Traffic state and emission estimation for urban expressways based on heterogeneous data

Urban expressways, as the backbone of a city's transportation network, are critical for reducing traffic congestion and improving transportation efficiency of the whole network. The estimation of traffic states and emissions for urban expressways supports traveler information provision and system-wide traffic management. This paper aims to modify the extended generalized Treiber-Helbing filter (EGTF) to fuse GPS data (probe vehicles) and traditional traffic data (loop detectors), so as to enhance more accurate estimations of traffic states and emissions on urban expressways. The speed field is first reconstructed based on heterogeneous data, and then travel time and emissions are estimated using a virtual trajectory method and the VT-Micro model, respectively. The algorithm is applied to a real-world case study for an urban expressway in Beijing, China. After the parameter tuning, the proposed algorithm is compared with existing algorithms from the literature. Numerical results show that data fusion using the proposed algorithm could make better use of heterogeneous data and increase the accuracy of travel time and emissions estimations.