Real-time Freeway Traffic State Estimation with Fixed and Mobile Sensing Data

This paper compares three filtering approaches to real-time freeway traffic state estimation based on the first-order and second-order macroscopic traffic flow models using fixed and mobile data together data together. The significance of online estimation of traffic flow model parameters along with traffic flow variables is also investigated in the context of heterogeneous sources of sensing data. The results indicate that the traffic state estimator based on a second-order model outperforms those based on the first-order models. In addition, the online model parameter estimation is significant for traffic state estimation especially when the penetration rate of mobile sensors is lower than 20%.