Predicting time-varying, speed-varying dilemma zones using machine learning and continuous vehicle tracking

Abstract

This paper proposes an innovative framework of predicting driver behavior under varying dilemma zone conditions using artificial intelligence-based machine learning. The framework utilizes multiple machine learning techniques to process vehicle attribute data (e.g., speed, location, and time-of-arrival) collected at the onset of the yellow indication, and eventually predicts drivers’ stop-or-go decision based on the data. A linear SVM was used to extract through vehicles from all approaching vehicles detected from radar sensors. A hierarchical clustering method was utilized to classify different traffic patterns by time-of-day. Finally, driver behavior prediction models were developed using three machine learning techniques (i.e., linear SVM, polynomial SVM, and ANN) widely adopted for binary classification problems. Model validation results showed that all the prediction models perform well with high prediction accuracies. The ANN model, which showed the best performance among the three, was selected to represent dilemma zone boundaries. Results show that the dilemma zone start- and end-points would both locate further from the stop bar with higher approaching speeds. Furthermore, the dilemma zone end-point would be more sensitive to the approaching speed than the start-point is. As a result, the dilemma zone length would become longer with higher approaching speeds. Results also showed that the dilemma zone length and location would vary by time-of-day regardless of the speed of approaching vehicles. The analysis showed that the dilemma zone length would be longer and its location would be much further from the stop bar for vehicles arriving during rush hours, as compared to those arriving during non-rush or nighttime hours. This indicates that drivers’ decision location to stop or go (when they are faced with a dilemma zone situation) is distributed farther from the intersection stop bar during rush hours. The proposed method shows an effective way of predicting driver behavior on signalized intersections. It is expected for the transportation agencies to use the method to improve intersection signal operations more effectively and safely.