The traditional method of estimating annual average daily traffic (AADT) involves several steps, with the most critical being the assignment process that involves allocating short-term counts to groups of seasonal adjustment factors (SAFs). The accuracy of AADT estimates highly depends on the assignment step, which is subject to errors stemming from human judgment. Support vector machines (SVMs), a supervised learning and statistical method, are employed to construct a series of assignment models that are compared with the traditional method and discriminant analysis (DA) models. Traffic volume data obtained from permanent traffic recorders are used to train and validate the models. The analysis is conducted with SAFs calculated for each direction of travel and separately for two-way traffic. The results reveal that the Gaussian kernel-based SVM model yields the lowest errors, improving AADT accuracy by 65% and decreasing the standard deviation of absolute percentage error by 73.7% over the traditional method. Another finding is that the assignment errors of the directional volume-based analysis are lower than those of the total volume-based analysis by 41.8%. The comparison between the two model parameters examinedthe average daily traffic and the hourly factorsindicates that the combined use of both parameters in SVMs is more effective than when hourly factors are used alone. However, in the case of DA, the opposite results are obtained. A possible explanation is that the SVM kernels transfer data from the input space to a feature space and thus provide the ability to assign counts effectively with different types of data within the same model.