Bayesian Calibration of the Intelligent Driver Model
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Accurate calibration of car-following models is essential for investigating microscopic human driving behaviors. This work proposes a memory-augmented Bayesian calibration approach, which leverages the Bayesian inference and stochastic processes (i.e., Gaussian processes) to calibrate an unbiased car-following model while extracting the serial correlations of residual. This calibration approach is applied to the intelligent driver model (IDM) and develops a novel model named MA-IDM. To evaluate the effectiveness of the developed approach, three models with different hierarchies (i.e., pooled, hierarchical, and unpooled) are tested. Experiments demonstrate that the MA-IDM can estimate the noise level of unrelated errors by decoupling the serial correlation of residuals. Furthermore, a stochastic simulation method is also developed based on our Bayesian calibration approach, which can obtain unbiased posterior motion states and generate anthropomorphic driving behaviors. Simulation results indicate that the MA-IDM outperforms Bayesian IDM in simulation accuracy and uncertainty quantification. With this Bayesian approach, we can generate enormous but nonidentical driving behaviors by sampling from the posteriors, which can help develop a realistic traffic simulator.
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