FREEWAY TRAFFIC PARAMETER AND STATE ESTIMATION WITH EULERIAN AND LAGRANGIAN DATA

The purpose of this study is to develop a traffic estimation framework which combines different data sources to better reconstruct the traffic states on the freeways. The framework combines both traffic parameter and state estimation in the same work flow, which resolves the inconsistency issue of most existing traffic state estimation methods.

To examine the quality of the traffic sensor data, the study starts with proposing the network sensor health problem (NSHP). The optimal set of sensors is selected from all sensors such that the violation of flow conservation is minimized. The health index for individual detector is then calculated based on the solutions. We also developed a tailored greedy search algorithm to find the solutions effectively. The proposed method is tested using the loop detector data from PeMS on a stretch of the SR-91 freeway. We compared the results with PeMS health status and found considerable level of consistency.

Two different traffic state estimation methods are proposed based on the data availability and traffic states. The LoopReid method is derived from the Newell's simplified kinematic wave model by assuming the whole road segment is fully congested. We formulate a least square optimization problem to find the initial states and traffic parameters based on the first-in-first-out principle and the congested part of the Newell's model. While developing the LoopCT method, we derived a counterpart of the Newell's kinematic wave model in the Lagrangian coordinates under Eulerian boundary conditions. This model also leads to a new method to estimate vehicle trajectories within a road segment. We formulate a least square optimization problem in initial states and traffic parameters which works for mixed traffic states. The two estimation methods turned out to be highly related and the LoopCT method degenerates to the LoopReid method when the traffic is fully congested. The two methods are validated using two datasets from the NGSIM project. Both methods achieved considerable level of accuracy at reconstructing the traffic states and parameters.