Online Parameter Identification for Traffic Simulation via Eulerian and Lagrangian Sensing

This paper deals with parameter identification for traffic models. Two fundamentally different approaches are presented, which are combined to the best advantage. With Eulerian sensing the sensors are spatially stationary, i.e. fixed in place, while with Lagrangian sensing the sensors move with the traffic flow as sensor-vehicles. We present a method to identify model parameters from both methods individually and investigate identifiability via the Fisher Information Matrix. Additionally an approach to combine both methods is presented, which is of particular importance where the data quality of one method is not sufficient to identify all model parameters. Results for both Eulerian and Lagrangian sensing as well as the combined method are presented.