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Dynamic Origin-Destination Matrix Estimation Using Metamodel-Based Model Predictive Control for Real Time Application
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DOI：10.1177/03611981251378484.png)
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Abstract
En 中文
This paper proposes a method to advance real time calibration methods for microscopic traffic simulations. Conventional dynamic origin-destination matrix estimation (DODE) methods utilize metamodels to achieve computational advantages. However, they may estimate inappropriate origin-destination (O-D) matrices because of inherent differences between metamodels and simulations. An optimal control problem formulation is proposed for the DODE problem, and a metamodel-based model predictive control (MPC) approach that considers the uncertainty of the metamodel. The primary inspiration for the proposed method is to control the input O-D matrix while monitoring, at every time step, how the input demand influences the traffic system across multiple time steps. A data-driven metamodel based on an attention mechanism is developed to represent the dynamics of the microscopic traffic simulation. In addition, the metamodel is combined with one of the MPC algorithms, a model predictive path integral. The experiments were conducted on the Nguyen-Dupuis network and in Siheung City, South Korea. Compared with benchmark methods on each target network, errors were reduced by 35.3% on the toy network and 26.2% on the real-world network. This approach exhibits robust calibration performance against misestimated O-D matrices while considering the uncertainty of the metamodel. This paper presents a novel form of an O-D matrix estimation method for real time calibration that complements the existing stream of literature and provides a basis for developing real time simulations.
Keywords:
real time calibration
dynamic origin-destination matrix estimation
metamodel
model predictive control
Journal
T
IF:
1.8
Papers:
854
Citations:
3.4W