A novel metamodel-based framework for large-scale dynamic origin-destination demand calibration

摘要

Calibrating dynamic traffic demand for stochastic traffic simulators is one of the big challenges due to computational burden. This paper proposes a novel framework to calibrate a dynamic car origin-destination matrix of large-scale networks, which has high computational efficiency. The proposed framework is relied on the metamodel optimization technique, based on the reference of aggregated traffic flow dynamics as expressed by the recent advance in traffic flow theory, namely the bi-modal macroscopic fundamental diagram (MFD). We validate our proposed approach with the Stochastic Perturbation Simultaneous Approximation (SPSA) algorithm which is widely used for the same purpose in the literature. Our result confirms that our approach can facilitate the demand calibration effectively.We also show that the traffic conditions at a link level are also reproduced realistically from our network level calibration, and that our approach outperforms the traditional link-level OD calibration by comparing with the Aimsun OD adjustment in addition to SPSA. Furthermore, we demonstrate via different realistic network studies that the proposed approach is computationally efficient compared to the existing state-of-the-art approach. Our result shows that the proposed approach can be applied effectively regardless of the topology of the network, and that the model parameters do not have significant influence on the optimization results. Our calibration results show that just a few iterations are needed to calibrate the OD demand in the proposed approach even for the large-scale complex network underpinned by a bi-modal MFD derived from multiple real data sources.