Real-time freeway traffic state prediction: A particle filter approach

Abstract

The research presented in this paper develops a multi-step traffic state prediction algorithm using spot speed measurements. The traditional Lighthill-Whitham-Richards (LWR) flow continuity equation is combined with the Van Aerde traffic stream model to generate a new partial differential equation (PDE) named the Van Aerde flow continuity model. The numerical solution of the PDE is obtained using the Godunov discretization scheme to generate a time series equation that characterizes the temporal and spatial relationship of traffic speed data. Because of the strong nonlinearity of the discretized speed update equation, a robust particle filter is applied to conduct a muti-step speed prediction using speed measurements. The prediction accuracy of the proposed approach is compared to the state-of-the-art Ensemble Kalman filter with the Greenshields traffic stream model using simulated loop detector data from Interstate 66. The results demonstrate that the proposed particle filter approach in combination with the discretized Van Aerde flow continuity model produces the lowest prediction error of 4.3 km/h for a five-minute prediction horizon, and accurately predicts the spatial and temporal propagation of shockwaves.