A hybrid approach for feature selection and freeway travel time prediction using biogeography-based optimization and support vector regression

As travelers make their choices based on travel time, its prior information can be helpful for them in choosing appropriate routes and departure times. To achieve this goal, travel time prediction models have been proposed in literature, but identification of important predictors has not received much attention. Identification of important predictors reduces dimensions of input data, which not only lessens computational load, but also provides better understanding of underlying relationship between important predictors and travel time. Therefore, this study proposes a hybrid approach for feature selection (identifying important predictors) along with developing a robust freeway travel time prediction model. A framework integrating biogeography-based optimization (BBO) algorithm and support vector regression (SVR) has been developed and implemented to predict travel time at 36.1 km long segment of National Taiwan Freeway No. 1. The proposed hybrid approach is able to develop a prediction model with only six predictors, which is found to have accuracy equivalent to a stand-alone SVR prediction model developed with forty-three predictors.