  As travelers make their choices based on travel time, its prior information
can be helpful for them in making more informed travel decisions. 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. Moreover,
collection of only important predictors can lead to a significant equipment
savings in data collection. 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) and support vector regression (SVR) has
been developed. It was validated by predicting 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 all
forty three predictors.

Signal Processing