Direct Forecasting of Freeway Corridor Travel Times Using Spectral Basis Neural Networks

Abstract

With the advent of advanced traveler information systems, the prediction of short-term link and corridor travel times has become increasingly important. The standard method for forecasting corridor travel times is a two-step process in which the link travel times are first forecast and then combined into a corridor travel time. If link travel times are not independent, however, there is the potential for erroneous corridor or route travel time estimates. As an alternative to the two-step approach, a direct or one-step approach for freeway corridor travel time forecasting is proposed that automatically takes into account interrelationships between link travel times. The use of spectral basis neural networks to directly forecast multiple-period freeway corridor travel times is examined first. The model is tested on observed travel times collected as part of the automatic vehicle identification component of the Houston Transtar system. The direct forecasting model is also compared with the two-step model, which uses forecast link travel times as input. It was found that the direct forecasting approach gave better results than any of the other models examined and that link travel time forecasting errors are not additive.