Bi-Level Optimization Model for DTA Flow and Speed Calibration

Dynamic traffic assignment (DTA) models are capable of capturing traffic dynamics and are well-known as a critical tool in controlling and predicting the traffic situation Ameli (2019). DTA simulation allows us to measure the results of deploying different technologies and applying different policies along with real experiments Ameli et al. (2020); Chen et al. (2021); Balzer et al. (2023). One of the crucial steps to achieve realistic results from simulation tools is calibration. It aims to determine the DTA model’s input such that the output represents traffic scenarios with a reliable level of accuracy (Antoniou, 2004; Zargayouna et al., 2006). The inputs can be divided into two groups: demand and supply. The supply parameters define the environment of the simulation and the field constraints, e.g., traffic network topology and capacity, traffic signals, speed limitation, etc. In contrast, the demand inputs represent the travelers and their behavior in the system, e.g., time-dependent origin-destination matrix, routing, lane changing, etc.