Integrated self-consistent macro-micro traffic flow modeling and calibration framework based on trajectory data

Calibrating microscopic car-following (CF) models is crucial in traffic flow theory as it allows for accurate reproduction and investigation of traffic behavior and phenomena. Typically, the calibration procedure is a complicated, non-convex optimization issue. When the traffic state is in equilibrium, the macroscopic flow model can be derived analytically from the corresponding CF model. In contrast to the microscopic CF model, calibrated based on trajectory data, the macroscopic representation of the fundamental diagram (FD) primarily adopts loop detector data for calibration. The different calibration approaches at the macro- and microscopic levels may lead to misaligned parameters with identical practical meanings in both macro- and micro-traffic models. This inconsistency arises from the difference between the parameter calibration processes used in macro- and microscopic traffic flow models. Hence, this study proposes an integrated multiresolution traffic flow modeling framework using the same trajectory data for parameter calibration based on the self-consistency concept. This framework incorporates multiple objective functions in the macro- and micro-dimensions. To expeditiously execute the proposed framework, an improved metaheuristic multi-objective optimization algorithm is presented that employs multiple enhancement strategies. Additionally, a deep learning technique based on attention mechanisms was used to extract stationary-state traffic data for the macroscopic calibration process, instead of directly using the entire aggregated data. We conducted experiments using real-world and synthetic trajectory data to validate our self-consistent calibration framework.