Predicting traffic congestion using driver behavior

Traffic congestion is one of the largest traffic problems and one solution is a prediction of its generation. Traffic congestion comes from three phases order: the free travel phase, the meta-stability phase, and the traffic congestion phase. Therefore, it can be considered that if the meta-stability phase can be detected, forecasting traffic congestion becomes possible. This paper proposes a driver model that forecasts traffic congestion based on changes in driving behavior and that does not rely on traffic flow monitoring infrastructure. As a result of evaluation in driving simulators, it was understood that the distribution of steering, throttle and speed input frequency changes based on changes in the travel phase. It is possible to distinguish these changes using support vector machines as shown in Figure 1, and it is possible to make this into a driver model that predicts traffic congestion. This method is the first propose that uses only CAN (Controller Area Network) data and needs no additional sensors to detect driving environments or any infrastructures.