Modeling of Cyclists' Decision for Left-Turn Vehicle at Unsignalized Intersection Using Logistic Regression Model and Gaussian Mixuter Model

In this paper, the cyclists’ decision-making behav- ior in the interaction with a car at unsignaled intersection is measured and analyzed. Based on the measured data, the cyclists’ decision-making model is identified by using logistic regression model. Since the data collection in the real world is hard to realize, we have used an interactive simulator in which the cycling simulator and the driving simulator are connected via network, and share the same virtual traffic environment. The cyclist’s decision states are defined by three states regarding their operation, pedaling-on, pedaling-off and brake-on. The models to estimate these three states were constructed using the logistic regression model and the Gaussian mixture model, respectively. Finally the accuracy of the constructed models are verified, and compared with each other.