An Automatic Traffic Peak Picking Method Based on Max Tree

Abstract

Traffic data analysis is a key step for intelligent transportation systems, and identifying traffic peaks is essential for subsequent traffic pattern analysis. Existing traffic peak detection methods consists of data smoothing and peak picking. We present a max-tree based traffic peak picking method, which constructs the max-tree of the input traffic flow data. Each node in the max tree is a component of an upper level set. We define the prominence of a component as the height difference between a top point and the higher of the left and right foot points of the component. The saliency of a peak is measured by the component prominence. The method generates candidate peaks of positive prominence. The method works directly on noisy traffic data, and the output candidate peaks and their prominences offer the subsequent analysis step the flexibility to choose peaks at any scale.