Knowledge of the normal traffic flow pattern is required for a number of transportation applications. Traditionally, the simple historic average has been considered as the best way to derive the traffic pattern. However, this method may often be significantly biased by the presence of incidents. One solution to avoid this bias is through visual inspection of the data by experts. The experts could identify anomalies caused by incidents and thereby identify the underlying normal traffic patterns. Three main challenges of this approach are (a) the bias introduced because of subjectivity, (b) the additional time required to analyze the data manually, and (c) the increasing sizes of the available traffic data sets. To address these challenges and also to exploit the potential of information technology, new data analysis tools are essential. In this research, a new tool, the quantum-frequency algorithm, was developed. This algorithm can aid in the automated identification of traffic flow patterns from large data sets. The paper presents the algorithm along with its theoretical basis. Finally, in the case study presented in the paper, the algorithm was able to identify a reasonable traffic pattern automatically from a large set of archived data. When compared with the historic average, it was found that the pattern identified by the quantum- frequency algorithm resulted in 39% lower cumulative deviation from the pattern identified manually by experts.