[2J4-GS-10] AI application:

[2J4-GS-10-01] Improvement of Parameter Estimation Accuracy by Using Data Assimilation for Traffic Flow Simulation

〇Takahiro Suzuki¹, Naoya Nomoto¹, Daisuke Okuya¹ (1. Oki Electric Industry Co., Ltd.)

Keywords:Traffic Flow Simulation, Data Assimilation, Probe Data

We aim to realize smooth traffic flow to solve social issues in transportation, such as traffic congestion and accidents. One of the methods is traffic flow simulation. By simulating various traffic conditions and their countermeasures, we believe decision-making for traffic measures that ensure smooth traffic flow can be conducted more efficiently. Appropriate parameterization of the model is important to accurately reproduce traffic conditions in simulations, but appropriate parameters are rarely known in advance. Therefore, we are researching parameter estimation through data assimilation using traffic probe data. In this study, we employ a data assimilation method called the Ensemble Kalman Filter, performing sequential data assimilation along with the time evolution of traffic flow simulations. In data assimilation, system noise is considered to express the uncertainty of the modeled system, and we found that the variance of system noise significantly affects the accuracy and stability of parameter estimation. This study confirmed that varying the system noise variance with time evolution, which is generally kept constant, can improve the accuracy of parameter estimation.