Multiobjective selection of input sensors for travel times forecasting using support vector regression

In this paper we propose a new method for travel time prediction using a support vector regression model (SVR). The inputs of the method are data from license plate detection systems and traffic sensors such as induction loops or radars placed in the area. This method is mainly designed to be capable of dealing with missing values in the traffic data. It is able to create many different SVR models with different input variables. These models are dynamically switched according to which traffic variables are currently available. The proposed method was compared with a basic license plate based prediction approach. The results showed that the proposed method provides the prediction of better quality. Moreover, it is available for a longer period of time.