa I. Alhadidi
; Department of Civil Engineering, Al-Ahliyya Amman University
Motasem S. Obeidat
; Department of Computer Science, Yarmouk University
Abstract
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. 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. 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.
Keywords
traffic signal; traffic safety; logistic regression; machine learning; yellow phase; red light running
Hrčak ID:
312927
URI
Publication date:
20.12.2023.
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