[Show abstract][Hide abstract] ABSTRACT: Forecasting traffic volume is an important task in controlling urban highways, guiding drivers' routes, and providing real-time transportation information. Previous research on traffic volume forecasting has concentrated on a single forecasting model and has reported positive results, which has been frequently better than those of other models. In addition, many previous researchers have claimed that neural network models are better than linear statistical models in terms of prediction accuracy. However, the forecasting power of a single model is limited to the typical cases to which the model fits best. In other words, even though many research efforts have claimed the general superiority of a single model over others in predicting future events, we believe it depends on the data characteristics used, the composition of the training data, the model architecture, and the algorithm itself.