Classification of Driver Stop/Run Behavior at the Onset of a Yellow Indication for Different Vehicles and Roadway Surface Conditions Using Historical Behavior

Abstract

The ability to classify driver stop/run behavior at signalized intersections considering the vehicle type and roadway surface conditions 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 three controlled field experiments and one data set collected using truck simulator. The field experiments are done on the Smart Road at the Virginia Tech Transportation Institute (VTTI) to model driver stop/run behavior at the onset of a yellow indication for different roadway surface conditions and different vehicle type. The paper offers two contributions. First, it introduces a new predictor related to driver aggressiveness and demonstrates that this measure enhances the modeling of driver stop/run behavior. Second, it applies well-known Artificial Intelligence techniques including: adaptive boosting (adaboost), artificial neural networks (ANN), and Support Vector Machine (SVM) algorithms on the data in order to develop a model that can be used by traffic signal controllers to predict driver stop/run decisions in a connected vehicle environment. The research demonstrates that by adding the driver aggressiveness predictor to the model, the increase in the model accuracy is significant for all models except SVM. However, the reduction in the false alarm rate was not statistically significant when using any of the approaches.