Traffic State Estimation Based on Kalman Filter Technique using Connected Vehicle V2V Basic Safety Messages

In the near future, vehicles will be equipped to receive and broadcast Basic Safety Messages (BSMs), which includes the vehicle position, speed, heading, and acceleration, to effectively avoid potential road collisions. This data with high resolution can be used to provide road information for traffic operation and management. This study proposed an algorithm using BSM data to estimate traffic states, including flow, density, and speed, based on the Kalman Filter and cell transmission model (CTM). The algorithm was tested using vehicle trajectory data generated by a CTM-based simulator. The result showed that the algorithm performed well with known parameters and had poor performance when parameter values were unknown, and the parameters were hard to be calibrated with the data from the CTM-based simulator.