Trajectory Reconstruction Using Automated Vehicles Motion Detection Data: A Hybrid Approach Integrating Wiedemann Model and Cellular Automation

In view of the increasing development of automated vehicles (AVs) technologies, it will be likely that road traffic is made up of a mixture of human-driven vehicles (HVs) and AVs in the coming years. To support traffic operation and management, this study proposed a hybrid approach integrating Wiedemann car-following model and cellular automation (CA) to reconstruct the trajectories of fully-sampled traffic flow on freeways. First, Wiedemann car-following model is applied to classify the vehicle driving states into following and closing. Then, human-driven vehicles (HVs) are inserted between the leading and following AVs based on the vehicle's behavior within the following AV's detection range. Next, the trajectories of inserted HVs are reconstructed by resorting to CA with four update rules set to determine vehicles' acceleration, deceleration, randomization and position. Last, the proposed hybrid approach is performed under different traffic densities and AVs penetration rates. Results show that the proposed method for trajectory reconstruction performs satisfactorily on freeways even at low penetration rates of AVs.