Neural Network-Wavelet Microsimulation Model for Delay and Queue Length Estimation at Freeway Work Zones

Abstract

Recently, the writers developed a new mesoscopic-wavelet model for simulating freeway traffic flow patterns and extracting congestion characteristics. As an extension of that research, in this paper, a new neural network-wavelet microsimulation model is presented to track the travel time of each individual vehicle for traffic delay and queue length estimation at work zones. The model incorporates the dynamics of a single vehicle in changing traffic flow conditions. The extracted congestion characteristics obtained from the mesoscopic-wavelet model are used in a Levenberg–Marquardt backpropagation (BP) neural network for classifying the traffic flow as free flow, transitional flow, and congested flow with stationary queue. The neural network model is trained using simulated data and tested using both simulated and real data. The computational model presented is applied to five examples of freeways with two and three lanes and one lane closure with varying entry flow or demand patterns. The new microsimulation model is more accurate than macroscopic models and substantially more efficient than microscopic models.