Modeling Traffic Vehicular Counts at an Urban Intersection Using Complex Seasonal Modeling Techniques

The accurate estimation of traffic counts is critical for Intelligent Transportation Systems (ITS). In this paper, we introduce novel model-based parametric techniques for modeling traffic counts with complex seasonality at urban intersections. The three distinct models to fit and predict vehicular counts with multiple seasonal effects are dynamic harmonic, BATS, and TBATS. We have used loop detectors to collect hourly data over 71 consecutive days. All three models effectively capture seasonality not only within a day, but also encompass hourly, daily, and weekly seasonal effects. In contrast, the traditional SARIMA model fails to capture more than one seasonal effect in the data. Furthermore, the…

• CS
  Cleveland State University