Accurate regional traffic volume projection is important for department of transportation to plan investments, and also helps forecast oil or electric energy demand and CO 2 emissions. Based on a 4.5 years’ daily traffic volume measurement data of the highway network of Guizhou province of China, this paper conducts a comprehensive measurement analysis of the network’s traffic volume growth pattern and proposes a new time series model, which improves the projection accuracy of non-holiday and holiday traffic considerably. We first find that the holiday traffic volume is considerably higher than that on the neighboring non-holidays (e.g., 1.88 times), which could bring tremendous pressure on the road network. We then find that the traffic of network increases exponentially, in particular, the increase rates in holidays are higher than those in non-holidays. Thus, we propose an Exponential-Growth (EG) holiday component model, which models the holiday component with exponential growth. Experimental results show that our model considerably improves the holiday traffic’s prediction accuracy compared with the existing models. For instance, for the first day of National Day holiday, which is usually the heaviest day in a whole year (from Jan. 1 to Dec. 31), the model decreases the prediction relative error from 18.7% to 7%.