Similarity-based Historical Input Selection to Predict Irregular Holiday Traffics in Real-time

The main contribution of this paper is to present a straightforward yet effective historical input selection method for real-time traffic forecasting. Historical inputs are crucial elements in traffic forecasting because they help models recognize historical traffic patterns. However, selecting appropriate historical inputs becomes challenging, especially on irregular holidays with abnormal traffic patterns.As a solution, we suggest a method with three clear strengths. First, it is simple enough to employ in real-time computation. Second, our method provides more sophisticated features than existing studies which roughly categorize and match similar days. Finally, our method outperforms existing methods remarkably, given the unusual traffic patterns on special holidays.Our experiments verified the outperformance through an industrial prediction model and traffic dataset from a leading mobility service provider in South Korea.