Mesoscopic-Wavelet Freeway Work Zone Flow and Congestion Feature Extraction Model

Abstract

A new mesoscopic-wavelet model is presented for simulating freeway traffic flow patterns and extracting congestion characteristics. A traffic speed–density relationship is introduced with a lane drop factor to take into account lane closures in freeway work zones. Patterns of multiple parameters are inputted to a congestion feature extraction algorithm. An approximate solution for this equation is found by space-time discretization. The high frequency fluctuations of the signal are not recognizable at normal resolutions. To overcome this problem, a multi-resolution wavelet filter is introduced in the proposed model to enhance traffic features and extract congestion characteristics from the traffic data. Fourth-order Coifman wavelets are used for filtering because of their good approximation for high-resolution scaling.