The ability to model driver stop/run behavior at signalized intersections is critical in the design of advanced driver assistance systems. Such systems can reduce intersection crashes and fatalities by predicting driver stop/run behavior. The research presented in this paper uses data collected from a controlled field experiment on the Smart Road at the Virginia Tech Transportation Institute (VTTI) to model driver stop/run behavior at the onset of a yellow indication. The paper offers three contributions. First, it evaluates the importance of various model predictors in the modeling of driver stop/run behavior in the vicinity of signalized intersections. Second, it introduces a new variable related to the driver aggressiveness and demonstrates that this measure enhances the modeling of driver stop/run behavior. Third it applies well-known machine learning techniques, including: K-nearest neighbors (K-NN), random forests, and Adaptive Boosting (AdaBoost) techniques on the data and compares their performance to standard logistic models in an attempt to identify the optimum modeling framework. The experimental work shows that by adding the driver aggressiveness predictor to the model, the model accuracy increases by approximately 10% for the logistic, random forest and K-NN models and by 7% for the AdaBoost model. The paper also demonstrates that all modeling frameworks produce similar prediction accuracies.
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